Next Article in Journal
Muscle or Heart? Functional Impact of Sarcopenia and Heart Failure in Geriatric Inpatients
Previous Article in Journal
Postpartum Depression: Interacting Biological Pathways and the Promising Validation of Blood-Based Biomarkers
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JCM
Volume 14
Issue 12
10.3390/jcm14124287
jcm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Comparative Effects of Dulaglutide and Semaglutide on Renal Function Decline and Proteinuria Reduction in Diabetic Patients: A Retrospective Cohort Study

by
Yuh-Mou Sue
1,2,3,
De-En Lu
2,
Te-I Chang
4,5,6,
Chun-You Chen
7,8,9,
Cheng-Hsien Chen
1,2,10,
Shih-Chang Hsu
11,12,
Yen-Ling Chu
2,
Nai-Jen Huang
2,
Tso-Hsiao Chen
1,2,
Feng-Yen Lin
1,13,
Chun-Ming Shih
1,13,
Po-Hsun Huang
14,
Hui-Ling Hsieh
15,16,† and
Chung-Te Liu
1,2,*,†
1
Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
2
Division of Nephrology, Department of Internal Medicine, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
3
Division of Nephrology, Hsin Kuo Min Hospital, Taipei Medical University, Taoyuan 320, Taiwan
4
Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
5
Division of Cardiovascular Surgery, Department of Surgery, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
6
Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
7
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
8
Artificial Intelligence Research and Development Center, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
9
Department of Radiation Oncology, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
10
Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235, Taiwan
11
Department of Emergency and Critical Medicine, Wan Fang Hospital, Taipei Medical University, Taipei 116, Taiwan
12
Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan
13
Division of Cardiology and Cardiovascular Research Center, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 110, Taiwan
14
Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital, Taipei 112, Taiwan
15
Second Degree Bachelor of Science in Nursing Collage of Medicine, National Taiwan University, Taipei 100, Taiwan
16
Department of Nursing, National Taiwan University Hospital Yunlin Branch, Yunlin 640, Taiwan
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Clin. Med. 2025, 14(12), 4287; https://doi.org/10.3390/jcm14124287
Submission received: 19 April 2025 / Revised: 21 May 2025 / Accepted: 12 June 2025 / Published: 16 June 2025
(This article belongs to the Section Nephrology & Urology)
Browse Figures
Versions Notes

Abstract

Background: GLP-1 receptor agonists (GLP-1 RAs) lower glucose and reduce cardiovascular events in type 2 diabetes, with noted renal benefits. Few studies directly compare GLP-1 RAs. This study aims to compare the effects of semaglutide and dulaglutide on renal function decline and proteinuria reduction in diabetic patients. Methods: The present study was conducted at Wanfang Hospital, Taipei Medical University. Diabetic patients using either semaglutide or dulaglutide for more than 1 year in the outpatient department from 1 January 2022 to 30 September 2024 were enrolled retrospectively. The outcome events in the present study included a decline in the estimated glomerular filtration rate (eGFR), an increase in the urine albumin–creatinine ratio (UACR), and patient death. Results: A total of 268 patients on dulaglutide and 747 on semaglutide were included. Baseline eGFR levels were similar in both groups. After 12 months, eGFR levels did not significantly decline in both groups. However, the dulaglutide group showed significantly higher UACR increases than the semaglutide group (p < 0.01). More death events also occurred in the dulaglutide group (p < 0.01). Multivariate logistic regression revealed a higher risk of UACR increase with dulaglutide (p < 0.01). Subgroup analysis found dulaglutide associated with higher UACR in patients younger than 60, males, those with hypertension, without heart failure, those using angiotensin receptor blockers, biguanides, and statins, and those not using sodium-glucose cotransporter-2 inhibitors. Conclusions: Dulaglutide and semaglutide had comparable effects on slowing eGFR decline. However, dulaglutide was less effective in reducing UACR, particularly in the subgroups mentioned above.

Graphical Abstract

1. Introduction

Diabetes mellitus (DM) is one of the most significant chronic diseases, affecting approximately 451 million adults worldwide [1]. DM is associated with the accelerated progression of both macrovascular as well as microvascular complications [2]. Diabetic kidney disease (DKD) is a serious microvascular complication of diabetes, affecting approximately 40% of patients with DM [3]. Globally, the prevalence of DM in patients with end-stage kidney disease (ESKD) was 29.7% in 2015, and DM was responsible for 31.3% of incident ESKD cases in the same year [4]. In Taiwan, the prevalence of DKD among patients with type 2 DM was 17.92% in 2014, and it was responsible for approximately 45% ESKD cases [5]. On a global scale, DKD is the leading cause of ESKD, highlighting the critical importance of preventing and treating the renal complications of DM [6].
While glycemic and blood pressure control form the cornerstone of DKD treatment [7,8], several therapies have been specifically approved to slow renal function decline or reduce proteinuria in DM patients. From 1993 to early 2000, multiple clinical trials demonstrated that renin–angiotensin system blockade slows the renal decline in DKD by 5–7 mL/min/year, establishing its central role in DKD treatment [9,10,11]. Since then, progress in DKD treatment stagnated until 2014, when multiple trials reported the effect of sodium-glucose cotransporter 2 (SGLT2) inhibitors on DKD outcomes [12,13,14,15]. Around the same time, the nonsteroidal mineralocorticoid receptor antagonist finerenone was also shown to slow the progression of DKD [16]. We are now fortunate to be in a period of rapid advancements in DKD treatment and are about to witness the emergence of novel therapies for DKD.
Glucagon-like peptide 1 receptor agonists (GLP-1 RAs) are incretin-based antidiabetic agents that show a promising glucose-lowering effect in patients with type 2 DM [17,18,19]. In clinical trials, GLP-1 RAs have demonstrated a significant reduction in cardiovascular events associated with atherosclerosis. This evidence prompted a revision of treatment guidelines in 2020 [20,21,22]. The post hoc analysis of these trials indicated that semaglutide and liraglutide were linked to reductions in albuminuria and a decline in renal function [23]. A later published clinical trial, titled “Effect of Efpeglenatide on Cardiovascular Outcomes”, demonstrated that efpeglenatide significantly reduced the risk in composite kidney outcomes compared with a placebo [24]. Another GLP-1 RA, dulaglutide, was shown to marginally reduce the incidence of combined cardiovascular death and heart failure events in the REWIND trial [25]. The post hoc analysis indicated that dulaglutide may slow the progression of DKD, the decline in renal function, and the advancement to ESKD, suggesting its potential as an additional treatment option for DKD [26,27].
Semaglutide and dulaglutide are both GLP-1 RAs of human GLP-1 backbone that stimulate insulin secretion via the incretin effect, inhibit glucagon production to decrease the blood glucose level, delay gastric emptying to decrease calory intake, and prevent pancreatic β-cell apoptosis [28,29,30]. One difference between these two GLP-1 RAs may be that the clinical trials of semaglutide showed stronger effects of weight reduction and cardiovascular protection [31]. Recently, a meta-analysis of randomized controlled trials showed that GLP-1 RAs also significantly reduced adverse kidney events and renal failure, which make GLP-1 RAs an optimal treatment choice of DKD [32]. There is a scarcity of studies that perform direct head-to-head comparisons between GLP-1 RAs, which are less common in clinical trial settings. However, these comparisons can be crucial in formulating optimal strategies for the treatment of DKD. Therefore, this study aims to compare the renal effects of semaglutide and dulaglutide in a retrospective cohort of diabetic patients, focusing specifically on their impact on renal function decline and proteinuria reduction.

2. Methods

2.1. Study Design and Participants

The present study was conducted at Wanfang Hospital, Taipei Medical University. Diabetic patients using either semaglutide or dulaglutide for more than 1 year in the outpatient department from 1 January 2022 to 30 September 2024 were enrolled retrospectively. Medical records were reviewed to confirm eligibility for inclusion, demographic characteristics, comorbidities, laboratory data, and outcomes of interest. From the initiation of semaglutide or dulaglutide use, patients were followed for one year. Semaglutide was administered as Ozempic, manufactured by Novo Nordisk (Bagsværd, Gladsaxe Municipality, Denmark), at 0.25 mg to 1 mg per week subcutaneously. Dulaglutide was administered as Trulicity, manufactured by Lilly (Indianapolis, IN, USA), at 0.75 mg to 1.5 mg per week subcutaneously. The exclusion criteria were as follows: (1) the interchangeable use of semaglutide and dulaglutide, and (2) age under 20 years. The present study was approved by the Ethics Committee and Institutional Review Board of Taipei Medical University on 29 November 2024 (N202411053). The requirement for informed consent was waived. This study was conducted in accordance with the tenets of the 1975 Declaration of Helsinki, revised in 2000.

2.2. Definitions of Parameters and Outcomes

Demographic profile and comorbidities were defined at the initiation of semaglutide or dulaglutide use. The presence of hypertension was defined as the use of any antihypertensive medication at baseline. The presence of chronic kidney disease (CKD) was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min or a urine albumin–creatinine ratio (UACR) > 30 mcg/g for more than 3 months. The presence of DM was defined as a previous glycated hemoglobin (HbA1c) > 6.5% and the use of any antidiabetic medication. The attending cardiologist diagnosed heart failure (HF) based on either a reduced ejection fraction (EF) of less than 40% or a preserved EF that met the diagnostic criteria recommended by the Heart Failure Association of the European Society of Cardiology [33]. Laboratory data were collected at the baseline (the time of semaglutide or dulaglutide initiation) and the end (12 months after initiation). In the present study, eGFR was calculated using the CKD-EPI equation, revised in 2021 [34]. The medications of interest that were co-administered included sodium-glucose transport protein 2 (SGLT2) inhibitors, biguanides, furosemide, angiotensin-converting enzyme inhibitors (ACEIs)/angiotensin receptor blockers (ARBs), spironolactone, thiazide, and statin. The use of a specific medication was defined as its prescription for a duration exceeding three months within the study period.
The difference between the end eGFR and the baseline eGFR was termed eGFR change. The difference between the end UACR and baseline UACR was termed UACR change. The outcome events in the present study included eGFR decline defined as the decrease in eGFR for more than 10% from its baseline, UACR increase defined as the increase in UACR for more than 10% from its baseline, and patient death by the 1-year study period.

2.3. Statistical Analyses

Continuous variables with a normal distribution were reported as mean ± standard deviation, while continuous variables that deviated from a normal distribution were expressed as medians (25th and 75th percentiles). Categorical variables were reported as frequency and percentage. Statistical analyses of continuous variables with a normal distribution were conducted using Student’s t-test for independent samples, analyses of continuous variables that deviated from a normal distribution were conducted using the Wilcoxon signed-rank test, and analyses of categorical variables were conducted using the chi square test. The changes in values between the baseline and the end of the study were analyzed using a dependent t-test for paired samples. The association between parameters and the outcomes was evaluated using logistic regression methods. Each parameter was pre-evaluated for its association with the outcomes using univariate logistic regression. Parameters significantly associated with the outcomes (p < 0.05) were included in the multivariate logistic regression model. Missing values were imputed using the last observation carried forward method. Statistical analysis was performed using SAS 9.4 (SAS Institute Inc., Cary, NC, USA).

3. Results

3.1. Demographic and Clinical Baseline Characteristics of Patients

During the data collection period, 268 patients using dulaglutide and 747 patients using semaglutide were eligible for inclusion in the study. Among all of the study patients, the mean age was 59.5 ± 14.2 years and 55.5% were male. Additionally, 71.2% had hypertension, 62.6% had chronic kidney disease (CKD), 12.9% had heart failure (HF), and 72.5% had dyslipidemia. Compared to the patients on semaglutide, the patients using dulaglutide were significantly older, included more males, and had a higher prevalence of hypertension. At baseline, the mean SCr was 0.9 ± 0.5 mg/dL and the mean eGFR was 90.0 ± 31.7 mL/min/1.73 m2. There were no significant differences in the baseline SCr and eGFR between the patients on dulaglutide and those on semaglutide. Patients on dulaglutide exhibited significantly higher HbA1c levels, lower serum albumin, and higher serum triglycerides. At baseline, the median UACR was 50.0 mcg/g in patients using dulaglutide and 65.0 mcg/g in patients on semaglutide, with no significant difference between the two groups (Table 1).
Out of the total 1015 patients included in the study, 21.9% were co-administered with SGLT2 inhibitors. Patients on dulaglutide had a significantly higher rate of co-administration with SGLT2 inhibitors compared to patients on semaglutide (34.3% versus 17.5%, p < 0.01). Regarding medications blocking the renin–angiotensin–aldosterone axis, 56.0% and 3.8% of the total included patients were using ARB and spironolactone, respectively, with no significant difference between the two groups. Additionally, patients on dulaglutide had significantly higher rates of co-administration of biguanides and statins, while the use of loop diuretics and thiazides was similar between the two groups (Table 2).

3.2. Comparative Analysis of Renal Outcomes in Patients Treated with Dulaglutide Versus Semaglutide

The baseline eGFR was 87.7 mL/min/1.72 m2 for the dulaglutide group and 90.8 mL/min/1.72 m2 for the semaglutide group, with no significant difference between the two groups (p = 0.20). For patients on dulaglutide, the mean eGFR was 86.8 mL/min/1.72 m2 after 12 months. The mean eGFR at baseline and after 12 months did not differ significantly (p = 0.32). For patients on semaglutide, the mean eGFR was 90.0 mL/min/1.72 m2 after 12 months, with no significant difference between the baseline and 12-month values (p = 0.13) (Figure 1A). The baseline UACR was 223.3 mcg/g for the dulaglutide group and 227.1 mcg/g for the semaglutide group, showing no significant difference between the two groups (p = 0.94). After 12 months, the mean UACR for the dulaglutide group increased to 293.7 mcg/g, which was significantly higher than the baseline value (p = 0.04). For the semaglutide group, the mean UACR was 226.4 mcg/g after 12 months, with no significant difference from the baseline value (p = 0.97) (Figure 1B).
In total, the mean eGFR change was −0.8 mL/min, and 243 (23.9%) of the 1015 included patients experienced an eGFR decline after 12 months of using GLP-1 RA. In the dulaglutide group, 25.7% of patients had an eGFR decline, while, in the semaglutide group, 23.2% of patients experienced an eGFR decline. The mean value of the eGFR change and the frequencies of the events of eGFR decline were not significantly different between the two groups. On the other hand, 220 (21.6%) of all included patients experienced a UACR increase, and the mean UACR change was 18.1 mcg/g after 12 months. In the dulaglutide group, the mean UACR change was 70.3 mcg/g and 36.5% of patients had a UACR increase, whereas in the semaglutide group, the mean UACR change was −0.6 mcg/g and 19.4% of patients had an increase. The frequency of a UACR increase event and the mean UACR change were significantly higher in the dulaglutide group (Table 3).
The above finding suggests that the dulaglutide group may have a higher chance of experiencing a UACR increase compared to the semaglutide group. To confirm this, a multivariate logistic regression model, adjusted for factors associated with an increased UACR, was constructed. Candidate risk factors significantly associated with the UACR increase in univariate logistic regression (p < 0.05) were included in the multivariate logistic regression model. These factors include the use of dulaglutide or semaglutide, age, gender, hypertension, CKD, dyslipidemia, baseline HbA1c, and serum low-density lipoprotein cholesterol (LDL), and the use of SGLT2 inhibitors, angiotensin II receptor blockers (ARBs), biguanides, and statins. In the multivariate logistic regression model, the dulaglutide group exhibited a significantly higher risk for a UACR increase compared to the semaglutide group, with an odds ratio (OR) of 1.6 and a 95% confidence interval (CI) of 1.2–2.3 (p < 0.01). Other significant risk factors for a UACR increase included male gender, hypertension, higher HbA1c, higher serum albumin, and the use of SGLT2 inhibitors (Table 4).

3.3. UACR Subgroup Outcomes in Patients Using Dulaglutide Versus Semaglutide

To elucidate the impacts of the identified risk factors on the association between dulaglutide and semaglutide use on UACR, subgroup analyses were conducted based on age, gender, the presence of hypertension, and HF. Among patients under 60 years of age, the dulaglutide group had a significantly higher risk of a UACR increase compared to the semaglutide group. In contrast, for patients aged 60 years and older, the dulaglutide group did not exhibit a significant risk of UACR increase compared to the semaglutide group. In male patients, the dulaglutide group exhibited a significantly higher risk of UACR increase compared to the semaglutide group. Conversely, in female patients, the dulaglutide group did not show a significantly higher risk of UACR increase compared to the semaglutide group. In patients with hypertension, the dulaglutide group showed a significantly higher risk for UACR increase compared to the semaglutide group. Conversely, in patients without hypertension, the dulaglutide group did not have a significantly higher risk for UACR increase compared to the semaglutide group. In patients with HF, there was no significant difference in the risk of UACR increase between the dulaglutide and semaglutide groups. Notably, in patients without HF, the dulaglutide group showed a significantly higher risk for UACR increase compared to the semaglutide group. These findings suggest that age, gender, and the presence of hypertension and HF may interact with GLP-1 RAs in their effect on UACR increase (Figure 2). The construction of the multivariate logistic regression models are referred to in Supplementary Tables S1–S8.
To elucidate the interactions between dulaglutide/semaglutide use and other medications in the multivariate logistic regression model, subgroup analyses were conducted based on the use of SGLT2 inhibitors, ARBs, biguanides, and statins. For the users of SGLT2 inhibitors, the dulaglutide group did not show a significant risk for UACR increase compared to the semaglutide group. In contrast, for nonusers of SGLT2 inhibitors, the dulaglutide group exhibited a significantly higher risk for UACR increase compared to the semaglutide group. Among ARB users, the dulaglutide group showed a significantly higher risk for UACR increase compared to the semaglutide group. However, in nonusers of ARB, the dulaglutide group did not show a significantly higher risk for UACR increase compared to the semaglutide group. For biguanide users, the dulaglutide group exhibited a significantly higher risk for UACR increase compared to the semaglutide group. Conversely, among nonusers of biguanides, the dulaglutide group did not demonstrate a significantly higher risk for UACR increase compared to the semaglutide group. Among statin users, the dulaglutide group exhibited a significantly higher risk for UACR increase compared to the semaglutide group. However, for nonusers of statins, the dulaglutide group did not show a significantly higher risk for UACR increase compared to the semaglutide group (Figure 3). The construction of multivariate logistic regression models are referred to in Supplementary Tables S9–S16.

4. Discussion

In summary, the main findings of the present study indicate that patients using dulaglutide experienced a significant increase in UACR after 12 months of treatment, whereas patients using semaglutide maintained similar UACR levels after the same period. Additionally, patients using dulaglutide exhibited a significantly higher UACR change, and more events of UACR increase and death, than the patients using semaglutide. The multivariate logistic regression model showed that the dulaglutide group had a significantly higher risk for UACR increase compared to the semaglutide group. Subgroup analysis revealed that the relationship between dulaglutide/semaglutide use and the higher risk of UACR increase remained consistent in patients aged younger than 60 years, male patients, patients with hypertension, patients without heart failure, patients using ARBs, biguanides, and statins, and patients not using SGLT2 inhibitors.
Previous studies have demonstrated the renal protective effects of GLP-1 RAs. A pooled analysis of clinical trials involving semaglutide revealed that semaglutide significantly slowed the decline in eGFR and the progression of UACR compared to placebo, especially in patients with pre-existing CKD [23,35]. Regarding dulaglutide, a post hoc analysis indicated that dulaglutide treatment was associated with fewer events of eGFR decline. However, it did not show a significant difference in the UACR compared to placebo [27]. In a retrospective study with a small sample size, dulaglutide was associated with a reduced decline in eGFR in patients with CKD [36]. The findings from these previous studies indicate that both dulaglutide and semaglutide may offer protective benefits against the decline in eGFR. However, only semaglutide appears to have protective effects against increases in the UACR, though further evidence is needed to confirm this. In response to these findings, the present study conducted a head-to-head comparison and demonstrated that dulaglutide and semaglutide exhibit similar protective effects in eGFR decline. However, semaglutide showed a significantly greater effect in reducing increases in UACR than did dulaglutide.
A recent meta-analysis comparing the efficacy of dulaglutide and semaglutide showed that both medications have similar efficacy in glycemic control. However, semaglutide demonstrated higher efficacy in inducing weight loss, and is therefore more suitable for patients requiring both weight reduction and glycemic control [37]. Additionally, an indirect treatment comparison demonstrated significantly greater reductions from baseline in HbA1c and body weight with semaglutide compared to dulaglutide [38]. Furthermore, a post hoc analysis suggested that semaglutide demonstrated superior efficacy compared to dulaglutide in terms of glycemic control and body weight reduction, irrespective of gender, diabetes duration, and baseline body mass index (BMI) [39]. Despite these findings, there are limited studies comparing which patient subgroups are most suitable for dulaglutide versus semaglutide based on age, gender, or comorbidities. The present study found that dulaglutide was associated with a higher UACR compared to semaglutide in patients who were younger, male, hypertensive, and without HF. This suggests that dulaglutide may be more favorable for patients who are older, female, non-hypertensive, and those with HF. These novel findings may help clinicians make individualized selections of GLP-1 RAs for patients.
Several studies have shown that the combined use of SGLT2 inhibitors and GLP-1 receptor agonists is associated with a reduction in major cardiovascular events and serious renal events in patients with type 2 DM [40,41,42]. However, studies investigating the comparative effects of dulaglutide versus semaglutide in combination with other medications remain absent to date. The present study showed that dulaglutide was associated with a higher risk of UACR increase versus semaglutide in the case of the combination with ARBs, biguanides, and statins, and without the combination of SGLT2 inhibitors. These findings suggest that dulaglutide may be a preferable option for patients not using ARBs, biguanides, and statins, and those who are using SGLT2 inhibitors. In contrast, semaglutide may be preferable in patients using ARBs, biguanides, and statins, and patients who are not using SGLT2 inhibitors.
The limitations of the present study include its single-center base, retrospective design, relatively small sample size, possible selection bias, lack of randomization, absent data on protein intake, and blood pressure monitoring during the follow-up period. To compensate for these limitations, the study ensured similar baseline eGFR and UACR levels between the dulaglutide and semaglutide groups, allowing for a comparative analysis of these outcomes. Additionally, a multivariate logistic method was employed to adjust for uncontrolled variables. While the mechanism underlying the renal effects of GLP-1 receptor agonists remains unclear, further investigation is needed to determine the differences in their impact on UACR reduction between semaglutide and dulaglutide.
In conclusion, the present study showed that dulaglutide was associated with a significantly higher UACR compared to semaglutide, especially in patients of younger age, male gender, with hypertension, without HF, those using ARBs, biguanides, and statins, and those who are not using SGLT2 inhibitors. These findings may help clinicians make individualized selections of GLP-1 RAs for diabetic patients.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jcm14124287/s1, Table S1: Subgroup Analysis of the Risk of UACR Increase in Patients Under 60 Years of Age; Table S2: Subgroup Analysis of the Risk of UACR Increase in Patients Above 60 Years of Age; Table S3: Subgroup Analysis of the Risk of UACR Increase in Male Patients; Table S4: Subgroup Analysis of the Risk of UACR Increase in Female Patients; Table S5: Subgroup Analysis of the Risk of UACR Increase in Patients with Hypertension; Table S6: Subgroup Analysis of the Risk of UACR Increase in Patients without Hypertension; Table S7: Subgroup Analysis of the Risk of UACR Increase in Patients with HF; Table S8: Subgroup Analysis of the Risk of UACR Increase in Patients without HF; Table S9: Subgroup Analysis of the Risk of UACR Increase in Patients Using SGLT2 inhibitors; Table S10: Subgroup Analysis of the Risk of UACR Increase in Patients Not Using SGLT2 inhibitors; Table S11: Subgroup Analysis of the Risk of UACR Increase in Patients Using ARBs; Table S12: Subgroup Analysis of the Risk of UACR Increase in Patients Not Using ARBs; Table S13: Subgroup Analysis of the Risk of UACR Increase in Patients Using Biguanides; Table S14: Subgroup Analysis of the Risk of UACR Increase in Patients Not Using Biguanides; Table S15: Subgroup Analysis of the Risk of UACR Increase in Patients Using Statins; Table S16: Subgroup Analysis of the Risk of UACR Increase in Patients Not Using Statins.

Author Contributions

Conception and Design: Y.-M.S., C.-Y.C., T.-I.C., H.-L.H., S.-C.H. and C.-T.L.; Analysis and Interpretation: D.-E.L., C.-Y.C., S.-C.H., C.-H.C., P.-H.H., H.-L.H. and C.-T.L.; Data Collection: D.-E.L., C.-Y.C., T.-I.C., Y.-L.C., N.-J.H., H.-L.H. and C.-T.L.; Writing the Manuscript: C.-Y.C. and C.-T.L.; Critical Revision: C.-H.C., S.-C.H., Y.-M.S., T.-H.C., F.-Y.L., C.-M.S., P.-H.H., H.-L.H. and C.-T.L.; Approval and Accountability: all co-authors approved and agreed to be accountable for the manuscript; Statistical Analysis: C.-Y.C., Y.-L.C., N.-J.H., H.-L.H. and C.-T.L.; Obtaining Funding: C.-T.L. and Y.-M.S. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported, in part, by research grants from Wanfang Hospital, Taipei Medical University (111-wf-f-1, 111-wf-f-2, 112-wf-swf-03, 113-wf-swf-04, 114-wf-eva-16) and the National Science and Technology Council, Taiwan (NSTC 113-2314-B-038-132, NSTC 112-2314-B-002-312 -MY3). These funding agencies had no influence on the study design, data collection or analysis, decision to publish, or manuscript preparation.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by Institutional Review Board of Taipei Medical University (protocol code: N202411053; date of approval: 29 November 2024).

Informed Consent Statement

The present study is a retrospective study, so the requirement for informed consent was waived.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author, Chung-Te Liu, upon reasonable request.

Conflicts of Interest

All authors declare no interest or relationship within the last three years directly related to this manuscript, including advisory positions, consulting fees, equity and stock ownership, and non-financial support. We also declare that no patents or copyrights by any of the authors are relevant to the work in this manuscript.

References

  1. Cho, N.H.; Shaw, J.E.; Karuranga, S.; Huang, Y.; da Rocha Fernandes, J.D.; Ohlrogge, A.W.; Malanda, B. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res. Clin. Pract. 2018, 138, 271–281. [Google Scholar] [CrossRef] [PubMed]
  2. Cade, W.T. Diabetes-related microvascular and macrovascular diseases in the physical therapy setting. Phys. Ther. 2008, 88, 1322–1335. [Google Scholar] [CrossRef] [PubMed]
  3. Garg, A.X.; Kiberd, B.A.; Clark, W.F.; Haynes, R.B.; Clase, C.M. Albuminuria and renal insufficiency prevalence guides population screening: Results from the NHANES III. Kidney Int. 2002, 61, 2165–2175. [Google Scholar] [CrossRef]
  4. Cheng, H.-T.; Xu, X.; Lim, P.S.; Hung, K.-Y. Worldwide Epidemiology of Diabetes-Related End-Stage Renal Disease, 2000–2015. Diabetes Care 2020, 44, 89–97. [Google Scholar] [CrossRef]
  5. Wang, J.; Yen, F.; Lin, K.; Shin, S.; Hsu, Y.; Hsu, C. Diabetes Kidney Disease Research Committee of the Diabetes Association of the Republic of China (Taiwan) Epidemiological characteristics of diabetic kidney disease in Taiwan. J. Diabetes Investig. 2021, 12, 2112–2123. [Google Scholar] [CrossRef]
  6. Duru, O.K.; Middleton, T.; Tewari, M.K.; Norris, K. The Landscape of Diabetic Kidney Disease in the United States. Curr. Diabetes Rep. 2018, 18, 14. [Google Scholar] [CrossRef] [PubMed]
  7. Kovesdy, C.P.; Sharma, K.; Kalantar-Zadeh, K. Glycemic control in diabetic CKD patients: Where do we stand? Am J Kidney Dis. 2008, 52, 766–777. [Google Scholar] [CrossRef]
  8. Tanaka, Y.; Atsumi, Y.; Matsuoka, K.; Onuma, T.; Tohjima, T.; Kawamori, R. Role of glycemic control and blood pressure in the development and progression of nephropathy in elderly Japanese NIDDM patients. Diabetes. Care. 1998, 21, 116–120. [Google Scholar] [CrossRef]
  9. Lewis, E.J.; Hunsicker, L.G.; Bain, R.P.; Rohde, R.D.; The Collaborative Study Group. The effect of angiotensin-converting-enzyme inhibition on diabetic nephropathy. N. Engl. J. Med. 1993, 329, 1456–1462. [Google Scholar] [CrossRef]
  10. Lewis, E.J.; Hunsicker, L.G.; Clarke, W.R.; Berl, T.; Pohl, M.A.; Lewis, J.B.; Ritz, E.; Atkins, R.C.; Rohde, R.; Raz, I.; et al. Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes. N. Engl. J. Med. 2001, 345, 851–860. [Google Scholar] [CrossRef]
  11. Brenner, B.M.; Cooper, M.E.; de Zeeuw, D.; Keane, W.F.; Mitch, W.E.; Parving, H.H.; Remuzzi, G.; Snapinn, S.M.; Zhang, Z.; Shahinfar, S.; et al. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy. N. Engl. J. Med. 2001, 345, 861–869. [Google Scholar] [CrossRef] [PubMed]
  12. McGuire, D.K.; Shih, W.J.; Cosentino, F.; Charbonnel, B.; Cherney, D.Z.I.; Dagogo-Jack, S.; Pratley, R.; Greenberg, M.; Wang, S.; Huyck, S.; et al. Association of SGLT2 Inhibitors with Cardiovascular and Kidney Outcomes in Patients with Type 2 Diabetes: A Meta-analysis. JAMA. Cardiol. 2021, 6, 148–158. [Google Scholar] [CrossRef] [PubMed]
  13. Perkovic, V.; Jardine, M.J.; Neal, B.; Bompoint, S.; Heerspink, H.J.L.; Charytan, D.M.; Edwards, R.; Agarwal, R.; Bakris, G.; Bull, S.; et al. Canagliflozin and Renal Outcomes in Type 2 Diabetes and Nephropathy. N. Engl. J. Med. 2019, 380, 2295–2306. [Google Scholar] [CrossRef]
  14. Heerspink, H.J.L.; Stefánsson, B.V.; Correa-Rotter, R.; Chertow, G.M.; Greene, T.; Hou, F.-F.; Mann, J.F.E.; McMurray, J.J.V.; Lindberg, M.; Rossing, P.; et al. Dapagliflozin in Patients with Chronic Kidney Disease. N. Engl. J. Med. 2020, 383, 1436–1446. [Google Scholar] [CrossRef]
  15. Herrington, W.G.; Staplin, N.; Wanner, C.; Green, J.B.; Hauske, S.J.; Emberson, J.R.; Preiss, D.; Judge, P.; Mayne, K.J.; Ng, S.Y.A.; et al. Empagliflozin in Patients with Chronic Kidney Disease. N. Engl. J. Med. 2023, 388, 117–127. [Google Scholar] [PubMed]
  16. Kintscher, U.; Bakris, G.L.; Kolkhof, P. Novel non-steroidal mineralocorticoid receptor antagonists in cardiorenal disease. Br. J. Pharmacol. 2022, 179, 3220–3234. [Google Scholar] [CrossRef]
  17. Eissele, R.; Göke, R.; Willemer, S.; Harthus, H.P.; Vermeer, H.; Arnold, R.; GÖKE, B. Glucagon-like peptide-1 cells in the gastrointestinal tract and pancreas of rat, pig and man. Eur. J. Clin. Investig. 1992, 22, 283–291. [Google Scholar] [CrossRef]
  18. Orskov, C.; Wettergren, A.; Holst, J.J. Secretion of the incretin hormones glucagon-like peptide-1 and gastric inhibitory polypeptide correlates with insulin secretion in normal man throughout the day. Scand. J. Gastroenterol. 1996, 31, 665–670. [Google Scholar] [CrossRef]
  19. Kuhre, R.E.; Albrechtsen, N.J.W.; Hartmann, B.; Deacon, C.F.; Holst, J.J. Measurement of the incretin hormones: Glucagon-like peptide-1 and glucose-dependent insulinotropic peptide. J. Diabetes Complicat. 2015, 29, 445–450. [Google Scholar] [CrossRef]
  20. American Diabetes Association. 10. Cardiovascular Disease and Risk Management: Standards of Medical Care in Diabetes—2021. Diabetes Care 2020, 44, S125–S150. [Google Scholar]
  21. Cosentino, F.; Grant, P.; Aboyans, V.; Bailey, C.J.; Ceriello, A.; Delgado, V.; ESC Scientific Document Group. 2019 ESC guidelines on diabetes, pre-diabetes, and cardiovascular diseases developed in collaboration with the EASD. Eur. Heart J. 2020, 41, 255–323. [Google Scholar] [CrossRef] [PubMed]
  22. Kidney Disease: Improving Global Outcomes Diabetes Working Group. KDIGO 2020 Clinical Practice Guideline for Diabetes Management in Chronic Kidney Disease. Kidney Int. 2020, 98, S1–S115. [Google Scholar] [CrossRef] [PubMed]
  23. Shaman, A.M.; Bain, S.C.; Bakris, G.L.; Buse, J.B.; Idorn, T.; Mahaffey, K.W.; Mann, J.F.; Nauck, M.A.; Rasmussen, S.; Rossing, P.; et al. Effect of the glucagon-like peptide-1 receptor agonists semaglutide and liraglutide on kidney outcomes in patients with type 2 diabetes: Pooled analysis of SUSTAIN 6 and LEADER. Circulation 2022, 145, 575–585. [Google Scholar] [CrossRef] [PubMed]
  24. Gerstein, H.C.; Khurmi, N.S. Efpeglenatide and Heart and Kidney Outcomes in Type 2 Diabetes. Reply. N. Engl. J. Med. 2021, 385, 2107. [Google Scholar]
  25. Branch, K.R.; Dagenais, G.R.; Avezum, A.; Basile, J.; Conget, I.; Cushman, W.C.; Jansky, P.; Lakshmanan, M.; Lanas, F.; Leiter, L.A.; et al. Dulaglutide and cardiovascular and heart failure outcomes in patients with and without heart failure: A post-hoc analysis from the REWIND randomized trial. Eur. J. Heart Fail. 2022, 24, 1805–1812. [Google Scholar] [CrossRef]
  26. Gerstein, H.C.; Colhoun, H.M.; Dagenais, G.R.; Diaz, R.; Lakshmanan, M.; Pais, P.; Probstfield, J.; Botros, F.T.; Riddle, M.C.; Rydén, L.; et al. Dulaglutide and renal outcomes in type 2 diabetes: An exploratory analysis of the REWIND randomised, placebo-controlled trial. Lancet 2019, 394, 131–138. [Google Scholar] [CrossRef]
  27. Botros, F.T.; Gerstein, H.C.; Malik, R.; Nicolay, C.; Hoover, A.; Turfanda, I.; Colhoun, H.M.; Shaw, J.E. Dulaglutide and Kidney Function-Related Outcomes in Type 2 Diabetes: A REWIND Post Hoc Analysis. Diabetes Care 2023, 46, 1524–1530. [Google Scholar] [CrossRef]
  28. Garber, A.J. Long-acting glucagon-like peptide 1 receptor agonists: A review of their efficacy and tolerability. Diabetes Care 2011, 34, S279–S284. [Google Scholar] [CrossRef]
  29. Gallwitz, B. GLP-1 agonists and dipeptidyl-peptidase IV inhibitors. Handb. Exp. Pharmacol. 2011, 203, 53–74. [Google Scholar]
  30. Okerson, T.; Chilton, R.J. The cardiovascular effects of GLP-1 receptor agonists. Cardiovasc. Ther. 2012, 30, e146–e155. [Google Scholar] [CrossRef]
  31. Papakonstantinou, I.; Tsioufis, K.; Katsi, V. Spotlight on the Mechanism of Action of Semaglutide. Curr. Issues Mol. Biol. 2024, 46, 14514–14541. [Google Scholar] [CrossRef] [PubMed]
  32. Badve, S.V.; Bilal, A.; Lee, M.M.Y.; Sattar, N.; Gerstein, H.C.; Ruff, C.T.; McMurray, J.J.V.; Rossing, P.; Bakris, G.; Mahaffey, K.W.; et al. Effects of GLP-1 receptor agonists on kidney and cardiovascular disease outcomes: A meta-analysis of randomised controlled trials. Lancet Diabetes Endocrinol. 2025, 13, 15–28. [Google Scholar] [CrossRef]
  33. Pieske, B.; Tschöpe, C.; A de Boer, R.; Fraser, A.G.; Anker, S.D.; Donal, E.; Edelmann, F.; Fu, M.; Guazzi, M.; Lam, C.S.P.; et al. How to diagnose heart failure with preserved ejection fraction: The HFA-PEFF diagnostic algorithm: A consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). Eur. Heart J. 2019, 40, 3297–3317. [Google Scholar] [CrossRef]
  34. CKD-EPI Creatinine Equation. 2021. Available online: https://www.kidney.org/ckd-epi-creatinine-equation-2021 (accessed on 3 February 2025).
  35. Apperloo, E.M.; I Cherney, D.Z.; Kuhlman, A.B.; E Mann, J.F.; Rasmussen, S.; Rossing, P.; Tuttle, K.R.; Vrhnjak, B.; Heerspink, H.J.L. Effect of semaglutide on kidney function across different levels of baseline HbA1c, blood pressure, body weight and albuminuria in SUSTAIN 6 and PIONEER 6. Nephrol. Dial. Transpl. 2025, 40, 352–359. [Google Scholar] [CrossRef]
  36. Kim, S.; An, J.N.; Song, Y.R.; Kim, S.G.; Lee, H.S.; Cho, A.; Kim, J.-K.; Bulum, T. Effect of once-weekly dulaglutide on renal function in patients with chronic kidney disease. PLoS ONE 2022, 17, e0273004. [Google Scholar] [CrossRef] [PubMed]
  37. Xia, L.; Li, H.; Huang, S.; Shen, L. Semaglutide vs. dulaglutide for glycemic and weight control in patients with type 2 diabetes mellitus: A systematic review and meta-analysis. Biomed. Rep. 2024, 22, 1–10. [Google Scholar] [CrossRef]
  38. Lingvay, I.; Bauer, R.; Baker-Knight, J.; Lawson, J.; Pratley, R. An Indirect Treatment Comparison of Semaglutide 2.0 mg vs Dulaglutide 3.0 mg and 4.5 mg Using Multilevel Network Meta-regression. J. Clin. Endocrinol. Metab. 2022, 107, 1461–1469. [Google Scholar]
  39. E Pratley, R.; Aroda, V.R.; Catarig, A.-M.; Lingvay, I.; Lüdemann, J.; Yildirim, E.; Viljoen, A. Impact of patient characteristics on efficacy and safety of once-weekly semaglutide versus dulaglutide: SUSTAIN 7 post hoc analyses. BMJ Open 2020, 10, e037883. [Google Scholar] [CrossRef]
  40. Mousavi, A.; Shojaei, S.; Soleimani, H.; Semirani-Nezhad, D.; Ebrahimi, P.; Zafari, A.; Ebrahimi, R.; Roozbehi, K.; Harrison, A.; Syed, M.A.; et al. Safety, efficacy, and cardiovascular benefits of combination therapy with SGLT-2 inhibitors and GLP-1 receptor agonists in patients with diabetes mellitus: A systematic review and meta-analysis of randomized controlled trials. Diabetol. Metab. Syndr. 2025, 17, 68. [Google Scholar] [CrossRef]
  41. Simms-Williams, N.; Treves, N.; Yin, H.; Lu, S.; Yu, O.; Pradhan, R.; Renoux, C.; Suissa, S.; Azoulay, L. Effect of combination treatment with glucagon-like peptide-1 receptor agonists and sodium-glucose cotransporter-2 inhibitors on incidence of cardiovascular and serious renal events: Population based cohort study. BMJ 2024, 385, e078242. [Google Scholar] [CrossRef]
  42. Gourdy, P.; Darmon, P.; Dievart, F.; Halimi, J.-M.; Guerci, B. Combining glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2is) in patients with type 2 diabetes mellitus (T2DM). Cardiovasc. Diabetol. 2023, 22, 79. [Google Scholar] [CrossRef] [PubMed]
Jcm 14 04287 g001
Figure 1. Evolving outcome values throughout the study duration. (A) Visualization of eGFR trends; (B) visualization of UACR trends. eGFR, estimated glomerular filtration rate; UACR, urine albumin–creatinine ratio. The changes in values between the baseline and the end of the study were analyzed using a dependent t-test for paired samples.
Figure 1. Evolving outcome values throughout the study duration. (A) Visualization of eGFR trends; (B) visualization of UACR trends. eGFR, estimated glomerular filtration rate; UACR, urine albumin–creatinine ratio. The changes in values between the baseline and the end of the study were analyzed using a dependent t-test for paired samples.
Jcm 14 04287 g001
Jcm 14 04287 g002
Figure 2. Subgroup analysis on the risk of UACR in patients treated with dulaglutide versus semaglutide. HTN, hypertension; HF, heart failure. Statistical analyses were performed by using logistic regression. The establishment of the multivariate logistic regression models are referred to in Supplementary Tables S1–S8.
Figure 2. Subgroup analysis on the risk of UACR in patients treated with dulaglutide versus semaglutide. HTN, hypertension; HF, heart failure. Statistical analyses were performed by using logistic regression. The establishment of the multivariate logistic regression models are referred to in Supplementary Tables S1–S8.
Jcm 14 04287 g002
Jcm 14 04287 g003
Figure 3. Impact of medication combinations on UACR risk in patients treated with dulaglutide versus semaglutide. SGLT2, sodium-glucose cotransporter-2; ARB, angiotensin receptor blockers. Statistical analyses were performed by using logistic regression. The establishment of the multivariate logistic regression models are referred to Supplementary Tables S9–S16.
Figure 3. Impact of medication combinations on UACR risk in patients treated with dulaglutide versus semaglutide. SGLT2, sodium-glucose cotransporter-2; ARB, angiotensin receptor blockers. Statistical analyses were performed by using logistic regression. The establishment of the multivariate logistic regression models are referred to Supplementary Tables S9–S16.
Jcm 14 04287 g003
Table 1. Baseline characteristics of the study patients.
Table 1. Baseline characteristics of the study patients.
CharacterTotalDulaglutideSemaglutidep Value
Number, n1015268747n/a
Age, yr59.5 ± 14.263.6 ± 15.158.0 ± 13.6<0.01
Male, n (%)564 (55.5)163 (60.8)401 (53.6)0.04
HTN, n (%)723 (71.2)208 (77.6)515 (68.9)<0.01
CKD, n (%)636 (62.6)169 (63.0)467 (62.5)0.87
HF, n (%)131 (12.9)43 (16.0)88 (11.7)0.07
Dyslipidemia, n (%)736 (72.5)195 (72.7)541 (72.4)0.91
Creatinine, mg/dL0.9 ± 0.51.0 ± 0.70.9 ± 0.50.08
eGFR, mL/min/1.73 m290.0 ± 31.787.7 ± 34.290.8 ± 30.80.20
HbA1c, %7.7 ± 1.88.7 ± 1.77.3 ± 1.7<0.01
Albumin, g/dL3.8 ± 0.43.6 ± 0.53.8 ± 0.3<0.01
ALT, U/L20.0 (17.0)21.0 (20.0)20.0 (17.0)0.09
LDL, mg/dL90.4 ± 31.391.6 ± 31.890.0 ± 31.10.47
TRIG, mg/dL136.0 (93.0)150.5 (114.5)132.0 (90.0)<0.01
UACR, mg/g50.0 (88.0)50.0 (133.0)65.0 (88.0)0.98
Yr, year; HTN, hypertension; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; HbA1c, glycated hemoglobin; ALT, alanine aminotransferase; LDL, low-density lipoprotein; TRIG, triglyceride; UACR, urine albumin-to-creatinine ratio; n/a, not applicable. Category variables were analyzed using chi square test. Continuous variables with normal distribution were expressed as mean ± standard deviation and analyzed using Student’s t-test for independent samples. Continuous variables deviated from normal distribution were expressed as median (interquartile range) and analyzed using Wilcoxon rank sum test.
Table 2. Medication profiles of the study patients.
Table 2. Medication profiles of the study patients.
CharacterTotalDulaglutideSemaglutidep Value
Number, n1015268747n/a
SGLT2 inhibitor, n (%)223 (21.9)92 (34.3)131 (17.5)<0.01
ARB, n (%)569 (56.0)156 (58.2)413 (55.2)0.40
Spironolactone, n (%)39 (3.8)14 (5.2)25 (3.3)0.17
Biguanides, n (%)618 (60.8)205 (76.4)413 (55.2)<0.01
Loop diuretics, n (%)43 (4.2)15 (5.6)28 (3.7)0.19
Thiazide, n (%)94 (9.2)24 (8.9)70 (9.3)0.84
Statins, n (%)726 (71.5)205 (76.4)521 (69.7)0.03
SGLT2, sodium-glucose cotransporter-2; ARB, angiotensin receptor blockers; n/a, not applicable. Category variables were analyzed using chi square test.
Table 3. One-year renal outcomes of dulaglutide versus semaglutide treatment.
Table 3. One-year renal outcomes of dulaglutide versus semaglutide treatment.
OutcomesTotalDulaglutideSemaglutidep Value
eGFR change, mL/min−0.8 ± 0.4−0.9 ± 0.9−0.8 ± 0.50.69
eGFR decline, n (%)243 (23.9)69 (25.7)174 (23.2)0.41
UACR change, mcg/g18.1 ± 15.970.3 ± 34.7−0.6 ± 17.7<0.01
UACR increase, n (%)220 (21.6)92 (34.3)128 (17.1)<0.01
Death, n (%)8 (0.8)6 (2.2)2 (0.2)<0.01
eGFR, estimated glomerular filtration rate; UACR, urine albumin-to-creatinine ratio. Event of eGFR decline was defined as eGFR decrease of more than 10% from baseline eGFR; event of UACR increase was defined as UACR increase of more than 10% from baseline UACR. Continuous variables were analyzed using Student’s t-test or Wilcoxon rank sum test, as appropriate. Categorical variables were analyzed using chi square method.
Table 4. Comparative analysis of the risk of UACR increase in patients treated with dulaglutide versus semaglutide.
Table 4. Comparative analysis of the risk of UACR increase in patients treated with dulaglutide versus semaglutide.
CharacterUnivariateMultivariate
OR95% CIp ValueOR95% CIp Value
Dulaglutide use2.41.8–3.3<0.011.61.1–2.3<0.01
Age, per 10 yr increment1.31.2–1.4<0.011.21.1–1.4<0.01
Male1.61.2–2.2<0.011.20.9–1.70.22
HTN2.01.4–2.9<0.011.40.9–2.10.11
CKD1.51.1–2.20.020.80.5–1.10.10
HF0.60.4–0.90.040.60.3–0.90.03
Dyslipidemia1.51.1–2.10.021.00.7–1.50.84
HbA1c, per 1% increment1.31.2–1.4<0.011.21.1–1.3<0.01
Albumin, per 1 g/dL increment1.10.8–1.50.58
ALT, per 30 U/L increment0.80.7–1.10.20
LDL, per 100 mg/dL increment0.50.3–0.8<0.010.60.3–1.00.07
TRIG, per 100 mg/dL increment1.00.9–1.00.26
SGLT2 inhibitors use2.11.5–2.9<0.011.71.2–2.4<0.01
ARB use1.81.3–2.4<0.011.10.7–1.60.56
Spironolactone use1.10.5–2.20.79
Biguanides use3.42.4–4.9<0.011.50.9–2.30.08
Loop diuretics use1.20.6–2.40.53
Thiazide use1.20.7–1.90.37
Statin use2.31.6–3.3<0.011.30.8–2.00.22
OR, odds ratio; CI, confidence interval; Yr, year; HTN, hypertension; CKD, chronic kidney disease; HF, heart failure; HbA1c, glycated hemoglobin; ALT, alanine aminotransferase; LDL, low-density lipoprotein; TRIG, triglyceride; SGLT2, sodium-glucose cotransporter-2; ARB, angiotensin receptor blockers; UACR, urine albumin-to-creatinine ratio. Risk analysis performed by using logistic regression model. Variables with p values of <0.05 in univariate logistic regression models were included into the multivariate logistic regression model.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sue, Y.-M.; Lu, D.-E.; Chang, T.-I.; Chen, C.-Y.; Chen, C.-H.; Hsu, S.-C.; Chu, Y.-L.; Huang, N.-J.; Chen, T.-H.; Lin, F.-Y.; et al. Comparative Effects of Dulaglutide and Semaglutide on Renal Function Decline and Proteinuria Reduction in Diabetic Patients: A Retrospective Cohort Study. J. Clin. Med. 2025, 14, 4287. https://doi.org/10.3390/jcm14124287

AMA Style

Sue Y-M, Lu D-E, Chang T-I, Chen C-Y, Chen C-H, Hsu S-C, Chu Y-L, Huang N-J, Chen T-H, Lin F-Y, et al. Comparative Effects of Dulaglutide and Semaglutide on Renal Function Decline and Proteinuria Reduction in Diabetic Patients: A Retrospective Cohort Study. Journal of Clinical Medicine. 2025; 14(12):4287. https://doi.org/10.3390/jcm14124287

Chicago/Turabian Style

Sue, Yuh-Mou, De-En Lu, Te-I Chang, Chun-You Chen, Cheng-Hsien Chen, Shih-Chang Hsu, Yen-Ling Chu, Nai-Jen Huang, Tso-Hsiao Chen, Feng-Yen Lin, and et al. 2025. "Comparative Effects of Dulaglutide and Semaglutide on Renal Function Decline and Proteinuria Reduction in Diabetic Patients: A Retrospective Cohort Study" Journal of Clinical Medicine 14, no. 12: 4287. https://doi.org/10.3390/jcm14124287

APA Style

Sue, Y.-M., Lu, D.-E., Chang, T.-I., Chen, C.-Y., Chen, C.-H., Hsu, S.-C., Chu, Y.-L., Huang, N.-J., Chen, T.-H., Lin, F.-Y., Shih, C.-M., Huang, P.-H., Hsieh, H.-L., & Liu, C.-T. (2025). Comparative Effects of Dulaglutide and Semaglutide on Renal Function Decline and Proteinuria Reduction in Diabetic Patients: A Retrospective Cohort Study. Journal of Clinical Medicine, 14(12), 4287. https://doi.org/10.3390/jcm14124287

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop