GLP-1 RAs and Cardiovascular and Kidney Outcomes by Body Mass Index in Type 2 Diabetes

Tien-Hsing Chen; En-Hao Hu; Dong-Yi Chen; Yuan Lin; Tien-Shin Chou; Ming-Shyan Lin; Ning-I Yang; Chao-Yung Wang; Ming-Jui Hung; Ming-Lung Tsai

doi:10.1001/jamanetworkopen.2025.30952

. 2025 Sep 8;8(9):e2530952. doi: 10.1001/jamanetworkopen.2025.30952

GLP-1 RAs and Cardiovascular and Kidney Outcomes by Body Mass Index in Type 2 Diabetes

Tien-Hsing Chen ^1,^8,¹⁰, En-Hao Hu ^2,⁸, Dong-Yi Chen ^3,⁸, Yuan Lin ^4,⁸, Tien-Shin Chou ^5,⁸, Ming-Shyan Lin ^6,⁸, Ning-I Yang ^1,⁸, Chao-Yung Wang ^3,⁸, Ming-Jui Hung ^1,⁸, Ming-Lung Tsai ^7,^8,^9,^✉

¹Division of Cardiology, Department of Internal Medicine, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan

²Department of Internal Medicine, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan

³Division of Cardiology, Department of Internal Medicine, Linkou Chang Gung Memorial Hospital, Taoyuan, Taiwan

⁴Department of Emergency Medicine, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan

⁵Division of Gastroenterology, Department of Internal Medicine, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan

⁶Division of Cardiology, Department of Internal Medicine, Chiayi Chang Gung Memorial Hospital, Chiayi, Taiwan

⁷Division of Cardiology, Department of Internal Medicine, New Taipei Municipal TuCheng Hospital, New Taipei, Taiwan

⁸College of Medicine, Chang Gung University, Taoyuan, Taiwan

⁹College of Management, Chang Gung University, Taoyuan, Taiwan

¹⁰School of Traditional Chinese Medicine, Chang Gung University, Taoyuan, Taiwan

Accepted for Publication: May 28, 2025.

Published: September 8, 2025. doi:10.1001/jamanetworkopen.2025.30952

^✉

Corresponding Author: Ming-Lung Tsai, MD, Division of Cardiology, Department of Internal Medicine, New Taipei Municipal TuCheng Hospital, No. 6, Sec. 2, Jincheng Road, TuCheng Dist. 236, New Taipei City, Taiwan (mltsai.cgmh@gmail.com).

Author Contributions: Dr Tsai had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs T. Chen and Hu contributed equally to this study.

Concept and design: T.-H. Chen, Hu, Chou, M. Lin, Wang, Tsai.

Acquisition, analysis, or interpretation of data: T.-H. Chen, Hu, D.-Y. Chen, Y. Lin, Yang, Hung, Tsai.

Drafting of the manuscript: T.-H. Chen, Hu, Tsai.

Critical review of the manuscript for important intellectual content: T.-H. Chen, D.-Y. Chen, Y. Lin, Chou, M.-S. Lin, Yang, Wang, Hung, Tsai.

Obtained funding: T.-H. Chen, Wang, Tsai.

Administrative, technical, or material support: Y. Lin, Chou, M.-S. Lin, Yang, Hung, Tsai.

Supervision: D.-Y. Chen, Y. Lin, Chou, Tsai.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the Chang Gung Medical Foundation, Taiwan (grant No. CORPVVP0161 and CORPVVP0101). We also acknowledge the support of Chang Gung Medical Foundation (grant No. CMRPVVN0221 and CMRPVVN0231), the National Science and Technology Council (grant No. 113-2314-B-182A-062-MY3), and the Maintenance Project of the Center of Data Science and Biostatistics (grant No. CGRPG2F0011, CLRPG2C0021, CLRPG2C0022, CLRPG2C0023, CLRPG2C0024, CLRPG2G0081, CLRPG2G0082, CLRPG2G0083, CLRPG2L0021, CLRPG2L0022, and CMRPG2N0041) at Keelung Chang Gung Memorial Hospital.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Data Sharing Statement: See Supplement 2.

Additional Contributions: We thank Alfred Hsing-Fen Lin, MSc; Zoe Ya-Jhu Syu, MSc; and Winnie Si-Hui Wu MSc (Raising Statistics Consultant Inc.), for their statistical assistance. These individuals received compensation and declared no competing interest between the findings of this study and their company.

^✉

Corresponding author.

PMCID: PMC12418133 PMID: 40920377

Key Points

Question

Are glucagon-like peptide-1 receptor agonists (GLP-1 RAs) associated with cardiovascular and kidney outcomes in patients with type 2 diabetes, and do these associations differ by body mass index (BMI)?

Findings

In this cohort study of 97 156 adults with type 2 diabetes, use of GLP-1 RAs was associated with significantly lower risks of major adverse cardiovascular events, cardiovascular death, and hospitalization for heart failure among patients with BMI of 25 or greater. Kidney benefits associated with GLP-1 RAs were consistent across BMI categories.

Meaning

These findings suggest that cardiovascular benefits of GLP-1 RAs may be modified by BMI, while kidney benefits were consistent regardless of BMI, supporting individualized treatment decisions in type 2 diabetes care.

This cohort study examines associations of glucagon-like peptide-1 receptor agonist (GLP-1 RA) use with cardiovascular and kidney outcomes across body mass index (BMI) categories in patients with type 2 diabetes.

Abstract

Importance

The cardiovascular benefits of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may vary by body mass index (BMI), but evidence on BMI-specific outcomes remains limited.

Objective

To investigate the associations of GLP-1 RA use with cardiovascular and kidney outcomes across BMI categories in patients with type 2 diabetes.

Design, Setting, and Participants

This retrospective cohort study used the Chang Gung Research Database, a clinical dataset covering multiple hospitals in Taiwan. Patients with type 2 diabetes who received GLP-1 RAs or dipeptidyl peptidase-4 (DPP-4) inhibitors between 2011 and 2022 were identified. DPP-4 inhibitors were selected as the comparator due to their widespread use as a second-line oral hypoglycemic agent and their relatively neutral cardiovascular and kidney effects reported in previous studies. Propensity score matching was applied separately within BMI categories less than 25 and 25 or greater using a comprehensive set of demographic, clinical, and biochemical variables to balance baseline characteristics between treatment groups. The analysis was conducted from December 15, 2023, to July 5, 2024.

Exposures

Initiation of GLP-1 RAs compared with DPP-4 inhibitors.

Main Outcomes and Measures

Primary outcomes included major adverse cardiovascular events (MACE; defined as cardiovascular death, myocardial infarction, ischemic stroke, or hospitalization for heart failure) and composite kidney outcomes (defined as estimated glomerular filtration rate decline ≥50% or progression to dialysis).

Results

Among 97 156 patients with diabetes identified, a total of 7200 matched patients (mean [SD] age, 57.4 [14.2] years; 7473 [51.9%] female) were included (1841 pairs with BMI <25 and 5359 pairs with BMI ≥25). Among patients with BMI 25 or greater, GLP-1 RAs were associated with lower risks of cardiovascular death (hazard ratio [HR], 0.62; 95% CI, 0.46-0.83) and hospitalization for heart failure (subdistribution HR, 0.77; 95% CI, 0.62-0.94). Kidney outcomes were consistent across BMI strata. Restricted cubic spline analysis revealed increasing cardiovascular benefit associated with GLP-1 RAs among patients with higher BMI.

Conclusions and Relevance

In this cohort study of patients with type 2 diabetes, GLP-1 RAs use was associated with BMI-dependent cardiovascular benefits and consistent kidney protection, suggesting the importance of BMI stratification in guiding treatment decisions.

Introduction

Type 2 diabetes is a major global public health issue, with cardiovascular (CV) and kidney complications contributing to its disease burden.^1,2 Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) have emerged as an important therapeutic class, offering both glycemic control and potential cardioprotective and nephroprotective benefits.^3,4,5 However, GLP-1 RAs are not a pharmacologically homogenous class, and their effects on specific CV outcomes appear to vary among agents and patient populations. For instance, semaglutide induces greater weight reduction than liraglutide, which may lead to distinct cardiorenal outcomes, especially in patients with varying body mass index (BMI; calculated as weight in kilograms divided by height in meters squared). In patients with heart failure (HF) with reduced ejection fraction, the LIVE trial⁶ showed no improvement in left ventricular ejection fraction with liraglutide and even reported increased cardiac adverse events. Similarly, the FIGHT trial^7,8 failed to demonstrate reductions in hospitalization for HF (HHF) or improvement in natriuretic peptides.

In contrast, evidence suggests that GLP-1 RAs may provide CV benefits in patients with obesity. The STEP-HFpEF trial⁹ demonstrated that semaglutide improved heart failure symptoms in patients with obesity without diabetes and with preserved ejection fraction. Furthermore, the SELECT trial¹⁰ showed that semaglutide reduced major adverse CV events (MACE) in individuals with overweight or obesity without diabetes.¹⁰ These findings suggest a potential interaction between BMI and the cardioprotective effects of GLP-1 RAs. However, few studies have directly examined this interaction in patients with type 2 diabetes.

We hypothesized that BMI may modify the CV and kidney outcomes associated with GLP-1 RAs in patients with type 2 diabetes. To evaluate this hypothesis, we conducted a large-scale retrospective cohort study comparing GLP-1 RAs with dipeptidyl peptidase-4 (DPP-4) inhibitors, a commonly used second-line therapy in Taiwan with a well-established neutral CV and kidney profile.^11,12 We analyzed data from a multi-institutional health care system, stratifying by BMI to evaluate whether treatment outcomes differed across weight categories.

Method

This cohort study was approved by the ethical review board of Chang Gung Medical Foundation with a waiver of informed consent because data were deidentified. This cohort study was conducted in accordance with the Declaration of Helsinki and followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.

Patients and Study Design

We conducted a retrospective cohort study using a new-user, active comparator design.¹³ Participants were patients with type 2 diabetes who initiated GLP-1 RAs or DPP-4 inhibitors in Chang-Gung Memorial Hospitals from December 15, 2011, to December 31, 2022. As the analysis was based on a clinical population with fixed inclusion criteria, no formal sample size calculation was performed. Key exclusion criteria included age younger than 20 years, type 1 diabetes, HF with reduced ejection fraction (<40%), bariatric surgery, prior use of sodium-glucose cotransporter 2 inhibitors, missing baseline hemoglobin A_1c or BMI, follow-up less than 90 days, and treatment switching within 90 days (eMethods in Supplement 1). Patients were stratified by BMI (≥25 vs <25) and treatment group (GLP-1 RAs vs DPP-4 inhibitors).^14,15

Data Source

We used the Chang Gung Research Database, a multi-institutional electronic medical record database managed by the Chang Gung Medical Foundation health care system in Taiwan. All data were anonymized and deidentified. Details regarding the Chang Gung Research Database have been published elsewhere.^16,17 Detailed information is provided in eMethods in Supplement 1.

Covariates and Baseline Characteristics

Baseline characteristics included demographics, comorbidities, laboratory values, and medications, all selected based on clinical relevance and previous literature on cardiorenal risk factors.¹⁸ These covariates were used for propensity score estimation and adjusted for in all outcome models. Details regarding covariate definitions and diagnostic coding are provided in the eMethods and eTable 1 in Supplement 1.

Outcomes Definition

Primary outcomes were MACE, including CV death, myocardial infarction, ischemic stroke, and HHF, and composite kidney outcomes, defined as at least 50% decline in estimated glomerular filtration rate (eGFR) or progression to dialysis. Secondary outcomes included all-cause mortality, infection-related admission, composite lower-limb events, hypoglycemia, diabetic ketoacidosis (DKA) and hyperosmolar hyperglycemic state (HHS), and pancreatitis. Detailed information is provided in the eMethods in Supplement 1. Mortality data were linked to the national death registry. Patients were followed-up from the date of initial dispensation (the index date) until the day of outcome occurrence, drug switch (eg, DPP4 inhibitor to GLP-1 RA), death, or the end of the database (December 31, 2022), whichever came first.

Statistical Analysis

We conducted 1:1 propensity score matching to reduce confounding between treatment groups. Matching was based on clinically relevant variables using a nearest-neighbor algorithm with a caliper of 0.2.¹⁹ Matching was conducted in a random order without replacement to ensure unbiased pair selection and performed separately for the nonobese and obese groups. Covariate balance was assessed using standardized mean differences.

Outcomes were analyzed in the matched cohort using Cox proportional hazards models for mortality and Fine-Gray subdistribution models for nonfatal events, accounting for the competing risk of death.²⁰ BMI was also modeled as a continuous variable using restricted cubic splines to evaluate interaction effects. A 2-sided P < .05 was considered statistically significant.

Additional details on the propensity score estimation, missing data imputation, and spline model specifications are provided in the eMethods in Supplement 1. The restricted cubic spline analysis was carried out using R version 4.3.2 (R Project for Statistical Computing) with the rms and interactionRCS packages. The remaining analyses (including matching) were conducted using SAS software version 9.4 (SAS Institute). Analyses were conducted from December 15, 2023, to July 5, 2024.

Sensitivity analyses included comparisons of individual GLP-1 RAs (liraglutide and dulaglutide), stratified analyses by prior sodium-glucose cotransporter 2 inhibitor use and concomitant insulin use, exclusion of thiazolidinediones or pancreatitis patients, inverse probability of treatment weighting, and multiple imputation for missing data. Full details are provided in the eMethods in Supplement 1.

Results

Patient Selection and Demographic Data

We identified 97 156 patients with type 2 diabetes who received their first prescription for GLP-1RAs or DPP-4 inhibitors between December 15, 2011, and December 31, 2022 (Figure 1). Among 8285 patients using GLP-1 RAs, dulaglutide (3433 patients [41.6%]) and liraglutide (2734 patients [33.1%]) were the most prescribed, followed by semaglutide (1239 patients [15.0%]), Soliqua (a fixed combination of lixisenatide and insulin glargine; 694 patients [8.4%]), and exenatide (185 patients [2.2%]). Before propensity score matching, GLP-1 RAs users were generally younger and had longer diabetes duration, higher rates of diabetic complications, and more frequent use of thiazolidinedione and insulin (Table 1). After matching, a total of 7200 matched patients (mean [SD] age, 57.4 [14.2] years; 7473 [51.9%] female) were included (1841 pairs with BMI <25 and 5359 pairs with BMI ≥25). Baseline characteristics were well balanced across groups in each BMI category (eTable 2 in Supplement 1).

Figure 1. — BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); DPP4i, DPP-4 inhibitors; EF, ejection fraction; GLP-1 RA, glucagon-like peptide-1 receptor agonist; HbA_1c, glycated hemoglobin A_1c; SLGT2i, sodium–glucose cotransporter 2 inhibitor.

Table 1. Baseline Characteristics of Patients With Type 2 Diabetes Treated With GLP-1 RAs vs DPP4i Stratified by BMI.

Variable	BMI <25				BMI ≥25
	Available No.	Patients, No. (%)		STD	Available No.	Patients, No. (%)		STD
	Available No.	GLP-1 RAs (n = 1953)	DPP4i (n = 37 443)	STD	Available No.	GLP-1 RAs (n = 6332)	DPP4i (n = 51 428)	STD
Age, mean (SD), y	39 396	61.0 (14.2)	66.1 (12.4)	−0.38	57 760	55.6 (14.0)	62.2 (12.9)	−0.49
Sex
Female	39 396	1062 (54.4)	17 422 (46.5)	0.16	57 760	3252 (51.4)	22 503 (43.8)	0.15
Male	39 396	891 (45.6)	20 021 (53.5)	−0.16	57 760	3080 (48.6)	28 925 (56.2)	−0.15
Smoking	39 396	307 (15.7)	6688 (17.9)	−0.06	57 760	1054 (16.6)	9071 (17.6)	−0.03
Alcohol consumption	39 396	149 (7.6)	3832 (10.2)	−0.09	57 760	504 (8.0)	5422 (10.5)	−0.09
BMI, mean (SD)	39 396	22.7 (1.9)	22.4(2.0)	0.15	57 760	31.1 (5.0)	29.1 (3.6)	0.46
Established ASCVD^a	39 396	651 (33.3)	11 475 (30.6)	0.06	57 760	1835 (29.0)	16 604 (32.3)	−0.07
Multiple risk factors for ASCVD^b	39 396	506 (25.9)	10 937 (29.2)	−0.07	57 760	1317 (20.8)	13 435 (26.1)	−0.13
Severity of diabetes
Insulin use	39 396	613 (31.4)	5644 (15.1)	0.39	57 760	1583 (25.0)	5305 (10.3)	0.39
Diabetes duration, y
Mean (SD)	39 396	8.9 (6.7)	5.0(5.4)	0.64	57 760	7.6 (6.2)	4.2 (4.9)	0.60
Median (IQR)	39 396	9.0 (2.1-15.2)	3.2 (0.1-9.2)	NA	57 760	7.0 (1.4-12.6)	2.5 (0.1-7.4)	NA
Glycated hemoglobin, mean (SD), %	39 396	8.8 (2.1)	8.4 (2.1)	0.18	57 760	8.7 (2.0)	8.3 (1.8)	0.19
Diabetic nephropathy	39 396	704 (36.0)	6489 (17.3)	0.43	57 760	2103 (33.2)	8301 (16.1)	0.40
Diabetic retinopathy	39 396	435 (22.3)	3736 (10.0)	0.34	57 760	1094 (17.3)	3776 (7.3)	0.31
Diabetic neuropathy	39 396	1012 (51.8)	9470 (25.3)	0.57	57 760	2871 (45.3)	11 181 (21.7)	0.52
Diabetic foot	39 396	103 (5.3)	1036 (2.8)	0.13	57 760	235 (3.7)	889 (1.7)	0.12
Outpatient visits for diabetes, No.
Mean (SD)	39 396	6.1 (5.0)	3.7 (4.1)	0.52	57 760	5.9 (4.8)	3.6(3.8)	0.55
Median (IQR)	39 396	5.0 (3.0-9.0)	3.0 (0.0-5.0)	NA	57 760	5.0 (3.0-8.0)	3.0 (0.0-5.0)	NA
LVEF
Mean (SD), %	12 704	67.3 (9.9)	67.8 (9.8)	−0.05	19 142	67.8 (8.8)	68.0 (9.2)	−0.02
<50%	12 704	36 (6.2)	723 (6.0)	0.01	19 142	65 (3.4)	720 (4.2)	0.04
≥50%	12 704	546 (93.8)	11 399 (94.0)		19 142	1821 (96.6)	16 536 (95.8)
HHF in the previous 1 y	39 396	56 (2.9)	966 (2.6)	0.02	57 760	96 (1.5)	931 (1.8)	−0.02
HHF history	39 396	105 (5.4)	1555 (4.2)	0.06	57 760	269 (4.2)	1900 (3.7)	0.03
Serum creatinine, mean (SD), mg/dL	37 623	1.2 (0.9)	1.1 (0.9)	0.06	55 384	1.1 (0.7)	1.1 (0.8)	−0.02
eGFR, ml/min/1.73m²	37 623	83.1 (39.7)	85.8 (38.9)	−0.07	55 384	86.5 (37.2)	84.4 (35.4)	0.06
eGFR stage
≥60 mL/min/1.73m²	38 398	1275 (67.4)	26 689 (73.1)	−0.13	56 038	4515 (73.6)	37 448 (75.0)	−0.03
30-59 mL/min/1.73m²	38 398	380 (20.1)	6515 (17.8)	0.06	56 038	1099 (17.9)	8934 (17.9)	<0.01
<30 mL/min/1.73m²	38 398	167 (8.8)	2597 (7.1)	0.06	56 038	393 (6.4)	2995 (6.0)	0.02
Dialysis	38 398	71 (3.8)	704 (1.9)	0.11	56 038	128 (2.1)	526 (1.1)	0.08
Baseline comorbidity
Hypertension	39 396	1185 (60.7)	22 962 (61.3)	−0.01	57 760	4532 (71.6)	37 207 (72.3)	−0.02
Dyslipidemia	39 396	1393 (71.3)	19 760 (52.8)	0.39	57 760	4800 (75.8)	31 312 (60.9)	0.32
Coronary artery disease	39 396	463 (23.7)	6934 (18.5)	0.13	57 760	1391 (22.0)	11 532 (22.4)	−0.01
Ischemic stroke	39 396	165 (8.4)	3924 (10.5)	−0.07	57 760	346 (5.5)	4524 (8.8)	−0.13
Myocardial infarction	39 396	117 (6.0)	1514 (4.0)	0.09	57 760	271 (4.3)	2079 (4.0)	0.01
Coronary intervention	39 396	190 (9.7)	2201 (5.9)	0.14	57 760	475 (7.5)	3362 (6.5)	0.04
Peripheral artery disease	39 396	154 (7.9)	1930 (5.2)	0.11	57 760	361 (5.7)	2103 (4.1)	0.07
Critical limb ischemia	39 396	55 (2.8)	514 (1.4)	0.10	57 760	110 (1.7)	394 (0.8)	0.09
Atrial fibrillation	39 396	77 (3.9)	1751 (4.7)	−0.04	57 760	194 (3.1)	2262 (4.4)	−0.07
Gout	39 396	236 (12.1)	3764 (10.1)	0.06	57 760	1091 (17.2)	7574 (14.7)	0.07
Pancreatitis	39 396	58 (3.0)	968 (2.6)	0.02	57 760	177 (2.8)	971 (1.9)	0.06
Malignant neoplasm	39 396	230 (11.8)	5743 (15.3)	−0.10	57 760	560 (8.8)	6125 (11.9)	−0.10
Charlson Comorbidity Index score, mean (SD)	39 396	3.6 (2.5)	3.2 (2.3)	0.14	57 760	3.3 (2.3)	3.0 (2.1)	0.11
Baseline vital sign, mean (SD)
Systolic blood pressure, mm Hg	38 809	134.8 (21.3)	135.8 (21.5)	−0.05	57 087	141.3 (20.1)	141.5 (20.3)	−0.01
Diastolic blood pressure, mm Hg	38 805	74.3 (11.8)	74.8 (11.9)	−0.04	57 082	79.4 (12.4)	78.9 (12.2)	0.04
Heart rate, beat/min	38 748	85.7 (13.6)	84.5 (14.4)	0.09	57 010	86.9 (13.6)	83.8 (14.0)	0.22
Biochemistry data, mean (SD)
Cholesterol, mg/dL
Low-density lipoprotein	35 366	98.1 (44.6)	102.6 (45.1)	−0.10	53 288	102.3 (49.8)	106.4 (51.2)	−0.08
High-density lipoprotein	33 535	48.3 (14.4)	46.0 (13.6)	0.16	50 296	43.3 (11.3)	43.6 (11.5)	−0.02
Total cholesterol	35 056	172.3 (43.4)	174.2 (42.6)	−0.04	52 331	174.7 (42.3)	177.5 (41.3)	−0.07
Triglyceride, mg/dL	34 892	156.4 (124.8)	148.3 (105.9)	0.07	52 370	200.8 (145.1)	179.7 (123.5)	0.16
Hemoglobin, g/dL	24 683	12.3 (2.1)	12.2 (2.2)	0.06	30 973	13.2 (2.1)	13.0 (2.3)	0.08
Uric acid, mg/dL	19 644	5.7 (1.8)	5.9 (1.9)	−0.11	31 107	6.2 (1.7)	6.3 (1.8)	−0.04
UACR, mg/g
<30	17 109	466 (42.3)	9129 (57.0)	−0.30	26 451	1489 (40.4)	12 455 (54.7)	−0.29
30-300	17 109	382 (34.7)	4527 (28.3)	0.14	26 451	1292 (35.1)	6854 (30.1)	0.11
>300	17 109	253 (23.0)	2352 (14.7)	0.21	26 451	901 (24.5)	3460 (15.2)	0.23
Concomitant oral antiglycemic drugs
Biguanide	39 396	1194 (61.1)	29 426 (78.6)	−0.39	57 760	4309 (68.1)	42 382 (82.4)	−0.34
Sulfonylurea	39 396	1201 (61.5)	20 537 (54.8)	0.14	57 760	3898 (61.6)	26 476 (51.5)	0.20
Thiazolidinedione	39 396	450 (23.0)	2238 (6.0)	0.50	57 760	1452 (22.9)	3634 (7.1)	0.46
α Glucosidase inhibitors	39 396	344 (17.6)	4263 (11.4)	0.18	57 760	1038 (16.4)	5099 (9.9)	0.19
Glinide	39 396	149 (7.6)	2699 (7.2)	0.02	57 760	309 (4.9)	2547 (5.0)	<0.01
Concomitant cardiovascular agents
RAASi	39 396	867 (44.4)	16 492 (44.0)	0.01	57 760	3678 (58.1)	29 678 (57.7)	0.01
β-blocker	39 396	426 (21.8)	7618 (20.3)	0.04	57 760	1580 (25.0)	13 229 (25.7)	−0.02
Calcium channel blocker	39 396	675 (34.6)	14 474 (38.7)	−0.09	57 760	2775 (43.8)	24 805 (48.2)	−0.09
Statin	39 396	1246 (63.8)	18 363 (49.0)	0.30	57 760	4134 (65.3)	28 937 (56.3)	0.19
Fibrates	39 396	132 (6.8)	2132 (5.7)	0.04	57 760	801 (12.7)	4747 (9.2)	0.11
Aspirin	39 396	489 (25.0)	9352 (25.0)	<0.01	57 760	1498 (23.7)	14 152 (27.5)	−0.09
P2Y12 receptor blockers	39 396	235 (12.0)	3481 (9.3)	0.09	57 760	450 (7.1)	4241 (8.2)	−0.04
Oral anticoagulants	39 396	57 (2.9)	1402 (3.7)	−0.05	57 760	161 (2.5)	1877 (3.6)	−0.06
Follow-up, y
Mean (SD)	39 396	2.5 (1.9	4.5 (3.0	−0.77	57 760	3.2 (2.5	4.9 (3.0	−0.63
Median (IQR)	39 396	2.0 (0.9-3.8)	4.1 (1.9-6.8)	NA	57 760	2.8 (1.1-4.6)	4.6 (2.3-7.4)	NA

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Abbreviations: ASCVD, atherosclerotic cardiovascular disease; BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); DPP4i, dipeptidyl peptidase-4 inhibitor; eGFR, estimated glomerular filtration rate; GLP-1 RA, glucagon-like peptide-1 receptor agonist; HHF, hospitalization for heart failure; LVEF, left ventricular ejection fraction; P2Y12, purinergic receptor P2Y G-protein coupled 12; RAASi, renin-angiotensin-aldosterone system inhibitor; STD, standardized difference; UACR, urine albumin to creatinine ratio.

SI conversion factors: To convert cholesterol to millimoles per liter, multiply by 0.0259; triglyceride to millimoles per liter, multiply by 0.0113; hemoglobin to grams per liter, multiply by 10; uric acid to millimoles per liter, multiply by 0.0595.

^{^a}

Any of coronary heart disease, coronary revascularization, ischemic stroke, intracerebral hemorrhage, carotid artery stent, myocardial infarction, peripheral artery disease, and lower-limb revascularization.

^{^b}

Male aged older than 55 years or female aged older than 60 years with hyperlipidemia, hypertension, and/or smoke.

CV Outcomes

The mean (SD) follow-up in the matched cohort was 3.1 (2.4) years. In patients with a BMI of less than 25, the risk of composite CV outcomes did not differ significantly between the GLP-1 RAs and DPP-4 inhibitors groups (8.6% vs 8.5%; hazard ratio [HR], 0.96; 95% CI, 0.77-1.20) (Table 2). Similarly, individual components of the CV outcomes, including CV death, MI, ischemic stroke, and HHF, showed no significant differences. However, in the BMI 25 or greater group, GLP-1 RA use was significantly associated with reduced risk of MACE compared with DPP-4 inhibitor use (6.3% vs 7.8%; HR, 0.79; 95% CI, 0.68-0.91), particularly noted in CV death (1.4% vs 2.2%; HR, 0.62; 95% CI, 0.46-0.83) and HHF (2.9% vs 3.8%; subdistribution HR [SHR], 0.77; 95% CI, 0.62-0.94) (Figure 2A-D).

Table 2. Outcomes of Patients With Diabetes Treated With GLP-1 RAs vs DPP4i in the Propensity Score–Matched Cohort Stratified by BMI.

Outcome	BMI <25			BMI ≥25
	Patients, No. (%)		GLP-1 RAs, HR/SHR (95% CI)	Patients, No. (%)		GLP-1 RAs, HR/SHR (95% CI)
	GLP-1 RAs (n = 1841)	DPP4i (n = 1841)	GLP-1 RAs, HR/SHR (95% CI)	GLP-1 RAs (n = 5359)	DPP4i (n = 5359)	GLP-1 RAs, HR/SHR (95% CI)
Major adverse cardiac events
Cardiovascular death^a	47 (2.6)	50 (2.7)	0.89 (0.60-1.32)	74 (1.4)	116 (2.2)	0.62 (0.46-0.83)^b
Myocardial infarction^c^,^d	33 (1.8)	35 (1.9)	0.92 (0.57-1.48)	65 (1.2)	81 (1.5)	0.80 (0.58-1.11)
Ischemic stroke^c	44 (2.4)	47 (2.6)	0.92 (0.61-1.38)	102 (1.9)	119 (2.2)	0.86 (0.66-1.12)
Hospitalization for heart failure^c	71 (3.9)	70 (3.8)	1.00 (0.71-1.39)	157 (2.9)	204 (3.8)	0.77 (0.62-0.94)^b
Composite MACE outcome^a^,^e	159 (8.6)	157 (8.5)	0.96 (0.77-1.20)	338 (6.3)	418 (7.8)	0.79 (0.68-0.91)^b
Kidney outcome
eGFR decline >50%^c	209 (11.4)	265 (14.4)	0.75 (0.62-0.90)^b	693 (12.9)	770 (14.4)	0.88 (0.79-0.98)^b
Progression to dialysis^c	75 (4.1)	111 (6.0)	0.66 (0.49-0.88)^b	215 (4.0)	268 (5.0)	0.79 (0.66-0.95)^b
Composite renal outcomes^c	233 (12.7)	302 (16.4)	0.73 (0.62-0.87)^b	734 (13.7)	820 (15.3)	0.87 (0.79-0.96)^b
Secondary outcome
All-cause death^a	140 (7.6)	208 (11.3)	0.64 (0.52-0.79)^b	278 (5.2)	445 (8.3)	0.61 (0.53-0.71)^b
Admission due to infection^c	289 (15.7)	328 (17.8)	0.85 (0.72-0.99)^b	757 (14.1)	877 (16.4)	0.84 (0.76-0.92)^b
Admission due to any cause^c	602 (32.7)	644 (35.0)	0.89 (0.79-0.99)^b	1779 (33.2)	1842 (34.4)	0.92 (0.87-0.99)^b
Composite MALE outcome^c^,^f	76 (4.1)	70 (3.8)	1.06 (0.77-1.47)	188 (3.5)	198 (3.7)	0.94 (0.77-1.15)
Hypoglycemia^c^,^g	86 (4.7)	54 (2.9)	1.57 (1.12-2.21)^b	180 (3.4)	138 (2.6)	1.31 (1.05-1.63)^b
DKA/HHS^c	189 (10.3)	136 (7.4)	1.39 (1.11-1.74)^b	455 (8.5)	354 (6.6)	1.30 (1.13-1.50)^b
Newly diagnosed pancreatitis^c	6 (0.3)	12 (0.7)	0.49 (0.18-1.30)	41 (0.8)	34 (0.6)	1.21 (0.77-1.91)

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Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); DKA, diabetic ketoacidosis; DPP4i, dipeptidyl peptidase-4 inhibitor; eGFR, estimated glomerular filtration rate; GLP-1 RA, glucagon-like peptide-1 receptor agonist; HHS, hyperosmolar hyperglycemic syndrome; HR, hazard ratio; MACE, major adverse cardiovascular events; MALE, major adverse lower-limb events; SHR, subdistribution hazard ratio.

^{^a}

Cox proportional hazards model.

^{^b}

P < .05.

^{^c}

Fine and Gray subdistribution hazard model.

^{^d}

Hospitalization for acute myocardial infarction and troponin-I greater than 0.5 or troponin-P greater than 100.

^{^e}

Composite of cardiovascular death, myocardial infarction, ischemic stroke, or hospitalization for heart failure.

^{^f}

Anyone of newly diagnosed peripheral artery disease (including critical limb ischemia), endovascular therapy/peripheral bypass and non-traumatic major amputation.

^{^g}

Fasting glucose less than 54 mg/dL.

Figure 2. — BMI indicates body mass index (calculated as weight in kilograms divided by height in meters squared); DPP4i, DPP-4 inhibitor; GLP-1 RA, glucagon-like peptide-1 receptor agonist.

Kidney Outcomes

GLP-1 RAs were associated with reduced risk of kidney events in both BMI groups (Table 2). In patients with BMI less than 25, fewer patients using GLP-1 RAs progressed to severe eGFR decline (11.4% vs 14.4%; SHR, 0.75; 95% CI, 0.62-0.90) or dialysis (4.1% vs 6.0%; SHR, 0.66; 95% CI, 0.49-0.88) compared with those using DPP-4 inhibitors. Similar protective associations were observed in the BMI 25 or greater group (eGFR decline >50%: 12.9% vs 14.4%; SHR, 0.88; 95% CI, 0.79-0.98; progression to dialysis: 4.0% vs 5.0%; SHR, 0.79; 95% CI, 0.66-0.95) (Figure 2E-F).

Secondary Outcomes

GLP-1 RAs also were associated with significantly reduced risks of all-cause mortality (BMI <25: 7.6% vs 11.3%; HR 0.64; 95% CI, 0.52-0.79; BMI ≥25: 5.2% vs 8.3%; HR, 0.61; 95% CI, 0.53-0.71), admissions due to infections (BMI <25: 15.7% vs 17.8%; SHR, 0.85; 95% CI, 0.72-0.99; BMI ≥25: 14.1% vs 16.4%; SHR, 0.84; 95% CI, 0.76-0.92), and admissions due to any cause (BMI <25: 32.7% vs 35.0%; SHR, 0.89; 95% CI, 0.79-0.99; BMI ≥25: 33.2% vs 34.4%; SHR 0.92; 95% CI, 0.87-0.99) in both BMI groups compared with patients using DPP-4 inhibitors (Table 2). Notably, there were more events of hypoglycemia (BMI <25: 4.7% vs 2.9%; SHR, 1.57; 95% CI, 1.12-2.21; BMI ≥25: 3.4% vs 2.6%; SHR, 1.31; 95% CI, 1.05-1.63) and DKA and HHS (BMI <25: 10.3% vs 7.4%; SHR, 1.39; 95% CI, 1.11-1.74; BMI ≥25: 8.5% vs 6.6%; SHR, 1.30; 95% CI, 1.13-1.50) in the GLP-1 RAs group across all BMI categories.

Additional and Sensitivity Analysis

Several subgroup and sensitivity analyses were conducted to assess the robustness of the main findings. The association of GLP-1 RAs with HHF and MACE was more pronounced among patients with higher BMI, as demonstrated by restricted cubic spline modeling (Figure 3), while kidney benefits were consistent across BMI values. Details on variable distributions, spline modeling, subgroup interactions, propensity score weighting, and complete-case vs imputed analyses are provided in the eResults, eFigures 2 to 5, and eTables 3 to 11 in Supplement 1.

Figure 3. — Body mass index is calculated as weight in kilograms divided by height in meters squared. HR indicates hazard ratio.

Discussion

To our knowledge, this cohort study is the first study focusing on the differential outcomes associated with GLP-1 RAs based on BMI. In this large retrospective cohort study, we found that GLP-1 RAs were associated with significantly lower CV risk in patients with type 2 diabetes and BMI 25 or more compared with matched patients using DPP-4 inhibitors. Meanwhile, the protective associations of GLP-1 RAs for kidney outcomes were consistent across all BMI categories. These findings suggest a potential interaction between BMI and CV—but not kidney—benefits of GLP-1 RAs.

CV Benefits in Overweight

Our observations align with the STEP-HFpEF and SELECT trials, which demonstrated CV benefit of semaglutide in individuals with overweight or obesity.^9,10 The CV benefits of GLP-1 RAs in patients with overweight may be attributed to their multiple mechanisms of action on insulin sensitivity and inflammation. Obesity is closely associated with heightened insulin resistance and chronic inflammation, both well-known risk factors for CV diseases. GLP-1 RAs enhance insulin sensitivity in skeletal muscle and adipose tissue, independent of weight changes.^21,22 while also facilitating the cross-talk of adipose tissue and liver during glucose metabolism. This results in reduced hepatic fat content and improved hepatic insulin sensitivity.²³ Additionally, GLP-1 RAs improve adipocyte function by reducing proinflammatory adipokines and increasing insulin-sensitizing adiponectin secretion.²⁴ These metabolic improvements are particularly significant in patients with higher BMI, who typically have more severe adipose dysfunction, suggesting a targeted therapeutic effect in obese patients.

The inflammatory component of obesity contributes significantly to CV complications through endothelial dysfunction, atherosclerotic plaque development, and myocardial remodeling.^23,25,26 GLP-1 RAs demonstrate substantial anti-inflammatory properties,²⁷ including reduction of macrophage infiltration in adipose tissue,²⁸ decrease in systemic inflammatory markers (eg, C-reactive protein and interleukin-6), and positively modulate immune cell functions, including those of T cells and macrophages.^29,30 These anti-inflammatory effects of GLP-1 RAs may be particularly beneficial in patients with higher BMI, who typically exhibit more intense inflammatory states. These agents effectively address obesity-associated CV risks by diminishing both endothelial activation and atherogenic modifications in GLP-1 receptor-expressing cells.³¹

Beyond insulin sensitivity and inflammation, GLP-1 RAs provide comprehensive CV protection through multiple pathways. They improve lipid profiles by reducing low-density lipoprotein cholesterol, total cholesterol, and triglycerides.³² Moreover, GLP-1 RAs have been consistently associated with clinically meaningful reductions in systolic blood pressure, which are thought to be mediated through mechanisms involving vasodilation and natriuresis.³³ The weight reduction effects, particularly significant in patients with higher baseline BMI, contribute to CV benefits.³⁴ This multitargeted approach creates more effective CV protection than therapies primarily focusing on glycemic control alone.³⁵

In contrast, neutral findings in leaner patients may reflect differences in baseline risk and treatment response. Weight loss induced by GLP-1 RA in patients with low baseline BMI might lead to loss of muscle mass, potentially influencing their overall condition.³⁶ Additionally, changes in fluid balance induced by GLP-1 RAs could be less tolerated in patients with preexisting cardiac dysfunction.³⁷ Although we did not observe harm in this subgroup, these findings suggest the necessity for cautious use of GLP-1 RAs in populations with low BMI, particularly those with preexisting CV conditions. Further research is needed to elucidate the mechanisms underlying the differential effects of GLP-1 RAs across BMI groups, particularly in patients with low BMI and preexisting HF.

Kidney Outcomes

Our study corroborates the nephroprotective potential of GLP-1 RAs, demonstrating beneficial associations with kidney outcomes in patients with diabetes irrespective of BMI. The differential associations of GLP-1 RAs with CV vs kidney outcomes across different weight groups may arise from various factors. GLP-1 improved kidney outcomes by inducing natriuresis, mitigating glomerular hyperfiltration, and reducing kidney inflammation and fibrosis.³⁸ These mechanisms are independent of body weight, which may explain our result. GLP-1 has been identified in the proximal tubules of rat and pig kidneys and the smooth muscle cells of both monkey and human vasculature, suggesting a systemic role in kidney function regulation.^39,40,41 The ability of GLP-1 to inhibit sodium-hydrogen exchanger pumps in the proximal tubules significantly contributes to blood pressure control, further benefiting kidney health.³⁸ Furthermore, GLP-1 RAs improve kidney hemodynamics by inhibiting pathways of glomerular hyperfiltration in patients with diabetes.⁴² The expression of GLP-1 RAs may also reduce diabetes-induced kidney inflammation and fibrosis.⁴³ These mechanisms, largely independent of body weight, support the role of GLP-1 RAs in improving kidney outcomes and suggest that their use could be particularly effective in patients with diabetes regardless of their BMI.

Several pivotal clinical trials supported the kidney protective capabilities of GLP-1 RAs. The LEADER study⁴ found that liraglutide significantly reduced the incidence of new-onset persistent macroalbuminuria. Similarly, the REWIND study³ reported improvements in new macroalbuminuria, a sustained decline in eGFR of 30% or more, and reductions in the need for chronic kidney replacement therapy. Moreover, the SUSTAIN-6 study⁵ observed that semaglutide effectively decreased persistent macroalbuminuria and doubled serum creatinine, maintained creatinine clearance above critical levels, and reduced the requirement for continuous kidney replacement therapy. A post hoc analysis of the LEADER and SUSTAIN-6 trials showed consistent kidney benefits of liraglutide and semaglutide across various BMI categories, revealing no heterogeneity in treatment effects.⁴⁴ This finding aligns with our results, confirming the broad nephroprotective potential of GLP-1 RAs, which appears to be effective irrespective of patient BMI. However, more research is needed to explore the relationship between BMI and kidney outcomes with GLP-1 RAs treatment.

Other Outcomes

Beyond their cardiometabolic outcomes, GLP-1 RAs were associated with reduced all-cause mortality and fewer infection-related hospitalizations across both BMI categories in our cohort. These findings suggest potential extraglycemic benefits of GLP-1 RAs. In patients with BMI 25 or more, a reduction in CV mortality was significantly associated with overall survival benefit. However, among those with BMI less than 25, the reduction in all-cause mortality was observed without significant changes in CV death, suggesting that improvements in non-CV outcomes, such as infection-related events. Recent studies showed that GLP-1 RAs may modulate immune responses, protecting organs in patients with sepsis.⁴⁵ The research by Steven et al⁴⁶ using an endotoxic shock model found that liraglutide could attenuate lipopolysaccharide-induced inflammatory pathways, thereby improving vascular function and reducing oxidative stress.⁴⁶ Another study in lipopolysaccharide-induced septic rats indicated that GLP-1 analogs maintain cardiac function and enhance survival rates.⁴⁷ These pleiotropic effects may contribute to the broader clinical benefits observed in our study.

We also observed a modest increase in hypoglycemia risk among patients using GLP-1 RAs, particularly when combined with other hypoglycemic agents. This risk likely reflects synergistic insulinotropic effects and has been reported in prior clinical trials.^48,49 Careful patient selection and medication adjustment may be necessary, especially in leaner patients or those receiving combination therapies.

Interestingly, our subgroup analysis indicated that patients receiving concomitant insulin therapy appeared to derive greater CV and kidney benefit from GLP-1 RAs. This may be explained by the fact that insulin-treated patients often have longer disease duration and greater cardiometabolic burden, and may therefore derive more benefit from the complementary mechanisms of GLP-1 RAs, including enhanced insulin sensitivity, reduced inflammation, and nephroprotective effects. The results are consistent with previous research suggesting that GLP-1 RAs remain beneficial even in populations with more advanced diabetes with high disease complexity.¹⁸

Limitations

This study has several limitations. First, the nonrandomized design introduces risks of selection bias, residual confounding, and reverse causality. Although propensity score matching improved baseline comparability, unmeasured variables, such as lifestyle, socioeconomic status, and cardiac function, may still influence outcomes. Furthermore, our analysis addressed only baseline covariates and did not account for time-varying confounders that may have arisen during the follow-up period, which could have impacted the observed associations. To mitigate early event bias, we excluded patients with follow-up less than 90 days; however, this approach does not fully address reverse causality.

Additionally, as all data were derived solely from the Chang Gung Research Database, our findings may be more representative of the Taiwanese population and may not be generalizable to other populations or health care settings. We also faced challenges with potential miscoding within the database. Efforts to mitigate this included correlating diagnostic codes with drug registration data, such as using direct eGFR measurements to define kidney function rather than relying on potentially inaccurately coded chronic kidney disease stage diagnoses. Additionally, medication adherence was inferred from prescription data, which may not accurately reflect actual usage, introducing another layer of uncertainty.

Conclusions

In this cohort study of patients with type 2 diabetes, the CV benefits associated with GLP-1 RAs were BMI-dependent, with significant reductions in CV death and heart failure hospitalization observed only in patients with BMI 25 or greater. In contrast, GLP-1 RAs demonstrated consistent kidney protective associations regardless of BMI, reducing the risk of eGFR decline and progression to dialysis in both BMI groups. Furthermore, GLP-1 RAs were associated with reduced all-cause mortality, infection-related hospitalizations, and all-cause hospitalizations across all BMI categories. However, careful monitoring is needed due to the increased risk of hypoglycemia and DKA or HHS events. Future studies should further investigate the mechanistic pathways of GLP-1 RAs, prescribing patterns in diverse populations, and evaluate their efficacy in patients with HF with preserved ejection fraction and lower BMI through randomized clinical trials.

Supplement 1.

eMethods.

eResults.

eReferences.

eTable 1. ICD codes for diseases used in this study

eTable 2. Baseline characteristics of type II diabetes mellitus patients treated with GLP-1 RAs versus DPP4i in the matched and imputed cohort stratified by the obesity status

eTable 3. Major outcomes of type II diabetes mellitus patients treated with dulaglutide versus DPP4i in the propensity matched cohort stratified by the obesity status

eTable 4. Major outcomes of type II diabetes mellitus patients treated with liraglutide versus DPP4i in the propensity matched cohort stratified by the obesity status

eTable 5. Subgroup analysis by previous use of SGLT2i in the propensity score matched cohort

eTable 6. Subgroup analysis by concomitant use of insulin in the propensity score matched cohort

eTable 7. Major outcomes of type II diabetes mellitus patients treated with GLP-1 RAs versus DPP4i in the propensity matched cohort, excluding patients exposed to thiazolidinediones

eTable 8. Major outcomes of type II diabetes mellitus patients treated with GLP-1 RAs versus DPP4i in the propensity matched cohort, excluding patients who were diagnosed with pancreatitis

eTable 9. Major outcomes of type II diabetes mellitus patients treated with GLP-1 RAs versus DPP4i in the IPTW-adjusted cohort

eTable 10. Baseline characteristics of patients with and without any missing data stratified by the obesity status

eTable 11. Major outcomes among patients with type 2 diabetes mellitus treated with GLP-1 receptor agonists versus DPP-4 inhibitors, analyzed using multivariable covariate adjustment and multiple imputation, with estimates pooled according to Rubin’s rules

eFigure 1. Body weight and eGFR changes in the propensity score matched cohort

eFigure 2. Quantile-quantile plot for the distribution of body mass index

eFigure 3. Quantile-quantile plot for the distribution of estimated glomerular filtration rate

eFigure 4. Quantile-quantile plot for the distribution of left ventricular ejection fraction

eFigure 5. Restricted cubic spline analysis of BMI thresholds for optimal benefits from GLP-1 RAs

jamanetwopen-e2530952-s001.pdf^{(1.2MB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2530952-s002.pdf^{(15.4KB, pdf)}

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