Next Article in Journal
Alternative Splicing Dysregulation in Retinitis Pigmentosa: Pathogenic Mechanisms and Therapeutic Opportunities
Previous Article in Journal
Circulating microRNAs and Plasma Gelsolin as Biomarkers of Sepsis: Molecular Insights and Prospects for Precision Medicine
Previous Article in Special Issue
The Role of LAIR1 as a Regulatory Receptor of Antitumor Immune Cell Responses and Tumor Cell Growth and Expansion
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomolecules
Volume 15
Issue 11
10.3390/biom15111622
biomolecules-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:
Systematic Review

Divergent Risks of Hematologic Malignancies Associated with GLP-1 Receptor Agonists and SGLT2 Inhibitors: Preliminary Findings from a Pilot Network Meta-Analysis

by
Pao-Yen Lin
1,2,†,
Bing-Yan Zeng
3,4,†,
Chih-Wei Hsu
1,
Mein-Woei Suen
5,6,7,8,
Chao-Ming Hung
9,10,
Brendon Stubbs
11,12,
Yen-Wen Chen
13,
Tien-Yu Chen
14,15,
Wei-Te Lei
16,17,
Jiann-Jy Chen
13,18,
Bing-Syuan Zeng
19,
Kuan-Pin Su
5,20,21,22,
Chih-Sung Liang
23,24,*,‡ and
Ping-Tao Tseng
3,5,13,25,*,‡
1
Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
2
Institute for Translational Research in Biomedical Sciences, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung 833, Taiwan
3
Institute of Biomedical Sciences, National Sun Yat-sen University, Kaohsiung 804, Taiwan
4
Department of Internal Medicine, E-Da Dachang Hospital, I-Shou University, Kaohsiung 807, Taiwan
5
Department of Psychology, College of Medical and Health Science, Asia University, Taichung 413, Taiwan
6
Gender Equality Education and Research Center, Asia University, Taichung 413, Taiwan
7
Department of Medical Research, Asia University Hospital, Asia University, Taichung 413, Taiwan
8
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung 404, Taiwan
9
Division of General Surgery, Department of Surgery, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan
10
School of Medicine, College of Medicine, I-Shou University, Kaohsiung 824, Taiwan
11
Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London WC2R 2LS, UK
12
Department of Sport, University of Vienna, Vienna 1030, Austria
13
Prospect Clinic for Otorhinolaryngology & Neurology, Kaohsiung 811, Taiwan
14
Department of Psychiatry, Tri-Service General Hospital, National Defense Medical University, Taipei 114, Taiwan
15
Department of Psychiatry, College of Medicine, National Defense Medical University, Taipei 114, Taiwan
16
Section of Immunology, Rheumatology, and Allergy Department of Pediatrics, Hsinchu Munipical MacKay Children’s Hospital, Hsinchu 300, Taiwan
17
Center for Molecular and Clinical Immunology, Chang Gung University, Taoyuan 333, Taiwan
18
Department of Otorhinolaryngology, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan
19
Department of Internal Medicine, E-Da Cancer Hospital, I-Shou University, Kaohsiung 824, Taiwan
20
Mind-Body Interface Research Center (MBI Lab & Care), China Medical University Hospital, Taichung 404, Taiwan
21
College of Medicine, China Medical University, Taichung 404, Taiwan
22
An-Nan Hospital, China Medical University, Tainan 709, Taiwan
23
Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, School of Medicine, National Defense Medical University, Taipei 112, Taiwan
24
Department of Psychiatry, National Defense Medical University, Taipei 114, Taiwan
25
Institute of Precision Medicine, National Sun Yat-sen University, Kaohsiung 804, Taiwan
 
add Show full affiliation list
 
remove Hide full affiliation list
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors also contributed equally to this work.
Biomolecules 2025, 15(11), 1622; https://doi.org/10.3390/biom15111622
Submission received: 11 September 2025 / Revised: 10 November 2025 / Accepted: 13 November 2025 / Published: 19 November 2025
(This article belongs to the Special Issue Advances in Cellular and Molecular Mechanisms in Immuno-Oncology and Onco-Hematology)
Browse Figures
Versions Notes

Abstract

Background: Although glucagon-like peptide-1 (GLP-1) receptor agonists and sodium–glucose cotransporter 2 (SGLT2) inhibitors have gained attention for their broad therapeutic effects, their influence on hematologic malignancy remains underexplored. Given the high mortality associated with hematologic cancers, clarifying the impact of these agents on such malignancies is essential. Objectives: This pilot network meta-analysis (NMA) aimed to assess the comparative risk of hematologic malignancies—including lymphoma, leukemia, and myeloma—associated with various GLP-1 receptor agonists and SGLT2 inhibitors. Methods: Following Cochrane-recommended confirmatory methods, we systematically searched multiple databases for randomized controlled trials (RCTs) published through 4 December 2024. The primary outcome was the incidence of overall hematologic malignancies. A frequentist random-effects NMA via the netmeta package was conducted, with additional validation through Bayesian NMA for solely sensitivity analyses. Results: Fifty-five RCTs (n = 200,606) were analyzed. Dulaglutide showed a significantly higher risk of overall hematologic malignancy [odds ratio (OR) = 2.18, 95% confidence interval (95%CI) = 1.14–4.19). In contrast, tirzepatide was linked to a significantly reduced risk (OR = 0.14, 95%CI = 0.03–0.60), especially for lymphoma. No statistically significant associations were identified for SGLT2 inhibitors (i.e., 95%CI across 1.0). Conclusions: Our preliminary findings reveal distinct and agent-specific effects of GLP-1 receptor agonists on hematologic malignancy risk. While dulaglutide may elevate the risk, tirzepatide appears protective, particularly against lymphoma. These results call for further long-term mechanistic studies to clarify causality and underlying pathways.

1. Introduction

Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium–glucose cotransporter 2 (SGLT2) inhibitors have emerged as innovative classes of antihyperglycemic agents, acting through mechanisms distinct from traditional therapies [1]. As the clinical application of these agents continues to grow, attention has also turned to their long-term safety profiles regarding cancer risk. While the relationship between these agents and solid malignancies [2] has been moderately explored, their influence on hematologic cancers remains largely uncharted. Previous reports had demonstrated that diabetes mellitus was associated with a higher risk of hematological malignancies and is an independent risk factor of all-cause and cause-specific mortality [3,4]. To date, only a few investigations have focused specifically on hematologic malignancies in the context of GLP-1 receptor agonist [5] or SGLT2 inhibitor [6] therapy. This knowledge gap is particularly significant given the generally poor prognosis associated with hematologic malignancies [7]. Fortunately, many large-scale randomized controlled trials (RCTs) have systematically captured adverse events, including hematologic cancers, within their safety assessments [8,9,10,11].
When RCTs are limited in duration or statistical power to assess rare adverse outcomes, meta-analytic methods can serve as a valuable tool to aggregate evidence. These methods harness data from extensive clinical trials and observational studies, offering insights that may reflect real-world clinical trends [12,13]. A well-executed network meta-analysis (NMA) is particularly advantageous, as it enables indirect comparisons among multiple interventions and facilitates the identification of superior regimens or doses [14]. Such approaches enhance evidence-based decision-making in clinical pharmacology.
Given the above, we undertook a pilot NMA to examine the comparative risks of hematologic malignancies associated with various GLP-1 receptor agonists and SGLT2 inhibitors. Our analytical framework builds upon previous studies examining neurological [15], colorectal cancer [2], metastatic cancer [16], and audiovestibular outcomes [17] related to these agents. This study represents the first NMA, to our knowledge, focusing specifically on hematologic malignancy risk in this drug class.

2. Methods

2.1. Study Design and Analytical Framework

This pilot NMA employed a hypothesis-driven design to preliminarily evaluate the hematologic malignancy risks linked to GLP-1 receptor agonists and SGLT2 inhibitors. Following the Cochrane Collaboration’s guidelines for adverse event-focused synthesis [18], we concentrated on predefined outcomes rather than general safety profiles. The review was conducted in accordance with the PRISMA extension statement for network meta-analyses (PRISMA-NMA) [19] (refer to Table S1). Our study was prospectively registered on PROSPERO (CRD42024622310) and approved by the Tri-Service General Hospital’s Institutional Review Board (TSGHIRB E202516007).

2.2. Literature Search Strategy

A broad and systematic literature search was conducted across eight major databases—PubMed, Embase, Cochrane CENTRAL, ClinicalTrials.gov, ProQuest, ScienceDirect, Web of Science, and ClinicalKey—up to 4 December 2024 (Table S2). Two independent reviewers (PT Tseng and BY Zeng) performed the selection process, starting with screening of titles and abstracts. Full-text evaluation was used for eligibility determination. Conflicts were resolved through mutual discussion. Additionally, reference lists from eligible reviews and prior meta-analyses were manually examined to identify supplementary studies. No filters for language were applied.

2.3. Eligibility Criteria

Study selection was guided by the PICOS model:
Population: Individuals without a prior diagnosis of hematologic malignancy
Intervention: Any GLP-1 receptor agonist or SGLT2 inhibitor, regardless of dosage
Comparator: Placebo, standard of care, or active control group
Outcome: Hematologic malignancy incidence
Study design: Randomized controlled trials (RCTs)
RCTs were eligible if they enrolled patients free from hematologic malignancy at baseline and evaluated the use of GLP-1 receptor agonists or SGLT2 inhibitors in human subjects, with either systematic adverse event tracking or explicit reporting of hematologic outcomes. Studies were excluded if they (1) involved pre-existing hematologic malignancies, (2) lacked a direct comparison group, (3) did not report hematologic events, (4) used a non-randomized design, or (5) were based on animal models. To reduce selective outcome reporting bias, only trials with structured monitoring of adverse events were retained [20].

2.4. Risk of Bias Assessment

Risk of bias was independently assessed by two reviewers using the Cochrane Risk of Bias Tool version 1.0 [21]. Any disagreement was resolved by consensus with a third investigator.

2.5. Outcome Definitions and Dose Categorization

Hematologic malignancies included in this pilot NMA were categorized as lymphoma, leukemia, and myeloma, based on definitions from the Global Burden of Disease project [22] and the 5th edition of the WHO classification of hematolymphoid neoplasms [23]. Dropout rate from trials, representing discontinuation for any cause, was used as a proxy for overall tolerability.
Medication dosages were stratified in accordance with definitions from their respective source trials:
Canagliflozin: Low: 100 mg; High: 300 mg.
Efpeglenatide: Low: 2 mg; Medium: 4 mg; High: 6 mg.
Ertugliflozin: Low: 5 mg; High: 15 mg.
Dapagliflozin: Low: 2.5 mg; Medium: 5 mg; High: 10 mg.
Injectable Semaglutide: Low: 0.5 mg; Medium: 1.0 mg; High: 2.4 mg.
Empagliflozin: Low: 1–10 mg; High: 25–50 mg.

2.6. Data Extraction and Handling

Two authors (PT Tseng and BY Zeng) independently extracted data on study design, patient demographics, intervention characteristics, malignancy outcomes, and trial discontinuations. Any unclear or missing information was clarified through correspondence with study authors. The extraction process followed Cochrane Handbook standards for systematic reviews [24].

2.7. Statistical Methodology

Random-effects models were utilized for all treatment comparisons involving more than one active comparator [25], and analyses were conducted using MetaInsight version 4.0.2 (Complex Reviews Support Unit, National Institute for Health Research, UK), which employs the netmeta package in R for frequentist NMAs [26].
When encountering single-zero-event arms, we applied a continuity correction to stabilize variance estimates, whereas studies with zero events in both arms were excluded to avoid underestimation of treatment effects [27,28]. Results were presented as odds ratios (ORs) with 95% confidence intervals (CIs), displayed via forest plots [29]. A network graph was constructed to visualize direct and indirect treatment connections. To assess agreement between direct and indirect comparisons, we implemented the node-splitting approach [26,30]. Statistical significance was defined as a two-sided p-value < 0.05.

2.8. Sensitivity and Subgroup Analyses

We conducted stratified analyses based on hematologic malignancy subtype—lymphoma, leukemia, and myeloma—to examine treatment-specific patterns. Further, a Bayesian NMA framework was used to validate the frequentist findings and assess model stability. Surface under the cumulative ranking curve (SUCRA) values were calculated to determine treatment hierarchy using Litmus Rank-O-Gram and radial SUCRA plots [31]. A deviation-from-model-fit analysis was conducted to assess potential inconsistency in treatment effects [32]. Finally, we applied the GRADE framework to evaluate the certainty of evidence across network estimates [33].

2.9. Ethics

All procedures adhered to the ethical principles of the Declaration of Helsinki.

3. Results

3.1. Study Selection and Characteristics

The literature review and selection process are illustrated in Figure 1. After removing 90 articles for failing to meet inclusion criteria (see Table S3), 54 publications encompassing 55 randomized controlled trials (RCTs) were included for analysis (Table S4) [8,9,10,11,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83]. These trials enrolled a total of 200,606 participants, with a mean age of 62.7 years (range: 41.2 to 71.9 years) and an average female proportion of 38.2% (range: 20.8–79.0%). The median follow-up period was 129.6 weeks, ranging from 12 to 281 weeks.
Across the included studies, we identified 24 distinct treatment arms: one standard-of-care or placebo group, and 23 active comparator groups representing various GLP-1 receptor agonists and SGLT2 inhibitors across multiple dosing regimens. GLP-1 receptor agonists examined included tirzepatide, efpeglenatide, liraglutide, albiglutide, dulaglutide, exenatide, semaglutide, and lixisenatide. SGLT2 inhibitors included bexagliflozin, canagliflozin, empagliflozin, ertugliflozin, dapagliflozin, and sotagliflozin.

3.2. Primary Outcome: Incidence of Overall Hematologic Malignancies

In this pilot NMA preliminarily evaluating all hematologic malignancies combined, dulaglutide was associated with a significantly elevated risk compared to control (OR = 2.18; 95% CI = 1.14–4.19). In contrast, tirzepatide demonstrated a significantly protective association, with markedly lower odds of hematologic malignancy compared to the control group (OR = 0.14; 95% CI = 0.03–0.60). Among all agents assessed, tirzepatide ranked as the safest in this domain (see Figure 2 and Figure 3, Figure S3A, and Table 1).

3.3. Subgroup Analysis: Specific Hematologic Cancer Types

When broken down by specific cancer subtype, tirzepatide continued to demonstrate a statistically significant reduction in lymphoma risk (OR = 0.18; 95% CI = 0.03–0.93) compared to the control arm. No other agent showed a significant increase or decrease in lymphoma risk relative to control (see Figures S1A, S2A and S3B, and Table S5A).
As for the risk of leukemia and myeloma, none of the investigated interventions exhibited statistically significant associations when compared with control groups (Figures S1B,C, S2B,C and S3C,D, and Table S5B,C).

3.4. Safety Profile: Dropout Rate

Dropout rate was used as a surrogate marker of overall tolerability. Several agents were associated with significantly lower discontinuation rates than the control group, including liraglutide (OR = 0.50; 95% CI = 0.38–0.66), tirzepatide (OR = 0.56; 95% CI = 0.42–0.74), medium-dose injectable semaglutide (OR = 0.65; 95% CI = 0.43–0.98), high-dose injectable semaglutide (OR = 0.71; 95% CI = 0.52–0.96), and high-dose empagliflozin (OR = 0.74; 95% CI = 0.56–0.97). Among all agents, liraglutide ranked highest for treatment retention, followed by tirzepatide (Figures S1D, S2D and S3E, and Table S5D).

3.5. Robustness Evaluation: Bayesian-Based Sensitivity Analysis

To verify the consistency of findings, we performed parallel analyses using a Bayesian framework. The direction and magnitude of treatment effects in Bayesian NMA aligned closely with those obtained from frequentist models (Figure S4A–E). Drug rankings by Bayesian SUCRA values are displayed in Table S6A–E and Figure S5A–J. No significant deviation was detected in model fit across treatment comparisons as assessed by deviation-from-consistency modeling (Figure S6A–O).

3.6. Risk of Bias and Inconsistency Assessment

Across all studies, 78.7% (303 of 385 items) were judged to have a low risk of bias, 15.8% (61 items) as unclear, and 5.5% (21 items) as high risk (Figure S7A,B). Evaluation of consistency between direct and indirect evidence did not reveal significant discrepancies (Table S7A–E). The overall quality of evidence supporting the primary and secondary outcomes was rated as moderate to high using the GRADE approach (Table S8A–E).

4. Discussion

This pilot NMA offers preliminary views into the distinct hematologic safety profiles of GLP-1 receptor agonists and SGLT2 inhibitors. Among the GLP-1 receptor agonists, tirzepatide stood out for its association with a significantly reduced incidence of hematologic malignancy—especially lymphoma—while dulaglutide was linked to an elevated risk. In contrast, no significant associations were detected between any SGLT2 inhibitor and the risk of blood cancers. The divergent trends observed between tirzepatide and dulaglutide are particularly noteworthy, considering their shared classification within the GLP-1 receptor agonist family. These findings suggest that potential hematologic effects may differ considerably across agents, even within the same drug class.
One of the most prominent observations in this pilot study was the elevated risk of hematologic malignancy associated with dulaglutide. A possible mechanistic explanation may involve dulaglutide’s influence on apoptosis-related signaling cascades. Preclinical studies have suggested that dulaglutide can enhance anti-apoptotic activity by modulating the BCL2/BAX/c-caspase3 axis, a pathway implicated in tumor progression and cell survival [84]. Though a case linking dulaglutide to medullary thyroid carcinoma has been reported [85], evidence for its association with hematologic malignancy is still lacking in clinical settings. Another theoretical mechanism involves dulaglutide’s impact on gene methylation. One study reported that dulaglutide induces demethylation of CDH1, the gene encoding E-cadherin—a cell adhesion molecule whose dysregulation has been implicated in certain leukemias [86,87]. While these molecular signals could hypothetically explain the observed associations, no direct clinical evidence has yet confirmed causality. Therefore, the potential linkage between hematologic malignancy and such medication still remained early speculation. Thus, further clinical and mechanistic investigations are required to establish a clearer link between dulaglutide use and hematologic cancer development.
Conversely, tirzepatide was associated with a protective effect against overall hematologic malignancies, most notably in reducing lymphoma risk. In fact, the tirzepatide exerted a different reaction on GLP-1 receptor from those of traditional GLP-1 receptor agonists. Tirzepatide is a biased agonist of GLP-1 receptor with significantly weaker affinity to GLP-1 receptor than natural GLP-1 receptor agonists (such as dulaglutide) [88]. Specifically, tirzepatide could act more on enhancing cAMP expression but less on β arrestin 2 recruitment [89]. Previous reports had demonstrated the potential role of β arrestin 2 on carcinogenesis [90]. Although evidence from clinical trials on tirzepatide’s role in oncology is still sparse [91], early preclinical studies suggest it may inhibit oncogenic pathways. For example, Kong et al. demonstrated that tirzepatide exerted weight-dependent anti-tumor effects in mice, modulating metabolic and signaling pathways based on the animals’ baseline obesity status [91]. In obese mice, the compound suppressed glycolytic and ErbB signaling—two pathways linked to cancer proliferation. In leaner models, tirzepatide enhanced O-linked glycosylation and phospholipase D signaling. Overactivation of ErbB pathways is well-documented in multiple tumor types [92], and inhibition of this axis is an emerging strategy in cancer therapeutics [93]. Although these findings are promising, they remain largely theoretical and early speculated based on animal models. Human studies explicitly designed to assess tirzepatide’s potential anti-cancer effects are still lacking and urgently needed. Future large-scale clinical trials and NMAs should be warranted to support or refute the result of our study.

Strengths and Limitations

Our analysis possesses several methodological advantages that enhance its clinical relevance. The use of NMA enabled us to perform indirect comparisons across a broad spectrum of GLP-1 receptor agonists and SGLT2 inhibitors, generating more granular insights than conventional meta-analyses. By restricting our inclusion criteria to RCTs, we ensured that the source data were of high methodological quality. Furthermore, we excluded patients with pre-existing hematologic malignancies, allowing for a more accurate assessment of drug-related causality or prophylaxis. We also carried out malignancy-type-specific subgroup analyses, providing more detailed results to inform targeted clinical strategies. Lastly, Bayesian sensitivity analysis reinforced the validity of our findings, as results remained consistent across modeling frameworks.
Nonetheless, several limitations merit discussion. First, the average trial duration of approximately 129.6 weeks (around 2.5 years) may be insufficient to fully capture the long-term risk of developing malignancy. Although the hematologic malignancy might have shorter period of latency (around 2 years) than solid tumors, the relatively shorter trial duration might still limit the property of hematologic malignancy detection [94]. Second, while our focus on RCTs enhances internal validity, it may exclude useful data from well-conducted observational studies. Although we recognized the fact that the numbers of included studies would matter a lot in clinical practice, we kept to only include RCTs due to avoidance of heterogeneity of study designation. Third, heterogeneity in diagnostic methodology across international trials could introduce variability in how hematologic cancers were identified, potentially affecting accuracy in pooled estimates. Fourth, given the small number of target events, the main results of this NMA should be interpreted with caution. Additionally, most included RCTs were not primarily designed to evaluate hematologic cancer outcomes, as they typically addressed glucose control in diabetes or weight reduction in obesity. Therefore, malignancy data may have been underreported or inconsistently monitored. Since adverse event reporting often receives less rigorous scrutiny than primary outcomes, there’s a possibility that hematologic events may have been missed or incompletely recorded. In addition, they were not initially designed to exclude the potential confounding factors, such as drug interactions, tolerance of these antidiabetics and further risk factors of these malignancies. Therefore, although the patient characteristic data of the current study fell within the hematologic malignancy range (i.e., age, gender distribution), we could not make further exploration regarding the confounding effects related to patient characteristics.

5. Conclusions

In summary, this pilot NMA provides preliminary evidence suggesting agent-specific differences in hematologic malignancy risk among GLP-1 receptor agonists. Specifically, dulaglutide was linked to a significantly higher incidence of overall hematologic malignancies, whereas tirzepatide demonstrated a notable risk-reducing effect—particularly for lymphoma. In contrast, no meaningful associations were identified for SGLT2 inhibitors regarding hematologic cancer risk. However, given the nature of the preliminary findings of this pilot NMA, we could only view the results as an exploratory finding but not final conclusion. Future research should prioritize large-scale RCTs directly addressing the risk of hematologic malignancy related to specific GLP-1 receptor agonists prescription. Further, to support or refute the results of the current NMA, large-scale and comprehensive NMA addressing various new generation antidiabetic agents should be warranted.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biom15111622/s1, Figure S1: Network structure of NMA of primary outcome and safety profile; Figure S2: Forest plot of NMA of primary outcome and safety profile; Figure S3: Individual study result of primary outcome and safety profile; Figure S4: Bayesian-based forest plot of NMA of primary outcome and safety profile; Figure S5: Bayesian-based Litmus Rank-O-Gram rank plot of primary outcome; Figure S6: Bayesian-based residual deviance NMA/UME model of primary outcome; Figure S7: Overview of risk of bias; Table S1: PRISMA 2020 checklist of the current network meta-analysis; Table S2: Keyword used in each database and search results; Table S3: Excluded studies and reason; Table S4: Characteristics of the included studies; Table S5: League table of NMA of primary outcome and safety profile; Table S6: SUCRA (Surface under the cumulative ranking) of primary outcome and safety profile; Table S7: Inconsistency within the network meta-analysis of primary outcome and safety profile; Table S8: GRADE of primary outcome and safety profile.

Author Contributions

P.-Y.L. and B.-Y.Z., who contributed equally as first authors, took the whole responsibility of literature search, data extraction, data analysis, and manuscript drafting. C.-W.H., M.-W.S., C.-M.H., B.S., Y.-W.C., T.-Y.C., W.-T.L., J.-J.C., B.-S.Z. and K.-P.S. contributed to study design, concept formation, and manuscript revision. C.-S.L. and P.-T.T., who contributed equally as corresponding authors, took the whole responsibility of collection of information from the other authors, manuscript major revision, and manuscript submission. All authors have read and agreed to the published version of the manuscript.

Funding

The authors of this work were supported by the following grants: Brendon Stubbs is supported by the NIHR Brendon Stubbs is part funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust. Brendon Stubbs is also supported by the Maudsley Charity, King’s College London.

Institutional Review Board Statement

The study protocol was approved by the Institutional Review Board of the Tri-Service General Hospital, National Defense Medical University (TSGHIRB E202516007) on 10 February 2025.

Informed Consent Statement

The current study did not directly involve individual participant so that we did not have the chance to approach individual participant or explore individual participant’s information. Therefore, it is impossible to obtain consent to participate in the current study.

Data Availability Statement

No new data were created or analyzed in this study.

Acknowledgments

This paper presents independent research. The views expressed in this publication are those of the authors and not necessarily those of the acknowledged institutions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Avogaro, A.; de Kreutzenberg, S.V.; Morieri, M.L.; Fadini, G.P.; Del Prato, S. Glucose-lowering drugs with cardiovascular benefits as modifiers of critical elements of the human life history. Lancet Diabetes Endocrinol. 2022, 10, 882–889. [Google Scholar] [CrossRef]
  2. Hung, C.M.; Zeng, B.Y.; Hsu, C.W.; Chen, P.H.; Sun, C.K.; Carvalho, A.F.; Stubbs, B.; Chen, Y.W.; Chen, T.Y.; Lei, W.T.; et al. The different colorectal tumor risk related to GLP-1 receptor agonists and SGLT2 inhibitors use: A network meta-analysis of 68 randomized controlled trials. Int. J. Surg. 2025. [Google Scholar] [CrossRef]
  3. Gong, I.Y.; Cheung, M.C.; Read, S.; Na, Y.; Lega, I.C.; Lipscombe, L.L. Association between diabetes and haematological malignancies: A population-based study. Diabetologia 2021, 64, 540–551. [Google Scholar] [CrossRef]
  4. Wuthikraikun, C.; Panja, P.; Decha-Umphai, C.; Sananpanichkul, N.; Lawsereevanich, P.; Kobkarnsakul, T.; Ngaosuwan, K. Impact of diabetes and glycemic control during cancer care on mortality in non-metastatic solid and hematologic malignancies. Ann. Med. 2025, 57, 2575108. [Google Scholar] [CrossRef]
  5. Yang, Z.; Lv, Y.; Yu, M.; Mei, M.; Xiang, L.; Zhao, S.; Li, R. GLP-1 receptor agonist-associated tumor adverse events: A real-world study from 2004 to 2021 based on FAERS. Front. Pharmacol. 2022, 13, 925377. [Google Scholar] [CrossRef] [PubMed]
  6. Lee, G.Y.; Huang, C.; Ma, K.S. Sodium Glucose Cotransporter-2 Inhibitor Is Associated with Lower Risk of Lymphoid Malignancy: Result from Large Real-World Cohorts. Blood 2023, 142, 379. [Google Scholar] [CrossRef]
  7. Yang, J.; Liu, X.; Zhong, Q.Z.; Yang, Y.; Wu, T.; Chen, S.Y.; Chen, B.; Song, Y.W.; Fang, H.; Wang, S.L.; et al. Disparities in mortality risk after diagnosis of hematological malignancies in 185 countries: A global data analysis. Cancer Lett. 2024, 595, 216793. [Google Scholar] [CrossRef] [PubMed]
  8. Hernandez, A.F.; Green, J.B.; Janmohamed, S.; D’Agostino, R.B., Sr.; Granger, C.B.; Jones, N.P.; Leiter, L.A.; Rosenberg, A.E.; Sigmon, K.N.; Somerville, M.C.; et al. Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): A double-blind, randomised placebo-controlled trial. Lancet 2018, 392, 1519–1529. [Google Scholar] [CrossRef]
  9. Jastreboff, A.M.; Aronne, L.J.; Ahmad, N.N.; Wharton, S.; Connery, L.; Alves, B.; Kiyosue, A.; Zhang, S.; Liu, B.; Bunck, M.C.; et al. Tirzepatide Once Weekly for the Treatment of Obesity. N. Engl. J. Med. 2022, 387, 205–216. [Google Scholar] [CrossRef]
  10. Neal, B.; Perkovic, V.; Mahaffey, K.W.; de Zeeuw, D.; Fulcher, G.; Erondu, N.; Shaw, W.; Law, G.; Desai, M.; Matthews, D.R.; et al. Canagliflozin and Cardiovascular and Renal Events in Type 2 Diabetes. N. Engl. J. Med. 2017, 377, 644–657. [Google Scholar] [CrossRef]
  11. Wiviott, S.D.; Raz, I.; Bonaca, M.P.; Mosenzon, O.; Kato, E.T.; Cahn, A.; Silverman, M.G.; Zelniker, T.A.; Kuder, J.F.; Murphy, S.A.; et al. Dapagliflozin and Cardiovascular Outcomes in Type 2 Diabetes. N. Engl. J. Med. 2019, 380, 347–357. [Google Scholar] [CrossRef]
  12. Wang, S.D. Opportunities and challenges of clinical research in the big-data era: From RCT to BCT. J. Thorac. Dis. 2013, 5, 721–723. [Google Scholar] [CrossRef]
  13. Zhang, Z. Big data and clinical research: Perspective from a clinician. J. Thorac. Dis. 2014, 6, 1659–1664. [Google Scholar] [CrossRef]
  14. Higgins, J.P.; Welton, N.J. Network meta-analysis: A norm for comparative effectiveness? Lancet 2015, 386, 628–630. [Google Scholar] [CrossRef]
  15. Tseng, P.T.; Zeng, B.Y.; Hsu, C.W.; Hung, C.M.; Carvalho, A.F.; Stubbs, B.; Chen, Y.W.; Chen, T.Y.; Lei, W.T.; Chen, J.J.; et al. The pharmacodynamics-based prophylactic benefits of GLP-1 receptor agonists and SGLT2 inhibitors on neurodegenerative diseases: Evidence from a network meta-analysis. BMC Med. 2025, 23, 197. [Google Scholar] [CrossRef]
  16. Hsu, C.W.; Zeng, B.S.; Liang, C.S.; Zeng, B.Y.; Hung, C.M.; Stubbs, B.; Chen, Y.W.; Lei, W.T.; Chen, J.J.; Chen, P.H.; et al. The Preventive Effects of GLP-1 Receptor Agonists and SGLT2 Inhibitors on Cancer Metastasis: A Network Meta-Analysis of 67 Randomized Controlled Trials. Int. J. Mol. Sci. 2025, 26, 8202. [Google Scholar] [CrossRef] [PubMed]
  17. Chen, J.J.; Hsu, C.W.; Hung, C.M.; Liang, C.S.; Su, K.P.; Carvalho, A.F.; Stubbs, B.; Chen, Y.W.; Chen, T.Y.; Lei, W.T.; et al. Risk of Hearing Loss in Patients Treated with Exendin-4 Derivatives: A Network Meta-Analysis of Glucagon-like Peptide-1 Receptor Agonists and Sodium-Glucose Cotransporter 2 Inhibitors. Pharmaceuticals 2025, 18, 735. [Google Scholar] [CrossRef] [PubMed]
  18. Peryer, G.; Golder, S.; Junqueira, D.; Vohra, S.; Loke, Y.K.; on Behalf of the Cochrane Adverse Effects Methods Group. Chapter 19: Adverse effects. In Cochrane Handbook for Systematic Reviews of Interventions; Higgins, J.P.T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M.J., Welch, V.A., Eds.; Cochrane: London, UK, 2023. [Google Scholar]
  19. Hutton, B.; Salanti, G.; Caldwell, D.M.; Chaimani, A.; Schmid, C.H.; Cameron, C.; Ioannidis, J.P.; Straus, S.; Thorlund, K.; Jansen, J.P.; et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: Checklist and explanations. Ann. Intern. Med. 2015, 162, 777–784. [Google Scholar] [CrossRef]
  20. Phillips, R.; Hazell, L.; Sauzet, O.; Cornelius, V. Analysis and reporting of adverse events in randomised controlled trials: A review. BMJ Open 2019, 9, e024537. [Google Scholar] [CrossRef] [PubMed]
  21. Higgins, J.; Green, S. Cochrane Handbook for Systematic Reviews of Interventions Version 5.0.2; The Cochrane Collaboration: London, UK, 2009. [Google Scholar]
  22. Keykhaei, M.; Masinaei, M.; Mohammadi, E.; Azadnajafabad, S.; Rezaei, N.; Saeedi Moghaddam, S.; Rezaei, N.; Nasserinejad, M.; Abbasi-Kangevari, M.; Malekpour, M.R.; et al. A global, regional, and national survey on burden and Quality of Care Index (QCI) of hematologic malignancies; global burden of disease systematic analysis 1990–2017. Exp. Hematol. Oncol. 2021, 10, 11. [Google Scholar] [CrossRef]
  23. Alaggio, R.; Amador, C.; Anagnostopoulos, I.; Attygalle, A.D.; Araujo, I.B.O.; Berti, E.; Bhagat, G.; Borges, A.M.; Boyer, D.; Calaminici, M.; et al. The 5th edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms. Leukemia 2022, 36, 1720–1748. [Google Scholar] [CrossRef]
  24. Chaimani, A.; Caldwell, D.M.; Li, T.; Higgins, J.P.T.; Salanti, G. Chapter 11: Undertaking network meta-analyses. In Cochrane Handbook for Systematic Reviews of Interventions; Cochrane: London, UK, 2018; Volume 7. [Google Scholar]
  25. Borenstein, M.; Hedges, L.V.; Higgins, J.P.; Rothstein, H.R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 2010, 1, 97–111. [Google Scholar] [CrossRef] [PubMed]
  26. Owen, R.K.; Bradbury, N.; Xin, Y.; Cooper, N.; Sutton, A. MetaInsight: An interactive web-based tool for analyzing, interrogating, and visualizing network meta-analyses using R-shiny and netmeta. Res. Synth. Methods 2019, 10, 569–581. [Google Scholar] [CrossRef] [PubMed]
  27. Cheng, J.; Pullenayegum, E.; Marshall, J.K.; Iorio, A.; Thabane, L. Impact of including or excluding both-armed zero-event studies on using standard meta-analysis methods for rare event outcome: A simulation study. BMJ Open 2016, 6, e010983. [Google Scholar] [CrossRef]
  28. Brockhaus, A.C.; Bender, R.; Skipka, G. The Peto odds ratio viewed as a new effect measure. Stat. Med. 2014, 33, 4861–4874. [Google Scholar] [CrossRef] [PubMed]
  29. Borenstein, M. Converting Among Effect Sizes. Available online: https://www.meta-analysis.com/downloads/Meta-analysis%20Converting%20among%20effect%20sizes.pdf (accessed on 30 December 2024).
  30. Dias, S.; Welton, N.J.; Caldwell, D.M.; Ades, A.E. Checking consistency in mixed treatment comparison meta-analysis. Stat. Med. 2010, 29, 932–944. [Google Scholar] [CrossRef]
  31. Nevill, C.R.; Cooper, N.J.; Sutton, A.J. A multifaceted graphical display, including treatment ranking, was developed to aid interpretation of network meta-analysis. J. Clin. Epidemiol. 2023, 157, 83–91. [Google Scholar] [CrossRef]
  32. Dias, S.; Ades, A.E.; Welton, N.J.; Jansen, J.P.; Sutton, A.J. Chapter 3: Model fit, model comparison and outlier detection. In Network Meta-Anlaysis for Decision-Making; Scott, M., Barnett, V., Eds.; John Wiley & Sons Ltd.: Chichester, UK, 2002; pp. 59–92. [Google Scholar]
  33. Brignardello-Petersen, R.; Izcovich, A.; Rochwerg, B.; Florez, I.D.; Hazlewood, G.; Alhazanni, W.; Yepes-Nunez, J.; Santesso, N.; Guyatt, G.H.; Schunemann, H.J.; et al. GRADE approach to drawing conclusions from a network meta-analysis using a partially contextualised framework. BMJ 2020, 371, m3907. [Google Scholar] [CrossRef]
  34. Anker, S.D.; Butler, J.; Filippatos, G.; Ferreira, J.P.; Bocchi, E.; Bohm, M.; Brunner-La Rocca, H.P.; Choi, D.J.; Chopra, V.; Chuquiure-Valenzuela, E.; et al. Empagliflozin in Heart Failure with a Preserved Ejection Fraction. N. Engl. J. Med. 2021, 385, 1451–1461. [Google Scholar] [CrossRef]
  35. Aronne, L.J.; Sattar, N.; Horn, D.B.; Bays, H.E.; Wharton, S.; Lin, W.Y.; Ahmad, N.N.; Zhang, S.; Liao, R.; Bunck, M.C.; et al. Continued Treatment With Tirzepatide for Maintenance of Weight Reduction in Adults With Obesity: The SURMOUNT-4 Randomized Clinical Trial. JAMA 2024, 331, 38–48. [Google Scholar] [CrossRef]
  36. Bailey, C.J.; Gross, J.L.; Pieters, A.; Bastien, A.; List, J.F. Effect of dapagliflozin in patients with type 2 diabetes who have inadequate glycaemic control with metformin: A randomised, double-blind, placebo-controlled trial. Lancet 2010, 375, 2223–2233. [Google Scholar] [CrossRef]
  37. Bhatt, D.L.; Szarek, M.; Pitt, B.; Cannon, C.P.; Leiter, L.A.; McGuire, D.K.; Lewis, J.B.; Riddle, M.C.; Inzucchi, S.E.; Kosiborod, M.N.; et al. Sotagliflozin in Patients with Diabetes and Chronic Kidney Disease. N. Engl. J. Med. 2021, 384, 129–139. [Google Scholar] [CrossRef] [PubMed]
  38. Buse, J.B.; Rosenstock, J.; Sesti, G.; Schmidt, W.E.; Montanya, E.; Brett, J.H.; Zychma, M.; Blonde, L.; for the LEAD-6 Study Group. Liraglutide once a day versus exenatide twice a day for type 2 diabetes: A 26-week randomised, parallel-group, multinational, open-label trial (LEAD-6). Lancet 2009, 374, 39–47. [Google Scholar] [CrossRef] [PubMed]
  39. Cannon, C.P.; Pratley, R.; Dagogo-Jack, S.; Mancuso, J.; Huyck, S.; Masiukiewicz, U.; Charbonnel, B.; Frederich, R.; Gallo, S.; Cosentino, F.; et al. Cardiovascular Outcomes with Ertugliflozin in Type 2 Diabetes. N. Engl. J. Med. 2020, 383, 1425–1435. [Google Scholar] [CrossRef]
  40. Cherney, D.Z.I.; Ferrannini, E.; Umpierrez, G.E.; Peters, A.L.; Rosenstock, J.; Powell, D.R.; Davies, M.J.; Banks, P.; Agarwal, R. Efficacy and safety of sotagliflozin in patients with type 2 diabetes and stage 3 chronic kidney disease. Diabetes Obes. Metab. 2023, 25, 1646–1657. [Google Scholar] [CrossRef]
  41. Danne, T.; Cariou, B.; Banks, P.; Brandle, M.; Brath, H.; Franek, E.; Kushner, J.A.; Lapuerta, P.; McGuire, D.K.; Peters, A.L.; et al. HbA(1c) and Hypoglycemia Reductions at 24 and 52 Weeks with Sotagliflozin in Combination with Insulin in Adults with Type 1 Diabetes: The European inTandem2 Study. Diabetes Care 2018, 41, 1981–1990. [Google Scholar] [CrossRef]
  42. Davies, M.; Faerch, L.; Jeppesen, O.K.; Pakseresht, A.; Pedersen, S.D.; Perreault, L.; Rosenstock, J.; Shimomura, I.; Viljoen, A.; Wadden, T.A.; et al. Semaglutide 2.4 mg once a week in adults with overweight or obesity, and type 2 diabetes (STEP 2): A randomised, double-blind, double-dummy, placebo-controlled, phase 3 trial. Lancet 2021, 397, 971–984. [Google Scholar] [CrossRef]
  43. Del Prato, S.; Kahn, S.E.; Pavo, I.; Weerakkody, G.J.; Yang, Z.; Doupis, J.; Aizenberg, D.; Wynne, A.G.; Riesmeyer, J.S.; Heine, R.J.; et al. Tirzepatide versus insulin glargine in type 2 diabetes and increased cardiovascular risk (SURPASS-4): A randomised, open-label, parallel-group, multicentre, phase 3 trial. Lancet 2021, 398, 1811–1824. [Google Scholar] [CrossRef] [PubMed]
  44. Ferrannini, E.; Berk, A.; Hantel, S.; Pinnetti, S.; Hach, T.; Woerle, H.J.; Broedl, U.C. Long-term safety and efficacy of empagliflozin, sitagliptin, and metformin: An active-controlled, parallel-group, randomized, 78-week open-label extension study in patients with type 2 diabetes. Diabetes Care 2013, 36, 4015–4021. [Google Scholar] [CrossRef]
  45. Frias, J.P.; Davies, M.J.; Rosenstock, J.; Perez Manghi, F.C.; Fernandez Lando, L.; Bergman, B.K.; Liu, B.; Cui, X.; Brown, K.; for the SURPASS-2 Investigators. Tirzepatide versus Semaglutide Once Weekly in Patients with Type 2 Diabetes. N. Engl. J. Med. 2021, 385, 503–515. [Google Scholar] [CrossRef]
  46. Gallo, S.; Charbonnel, B.; Goldman, A.; Shi, H.; Huyck, S.; Darekar, A.; Lauring, B.; Terra, S.G. Long-term efficacy and safety of ertugliflozin in patients with type 2 diabetes mellitus inadequately controlled with metformin monotherapy: 104-week VERTIS MET trial. Diabetes Obes. Metab. 2019, 21, 1027–1036. [Google Scholar] [CrossRef]
  47. Garvey, W.T.; Frias, J.P.; Jastreboff, A.M.; le Roux, C.W.; Sattar, N.; Aizenberg, D.; Mao, H.; Zhang, S.; Ahmad, N.N.; Bunck, M.C.; et al. Tirzepatide once weekly for the treatment of obesity in people with type 2 diabetes (SURMOUNT-2): A double-blind, randomised, multicentre, placebo-controlled, phase 3 trial. Lancet 2023, 402, 613–626. [Google Scholar] [CrossRef] [PubMed]
  48. Gerstein, H.C.; Colhoun, H.M.; Dagenais, G.R.; Diaz, R.; Lakshmanan, M.; Pais, P.; Probstfield, J.; Riesmeyer, J.S.; Riddle, M.C.; Ryden, L.; et al. Dulaglutide and cardiovascular outcomes in type 2 diabetes (REWIND): A double-blind, randomised placebo-controlled trial. Lancet 2019, 394, 121–130. [Google Scholar] [CrossRef]
  49. Gerstein, H.C.; Sattar, N.; Rosenstock, J.; Ramasundarahettige, C.; Pratley, R.; Lopes, R.D.; Lam, C.S.P.; Khurmi, N.S.; Heenan, L.; Del Prato, S.; et al. Cardiovascular and Renal Outcomes with Efpeglenatide in Type 2 Diabetes. N. Engl. J. Med. 2021, 385, 896–907. [Google Scholar] [CrossRef]
  50. Giorgino, F.; Benroubi, M.; Sun, J.H.; Zimmermann, A.G.; Pechtner, V. Efficacy and Safety of Once-Weekly Dulaglutide Versus Insulin Glargine in Patients With Type 2 Diabetes on Metformin and Glimepiride (AWARD-2). Diabetes Care 2015, 38, 2241–2249. [Google Scholar] [CrossRef]
  51. Grunberger, G.; Camp, S.; Johnson, J.; Huyck, S.; Terra, S.G.; Mancuso, J.P.; Jiang, Z.W.; Golm, G.; Engel, S.S.; Lauring, B. Ertugliflozin in Patients with Stage 3 Chronic Kidney Disease and Type 2 Diabetes Mellitus: The VERTIS RENAL Randomized Study. Diabetes Ther. 2018, 9, 49–66. [Google Scholar] [CrossRef] [PubMed]
  52. Hadjadj, S.; Rosenstock, J.; Meinicke, T.; Woerle, H.J.; Broedl, U.C. Initial Combination of Empagliflozin and Metformin in Patients With Type 2 Diabetes. Diabetes Care 2016, 39, 1718–1728. [Google Scholar] [CrossRef] [PubMed]
  53. Heerspink, H.J.L.; Stefansson, 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]
  54. Holman, R.R.; Bethel, M.A.; Mentz, R.J.; Thompson, V.P.; Lokhnygina, Y.; Buse, J.B.; Chan, J.C.; Choi, J.; Gustavson, S.M.; Iqbal, N.; et al. Effects of Once-Weekly Exenatide on Cardiovascular Outcomes in Type 2 Diabetes. N. Engl. J. Med. 2017, 377, 1228–1239. [Google Scholar] [CrossRef]
  55. Home, P.D.; Ahren, B.; Reusch, J.E.B.; Rendell, M.; Weissman, P.N.; Cirkel, D.T.; Miller, D.; Ambery, P.; Carr, M.C.; Nauck, M.A. Three-year data from 5 HARMONY phase 3 clinical trials of albiglutide in type 2 diabetes mellitus: Long-term efficacy with or without rescue therapy. Diabetes Res. Clin. Pract. 2017, 131, 49–60. [Google Scholar] [CrossRef]
  56. Husain, M.; Birkenfeld, A.L.; Donsmark, M.; Dungan, K.; Eliaschewitz, F.G.; Franco, D.R.; Jeppesen, O.K.; Lingvay, I.; Mosenzon, O.; Pedersen, S.D.; et al. Oral Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N. Engl. J. Med. 2019, 381, 841–851. [Google Scholar] [CrossRef] [PubMed]
  57. Kaku, K.; Yamada, Y.; Watada, H.; Abiko, A.; Nishida, T.; Zacho, J.; Kiyosue, A. Safety and efficacy of once-weekly semaglutide vs additional oral antidiabetic drugs in Japanese people with inadequately controlled type 2 diabetes: A randomized trial. Diabetes Obes. Metab. 2018, 20, 1202–1212. [Google Scholar] [CrossRef] [PubMed]
  58. Lincoff, A.M.; Brown-Frandsen, K.; Colhoun, H.M.; Deanfield, J.; Emerson, S.S.; Esbjerg, S.; Hardt-Lindberg, S.; Hovingh, G.K.; Kahn, S.E.; Kushner, R.F.; et al. Semaglutide and Cardiovascular Outcomes in Obesity without Diabetes. N. Engl. J. Med. 2023, 389, 2221–2232. [Google Scholar] [CrossRef]
  59. Lock, J.P. Bexagliflozin Efficacy and Safety Trial (BEST). Available online: https://clinicaltrials.gov/study/NCT02558296?cond=NCT02558296&rank=1 (accessed on 28 October 2024).
  60. Marso, S.P.; Bain, S.C.; Consoli, A.; Eliaschewitz, F.G.; Jodar, E.; Leiter, L.A.; Lingvay, I.; Rosenstock, J.; Seufert, J.; Warren, M.L.; et al. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N. Engl. J. Med. 2016, 375, 1834–1844. [Google Scholar] [CrossRef]
  61. Marso, S.P.; Daniels, G.H.; Brown-Frandsen, K.; Kristensen, P.; Mann, J.F.; Nauck, M.A.; Nissen, S.E.; Pocock, S.; Poulter, N.R.; Ravn, L.S.; et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N. Engl. J. Med. 2016, 375, 311–322. [Google Scholar] [CrossRef] [PubMed]
  62. McMurray, J.J.V.; Solomon, S.D.; Inzucchi, S.E.; Kober, L.; Kosiborod, M.N.; Martinez, F.A.; Ponikowski, P.; Sabatine, M.S.; Anand, I.S.; Belohlavek, J.; et al. Dapagliflozin in Patients with Heart Failure and Reduced Ejection Fraction. N. Engl. J. Med. 2019, 381, 1995–2008. [Google Scholar] [CrossRef]
  63. Nauck, M.; Frid, A.; Hermansen, K.; Shah, N.S.; Tankova, T.; Mitha, I.H.; Zdravkovic, M.; During, M.; Matthews, D.R.; for the LEAD-2 Study Group. Efficacy and safety comparison of liraglutide, glimepiride, and placebo, all in combination with metformin, in type 2 diabetes: The LEAD (liraglutide effect and action in diabetes)-2 study. Diabetes Care 2009, 32, 84–90. [Google Scholar] [CrossRef]
  64. Nauck, M.; Rizzo, M.; Johnson, A.; Bosch-Traberg, H.; Madsen, J.; Cariou, B. Once-Daily Liraglutide Versus Lixisenatide as Add-on to Metformin in Type 2 Diabetes: A 26-Week Randomized Controlled Clinical Trial. Diabetes Care 2016, 39, 1501–1509. [Google Scholar] [CrossRef]
  65. Nauck, M.A.; Stewart, M.W.; Perkins, C.; Jones-Leone, A.; Yang, F.; Perry, C.; Reinhardt, R.R.; Rendell, M. Efficacy and safety of once-weekly GLP-1 receptor agonist albiglutide (HARMONY 2): 52 week primary endpoint results from a randomised, placebo-controlled trial in patients with type 2 diabetes mellitus inadequately controlled with diet and exercise. Diabetologia 2016, 59, 266–274. [Google Scholar] [CrossRef]
  66. Packer, M.; Anker, S.D.; Butler, J.; Filippatos, G.; Pocock, S.J.; Carson, P.; Januzzi, J.; Verma, S.; Tsutsui, H.; Brueckmann, M.; et al. Cardiovascular and Renal Outcomes with Empagliflozin in Heart Failure. N. Engl. J. Med. 2020, 383, 1413–1424. [Google Scholar] [CrossRef]
  67. 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]
  68. Pfeffer, M.A.; Claggett, B.; Diaz, R.; Dickstein, K.; Gerstein, H.C.; Kober, L.V.; Lawson, F.C.; Ping, L.; Wei, X.; Lewis, E.F.; et al. Lixisenatide in Patients with Type 2 Diabetes and Acute Coronary Syndrome. N. Engl. J. Med. 2015, 373, 2247–2257. [Google Scholar] [CrossRef]
  69. Pi-Sunyer, X.; Astrup, A.; Fujioka, K.; Greenway, F.; Halpern, A.; Krempf, M.; Lau, D.C.; le Roux, C.W.; Violante Ortiz, R.; Jensen, C.B.; et al. A Randomized, Controlled Trial of 3.0 mg of Liraglutide in Weight Management. N. Engl. J. Med. 2015, 373, 11–22. [Google Scholar] [CrossRef]
  70. Ridderstrale, M.; Andersen, K.R.; Zeller, C.; Kim, G.; Woerle, H.J.; Broedl, U.C.; on Behalf of the EMPA-REG H2H-SU Trial Investigators. Comparison of empagliflozin and glimepiride as add-on to metformin in patients with type 2 diabetes: A 104-week randomised, active-controlled, double-blind, phase 3 trial. Lancet Diabetes Endocrinol. 2014, 2, 691–700. [Google Scholar] [CrossRef]
  71. Rosenstock, J.; Allison, D.; Birkenfeld, A.L.; Blicher, T.M.; Deenadayalan, S.; Jacobsen, J.B.; Serusclat, P.; Violante, R.; Watada, H.; Davies, M.; et al. Effect of Additional Oral Semaglutide vs Sitagliptin on Glycated Hemoglobin in Adults With Type 2 Diabetes Uncontrolled With Metformin Alone or With Sulfonylurea: The PIONEER 3 Randomized Clinical Trial. JAMA 2019, 321, 1466–1480. [Google Scholar] [CrossRef] [PubMed]
  72. Rosenstock, J.; Fonseca, V.A.; Gross, J.L.; Ratner, R.E.; Ahren, B.; Chow, F.C.; Yang, F.; Miller, D.; Johnson, S.L.; Stewart, M.W.; et al. Advancing basal insulin replacement in type 2 diabetes inadequately controlled with insulin glargine plus oral agents: A comparison of adding albiglutide, a weekly GLP-1 receptor agonist, versus thrice-daily prandial insulin lispro. Diabetes Care 2014, 37, 2317–2325. [Google Scholar] [CrossRef]
  73. Rubino, D.; Abrahamsson, N.; Davies, M.; Hesse, D.; Greenway, F.L.; Jensen, C.; Lingvay, I.; Mosenzon, O.; Rosenstock, J.; Rubio, M.A.; et al. Effect of Continued Weekly Subcutaneous Semaglutide vs Placebo on Weight Loss Maintenance in Adults With Overweight or Obesity: The STEP 4 Randomized Clinical Trial. JAMA 2021, 325, 1414–1425. [Google Scholar] [CrossRef] [PubMed]
  74. Solomon, S.D.; McMurray, J.J.V.; Claggett, B.; de Boer, R.A.; DeMets, D.; Hernandez, A.F.; Inzucchi, S.E.; Kosiborod, M.N.; Lam, C.S.P.; Martinez, F.; et al. Dapagliflozin in Heart Failure with Mildly Reduced or Preserved Ejection Fraction. N. Engl. J. Med. 2022, 387, 1089–1098. [Google Scholar] [CrossRef] [PubMed]
  75. Herrington, W.G.; Staplin, N.; Wanner, C.; Green, J.B.; Hauske, S.J.; Emberson, J.R.; Preiss, D.; Judge, P.; Mayne, K.J.; The EMPA-KIDNEY Collaborative Group; et al. Empagliflozin in Patients with Chronic Kidney Disease. N. Engl. J. Med. 2023, 388, 117–127. [Google Scholar] [CrossRef]
  76. Tuttle, K.R.; Levin, A.; Nangaku, M.; Kadowaki, T.; Agarwal, R.; Hauske, S.J.; Elsasser, A.; Ritter, I.; Steubl, D.; Wanner, C.; et al. Safety of Empagliflozin in Patients With Type 2 Diabetes and Chronic Kidney Disease: Pooled Analysis of Placebo-Controlled Clinical Trials. Diabetes Care 2022, 45, 1445–1452. [Google Scholar] [CrossRef]
  77. Umpierrez, G.; Tofe Povedano, S.; Perez Manghi, F.; Shurzinske, L.; Pechtner, V. Efficacy and safety of dulaglutide monotherapy versus metformin in type 2 diabetes in a randomized controlled trial (AWARD-3). Diabetes Care 2014, 37, 2168–2176. [Google Scholar] [CrossRef]
  78. Wada, T.; Mori-Anai, K.; Takahashi, A.; Matsui, T.; Inagaki, M.; Iida, M.; Maruyama, K.; Tsuda, H. Effect of canagliflozin on the decline of estimated glomerular filtration rate in chronic kidney disease patients with type 2 diabetes mellitus: A multicenter, randomized, double-blind, placebo-controlled, parallel-group, phase III study in Japan. J. Diabetes Investig. 2022, 13, 1981–1989. [Google Scholar] [CrossRef]
  79. Wason, S. Efficacy and Safety of Sotagliflozin Versus Placebo in Participants with Type 2 Diabetes Mellitus Who Have Inadequate Glycemic Control While Taking Insulin Alone or with Other Oral Antidiabetic Agents (SOTA-INS). Available online: https://clinicaltrials.gov/study/NCT03285594?cond=NCT03285594&rank=1 (accessed on 28 October 2024).
  80. Weissman, P.N.; Carr, M.C.; Ye, J.; Cirkel, D.T.; Stewart, M.; Perry, C.; Pratley, R. HARMONY 4: Randomised clinical trial comparing once-weekly albiglutide and insulin glargine in patients with type 2 diabetes inadequately controlled with metformin with or without sulfonylurea. Diabetologia 2014, 57, 2475–2484. [Google Scholar] [CrossRef]
  81. Wilding, J.P.; Woo, V.; Soler, N.G.; Pahor, A.; Sugg, J.; Rohwedder, K.; Parikh, S.; for the Dapagliflozin 006 Study Group. Long-term efficacy of dapagliflozin in patients with type 2 diabetes mellitus receiving high doses of insulin: A randomized trial. Ann. Intern. Med. 2012, 156, 405–415. [Google Scholar] [CrossRef]
  82. Wilding, J.P.H.; Batterham, R.L.; Calanna, S.; Davies, M.; Van Gaal, L.F.; Lingvay, I.; McGowan, B.M.; Rosenstock, J.; Tran, M.T.D.; Wadden, T.A.; et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N. Engl. J. Med. 2021, 384, 989–1002. [Google Scholar] [CrossRef]
  83. Zinman, B.; Wanner, C.; Lachin, J.M.; Fitchett, D.; Bluhmki, E.; Hantel, S.; Mattheus, M.; Devins, T.; Johansen, O.E.; Woerle, H.J.; et al. Empagliflozin, Cardiovascular Outcomes, and Mortality in Type 2 Diabetes. N. Engl. J. Med. 2015, 373, 2117–2128. [Google Scholar] [CrossRef] [PubMed]
  84. Guan, T.; Xiao, Y.; Xie, X.; Meng, N.; Qi, Q.; Xu, J.; Jiang, X.; Zhang, Z.; Teng, Z.; Lv, P. Dulaglutide Improves Gliosis and Suppresses Apoptosis/Autophagy Through the PI3K/Akt/mTOR Signaling Pathway in Vascular Dementia Rats. Neurochem. Res. 2023, 48, 1561–1579. [Google Scholar] [CrossRef] [PubMed]
  85. Vemula, H.; Villanueva, F.S.; Nguyen, H.D.; Mohan, A.; Potluri, S. Medullary Carcinoma of Thyroid Due to GLP-1 Receptor Agonist. J. Endocr. Soc. 2021, 5, A893. [Google Scholar] [CrossRef]
  86. Tatsch, J.M.; Furman, D.P.; Nobre, R.M.; Wurzer, K.M.; da Silva, L.C.; Picheth, G.F.; Ramos, E.A.; Acco, A.; Klassen, G. Dulaglutide as a demethylating agent to improve the outcome of breast cancer. Epigenomics 2023, 15, 1309–1322. [Google Scholar] [CrossRef]
  87. Zebardast, S.; Sahmani, M.; Mohammadi, S.; Foroughi, F.; Dehghani Fard, A.; Mohammadi, Z.; Khojastepour, S.; Azad, M. The Gene Expression Profile and DNA Methylation Pattern of CDH1 and DNMT1 Genes in Acute Promyelocytic Leukemia (APL). Rep. Biochem. Mol. Biol. 2020, 8, 454–457. [Google Scholar]
  88. Ma, Z.; Jin, K.; Yue, M.; Chen, X.; Chen, J. Research Progress on the GIP/GLP-1 Receptor Coagonist Tirzepatide, a Rising Star in Type 2 Diabetes. J. Diabetes Res. 2023, 2023, 5891532. [Google Scholar] [CrossRef]
  89. Willard, F.S.; Douros, J.D.; Gabe, M.B.; Showalter, A.D.; Wainscott, D.B.; Suter, T.M.; Capozzi, M.E.; van der Velden, W.J.; Stutsman, C.; Cardona, G.R.; et al. Tirzepatide is an imbalanced and biased dual GIP and GLP-1 receptor agonist. JCI Insight 2020, 5, e140532. [Google Scholar] [CrossRef] [PubMed]
  90. Song, Q.; Ji, Q.; Li, Q. The role and mechanism of beta-arrestins in cancer invasion and metastasis (Review). Int. J. Mol. Med. 2018, 41, 631–639. [Google Scholar] [CrossRef] [PubMed]
  91. Kong, W.; Deng, B.; Shen, X.; John, C.; Haag, J.; Sinha, N.; Lee, D.; Sun, W.; Chen, S.; Zhang, H.; et al. Tirzepatide as an innovative treatment strategy in a pre-clinical model of obesity-driven endometrial cancer. Gynecol. Oncol. 2024, 191, 116–123. [Google Scholar] [CrossRef] [PubMed]
  92. Cho, H.S.; Leahy, D.J. Structure of the extracellular region of HER3 reveals an interdomain tether. Science 2002, 297, 1330–1333. [Google Scholar] [CrossRef] [PubMed]
  93. Arteaga, C.L.; Engelman, J.A. ERBB receptors: From oncogene discovery to basic science to mechanism-based cancer therapeutics. Cancer Cell 2014, 25, 282–303. [Google Scholar] [CrossRef]
  94. Hicks, B.; Kaye, J.A.; Azoulay, L.; Kristensen, K.B.; Habel, L.A.; Pottegard, A. The application of lag times in cancer pharmacoepidemiology: A narrative review. Ann. Epidemiol. 2023, 84, 25–32. [Google Scholar] [CrossRef]
Biomolecules 15 01622 g001
Figure 1. PRISMA2020 Flowchart of current network meta-analysis.
Figure 1. PRISMA2020 Flowchart of current network meta-analysis.
Biomolecules 15 01622 g001
Biomolecules 15 01622 g002
Figure 2. Network structure of the primary outcome: overall hematologic malignancy. The overall structure of the network meta-analysis. The lines between nodes represent direct comparisons from various trials, with the green numbers over the lines indicating the number of trials providing these comparisons for each specific treatment. The thickness of the lines corresponds to the number of trials linked to the network.
Figure 2. Network structure of the primary outcome: overall hematologic malignancy. The overall structure of the network meta-analysis. The lines between nodes represent direct comparisons from various trials, with the green numbers over the lines indicating the number of trials providing these comparisons for each specific treatment. The thickness of the lines corresponds to the number of trials linked to the network.
Biomolecules 15 01622 g002
Biomolecules 15 01622 g003
Figure 3. Forest plot of primary outcome: overall hematologic malignancy. When the effect size (expressed as odds ratio) is less than 1, the specified treatment is associated with fewer hematologic malignancy events compared to placebo/controls. Abbreviation: 95%CIs: 95% confidence intervals; GLP-1 agonist: glucagon-like peptide-1 agonist; NMA: network meta-analysis; OR: odds ratio; RCT: randomized controlled trial; SGLT2 inhibitor: sodium–glucose cotransporter 2 inhibitor.
Figure 3. Forest plot of primary outcome: overall hematologic malignancy. When the effect size (expressed as odds ratio) is less than 1, the specified treatment is associated with fewer hematologic malignancy events compared to placebo/controls. Abbreviation: 95%CIs: 95% confidence intervals; GLP-1 agonist: glucagon-like peptide-1 agonist; NMA: network meta-analysis; OR: odds ratio; RCT: randomized controlled trial; SGLT2 inhibitor: sodium–glucose cotransporter 2 inhibitor.
Biomolecules 15 01622 g003
Table 1. League table of the primary outcome: overall hematologic malignancy.
Table 1. League table of the primary outcome: overall hematologic malignancy.
Tirzepatide0.11 [0.00; 2.72]* 0.19 [0.04; 0.90]............
0.37 [0.05; 2.85]Inject_
semaglutide_
low_dosage		0.95 [0.14; 6.52]0.34 [0.07; 1.59]............
0.33 [0.06; 1.85]0.91 [0.16; 5.19]Inject_
semaglutide_
medium_dosage		0.41 [0.10; 1.64]0.20 [0.01; 4.17]...........
0.44 [0.03; 6.50]1.21 [0.08; 18.32]1.33 [0.10; 17.31]Dapagliflozin_
medium_dosage		0.32 [0.03; 3.09]............
0.43 [0.03; 6.35]1.18 [0.08; 17.88]1.30 [0.10; 16.90]0.98 [0.04; 24.14]Dapagliflozin_
low_dosage		0.33 [0.03; 3.16]............
0.31 [0.05; 2.13]0.86 [0.12; 6.08]0.94 [0.17; 5.37]0.71 [0.05; 9.50]0.73 [0.05; 9.73]Canagliflozin_
high_dosage		0.75 [0.17; 3.36]0.56 [0.14; 2.17]............
0.24 [0.04; 1.53]0.65 [0.10; 4.39]0.72 [0.13; 3.85]0.54 [0.04; 6.94]0.55 [0.04; 7.11]0.76 [0.14; 4.27]Lixisenatide0.33 [0.01; 8.19]0.67 [0.19; 2.36]............
0.20 [0.03; 1.22]0.55 [0.09; 3.50]0.61 [0.12; 3.05]0.46 [0.04; 5.63]0.47 [0.04; 5.77]0.64 [0.18; 2.35]0.85 [0.17; 4.20]Canagliflozin_
low_dosage		0.87 [0.28; 2.68]............
0.19 [0.03; 1.22]0.53 [0.08; 3.50]0.58 [0.11; 3.07]0.44 [0.03; 5.57]0.45 [0.04; 5.70]0.62 [0.11; 3.40]0.81 [0.16; 4.23]0.96 [0.20; 4.63]Empagliflozin_
high_dosage		0.59 [0.15; 2.28].........0.37 [0.11; 1.33]..
* 0.17 [0.04; 0.79]0.45 [0.09; 2.28]0.50 [0.13; 1.90]0.38 [0.04; 3.92]0.39 [0.04; 4.01]0.53 [0.13; 2.13]0.70 [0.19; 2.52]0.82 [0.24; 2.81]0.86 [0.24; 3.14]Liraglutide0.85 [0.47; 1.56].0.33 [0.01; 8.08]..........
0.16 [0.02; 1.16]0.44 [0.06; 3.30]0.48 [0.08; 2.93]0.36 [0.03; 5.09]0.37 [0.03; 5.21]0.51 [0.08; 3.24]0.68 [0.11; 4.05]0.80 [0.14; 4.46]0.83 [0.14; 4.90]0.97 [0.22; 4.19]Oral_
semaglutide		0.88 [0.23; 3.36]............
* 0.14 [0.03; 0.60]0.39 [0.09; 1.73]0.42 [0.13; 1.41]0.32 [0.03; 3.09]0.33 [0.03; 3.16]0.45 [0.13; 1.59]0.59 [0.18; 1.93]0.70 [0.24; 2.05]0.73 [0.23; 2.32]0.85 [0.47; 1.52]0.88 [0.23; 3.36]Placebo_
or_Control		0.99 [0.56; 1.75]0.95 [0.53; 1.71]0.83 [0.25; 2.73]0.87 [0.58; 1.30]0.66 [0.03; 16.34]0.81 [0.28; 2.35]0.62 [0.21; 1.88]0.33 [0.01; 8.20]0.61 [0.22; 1.70]* 0.52 [0.28; 0.99]0.20 [0.01; 4.18]* 0.46 [0.24; 0.88]
* 0.14 [0.03; 0.63]0.37 [0.07; 1.83]0.41 [0.11; 1.48]0.31 [0.03; 3.18]0.31 [0.03; 3.26]0.43 [0.11; 1.72]0.57 [0.15; 2.11]0.67 [0.20; 2.27]0.70 [0.19; 2.54]0.82 [0.36; 1.84]0.84 [0.20; 3.63]0.96 [0.55; 1.69]Inject_
semaglutide_
high_dosage		...........
* 0.13 [0.03; 0.62]0.36 [0.07; 1.78]0.39 [0.10; 1.49]0.30 [0.03; 3.07]0.30 [0.03; 3.14]0.42 [0.10; 1.67]0.55 [0.15; 2.04]0.65 [0.19; 2.20]0.68 [0.19; 2.46]0.79 [0.35; 1.76]0.81 [0.19; 3.50]0.93 [0.52; 1.65]0.96 [0.43; 2.16]Exenatide..........
* 0.12 [0.02; 0.73]0.32 [0.05; 2.10]0.36 [0.07; 1.83]0.27 [0.02; 3.36]0.28 [0.02; 3.44]0.38 [0.07; 2.03]0.50 [0.10; 2.53]0.59 [0.12; 2.77]0.61 [0.12; 3.06]0.71 [0.20; 2.51]0.74 [0.13; 4.22]0.84 [0.28; 2.56]0.87 [0.25; 3.05]0.91 [0.26; 3.18]Ertugliflozin_
low_dosage		.....0.71 [0.27; 1.91]...
* 0.12 [0.03; 0.55]0.34 [0.07; 1.59]0.37 [0.10; 1.31]0.28 [0.03; 2.78]0.28 [0.03; 2.85]0.39 [0.10; 1.47]0.52 [0.15; 1.80]0.61 [0.19; 1.92]0.64 [0.19; 2.16]0.74 [0.36; 1.50]0.76 [0.19; 3.10]0.87 [0.58; 1.30]0.90 [0.45; 1.81]0.94 [0.47; 1.90]1.04 [0.32; 3.38]Dapagliflozin_
high_dosage		........
0.09 [0.00; 3.15]0.26 [0.01; 8.80]0.28 [0.01; 8.63]0.21 [0.00; 10.74]0.22 [0.00; 11.01]0.30 [0.01; 9.35]0.39 [0.01; 11.97]0.46 [0.02; 13.62]0.49 [0.02; 14.62]0.56 [0.02; 14.63]0.58 [0.02; 18.78]0.66 [0.03; 16.34]0.69 [0.03; 17.85]0.72 [0.03; 18.59]0.79 [0.03; 23.46]0.76 [0.03; 19.25]Bexagliflozin.......
* 0.12 [0.02; 0.69]0.31 [0.05; 1.97]0.35 [0.07; 1.71]0.26 [0.02; 3.18]0.27 [0.02; 3.26]0.37 [0.07; 1.90]0.48 [0.10; 2.36]0.57 [0.13; 2.58]0.59 [0.12; 2.85]0.69 [0.21; 2.32]0.71 [0.13; 3.95]0.81 [0.28; 2.35]0.85 [0.25; 2.81]0.88 [0.26; 2.94]0.97 [0.21; 4.50]0.93 [0.30; 2.91]1.22 [0.04; 35.71]Sotagliflozin......
* 0.09 [0.01; 0.54]0.24 [0.04; 1.55]0.27 [0.05; 1.35]0.20 [0.02; 2.48]0.20 [0.02; 2.54]0.28 [0.05; 1.50]0.37 [0.07; 1.86]0.44 [0.09; 2.04]0.46 [0.09; 2.25]0.53 [0.15; 1.85]0.55 [0.10; 3.11]0.62 [0.21; 1.88]0.65 [0.19; 2.24]0.67 [0.19; 2.34]0.74 [0.15; 3.56]0.72 [0.22; 2.32]0.94 [0.03; 27.76]0.77 [0.17; 3.55]Albiglutide.....
0.05 [0.00; 1.58]0.13 [0.00; 4.42]0.14 [0.00; 4.33]0.11 [0.00; 5.39]0.11 [0.00; 5.52]0.15 [0.00; 4.69]0.20 [0.01; 6.01]0.23 [0.01; 6.84]0.24 [0.01; 7.34]0.28 [0.01; 7.34]0.29 [0.01; 9.43]0.33 [0.01; 8.20]0.35 [0.01; 8.96]0.36 [0.01; 9.33]0.40 [0.01; 11.77]0.38 [0.02; 9.66]0.50 [0.01; 46.50]0.41 [0.01; 11.97]0.53 [0.02; 15.80]Efpeglenatide_
high_dosage		..0.60 [0.08; 4.55].
* 0.08 [0.01; 0.47]0.22 [0.04; 1.35]0.24 [0.05; 1.17]0.18 [0.02; 2.19]0.19 [0.02; 2.24]0.26 [0.05; 1.30]0.34 [0.07; 1.61]0.40 [0.09; 1.75]0.42 [0.09; 1.94]0.49 [0.15; 1.56]0.50 [0.09; 2.70]0.57 [0.21; 1.57]0.60 [0.19; 1.89]0.62 [0.19; 1.98]0.68 [0.26; 1.79]0.66 [0.22; 1.95]0.86 [0.03; 24.76]0.70 [0.16; 3.05]0.92 [0.21; 4.09]1.72 [0.06; 49.25]Ertugliflozin_
high_dosage		...
* 0.08 [0.02; 0.36]0.21 [0.04; 1.05]* 0.23 [0.06; 0.88]0.17 [0.02; 1.79]0.17 [0.02; 1.84]* 0.24 [0.06; 0.98]0.32 [0.08; 1.20]0.37 [0.11; 1.30]0.39 [0.13; 1.19]0.45 [0.19; 1.07]0.47 [0.11; 2.06]0.53 [0.29; 1.00]0.55 [0.24; 1.29]0.58 [0.25; 1.35]0.64 [0.18; 2.28]0.61 [0.29; 1.29]0.80 [0.03; 20.98]0.66 [0.19; 2.25]0.86 [0.24; 3.04]1.60 [0.06; 41.73]0.93 [0.28; 3.05]Empagliflozin_
low_dosage		..
* 0.03 [0.00; 0.82]0.08 [0.00; 2.29]0.09 [0.00; 2.23]0.06 [0.00; 2.84]0.07 [0.00; 2.90]0.09 [0.00; 2.42]0.12 [0.00; 3.09]0.14 [0.01; 3.52]0.15 [0.01; 3.78]0.17 [0.01; 3.75]0.18 [0.01; 4.87]0.20 [0.01; 4.18]0.21 [0.01; 4.58]0.22 [0.01; 4.77]0.24 [0.01; 6.06]0.23 [0.01; 4.93]0.30 [0.00; 24.90]0.25 [0.01; 6.15]0.32 [0.01; 8.13]0.60 [0.08; 4.55]0.35 [0.01; 8.59]0.38 [0.02; 8.34]Efpeglenatide_
medium_dosage		.
* 0.07 [0.01; 0.31]* 0.18 [0.03; 0.91]* 0.19 [0.05; 0.76]0.15 [0.01; 1.55]0.15 [0.01; 1.59]* 0.21 [0.05; 0.85]0.27 [0.07; 1.05]0.32 [0.09; 1.13]0.34 [0.09; 1.26]* 0.39 [0.16; 0.94]0.40 [0.09; 1.79]* 0.46 [0.24; 0.88]0.48 [0.20; 1.13]0.50 [0.21; 1.18]0.55 [0.15; 1.98]0.53 [0.25; 1.13]0.69 [0.03; 18.12]0.56 [0.16; 1.96]0.73 [0.20; 2.65]1.37 [0.05; 36.05]0.80 [0.24; 2.66]0.86 [0.35; 2.12]2.29 [0.10; 51.12]Dulaglutide
Gray background represent the indicated medicatoin. Data present as OR [95%CIs]. Pairwise (upper-right portion) and network (lower-left portion) meta-analysis results are presented as estimate effect sizes for the outcome of overall events of hematologic malignancy. Interventions are reported in order of mean ranking of beneficially prophylactic effect on overall events of hematologic malignancy, and outcomes are expressed as odds ratio (OR) (95% confidence intervals) (95%CIs). For the pairwise meta-analyses, OR of less than 1 indicate that the treatment specified in the row got more beneficial effect than that specified in the column. For the network meta-analysis (NMA), OR of less than 1 indicate that the treatment specified in the column got more beneficial effect than that specified in the row. Bold results marked with * indicate statistical significance. Abbreviation: 95%CIs: 95% confidence intervals; GLP-1 agonist: glucagon-like peptide-1 agonist; NMA: network meta-analysis; OR: odds ratio; RCT: randomized controlled trial; SGLT2 inhibitor: sodium–glucose cotransporter 2 inhibitor.
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

Lin, P.-Y.; Zeng, B.-Y.; Hsu, C.-W.; Suen, M.-W.; Hung, C.-M.; Stubbs, B.; Chen, Y.-W.; Chen, T.-Y.; Lei, W.-T.; Chen, J.-J.; et al. Divergent Risks of Hematologic Malignancies Associated with GLP-1 Receptor Agonists and SGLT2 Inhibitors: Preliminary Findings from a Pilot Network Meta-Analysis. Biomolecules 2025, 15, 1622. https://doi.org/10.3390/biom15111622

AMA Style

Lin P-Y, Zeng B-Y, Hsu C-W, Suen M-W, Hung C-M, Stubbs B, Chen Y-W, Chen T-Y, Lei W-T, Chen J-J, et al. Divergent Risks of Hematologic Malignancies Associated with GLP-1 Receptor Agonists and SGLT2 Inhibitors: Preliminary Findings from a Pilot Network Meta-Analysis. Biomolecules. 2025; 15(11):1622. https://doi.org/10.3390/biom15111622

Chicago/Turabian Style

Lin, Pao-Yen, Bing-Yan Zeng, Chih-Wei Hsu, Mein-Woei Suen, Chao-Ming Hung, Brendon Stubbs, Yen-Wen Chen, Tien-Yu Chen, Wei-Te Lei, Jiann-Jy Chen, and et al. 2025. "Divergent Risks of Hematologic Malignancies Associated with GLP-1 Receptor Agonists and SGLT2 Inhibitors: Preliminary Findings from a Pilot Network Meta-Analysis" Biomolecules 15, no. 11: 1622. https://doi.org/10.3390/biom15111622

APA Style

Lin, P.-Y., Zeng, B.-Y., Hsu, C.-W., Suen, M.-W., Hung, C.-M., Stubbs, B., Chen, Y.-W., Chen, T.-Y., Lei, W.-T., Chen, J.-J., Zeng, B.-S., Su, K.-P., Liang, C.-S., & Tseng, P.-T. (2025). Divergent Risks of Hematologic Malignancies Associated with GLP-1 Receptor Agonists and SGLT2 Inhibitors: Preliminary Findings from a Pilot Network Meta-Analysis. Biomolecules, 15(11), 1622. https://doi.org/10.3390/biom15111622

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