Evidence mapPaperPMID 40836299Full record

ArticleCardiovascular diabetology2025

Comparative cardiovascular outcomes and safety of hypoglycemic drug classes in patients with type 2 diabetes and hypertension: a multicenter cohort analysis.

Zhiyuan Wei, Wanqian Xu, Yu Wang, Yu Tian, Zhongmin Wang, Shenqi Jing, Weina Liu, Sipeng Shen, Chenlong Qin, Xin Zhang and 2 more

Abstract readMulticenter StudyComparative Study
In one paragraph

Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Observational
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Zhiyuan Wei *Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.ORCID http://orcid.org/0009-0000-9341-4111
Wanqian Xu *General Practice Medical Center, Chinese Evidence-Based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, Sichuan, China.ORCID http://orcid.org/0009-0003-0111-2338
Yu Wang *Research Center for Scientific Data Hub , Zhejiang Lab , Hangzhou, 311100, Zhejiang, China.ORCID http://orcid.org/0000-0002-8059-8051
Yu TianEngineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University , Hangzhou, 310027 , Zhejiang , China.ORCID http://orcid.org/0000-0002-6791-8217
Zhongmin WangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.ORCID http://orcid.org/0009-0000-7561-7775
Shenqi JingDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.ORCID http://orcid.org/0000-0002-8529-0566
Weina LiuDepartment of Nutrition and Food Hygiene, School of Public Health , Southeast University , Nanjing, 210009 , Jiangsu , China.ORCID http://orcid.org/0000-0001-5377-9714
Sipeng ShenDepartment of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.ORCID http://orcid.org/0000-0003-0436-4736
Chenlong QinDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.ORCID http://orcid.org/0009-0009-0912-5639
Xin ZhangDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, Jiangsu, China. zx6800@njmu.edu.cn.ORCID http://orcid.org/0000-0003-0047-3134
Jingsong LiResearch Center for Scientific Data Hub , Zhejiang Lab , Hangzhou, 311100, Zhejiang, China. ljs@zju.edu.cn.ORCID http://orcid.org/0000-0002-1064-637X
Yun LiuDepartment of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, 211166, Jiangsu, China. liuyun@njmu.edu.cn.ORCID http://orcid.org/0000-0002-6431-4469

Funding

Basic Research Program of Jiangsu Province BK20241308Leading Major Projects in Basic Research at the Forefront of the Institute (China) QY202403Major Special Projects of Jiangsu Provincial Department of Science and Technology (China) BK20243054National Key Research and Development Program of China 2023YFC3605800National Natural Science Foundation of China (NSFC) Youth Science Fund 82404291National Science and Technology Major Project 2024ZD0531800Social Development Plan of Jiangsu Provincial Department of Science and Technology (China) BE2023781
6 · The paper itself

Abstract

backgroundPatients with type 2 diabetes (T2D) and hypertension are at increased risk of adverse cardiovascular (CV) events. However, real-world evidence comparing the CV effectiveness and safety of major hypoglycemic drug classes remains limited in this population. This multicenter pooled analysis aims to directly compare the CV outcomes and safety profiles of these key agents in patients with T2D and hypertension.

methodsWe analyzed electronic health records from two databases in a cohort study of T2D patients with hypertension who had initiated metformin as first-line therapy. Propensity score matching (PSM) and Cox proportional hazards models were used to compare the risks of 3-/4-point major adverse cardiovascular events (MACE) and safety outcomes across drug classes added to metformin: insulin, sulfonylureas (SUs), glucagon-like peptide-1 receptor agonists (GLP-1 RAs), dipeptidyl peptidase-4 inhibitors (DPP4is), glinides, acarbose, and sodium-glucose transporter 2 inhibitors (SGLT2is).

resultsCompared with insulin, GLP-1 RAs, DPP4is, and glinides were associated with a lower risk of 3-point MACE (HR: 0.48 [0.31-0.76], 0.70 [0.57-0.85], and 0.70 [0.52-0.94], respectively). SUs were associated with a higher risk of 3-point MACE compared with DPP4is (HR: 1.30 [1.06-1.59]). DPP4is, GLP-1 RAs, and glinides showed a lower risk of 3-point MACE compared with acarbose (HR: 0.62 [0.51-0.76], 0.47 [0.29-0.75], and 0.59 [0.43-0.81], respectively). Similar patterns were observed for 4-point MACE. For safety outcomes, DPP4is were associated with a reduced risk of chronic kidney disease, while insulin use was associated with reduced risks of inflammatory polyarthritis and insomnia. However, DPP4is were associated with higher risks of coronary atherosclerotic diseases and hypertensive heart disease.

conclusionsThis study highlights the differential cardiovascular effectiveness and safety profiles of hypoglycemic therapies in real-world settings, providing valuable insights for optimizing T2D management, particularly in patients with comorbid hypertension.

Indexed as

Antihypertensive AgentsBlood GlucoseCardiovascular DiseasesDiabetes Mellitus, Type 2HypertensionHypoglycemic AgentsIncretinsAgedDatabases, FactualDipeptidyl-Peptidase IV InhibitorsDrug Therapy, CombinationElectronic Health RecordsFemaleGlucagon-Like Peptide-1 Receptor AgonistsHeart Disease Risk FactorsHumansAntihypertensive AgentsBlood GlucoseDipeptidyl-Peptidase IV InhibitorsGlucagon-Like Peptide-1 Receptor AgonistsHypoglycemic AgentsIncretinsCardiovascular safetyHypertensionMajor adverse cardiovascular events (MACE)Real-world evidenceRetrospective studyType 2 diabetes

Identifiers

PMID40836299
PMCPMC12369064

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.