Evidence map›Paper›PMID 39723481›Full record

SynthesisDiabetes, obesity & metabolism2025

Assessing the benefit-risk profile of newer glucose-lowering drugs: A systematic review and network meta-analysis of randomized outcome trials.

Huilin Tang, Bingyu Zhang, Yiwen Lu, William T Donahoo, Naykky Singh Ospina, Pareeta Kotecha, Ying Lu, Jiayi Tong, Steven M Smith, Eric I Rosenberg and 4 more

Abstract readSystematic ReviewNetwork Meta-Analysis
In one paragraph

Synthesis in Diabetes, obesity & metabolism, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–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

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
  8. 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

14 authors.

Huilin TangDepartment of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, Florida, USA.ORCID 0000-0002-5814-6657
Bingyu ZhangThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Yiwen LuThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, Pennsylvania, USA.
William T DonahooDivision of Endocrinology, Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA.
Naykky Singh OspinaDivision of Endocrinology, Department of Medicine, University of Florida College of Medicine, Gainesville, Florida, USA.
Pareeta KotechaDepartment of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, Florida, USA.ORCID 0009-0009-4864-9662
Ying LuDepartment of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, Florida, USA.
Jiayi TongThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Steven M SmithDepartment of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, Florida, USA.ORCID 0000-0002-0201-839X
Eric I RosenbergDivision of General Internal Medicine, Department of Medicine, University of Florida, Gainesville, Florida, USA.
Stephen E KimmelDepartment of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, Florida, USA.
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.
Jingchuan GuoDepartment of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy, Gainesville, Florida, USA.ORCID 0000-0001-9799-2592
Yong ChenThe Center for Health AI and Synthesis of Evidence (CHASE), University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Funding

Supplement of NIDDK R01 newer GLDs and Clinical OutcomesR01DK133465 · NIDDK · UNIVERSITY OF FLORIDA · PI GUO, JINGCHUAN, SHAO, HUI · 2022 to 2025
$2.8M
Adequate selection of patients for thyroid biopsy: evaluation of a shared decision making conversation aidK08CA248972 · NCI · UNIVERSITY OF FLORIDA · PI SINGH OSPINA, NAYKKY · 2020 to 2024
$1.1M
Division of Diabetes, Endocrinology, and Metabolic Diseases R01DK133465NCI NIH HHS K08 CA248972NIDDK NIH HHS R01 DK133465
6 · The paper itself

Abstract

aimTo comprehensively evaluate the benefits and risks of glucagon-like peptide-1 receptor agonists (GLP-1RA), dipeptidyl peptidase 4 inhibitors (DPP4i), and sodium-glucose cotransporter 2 inhibitors (SGLT2i). MATERIALS AND

methodsA systematic search of PubMed, EMBASE, and Cochrane Central Register of Controlled Trials (CENTRAL) from inception to November 2023 to identify randomized cardiovascular and kidney outcome trials that enrolled adults with type 2 diabetes, heart failure, or chronic kidney disease and compared DPP4i, GLP-1RAs, or SGLT2i to placebo. Twenty-one outcomes (e.g., major adverse cardiovascular events [MACE], stroke, and hospitalization for heart failure [HHF]) were assessed. Data were pooled using population-averaged odds ratios (ORs) with 95% CIs.

resultsTwenty-six trials enrolling 198 177 participants were included. GLP-1RAs were most effective in lowering the risks of MACE (OR, 0.85, [95% CI, 0.79 to 0.92]) and stroke (0.84 [0.77, 0.91]), but increased the risk of thyroid cancer (1.58 [1.36, 2.50]). SGLT2i showed the greatest benefits in reducing the risk of HHF (0.68 [0.64, 0.73]) and improving composite renal outcomes (0.67 [0.58, 0.77]), but increased the risk of genital infections (3.11 [2.15, 4.50]). DPP4i were associated with a lower risk of certain psychiatric disorders, Parkinson's disease (0.54 [0.32, 0.92]), and amputation (0.70 [0.86, 0.93]), but an increased risk of neuropathy (1.10 [1.02, 1.18]) and pancreatitis (1.63 [1.40, 1.91]). The weighted origami plot suggested that GLP-1RAs were more suitable for reducing macrovascular and microvascular outcomes, while DPP4i might be better for neurodegenerative diseases and cancer concerns.

conclusionsGiven the distinct benefit-risk profiles, the selection of glucose-lowering drugs should be individualized based on patient characteristics and risk factors.

Indexed as

Diabetes Mellitus, Type 2Dipeptidyl-Peptidase IV InhibitorsHypoglycemic AgentsSodium-Glucose Transporter 2 InhibitorsBlood GlucoseCardiovascular DiseasesGlucagon-Like Peptide-1 Receptor AgonistsHumansRandomized Controlled Trials as TopicRisk AssessmentBlood GlucoseDipeptidyl-Peptidase IV InhibitorsGlucagon-Like Peptide-1 Receptor AgonistsHypoglycemic AgentsSodium-Glucose Transporter 2 InhibitorsDPP‐IV inhibitorGLP‐1 analoguemeta‐analysisSGLT2 inhibitortype 2 diabetes

Identifiers

PMID39723481
PMCPMC12135018

What OpenQuestion holds

Textmetadata
LicenceTDM
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 OpenQuestion graph.