Injection‐site and dermatologic reactions associated with glucagon‐like peptide‐1 receptor agonists: Insights from meta‐analysis of randomised controlled trials and real‐world evidence
Bibliographic record
Abstract
Abstract Aims Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) are widely used for type 2 diabetes mellitus and obesity, with once‐weekly dosing that supports adherence. However, injection‐site reactions (ISRs) and dermatologic events have been recognised, ranging from mild local events to rare systemic hypersensitivity reactions that may cause discontinuation. To evaluate dermatologic and ISR safety of GLP‐1 RAs through a meta‐analysis of randomised controlled trials (RCTs) and disproportionality analysis of data from the United States Food and Drug Administration Adverse Event Reporting System (FAERS). Materials and Methods PubMed, Embase and Web of Science were searched through December 2024 to identify RCTs reporting ISR or dermatologic outcomes for GLP‐1 RAs. Random‐effects meta‐analysis synthesised trial evidence. A retrospective disproportionality analysis of FAERS data evaluated all approved GLP‐1 RAs. Lower bound reporting odds ratios (LB ROR), proportional reporting ratios (PRR) and information components (IC) were calculated. Results The pooled meta‐analysis of 14 RCTs that reported ISRs (4861 patients; 396 ISR events) showed increased ISR risk with GLP‐1 RAs versus comparators (risk ratio 3.55; 95% confidence interval, 2.35–5.36; I 2 = 41.4%). Dermatologic events were infrequent and not significantly elevated. FAERS data analysis revealed potential ISR signals for exenatide and dulaglutide. Exenatide was associated with injection‐site haemorrhage (PRR: 27.6; LB ROR: 29.4; IC 025 : 4.6). Dulaglutide showed disproportionate reporting for injection‐site haemorrhage (PRR: 11.5; LB ROR: 11.5; IC 025 : 3.4). Conclusions GLP‐1 RAs are consistently linked to higher ISR risk, especially with exenatide and dulaglutide, while generalised dermatologic events are rare. Clinicians should counsel patients about ISR risk to support adherence and optimise outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.072 | 0.131 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.059 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".