Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
Neurology
Glucagon-like Peptide-1 Receptor Agonists and Risk of Nonarteritic Anterior Ischemic Optic Neuropathy: Systematic Review and Meta-Analysis
BACKGROUND AND OBJECTIVES: Recent observational studies have reported conflicting evidence regarding an association between glucagon-like peptide-1 receptor agonists (GLP-1RAs), particularly semaglutide, and nonarteritic anterior ischemic optic neuropathy (NAION). We aimed to synthesize the pooled evidence assessing the association between GLP-1RA use and NAION risk. METHODS: Embase, Medline, and Cochrane CENTRAL databases were searched from inception to April 5, 2025. Observational studies and randomized controlled trials comparing NAION risk in GLP-1RA users with non-GLP-1RA users were included. Title/abstract and full-text screening were conducted in duplicate by 2 independent reviewers. Discrepancies were resolved through discussion or adjudication by a third reviewer. Risk of bias was assessed using the Risk of Bias In Nonrandomized Studies of Interventions (ROBINS-I) tool, and the certainty of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation framework. Pooled relative risks (RRs) were estimated using Bayesian random-effects models, incorporating half-normal, between-study, and weakly informative priors. The RR with 95% credible interval (CrI) was computed to analyze the development of NAION associated with GLP-1RA exposure. RESULTS: Five studies were included in the primary analysis, encompassing 1,593,554 patients (682,456 semaglutide users and 911,098 non-GLP-1RA users). Semaglutide use was associated with a high probability of increased risk of NAION compared with non-GLP-1RA use (RR: 2.52, 95% CrI [1.56, 4.72], I2: 57.8%, P(RR > 1): 99.9%). This association was particularly pronounced among patients with diabetes (RR: 2.41, 95% CrI [1.57, 4.10], I2: 47.2%, P(RR > 1): 99.9%). The overall incidence of NAION across 5 studies and 7 comparisons, encompassing 1,460,760 patients (semaglutide: 1,108,542; dulaglutide: 326,282; and exenatide: 25,936) and 272 NAION events (semaglutide: 198; dulaglutide: 54; and exenatide: 20) was 85 cases per 100,000 GLP-1RA users (95% CrI [29, 263], I2: 98.8%, P(incidence >0.00001): 100%). The incidence of NAION across 1,108,542 semaglutide users was 118 cases per 100,000 (95% CrI [32, 451], I2: 98.8%, P(incidence >0.00001): 100%). Leave-one-out sensitivity analyses consistently supported these findings. DISCUSSION: Low-to-moderate certainty evidence indicates that semaglutide significantly increases risk of NAION relative to non-GLP-1RAs, particularly among patients with diabetes. These findings warrant further investigation and should inform clinical risk-benefit discussions.
26 May 2026
Read appraisal →European journal of clinical pharmacology
Dysesthesia associated with GLP-1 agonist therapies: data-mining analysis and literature review
PURPOSE: An increasing number of anecdotal reports on social media platforms and medical blogs describe dysesthesia, particularly burning skin sensations, in association with glucagon-like peptide-1 receptor (GLP-1R) agonists. We performed a pharmacovigilance data-mining analysis to characterise cases of dysesthesia related to GLP-1R. METHODS: We conducted a disproportionality analysis using VigiBase data on GLP-1R (Anatomical Therapeutic Chemical classification: ATC code A10BJ) and tirzepatide, focusing on the HLT (High Level Term) "Paraesthesia and dysesthesia", with the Information Component (IC). Additionally, we reviewed all narratives of dysesthesia cases in the French Pharmacovigilance database to extract clinical and pharmacological characteristics. A literature review complemented this analysis. RESULTS: Exenatide was significantly associated with hypoesthesia or oral paraesthesia, semaglutide and tirzepatide with hyperaesthesia, and semaglutide with dysesthesia and burning sensation. Dysesthesia appears to be dose-dependent, occurring more frequently at higher doses and with more potent GLP-1R, whether used for weight management or type 2 diabetes. Discontinuation was often performed, followed by spontaneous favourable outcomes, and cases of rechallenge were observed. Skin burning sensations represent a distinctive form of dysesthesia. CONCLUSION: Pharmacovigilance quantitative and qualitative data strengthens evidence for dysesthesias associated with GLP-1R agonists already observed in clinical trials of semaglutide, tirzepatide, and retatrutide.
25 May 2026
Read appraisal →Journal of Korean medical science
Cardiovascular Risk Stratification in Youth-Onset Type 2 Diabetes Using Machine Learning
BACKGROUND: The rising incidence of youth-onset type 2 diabetes mellitus (T2DM), along with the risk of early cardiovascular complications, is concerning. Brachial-ankle pulse wave velocity (baPWV) and carotid intima-media thickness (cIMT) are noninvasive markers of arterial stiffness and atherosclerosis. They serve as important markers for cardiovascular risk assessment. This study aimed to apply machine learning models to identify high-risk individuals who may benefit from early and targeted cardiovascular screening. METHODS: This retrospective study included 129 patients with youth-onset T2DM who underwent baPWV and cIMT measurements between January 2018 and July 2024. High-risk groups were defined as having values of ≥ mean + 1 standard deviation for baPWV (arterial stiffness) and cIMT (atherosclerosis). Clinical predictors were evaluated using linear, logistic regression, and machine learning analyses. Multiple machine learning models were trained using oversampling and cross-validation techniques to enhance prediction performance. RESULTS: Among the models tested, the gradient boosting model with adaptive synthetic sampling oversampling achieved the best performance in predicting both arterial stiffness (accuracy 0.81) and atherosclerosis prediction (accuracy 0.92). Age and hypertension were consistently identified as the most important factors for arterial stiffness. For atherosclerosis risk, traditional analysis identified dyslipidemia, male sex, and duration of illness as relevant factors; machine learning more clearly emphasized low-density lipoprotein cholesterol and triglyceride levels as key predictors of increased cIMT. CONCLUSION: Hypertension and age were consistent predictors of arterial stiffness, while atherosclerosis risk factors were further clarified with lipid parameters by machine learning analysis. These findings suggest that conventional and machine learning analyses offer complementary strengths. Their combined use may enable earlier to detect nuanced cardiovascular risk patterns and support early and targeted vascular screening in youth-onset T2DM.
14 May 2026
Read appraisal →Science advances
Augmenting ultrasound for continuous glucose monitoring via a wearable acoustically readable microneedle patch
Continuous glucose monitoring (CGM) represents substantial advancement, yet poses a challenge in wearable health care development. Current enzymatic CGM faces limitations in stability, cost, and durability. We introduce an enzyme-free, wearable acoustically readable microneedle patch (ARMPatch) composed of glucose-responsive hydrogel. Positioned between a standard ultrasound probe and the skin, ARMPatch acts as an acoustic interface enabling CGM using conventional ultrasound. Its hydrogel microneedles minimally penetrate the epidermis, allowing interstitial fluid to trigger variable swelling of the microneedles in response to glucose fluctuations. ARMPatch delivers stable and selective glucose readings for up to 56 days. Glucose variations with millimolar resolution are obtained via ultrasound within a response time of 30 to 60 minutes. In vivo CGM in animal models for 7 days reveals a reversible correlation between microneedle swelling and glucose variations. Acting as "accessory" for standard ultrasound probes, this approach offers a minimally invasive, cost-effective, and long-lasting solution to non-enzymatic CGM, expanding the utility of ultrasound in wearable biosensing.
5 May 2026
Read appraisal →Journal of diabetes science and technology
Plantar Thermogram Analysis Using Deep Learning for Diabetic Foot Risk Classification
AIMS: Thermography is a noninvasive method to identify patients at risk of diabetic foot ulcers. In this study, we employed thermography and deep learning to stratify patients with diabetes at risk of developing foot ulcers. METHODS: We prospectively recorded clinical data and plantar thermograms for adult patients with diabetes who underwent diabetic foot screening. A total of 153 thermal images were analyzed using a deep learning algorithm to determine the risk of diabetic foot ulcers. The neural network was trained using a balanced dataset consisting of 98 thermal images (49 normal and 49 abnormal), with 80% allocated for training and 20% for validation. The trained model was then validated on a separate testing dataset consisting of 55 thermal images (42 normal and 13 abnormal). The neural network was trained to prioritize higher sensitivity in identifying at-risk feet for screening purposes. RESULTS: Participants had a mean age of 63.1 ± 12.6 years (52.3% female), and 62.1% had been diagnosed with diabetes for more than 10 years. The average body mass index was 27.5 ± 5.6 kg/m2. Of the thermal images, 91 were classified as category 0 and 62 as categories 1 to 3, according to the diabetic foot risk classification system of the International Working Group on the Diabetic Foot. Using five-fold cross-validation, the neural network model achieved an overall accuracy of 71.8 ± 4.9%, a sensitivity of 81.2 ± 10.0%, and a specificity of 64.0 ± 7.4%. Additionally, the Matthews correlation coefficient was 0.46 ± 0.08%Conclusions:These results suggest that thermography combined with deep learning could be developed for screening purposes to stratify patients at risk of developing diabetic foot ulcers.
4 May 2026
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