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Smoking and idiopathic intracranial hypertension: a meta-analysis
Ryan S Huang1, Andrew Mihalache1, Brendan K Tao2
1Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Purpose:
To examine the association between smoking and idiopathic intracranial hypertension (IIH).
Methods:
A systematic literature search was conducted on Ovid MEDLINE, Embase, and the Cochrane Library from inception to March 12th, 2025, to identify eligible observational studies evaluating the association between smoking and IIH in adult populations. A random-effects meta-analysis using the Mantel-Haenszel method was conducted to calculate pooled odds ratios (ORs) with 95% confidence intervals (CIs). Leave-one-out sensitivity analyses and subgroup analyses stratified by study continent were performed to assess the robustness of findings. Study quality was appraised using the ROBINS-E tool and the certainty of evidence was evaluated through the GRADE framework. Publication bias was assessed using Egger's test.
Results:
Eleven observational studies comprising 8,098 IIH patients and 90,381 non-IIH controls were included. Patients with IIH exhibited higher odds of current smoking status compared to controls (OR = 1.46, 95%CI = [1.39-1.54], p < 0.001, I2 = 0%). This association remained consistent in the leave-one-out sensitivity analysis and across geographical subgroups: European studies (OR = 1.45, 95%CI = [1.33-1.59], p < 0.001, I2 = 24%) and North American studies (OR = 1.42, 95%CI = [1.21-1.67], p < 0.001, I2 = 0%). Additionally, IIH patients had higher odds of former smoking status compared to controls (OR = 1.13, 95%CI = [1.05-1.21], p < 0.001, I2 = 0%). Quality assessments ranged from some concerns to low risk of bias, with no publication bias (p = 0.59).
Conclusions:
Low certainty of evidence suggests that current and former smoking status are associated with IIH. Clinicians should consider smoking history as part of risk assessment in patients with suspected IIH. Further prospective studies with detailed exposure metrics are needed to confirm causality and explore dose-dependent relationships.
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