Journal Article

Association of allergic diseases and epilepsy with risk of glioma, meningioma and acoustic neuroma: results from the INTERPHONE international case–control study

Blettner, Maria, Feychting, Maria, Cardis, Elisabeth, Klaboe, Lars, Yamaguchi, Naohito, Hours, Martine, Krewski, Daniel, Cook, Angus, Siemiatycki, Jack, Moissonnier, Monika, Woodward, Alistair, Takebayashi, Toru, Sadetzki, Siegal, Schoemaker, Minouk, Auvinen, Anssi, Berg-Beckhoff, Gabriele, Feltbower, Richard, Schlehofer, Brigitte, Parent, Marie-Elise, Swerdlow, Anthony, Giles, Graham G, Lahkola, Anna, Johansen, Christoffer, Tynes, Tore, Lagorio, Susanna, Deltour, Isabelle, Armstrong, Bruce, Schüz, Joachim

2022

Overview

We investigated the association of allergic diseases and epilepsy with risk of brain tumours, in Interphone, a 13-country case–control study. Data were obtained from 2693 glioma cases, 2396 meningioma cases, and 1102 acoustic neuroma cases and their 6321 controls. Conditional logistic regression models were used to estimate pooled odds ratios (ORs) and their respective 95% confidence intervals (CIs), adjusted for education and time at interview. Reduced ORs were observed for glioma in relation to physician-diagnosed asthma (OR = 0.73; CI 0.58–0.92), hay fever (OR 0.72; CI 0.61–0.86), and eczema (OR 0.78, CI 0.64–0.94), but not for meningioma or acoustic neuroma. Previous diagnosis of epilepsy was associated with an increased OR for glioma (2.94; CI 1.87–4.63) and for meningioma (2.12; CI 1.27–3.56), but not for acoustic neuroma. This large-scale case–control study adds to the growing evidence that people with allergies have a lower risk of developing glioma, but not meningioma or acoustic neuroma. It also supports clinical observations of epilepsy prior to the diagnosis of glioma and meningioma.

• Publisher: Springer Nature B.V
• Subject: Acoustics / Allergic diseases / Allergies / Asthma / Brain cancer / Brain tumors / Case studies / Confidence intervals / Diagnosis / Eczema / Epilepsy / Glioma / Hay fever / Meningioma / Regression analysis / Regression models / Risk / Skin diseases / Statistical analysis