Objective: We aimed to investigate the prevalence of idiopathic intracranial hypertension (IIH) in patients with migraine by screening for papilledema.
Materials And Methods: We have included all the patients with migraine who applied to our neurology clinic during December 2019 and accepted to participate in the study. The demographic and clinical characteristics including migraine subtype (episodic/chronic), headache frequency per month, and headache characteristics of all patients were interrogated. Besides, the presence of fibromyalgia (FM) and chronic fatigue syndrome (CFS) was noted. Fundus examination was performed in all of the patients and the presence of papilledema was noted.
Results: Overall, 158 consecutive migraineurs were included in this study. The mean age of the group was 35.9 ± 9.9 and the female/male ratio was 134/24. Papilledema was determined in 10 (6%) patients. There was a past medical history of having IIH in one of these patients. In four of the patients, the diagnosis of IIH was newly established. Comparative analyses between episodic migraineurs and chronic migraineurs revealed that female gender was more prevalent in chronic migraineurs (p = 0.00) and the comorbidities of FM and CFS were more common in chronic migraineurs. Remarkably, papilledema was found to be more common in chronic migraineurs. The results of the logistic regression analyses revealed that obesity was the only predictor for the presence of papilledema (p = 0.014).
Conclusion: Our results may suggest that IIH should be kept in mind as a notable comorbidity in migraineurs, particularly in the subgroup of obese patients with chronic migraine.
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http://dx.doi.org/10.1007/s10072-020-04473-8 | DOI Listing |
PLoS One
December 2024
ASU-Mayo Center for Innovative Imaging, Tempe, Arizona, United States of America.
Multicenter and multi-scanner imaging studies may be necessary to ensure sufficiently large sample sizes for developing accurate predictive models. However, multicenter studies, incorporating varying research participant characteristics, MRI scanners, and imaging acquisition protocols, may introduce confounding factors, potentially hindering the creation of generalizable machine learning models. Models developed using one dataset may not readily apply to another, emphasizing the importance of classification model generalizability in multi-scanner and multicenter studies for producing reproducible results.
View Article and Find Full Text PDFClin Exp Immunol
December 2024
Department of Neurology, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
Introduction: To evaluate the characteristics of hematological parameters and peripheral inflammatory markers in migraine, including chronic migraine (CM) and episodic migraine (EM), and to explore their underlying mechanisms.
Method: A total of 88 subjects were enrolled, 58 with migraine (28 with chronic migraine and 30 with episodic migraine) and 30 healthy controls. All subjects were matched for age, gender and body mass index (BMI), and peripheral blood was collected.
Ideggyogy Sz
November 2024
Fejér Megyei Szent György Egyetemi Oktató Kórház, Neurológiai Osztály, Székesfehérvár.
Migraine is a disabling primary headache disorder that directly affects a significant proportion of the population. Despite its widespread prevalence, migraine remains under-diagnosed and under-treated. To support clinical decision-making, we convened a panel of experts to develop a clinical approach to the diagnosis and management of migraine.
View Article and Find Full Text PDFJ Clin Med Res
December 2024
College of Clinical Pharmacy, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia.
Neurol Int
October 2024
Clinical Neurology Unit, Department of Head, Neck and Neurosurgery, Udine University Hospital, Piazzale Santa Maria della Misericordia 15, 33100 Udine, Italy.
Aims: The aim of this study is to assess the sleep quality and daytime sleepiness improvement in chronic migraineurs after 6 months of a 2:1 KD (ketogenic diet) and LGID (low-glycemic-index diet).
Methods: Twenty-six patients underwent 2:1 KD (11 patients) and LGID (15 patients). PSQI (Pittsburgh sleep quality index) and ESS (Epworth sleepiness scale) were administered at the baseline and the 3-month and 6-month follow-up.
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