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Pozzilli V, Haggiag S, Di Filippo M, Capone F, Di Lazzaro V, Tortorella C, Gasperini C, Prosperini L. Incidence and determinants of seizures in multiple sclerosis: a meta-analysis of randomised clinical trials. J Neurol Neurosurg Psychiatry 2024; 95:612-619. [PMID: 38383156 DOI: 10.1136/jnnp-2023-332996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 01/29/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Seizures are reported to be more prevalent in individuals with multiple sclerosis (MS) compared with the general population. Existing data predominantly originate from population-based studies, which introduce variability in methodologies and are vulnerable to selection and reporting biases. METHODS This meta-analysis aims to assess the incidence of seizures in patients participating in randomised clinical trials and to identify potential contributing factors. Data were extracted from 60 articles published from 1993 to 2022. The pooled effect size, representing the incidence rate of seizure events, was estimated using a random-effect model. Metaregression was employed to explore factors influencing the pooled effect size. RESULTS The meta-analysis included data from 53 535 patients and 120 seizure events in a median follow-up of 2 years. The pooled incidence rate of seizures was 68.0 per 100 000 patient-years, significantly higher than the general population rate of 34.6. Generalised tonic-clonic seizures were the most common type reported, although there was a high risk of misclassification for focal seizures with secondary generalisation. Disease progression, longer disease duration, higher disability levels and lower brain volume were associated with a higher incidence of seizures. Particularly, sphingosine-1-phosphate receptor (S1PR) modulators exhibited a 2.45-fold increased risk of seizures compared with placebo or comparators, with a risk difference of 20.5 events per 100 000 patient-years. CONCLUSIONS Patients with MS face a nearly twofold higher seizure risk compared with the general population. This risk appears to be associated not only with disease burden but also with S1PR modulators. Our findings underscore epilepsy as a significant comorbidity in MS and emphasise the necessity for further research into its triggers, preventive measures and treatment strategies.
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Affiliation(s)
- Valeria Pozzilli
- Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Campus Bio-Medico University, Roma, Lazio, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Shalom Haggiag
- MS Centre, Department of Neurosciences, San Camillo Forlanini Hospital, Roma, Italy
| | - Massimiliano Di Filippo
- Section of Neurology, Department of Medicine and Surgery, University of Perugia, Perugia, Italy
| | - Fioravante Capone
- Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Campus Bio-Medico University, Roma, Lazio, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Campus Bio-Medico University, Roma, Lazio, Italy
- Fondazione Policlinico Universitario Campus Bio-Medico, Rome, Italy
| | - Carla Tortorella
- MS Centre, Department of Neurosciences, San Camillo Forlanini Hospital, Roma, Italy
| | - Claudio Gasperini
- MS Centre, Department of Neurosciences, San Camillo Forlanini Hospital, Roma, Italy
| | - Luca Prosperini
- MS Centre, Department of Neurosciences, San Camillo Forlanini Hospital, Roma, Italy
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Ioannidis JP. Therapeutic interventions increasing seizure risk in multiple sclerosis: resolving discordant meta-analyses. J Neurol Neurosurg Psychiatry 2024; 95:594. [PMID: 38383155 DOI: 10.1136/jnnp-2024-333329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/21/2024] [Accepted: 01/29/2024] [Indexed: 02/23/2024]
Affiliation(s)
- John P Ioannidis
- Stanford Prevention Research Center, Department of Medicine and Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, USA
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Xu W, Rong Z, Ma W, Zhu B, Li N, Huang J, Liu Z, Yu Y, Zhang F, Zhang X, Ge M, Hou Y. Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network. Comput Biol Med 2024; 176:108530. [PMID: 38749324 DOI: 10.1016/j.compbiomed.2024.108530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2024] [Revised: 04/14/2024] [Accepted: 04/28/2024] [Indexed: 05/31/2024]
Abstract
As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown pathogenesis. This is due to their similar clinical presentations and imaging manifestations. That misdiagnosis can significantly increase the occurrence of adverse events. Delayed or incorrect treatment is one of the most important causes of MS progression. Therefore, the development of a practical diagnostic imaging aid could significantly reduce the risk of misdiagnosis and improve patient prognosis. We propose an interpretable deep learning (DL) model that differentiates MS and cSVD using T2-weighted fluid-attenuated inversion recovery (FLAIR) images. Transfer learning (TL) was utilized to extract features from the ImageNet dataset. This pioneering model marks the first of its kind in neuroimaging, showing great potential in enhancing differential diagnostic capabilities within the field of neurological disorders. Our model extracts the texture features of the images and achieves more robust feature learning through two attention modules. The attention maps provided by the attention modules provide model interpretation to validate model learning and reveal more information to physicians. Finally, the proposed model is trained end-to-end using focal loss to reduce the influence of class imbalance. The model was validated using clinically diagnosed MS (n=112) and cSVD (n=321) patients from the Beijing Tiantan Hospital. The performance of the proposed model was better than that of two commonly used DL approaches, with a mean balanced accuracy of 86.06 % and a mean area under the receiver operating characteristic curve of 98.78 %. Moreover, the generated attention heat maps showed that the proposed model could focus on the lesion signatures in the image. The proposed model provides a practical diagnostic imaging aid for the use of routinely available imaging techniques such as magnetic resonance imaging to classify MS and cSVD by linking DL to human brain disease. We anticipate a substantial improvement in accurately distinguishing between various neurological conditions through this novel model.
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Affiliation(s)
- Wangshu Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Zhiwei Rong
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Wenping Ma
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Bin Zhu
- Department of Pharmacy, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Na Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China
| | - Jiansong Huang
- Peking University Health Science Center, Beijing, 100191, China
| | - Zhilin Liu
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Yipei Yu
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China
| | - Fa Zhang
- The School of Medical Technology, Beijing Institute of Technology, Beijing, 100081, China.
| | - Xinghu Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China.
| | - Ming Ge
- Department of Neurosurgery, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China.
| | - Yan Hou
- Department of Biostatistics, School of Public Health, Peking University, Beijing, 100191, China; Peking University Clinical Research Center, Beijing, 100191, China.
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Sanchez M, Marone A, Silva WH, Marrodan M, Correale J. Clinical characteristics, course and prognosis of Multiple Sclerosis patients with epilepsy. A case control study: MS and epilepsy. Mult Scler Relat Disord 2024; 83:105422. [PMID: 38219299 DOI: 10.1016/j.msard.2024.105422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 12/27/2023] [Accepted: 01/01/2024] [Indexed: 01/16/2024]
Abstract
BACKGROUND AND PURPOSE Although more common than in the general population, seizures are an atypical manifestation of multiple sclerosis (MS) and their pathophysiology is not well understood. This study aims to examine the prevalence, clinical characteristics, brain imaging findings and course of epilepsy, presenting in patients with MS. METHODS Observational retrospective study of MS patients evaluated at a single MS reference center in Buenos Aires, Argentina, between 2011 and 2022, focusing on those who developed epilepsy (EMS). Clinical, demographic, and prognostic factors were evaluated and compared to a control group of non-epileptic MS patients (NEMS). To analyze specific epilepsy characteristics, a second control group of patients with non-lesional focal epilepsy (FNLE) was also included. RESULTS Twenty-five patients (18 women), were diagnosed with epilepsy, corresponding to a prevalence of 1.95%. Comparison of brain imaging characteristics between EMS and NEMS patients showed brain atrophy (32% vs 6.1%, p<0.01), as well as cortical (26% vs 4%, p=0.03) and juxtacortical lesions (84% vs 55%, p=0.05), were more frequent in EMS patients. However, after multivariate analysis, cortical atrophy was the only variable linked to a significant increase in risk of epilepsy (OR 24, 95%CI=2.3-200, p<0.01). No significant differences in clinical characteristics, disease activity, disability levels, response to disease modified treatment (DMT) or lack of DMT efficacy were observed between MS patients with or without epilepsy. Most patients received anti-seizure medication (ASM), and seizure control was better in EMS than in FNLE patients (92% vs 58%, p=0.022) with no differences found in drug resistance. We did not find predictors of seizure recurrence in the population studied. CONCLUSION We observed a lower prevalence of epilepsy in this group of MS patients, compared to other reported cohorts. Although epilepsy seems to have a benign course in MS patients, cortical atrophy appears to be an important contributor to the development of secondary epilepsy in MS patients. Further investigations will be necessary to identify risk factors or biomarkers predicting increased epilepsy risk in MS.
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Affiliation(s)
| | - Abril Marone
- Departamento de Neurología, Fleni, Buenos Aires, Argentina
| | - Walter H Silva
- Departamento de Neurología, Fleni, Buenos Aires, Argentina
| | | | - Jorge Correale
- Departamento de Neurología, Fleni, Buenos Aires, Argentina; Instituto de Química y Fisicoquímica Biológicas (IQUIFIB), Universidad de Buenos Aires/CONICET, Buenos Aires, Argentina
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Milosevic A, Lavrnja I, Savic D, Milosevic K, Skuljec J, Bjelobaba I, Janjic MM. Rat Ovarian Function Is Impaired during Experimental Autoimmune Encephalomyelitis. Cells 2023; 12:cells12071045. [PMID: 37048118 PMCID: PMC10093247 DOI: 10.3390/cells12071045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/21/2023] [Accepted: 03/28/2023] [Indexed: 04/14/2023] Open
Abstract
Multiple sclerosis (MS) is an autoimmune disease affecting the CNS and occurring far more prevalently in women than in men. In both MS and its animal models, sex hormones play important immunomodulatory roles. We have previously shown that experimental autoimmune encephalomyelitis (EAE) affects the hypothalamic-pituitary-gonadal axis in rats of both sexes and induces an arrest in the estrous cycle in females. To investigate the gonadal status in female rats with EAE, we explored ovarian morphometric parameters, circulating and intraovarian sex steroid levels, and the expression of steroidogenic machinery components in the ovarian tissue. A prolonged state of diestrus was recorded during the peak of EAE, with maintenance of the corpora lutea, elevated intraovarian progesterone levels, and increased gene and protein expression of StAR, similar to the state of pseudopregnancy. The decrease in CYP17A1 protein expression was followed by a decrease in ovarian testosterone and estradiol levels. On the contrary, serum testosterone levels were slightly increased. With unchanged serum estradiol levels, these results point at extra-gonadal sites of sex steroid biosynthesis and catabolism as important regulators of their circulating levels. Our study suggests alterations in the function of the female reproductive system during central autoimmunity and highlights the bidirectional relationships between hormonal status and EAE.
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Affiliation(s)
- Ana Milosevic
- Institute for Biological Research "Siniša Stanković"-National Institute of Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Irena Lavrnja
- Institute for Biological Research "Siniša Stanković"-National Institute of Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Danijela Savic
- Institute for Biological Research "Siniša Stanković"-National Institute of Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Katarina Milosevic
- Institute for Biological Research "Siniša Stanković"-National Institute of Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Jelena Skuljec
- Department of Neurology, University Medicine Essen, 45147 Essen, Germany
- Center for Translational Neuro- and Behavioral Sciences, University Hospital Essen, 45147 Essen, Germany
| | - Ivana Bjelobaba
- Institute for Biological Research "Siniša Stanković"-National Institute of Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
| | - Marija M Janjic
- Institute for Biological Research "Siniša Stanković"-National Institute of Republic of Serbia, University of Belgrade, 11000 Belgrade, Serbia
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