1
|
Hechenberger S, Helmlinger B, Penner IK, Pirpamer L, Fruhwirth V, Heschl B, Ropele S, Wurth S, Damulina A, Eppinger S, Demjaha R, Khalil M, Pinter D, Enzinger C. Psychological factors and brain magnetic resonance imaging metrics associated with fatigue in persons with multiple sclerosis. J Neurol Sci 2023; 454:120833. [PMID: 37866195 DOI: 10.1016/j.jns.2023.120833] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 09/11/2023] [Accepted: 10/08/2023] [Indexed: 10/24/2023]
Abstract
BACKGROUND Besides demographics and clinical factors, psychological variables and brain-tissue changes have been associated with fatigue in persons with multiple sclerosis (pwMS). Identifying predictors of fatigue could help to improve therapeutic approaches for pwMS. Therefore, we investigated predictors of fatigue using a multifactorial approach. METHODS 136 pwMS and 49 normal controls (NC) underwent clinical, neuropsychological, and magnetic resonance imaging examinations. We assessed fatigue using the "Fatigue Scale for Motor and Cognitive Functions", yielding a total, motor, and cognitive fatigue score. We further analyzed global and subcortical brain volumes, white matter lesions and microstructural changes (examining fractional anisotropy; FA) along the cortico striatal thalamo cortical (CSTC) loop. Potential demographic, clinical, psychological, and magnetic resonance imaging predictors of total, motor, and cognitive fatigue were explored using multifactorial linear regression models. RESULTS 53% of pwMS and 20% of NC demonstrated fatigue. Besides demographics and clinical data, total fatigue in pwMS was predicted by higher levels of depression and reduced microstructural tissue integrity in the CSTC loop (adjusted R2 = 0.52, p < 0.001). More specifically, motor fatigue was predicted by lower education, female sex, higher physical disability, higher levels of depression, and self-efficacy (adjusted R2 = 0.54, p < 0.001). Cognitive fatigue was also predicted by higher levels of depression and lower self-efficacy, but in addition by FA reductions in the CSTC loop (adjusted R2 = 0.45, p < 0.001). CONCLUSIONS Our results indicate that depression and self-efficacy strongly predict fatigue in MS. Incremental variance in total and cognitive fatigue was explained by microstructural changes along the CSTC loop, beyond demographics, clinical, and psychological variables.
Collapse
Affiliation(s)
- Stefanie Hechenberger
- Medical University of Graz, Research Unit for Neuronal Plasticity and Repair, Graz, Austria; Medical University of Graz, Department of Neurology, Graz, Austria
| | - Birgit Helmlinger
- Medical University of Graz, Research Unit for Neuronal Plasticity and Repair, Graz, Austria; Medical University of Graz, Department of Neurology, Graz, Austria
| | - Iris-Katharina Penner
- Department of Neurology. Inselspital, Bern University Hospital, University of Bern, Switzerland
| | - Lukas Pirpamer
- Medical University of Graz, Department of Neurology, Graz, Austria; Medical Image Analysis Center (MIAC) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Viktoria Fruhwirth
- Medical University of Graz, Research Unit for Neuronal Plasticity and Repair, Graz, Austria; Medical University of Graz, Department of Neurology, Graz, Austria
| | - Bettina Heschl
- Medical University of Graz, Department of Neurology, Graz, Austria
| | - Stefan Ropele
- Medical University of Graz, Department of Neurology, Graz, Austria
| | - Sebastian Wurth
- Medical University of Graz, Department of Neurology, Graz, Austria
| | - Anna Damulina
- Medical University of Graz, Department of Neurology, Graz, Austria
| | - Sebastian Eppinger
- Medical University of Graz, Department of Neurology, Graz, Austria; Medical University of Graz, Division of Neuroradiology & Interventional Radiology, Department of Radiology, Graz, Austria
| | - Rina Demjaha
- Medical University of Graz, Department of Neurology, Graz, Austria; Medical University of Graz, Neurology Biomarker Research Unit, Graz, Austria
| | - Michael Khalil
- Medical University of Graz, Department of Neurology, Graz, Austria; Medical University of Graz, Neurology Biomarker Research Unit, Graz, Austria
| | - Daniela Pinter
- Medical University of Graz, Research Unit for Neuronal Plasticity and Repair, Graz, Austria; Medical University of Graz, Department of Neurology, Graz, Austria.
| | - Christian Enzinger
- Medical University of Graz, Research Unit for Neuronal Plasticity and Repair, Graz, Austria; Medical University of Graz, Department of Neurology, Graz, Austria
| |
Collapse
|
2
|
Rose DR, Amin M, Ontaneda D. Prediction in treatment outcomes in multiple sclerosis: challenges and recent advances. Expert Rev Clin Immunol 2021; 17:1187-1198. [PMID: 34570656 DOI: 10.1080/1744666x.2021.1986005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
INTRODUCTION Multiple Sclerosis (MS) is a chronic autoimmune and neurodegenerative disease of the central nervous system with a course dependent on early treatment response. Increasing evidence also suggests that despite eliminating disease activity (relapses and lesions), many patients continue to accrue disability, highlighting the need for a more comprehensive definition of treatment success. Optimizing disability outcome measures, as well as continuously improving our understanding of neuroinflammatory and neurodegenerative biomarkers is required. AREAS COVERED This review describes the challenges inherent in classifying and monitoring disease phenotype in MS. The review also provides an assessment of clinical, radiological, and blood biomarker tools for current and future practice. EXPERT OPINION Emerging MRI techniques and standardized patient outcome assessments will increase the accuracy of initial diagnosis and understanding of disease progression.
Collapse
Affiliation(s)
- Deja R Rose
- Cleveland Clinic, Mellen Center for Multiple Sclerosis, Cleveland Ohio, United States
| | - Moein Amin
- Cleveland Clinic, Mellen Center for Multiple Sclerosis, Cleveland Ohio, United States.,Department of Neurology, Cleveland Clinic, Cleveland Ohio, United States
| | - Daniel Ontaneda
- Cleveland Clinic, Mellen Center for Multiple Sclerosis, Cleveland Ohio, United States.,Department of Neurology, Cleveland Clinic, Cleveland Ohio, United States
| |
Collapse
|
3
|
Beyond Alzheimer's disease: Can bilingualism be a more generalized protective factor in neurodegeneration? Neuropsychologia 2020; 147:107593. [PMID: 32882240 DOI: 10.1016/j.neuropsychologia.2020.107593] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/10/2020] [Accepted: 08/26/2020] [Indexed: 01/18/2023]
Abstract
Bilingualism has been argued to have an impact on cognition and brain structure. Effects have been reported across the lifespan: from healthy children to ageing adults, including clinical (ageing) populations. It has been argued that active bilingualism may significantly contribute to the delaying of the expression of Alzheimer's disease symptoms. If bilingualism plays an ameliorative role against the expression of neurodegeneration in dementia, it is possible that it could have similar effects for other neurodegenerative disorders, including Multiple Sclerosis, Parkinson's and Huntington's Diseases. To date, however, direct relevant evidence remains limited, not least because the necessary scientific motivations for investigating this with greater depth have not yet been fully articulated. Herein, we provide a roadmap that reviews the relevant literatures, highlighting potential links across neurodegenerative disorders and bilingualism more generally.
Collapse
|