James H Cole1,2,3,4, Joel Raffel5, Tim Friede6, Arman Eshaghi7, Wallace J Brownlee7, Declan Chard7,8, Nicola De Stefano9, Christian Enzinger10, Lukas Pirpamer11, Massimo Filippi12, Claudio Gasperini13, Maria Assunta Rocca12, Alex Rovira14, Serena Ruggieri13, Jaume Sastre-Garriga15, Maria Laura Stromillo9, Bernard M J Uitdehaag16, Hugo Vrenken8, Frederik Barkhof1,7,17, Richard Nicholas5,18, Olga Ciccarelli1,7. 1. Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK. 2. Dementia Research Centre, Institute of Neurology, University College London, London, UK. 3. Department of Neuroimaging, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK. 4. Computational, Cognitive, and Clinical Neuroimaging Laboratory, Department of Medicine, Imperial College London, London, UK. 5. Centre for Neuroinflammation and Neurodegeneration, Faculty of Medicine, Imperial College London, London, UK. 6. Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany. 7. Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK. 8. Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands. 9. Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy. 10. Research Unit for Neural Repair and Plasticity, Department of Neurology and Division of Neuroradiology, Vascular and Interventional Radiology, Department of Radiology, Medical University of Graz, Graz, Austria. 11. Neuroimaging Research Unit, Department of Neurology, Medical University of Graz, Graz, Austria. 12. Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy. 13. Department of Neurosciences, San Camillo-Forlanini Hospital, Rome, Italy. 14. MR Unit and Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain. 15. Department of Neurology / Neuroimmunology, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain. 16. Department of Neurology, VU University Medical Center, Amsterdam, The Netherlands. 17. National Institute for Health Research (NIHR), University College London Hospitals (UCLH) Biomedical Research Centre (BRC), London, UK. 18. Department of Visual Neuroscience, UCL Institute of Ophthalmology, London, UK.
Abstract
OBJECTIVE: During the natural course of multiple sclerosis (MS), the brain is exposed to aging as well as disease effects. Brain aging can be modeled statistically; the so-called "brain-age" paradigm. Here, we evaluated whether brain-predicted age difference (brain-PAD) was sensitive to the presence of MS, clinical progression, and future outcomes. METHODS: In a longitudinal, multicenter sample of 3,565 magnetic resonance imaging (MRI) scans, in 1,204 patients with MS and clinically isolated syndrome (CIS) and 150 healthy controls (mean follow-up time: patients 3.41 years, healthy controls 1.97 years), we measured "brain-predicted age" using T1-weighted MRI. We compared brain-PAD among patients with MS and patients with CIS and healthy controls, and between disease subtypes. Relationships between brain-PAD and Expanded Disability Status Scale (EDSS) were explored. RESULTS: Patients with MS had markedly higher brain-PAD than healthy controls (mean brain-PAD +10.3 years; 95% confidence interval [CI] = 8.5-12.1] versus 4.3 years; 95% CI = 2.1 to 6.4; p < 0.001). The highest brain-PADs were in secondary-progressive MS (+13.3 years; 95% CI = 11.3-15.3). Brain-PAD at study entry predicted time-to-disability progression (hazard ratio 1.02; 95% CI = 1.01-1.03; p < 0.001); although normalized brain volume was a stronger predictor. Greater annualized brain-PAD increases were associated with greater annualized EDSS score (r = 0.26; p < 0.001). INTERPRETATION: The brain-age paradigm is sensitive to MS-related atrophy and clinical progression. A higher brain-PAD at baseline was associated with more rapid disability progression and the rate of change in brain-PAD related to worsening disability. Potentially, "brain-age" could be used as a prognostic biomarker in early-stage MS, to track disease progression or stratify patients for clinical trial enrollment. ANN NEUROL 2020 ANN NEUROL 2020;88:93-105.
OBJECTIVE: During the natural course of multiple sclerosis (MS), the brain is exposed to aging as well as disease effects. Brain aging can be modeled statistically; the so-called "brain-age" paradigm. Here, we evaluated whether brain-predicted age difference (brain-PAD) was sensitive to the presence of MS, clinical progression, and future outcomes. METHODS: In a longitudinal, multicenter sample of 3,565 magnetic resonance imaging (MRI) scans, in 1,204 patients with MS and clinically isolated syndrome (CIS) and 150 healthy controls (mean follow-up time: patients 3.41 years, healthy controls 1.97 years), we measured "brain-predicted age" using T1-weighted MRI. We compared brain-PAD among patients with MS and patients with CIS and healthy controls, and between disease subtypes. Relationships between brain-PAD and Expanded Disability Status Scale (EDSS) were explored. RESULTS:Patients with MS had markedly higher brain-PAD than healthy controls (mean brain-PAD +10.3 years; 95% confidence interval [CI] = 8.5-12.1] versus 4.3 years; 95% CI = 2.1 to 6.4; p < 0.001). The highest brain-PADs were in secondary-progressive MS (+13.3 years; 95% CI = 11.3-15.3). Brain-PAD at study entry predicted time-to-disability progression (hazard ratio 1.02; 95% CI = 1.01-1.03; p < 0.001); although normalized brain volume was a stronger predictor. Greater annualized brain-PAD increases were associated with greater annualized EDSS score (r = 0.26; p < 0.001). INTERPRETATION: The brain-age paradigm is sensitive to MS-related atrophy and clinical progression. A higher brain-PAD at baseline was associated with more rapid disability progression and the rate of change in brain-PAD related to worsening disability. Potentially, "brain-age" could be used as a prognostic biomarker in early-stage MS, to track disease progression or stratify patients for clinical trial enrollment. ANN NEUROL 2020 ANN NEUROL 2020;88:93-105.
Authors: Torgeir Hellstrøm; Nada Andelic; Ann-Marie G de Lange; Eirik Helseth; Kristin Eiklid; Lars T Westlye Journal: J Clin Med Date: 2021-01-22 Impact factor: 4.241
Authors: Lea Baecker; Jessica Dafflon; Pedro F da Costa; Rafael Garcia-Dias; Sandra Vieira; Cristina Scarpazza; Vince D Calhoun; João R Sato; Andrea Mechelli; Walter H L Pinaya Journal: Hum Brain Mapp Date: 2021-03-19 Impact factor: 5.038
Authors: Arinbjörn Kolbeinsson; Sarah Filippi; Yannis Panagakis; Paul M Matthews; Paul Elliott; Abbas Dehghan; Ioanna Tzoulaki Journal: Sci Rep Date: 2020-11-17 Impact factor: 4.379
Authors: Sophie A H Jacobs; Paolo A Muraro; Maria T Cencioni; Sarah Knowles; James H Cole; Richard Nicholas Journal: Front Neurol Date: 2022-01-06 Impact factor: 4.003
Authors: Sebastian G Popescu; Alex Whittington; Roger N Gunn; Paul M Matthews; Ben Glocker; David J Sharp; James H Cole Journal: Hum Brain Mapp Date: 2020-07-09 Impact factor: 5.399