Accelerated brain ageing and disability in multiple sclerosis

Background Brain atrophy occurs in both normal ageing and in multiple sclerosis (MS), but it occurs at a faster rate in MS, where it is the major driver of disability progression. Here, we employed a neuroimaging biomarker of structural brain ageing to explore how MS influences the brain ageing process. Methods In a longitudinal, multi-centre sample of 3,565 MRI scans in 1,204 MS/clinically isolated syndrome (CIS) patients and 150 healthy controls (HCs) (mean follow-up time: patients 3⋅41 years, HCs 1⋅97 years) we measured ‘brain-predicted age’ using T1-weighted MRI. Brain-predicted age difference (brain-PAD) was calculated as the difference between the brain-predicted age and chronological age. Positive brain-PAD indicates a brain appears older than its chronological age. We compared brain-PAD between MS/CIS patients and HCs, and between disease subtypes. In patients, the relationship between brain-PAD and Expanded Disability Status Scale (EDSS) at study entry and over time was explored. Findings Adjusted for age, sex, intracranial volume, cohort and scanner effects MS/CIS patients had markedly older-appearing brains than HCs (mean brain-PAD 11⋅8 years [95% CI 9⋅1—14⋅5] versus −0⋅01 [−3⋅0—3⋅0], p<0⋅0001). All MS subtypes had greater brain-PAD scores than HCs, with the oldest-appearing brains in secondary-progressive MS (mean brain-PAD 18⋅0 years [15⋅4—20⋅5], p<0⋅05). At baseline, higher brain-PAD was associated with a higher EDSS, longer time since diagnosis and a younger age at diagnosis. Brain-PAD at study entry significantly predicted time-to-EDSS progression (hazard ratio 1⋅02 [1⋅01—1⋅03], p<0⋅0001): for every 5 years of additional brain-PAD, the risk of progression increased by 14⋅2%. Interpretation MS increases brain ageing across all MS subtypes. An older-appearing brain at baseline was associated with more rapid disability progression, suggesting ‘brain-age’ could be an individualised prognostic biomarker from a single, cross-sectional assessment. Funding UK MS Society; National Institute for Health Research University College London Hospitals Biomedical Research Centre.

[1]  J. Mostert,et al.  Progression in multiple sclerosis: Further evidence of an age dependent process , 2007, Journal of the Neurological Sciences.

[2]  M. Mattson,et al.  Hallmarks of Brain Aging: Adaptive and Pathological Modification by Metabolic States. , 2018, Cell metabolism.

[3]  Eva Kubala Havrdova,et al.  Pathological cut-offs of global and regional brain volume loss in multiple sclerosis , 2019, Multiple sclerosis.

[4]  Julio Acosta-Cabronero,et al.  Brain-predicted age in Down syndrome is associated with beta amyloid deposition and cognitive decline , 2017, Neurobiology of Aging.

[5]  M. Battaglini,et al.  Deep gray matter volume loss drives disability worsening in multiple sclerosis , 2018, Annals of neurology.

[6]  Thomas Thesen,et al.  Structural brain changes in medically refractory focal epilepsy resemble premature brain aging , 2017, Epilepsy Research.

[7]  Christian Gaser,et al.  Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease , 2012 .

[8]  David H. Miller,et al.  ECTRIMS/EAN Guideline on the pharmacological treatment of people with multiple sclerosis , 2018, Multiple sclerosis.

[9]  Janaina Mourão Miranda,et al.  PRoNTo: Pattern Recognition for Neuroimaging Toolbox , 2013, Neuroinformatics.

[10]  J. Cole,et al.  Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases , 2018, BioEssays : news and reviews in molecular, cellular and developmental biology.

[11]  Erlend S. Dørum,et al.  Genetics of brain age suggest an overlap with common brain disorders , 2018, bioRxiv.

[12]  David J. Sharp,et al.  Increased brain-predicted aging in treated HIV disease , 2017, Neurology.

[13]  Jeffrey A. Cohen,et al.  Defining the clinical course of multiple sclerosis: the 2013 revisions. , 2014, Neurology.

[14]  M. Battaglini,et al.  Brain MRI atrophy quantification in MS , 2017, Neurology.

[15]  J. Kurtzke Rating neurologic impairment in multiple sclerosis , 1983, Neurology.

[16]  J. Laman,et al.  Targeting senescence to delay progression of multiple sclerosis , 2018, Journal of Molecular Medicine.

[17]  B. Weinshenker,et al.  Onset of progressive phase is an age-dependent clinical milestone in multiple sclerosis , 2013, Multiple sclerosis.

[18]  Gilles Edan,et al.  Evidence for a two-stage disability progression in multiple sclerosis , 2010, Brain : a journal of neurology.

[19]  Etienne E. Pracht,et al.  The burden of neurological disease in the United States: A summary report and call to action , 2017, Annals of neurology.

[20]  Giovanni Montana,et al.  Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker , 2016, NeuroImage.

[21]  M. Battaglini,et al.  Evaluating and reducing the impact of white matter lesions on brain volume measurements , 2012, Human brain mapping.

[22]  Stefan Klöppel,et al.  BrainAGE in Mild Cognitive Impaired Patients: Predicting the Conversion to Alzheimer’s Disease , 2013, PloS one.

[23]  Stuart J. Ritchie,et al.  Brain age predicts mortality , 2017, Molecular Psychiatry.

[24]  M Daumer,et al.  The relationship of age with the clinical phenotype in multiple sclerosis , 2016, Multiple sclerosis.

[25]  C. Franceschi,et al.  The Continuum of Aging and Age-Related Diseases: Common Mechanisms but Different Rates , 2018, Front. Med..

[26]  Robert Leech,et al.  Prediction of brain age suggests accelerated atrophy after traumatic brain injury , 2015, Annals of neurology.

[27]  Jeffrey A. Cohen,et al.  Diagnostic criteria for multiple sclerosis: 2010 Revisions to the McDonald criteria , 2011, Annals of neurology.

[28]  George C. Ebers,et al.  Clinical prognostic factors in multiple sclerosis: a natural history review , 2009, Nature Reviews Neurology.

[29]  M Daumer,et al.  Age and disability accumulation in multiple sclerosis , 2011, Neurology.

[30]  J. Cole,et al.  Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers , 2017, Trends in Neurosciences.