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Premorbid brain structure influences risk of amyotrophic lateral sclerosis
  1. Alexander G Thompson,
  2. Bernd Taschler,
  3. Stephen M Smith,
  4. Martin R Turner
  1. Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
  1. Correspondence to Dr Alexander G Thompson, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; alexander.thompson{at}ndcn.ox.ac.uk; Dr Martin R Turner; martin.turner{at}ndcn.ox.ac.uk

Abstract

Background Amyotrophic lateral sclerosis (ALS) is a disease of the motor network associated with brain structure and functional connectivity alterations that are implicated in disease progression. Whether such changes have a causal role in ALS, fitting with a postulated influence of premorbid cerebral architecture on the phenotypes associated with neurodegenerative disorders is not known.

Methods This study considered causal effects and shared genetic risk of 2240 structural and functional MRI brain scan imaging-derived phenotypes (IDPs) on ALS using two sample Mendelian randomisation, with putative associations further examined with extensive sensitivity analysis. Shared genetic predisposition between IDPs and ALS was explored using genetic correlation analysis.

Results Increased white matter volume in the cerebral hemispheres was causally associated with ALS. Weaker causal associations were observed for brain stem grey matter volume, parieto-occipital white matter surface and volume of the left thalamic ventral anterior nucleus. Genetic correlation was observed between ALS and intracellular volume fraction and isotropic free water volume fraction within the posterior limb of the internal capsule.

Conclusions This study provides evidence that premorbid brain structure, in particular white matter volume, contributes to the risk of ALS.

  • MRI
  • ALS

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Footnotes

  • Contributors All authors contributed to study design. AGT undertook the analysis, prepared the figures and manuscript. All authors reviewed and edited the manuscript.

  • Funding AGT holds a Clinician Scientist Fellowship from the Medical Research Council (MR/T006927/1) and Lady Edith Wolfson Fellowship from the Motor Neuron Disease Association (Thompson/Jan20/952-795). Computation used the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. This research was funded in whole, or in part, by the UKRI MR/T006927/1.

  • Disclaimer The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.