Evidence map›Paper›PMID 29331092›Full record

ArticleAnnals of neurology2018

Deep gray matter volume loss drives disability worsening in multiple sclerosis.

Arman Eshaghi, Ferran Prados, Wallace J Brownlee, Daniel R Altmann, Carmen Tur, M Jorge Cardoso, Floriana De Angelis, Steven H van de Pavert, Niamh Cawley, Nicola De Stefano and 21 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of neurology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 268 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
268citing papers in PubMed, 9 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

268 citing papers in PubMed, 9 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. The effect of ibudilast on thalamic volume in progressive multiple sclerosis.Multiple sclerosis (Houndmills, Basingstoke, England) · 2023
    Trial
  11. Trial
  12. Trial
  13. Trial
  14. Trial
  15. Trial
  16. Trial
  17. Article
  18. Review
  19. Article
  20. Review

208 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

31 authors.

Arman EshaghiQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.ORCID 0000-0002-6652-3512
Ferran PradosQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Wallace J BrownleeQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Daniel R AltmannQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Carmen TurQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
M Jorge CardosoCentre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, United Kingdom.
Floriana De AngelisQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Steven H van de PavertQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Niamh CawleyQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Nicola De StefanoDepartment of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.ORCID 0000-0003-0272-4347
M Laura StromilloDepartment of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
Marco BattagliniDepartment of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.ORCID 0000-0002-9188-4408
Serena RuggieriDepartment of Neurosciences, S Camillo Forlanini Hospital, Rome, Italy.
Claudio GasperiniDepartment of Neurosciences, S Camillo Forlanini Hospital, Rome, Italy.
Massimo FilippiNeuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
Maria A RoccaNeuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy.
Alex RoviraMR Unit and Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
Jaume Sastre-GarrigaDepartment of Neurology/Neuroimmunology, Multiple Sclerosis Centre of Catalonia (Cemcat), Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain.
Hugo VrenkenDepartment of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam, The Netherlands.
Cyra E LeursDepartment of Neurology, MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Joep KillesteinDepartment of Neurology, MS Center Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.
Lukas PirpamerDepartment of Neurology, Medical University of Graz, Graz, Austria.
Christian EnzingerDepartment of Neurology, Medical University of Graz, Graz, Austria.
Sebastien OurselinCentre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, United Kingdom.
Claudia A M Gandini Wheeler-KingshottQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Declan ChardQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Alan J ThompsonQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Daniel C AlexanderCentre for Medical Image Computing (CMIC), Department of Computer Science, University College London, London, United Kingdom.ORCID 0000-0003-2439-350X
Frederik BarkhofQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
Olga CiccarelliQueen Square Multiple Sclerosis Centre, UCL Institute of Neurology, Faculty of Brain Sciences, University College London.
MAGNIMS study group

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveGray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.

methodsWe analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression.

resultsSPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).

interpretationThis large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions. Ann Neurol 2018;83:210-222.

Indexed as

AdultAtrophyBrainDisability EvaluationDisease ProgressionFemaleGray MatterHumansImage Interpretation, Computer-AssistedLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedMultiple SclerosisNeuroimagingRetrospective Studies

Identifiers

PMID29331092
PMCPMC5838522

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.