Retinal small vessel pathology is associated with disease burden in multiple sclerosis.

Multiple sclerosis deep learning neuroophthalmology optical coherence tomography angiography pathophysiology retinal microvasculature

Journal

Multiple sclerosis (Houndmills, Basingstoke, England)
ISSN: 1477-0970
Titre abrégé: Mult Scler
Pays: England
ID NLM: 9509185

Informations de publication

Date de publication:
15 May 2024
Historique:
medline: 16 5 2024
pubmed: 16 5 2024
entrez: 16 5 2024
Statut: aheadofprint

Résumé

Alterations of the superficial retinal vasculature are commonly observed in multiple sclerosis (MS) and can be visualized through optical coherence tomography angiography (OCTA). This study aimed to examine changes in the retinal vasculature during MS and to integrate findings into current concepts of the underlying pathology. In this cross-sectional study, including 259 relapsing-remitting MS patients and 78 healthy controls, we analyzed OCTAs using deep-learning-based segmentation algorithm tools. We identified a loss of small-sized vessels (diameter < 10 µm) in the superficial vascular complex in all MS eyes, irrespective of their optic neuritis (ON) history. This alteration was associated with MS disease burden and appears independent of retinal ganglion cell loss. In contrast, an observed reduction of medium-sized vessels (diameter 10-20 µm) was specific to eyes with a history of ON and was closely linked to ganglion cell atrophy. These findings suggest distinct atrophy patterns in retinal vessels in patients with MS. Further studies are necessary to investigate retinal vessel alterations and their underlying pathology in MS.

Sections du résumé

BACKGROUND UNASSIGNED
Alterations of the superficial retinal vasculature are commonly observed in multiple sclerosis (MS) and can be visualized through optical coherence tomography angiography (OCTA).
OBJECTIVES UNASSIGNED
This study aimed to examine changes in the retinal vasculature during MS and to integrate findings into current concepts of the underlying pathology.
METHODS UNASSIGNED
In this cross-sectional study, including 259 relapsing-remitting MS patients and 78 healthy controls, we analyzed OCTAs using deep-learning-based segmentation algorithm tools.
RESULTS UNASSIGNED
We identified a loss of small-sized vessels (diameter < 10 µm) in the superficial vascular complex in all MS eyes, irrespective of their optic neuritis (ON) history. This alteration was associated with MS disease burden and appears independent of retinal ganglion cell loss. In contrast, an observed reduction of medium-sized vessels (diameter 10-20 µm) was specific to eyes with a history of ON and was closely linked to ganglion cell atrophy.
CONCLUSION UNASSIGNED
These findings suggest distinct atrophy patterns in retinal vessels in patients with MS. Further studies are necessary to investigate retinal vessel alterations and their underlying pathology in MS.

Identifiants

pubmed: 38751230
doi: 10.1177/13524585241247775
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

13524585241247775

Déclaration de conflit d'intérêts

Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: R.W. received a poster grant by Novartis. L.A. received a research grant by Novartis and travel grants by Novartis, Sanofi and Horizon therapeutics. B.H. has served on scientific advisory boards for Novartis; he has served as DMSC member for AllergyCare, Sandoz, Polpharma, Biocon and TG therapeutics; he or his institution have received speaker honoraria from Desitin; his institution received research grants from Regeneron and Roche for MS research. He holds part of two patents; one for the detection of antibodies against KIR4.1 in a subpopulation of patients with MS and one for genetic determinants of neutralizing antibodies to interferon. All conflicts are not relevant to the topic of the study. B.K. received travel support and speaking honoraria from Novartis Deutschland GmbH, Teva Deutschland, Merck, and Heidelberg Engineering and served at the advisory board of Merck. He received a research grant from Novartis. L.K., A.W., D.R., T.K. and M.J.M. have nothing to disclose.

Auteurs

Rebecca Wicklein (R)

Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.

Linus Kreitner (L)

Institute for AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.

Anna Wild (A)

Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.

Lilian Aly (L)

Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.

Daniel Rueckert (D)

Institute for AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
BioMedIA, Imperial College London, London, UK.

Bernhard Hemmer (B)

Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.

Thomas Korn (T)

Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.
Institute for Experimental Neuroimmunology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.

Martin J Menten (MJ)

Institute for AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
BioMedIA, Imperial College London, London, UK.

Benjamin Knier (B)

Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany.
Department of Neurology and Geriatric Neurology, Diakonie Klinikum Schwäbisch Hall, Schwäbisch Hall, Germany.

Classifications MeSH