Evidence map›Paper›PMID 42607283›Full record

ArticleNeurology(R) neuroimmunology & neuroinflammation2026

Data-Driven Fatigue Trajectories in Multiple Sclerosis and Their Predictors.

Alessandro Cruciani, Emanuele Olivieri, Ronja Christensen, Sarmad Al-Araji, Samih Fourali, Weaam Hamed, Anna He, Baris Kanber, Davide Maccarrone, Suraya Mohamud and 10 more

Abstract read
In one paragraph

Article in Neurology(R) neuroimmunology & neuroinflammation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

20 authors.

Alessandro CrucianiQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-2631-4185
Emanuele OlivieriQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0009-0002-2732-5270
Ronja ChristensenQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-5101-0896
Sarmad Al-ArajiQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0009-0007-0703-6849
Samih FouraliQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.
Weaam HamedQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0001-5991-9060
Anna HeQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-6924-8798
Baris KanberQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0003-2443-8800
Davide MaccarroneQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-5873-0191
Suraya MohamudQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0009-0003-9850-1785
Riccardo NistriQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-5479-8201
Anestis PassalisQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0009-0008-9031-1107
Valeria PozzilliQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-0556-0176
Ferran PradosQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-7872-0142
Charmaine YamQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-4146-2047
Marios C YiannakasQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0003-4986-446X
Alan J ThompsonQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-4333-8496
Frederik BarkhofQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0003-3543-3706
Ahmed T ToosyQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0002-4441-3750
Olga CiccarelliQueen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, United Kingdom.ORCID 0000-0001-7485-1367

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesFatigue is one of the most disabling symptoms of multiple sclerosis (MS), yet its longitudinal trajectories and determinants remain poorly characterized. We aimed to identify trajectories of cognitive and physical fatigue in relapsing-remitting MS (RRMS) and to determine baseline clinical and MRI predictors of worsening fatigue.

methodsWe conducted a prospective, single-center cohort study embedded in the Predicting Optimal INdividualised Treatment response in MS (POINT-MS) program at the Queen Square MS Centre (London, United Kingdom). Adults with RRMS initiating a new disease-modifying therapy within 3 months of baseline were enrolled and underwent clinical and MRI assessments at baseline, 6 months, and 18 months. Key inclusion criteria were a diagnosis of RRMS, availability of baseline brain and cervical spinal cord MRI, and completion of at least 3 Modified Fatigue Impact Scale (MFIS) assessments. The primary outcome was the longitudinal change in MFIS cognitive and physical subscale scores. Fatigue trajectories were identified using growth mixture modeling. Baseline predictors included demographic factors, disability, cognitive performance, depression diagnosis, proportion of disease duration on disease-modifying treatments (DMTs), and MRI-derived measures of cervical spinal cord cross-sectional area (C-CSA) and regional brain volumes standardized to healthy-control norms. Predictor importance was assessed with conditional random forests, followed by multivariable modeling.

resultsWe enrolled 225 participants. Three distinct fatigue trajectories emerged for both cognitive and physical MFIS subscales, including improving, stable/mild-worsening, and worsening classes. Worsening trajectories showed annualized increases of +3.32 points/year (cognitive) and +3.37 points/year (physical). Smaller baseline C-CSA was the strongest MRI predictor of worsening trajectories. Clinically, the proportion of disease duration spent on DMTs was the most influential predictor. DISCUSSION: Fatigue in RRMS follows heterogeneous trajectories over short-term follow-up. Smaller cervical spinal cord area was the strongest MRI predictor of worsening fatigue. Damage to ascending somatosensory axons within the cervical cord may reduce afferent input to the thalamus and diminish thalamocortical drive, contributing to both the motor and the cognitive-arousal dimensions of fatigue. These findings support incorporating cervical cord MRI into prognostic models and future fatigue research.

Indexed as

Disease ProgressionFatigueMultiple Sclerosis, Relapsing-RemittingAdultFemaleHumansLongitudinal StudiesMagnetic Resonance ImagingMaleMiddle AgedProspective Studies

Identifiers

PMID42607283
PMCPMC13485140

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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.