Dataset Information

The course of fatigue during the development of rheumatoid arthritis and its relation with inflammation: a longitudinal study.

ABSTRACT:

Objective

Fatigue is a prominent and disabling symptom in patients with rheumatoid arthritis (RA), that is only partially explained by inflammation and responds poorly to DMARD-therapy. We hypothesized that inflammation explains fatigue to a larger extent in the phase of clinically suspect arthralgia (CSA), when persistent clinical arthritis is still absent and fatigue has not yet become chronic. We therefore studied the course of fatigue in CSA during progression to RA and the association with inflammation at CSA-onset and at RA-diagnosis.

Methods

600 consecutive CSA-patients were followed for RA-development. Additionally, 710 early RA-patients were studied at diagnosis. Fatigue was assessed every study visit and expressed on a 0-100 scale. Inflammation was measured with the DAS44-CRP, with and without including subclinical inflammation. The course of fatigue over time was studied with linear mixed models. Associations between fatigue and inflammation were studied with linear regression. Analyses were stratified by ACPA-status.

Results

In 88 CSA-patients who developed RA, pre-arthritis fatigue-levels increased gradually with 7 points/year, towards 48 (95%CI=41-55) at RA-development (P=ns). Fatigue decreased in CSA-patients who did not develop RA (4 points/year, P<0.001). At CSA-onset, inflammation was associated with fatigue (β=18, meaning 18 points more fatigue per point increase DAS-score, P<0.01). This association was stronger than at RA-diagnosis (β=5, P<0.001). Fatigue-levels were lower in ACPA-positive pre-RA, but its association with inflammation was stronger compared to ACPA-negative pre-RA.

Conclusion

Fatigue increased gradually during progression from arthralgia to clinical arthritis, and fatigue was better explained by inflammation in CSA than in RA. This implies a 'phase-dependent relation' between inflammation and fatigue.

SUBMITTER: Khidir SJH

PROVIDER: S-EPMC7615874 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

The course of fatigue during the development of rheumatoid arthritis and its relation with inflammation: a longitudinal study.

Khidir Sarah J H SJH Wouters Fenne F van der Helm-van Mil Annette H M AHM van Mulligen Elise E

Joint bone spine 20220628 6

<h4>Objective</h4>Fatigue is a prominent and disabling symptom in patients with rheumatoid arthritis (RA), that is only partially explained by inflammation and responds poorly to DMARD-therapy. We hypothesized that inflammation explains fatigue to a larger extent in the phase of clinically suspect arthralgia (CSA), when persistent clinical arthritis is still absent and fatigue has not yet become chronic. We therefore studied the course of fatigue in CSA during progression to RA and the associati ...[more]

PMID: 35777555

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Project description:BackgroundFatigue is a common and burdensome symptom in Rheumatoid Arthritis (RA), yet is poorly understood. Currently, clinicians rely solely on fatigue questionnaires, which are inherently subjective measures. For the effective development of future therapies and stratification, it is of vital importance to identify biomarkers of fatigue. In this study, we identify brain differences between RA patients who improved and did not improve their levels of fatigue based on Chalder Fatigue Scale variation (ΔCFS≥ 2), and we compared the performance of different classifiers to distinguish between these samples at baseline.MethodsFifty-four fatigued RA patients underwent a magnetic resonance (MR) scan at baseline and 6 months later. At 6 months we identified those whose fatigue levels improved and those for whom it did not. More than 900 brain features across three data sets were assessed as potential predictors of fatigue improvement. These data sets included clinical, structural MRI (sMRI) and diffusion tensor imaging (DTI) data. A genetic algorithm was used for feature selection. Three classifiers were employed in the discrimination of improvers and non-improvers of fatigue: a Least Square Linear Discriminant (LSLD), a linear Support Vector Machine (SVM) and a SVM with Radial Basis Function kernel.ResultsThe highest accuracy (67.9%) was achieved with the sMRI set, followed by the DTI set (63.8%), whereas classification performance using clinical features was at the chance level. The mean curvature of the left superior temporal sulcus was most strongly selected during the feature selection step, followed by the surface are of the right frontal pole and the surface area of the left banks of the superior temporal sulcus.ConclusionsThe results presented evidence a superiority of brain metrics over clinical metrics in predicting fatigue changes. Further exploration of these methods may support clinicians to triage patients towards the most appropriate fatigue alleviating therapies.

Dataset Information

The course of fatigue during the development of rheumatoid arthritis and its relation with inflammation: a longitudinal study.

Objective

Methods

Results

Conclusion

Publications

The course of fatigue during the development of rheumatoid arthritis and its relation with inflammation: a longitudinal study.

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