Biochemical Correlations with Fatigue in Multiple Sclerosis Detected by MR 2D Localized Correlated Spectroscopy. Issue 3 (22nd February 2021)

• Record Type: Journal Article
• Authors: ARM, Jameen; Al‐iedani, Oun; Ribbons, Karen; Lea, Rod; Lechner‐Scott, Jeannette; Ramadan, Saadallah
• Is Part Of: Journal of neuroimaging. Volume 31:Issue 3(2021)
• Page Start: 508
• Page End: 516
• Publication Date: 2021-02-22
• Subjects: 2D L‐COSY -- MS fatigue -- multiple sclerosis; Diagnostic imaging -- Periodicals; Nervous system -- Diseases -- Diagnosis -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Système nerveux -- Maladies -- Diagnostic -- Périodiques; Imagerie médicale; Neuroimagerie; Neurologie; Système nerveux; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.804754
• DOI: 10.1111/jon.12836
• Languages: English
• ISSNs: 1051-2284

Abstract

ABSTRACT: BACKGROUND AND PURPOSE: Fatigue is the common symptom in patients with multiple sclerosis (MS), yet its pathophysiological mechanism is poorly understood. We investigated the metabolic changes in fatigue in a group of relapsing‐remitting MS (RRMS) patients using MR two‐dimensional localized correlated spectroscopy (2D L‐COSY). METHODS: Sixteen RRMS and 16 healthy controls were included in the study. Fatigue impact was assessed with the Modified Fatigue Impact Scale (MFIS). MR 2D L‐COSY data were collected from the posterior cingulate cortex. Nonparametric statistical analysis was used to calculate the changes in creatine scaled metabolic ratios and their correlations with fatigue scores. RESULTS: Compared to healthy controls, the RRMS group showed significantly higher fatigue and lower metabolic ratios for tyrosine, glutathione, homocarnosine (GSH+Hca), fucose‐3, glutamine+glutamate (Glx), glycerophosphocholine (GPC), total choline, and N‐acetylaspartate (NAA‐2), while increased levels for isoleucine and glucose ( P ≤ .05). Only GPC showed positive correlation with all fatigue domains ( r = .537, P ≤ .05). On the other hand, Glx‐upper, NAA‐2, GSH+Hca, and fucose‐3 showed negative correlations with all fatigue domains ( r = –.345 to –.580, P ≤ .05). While tyrosine showed positive correlation with MFIS ( r = .499, P ≤ .05), cognitive fatigue was negatively correlated with total GSH ( r = –.530, P ≤ .05). No correlations were found between lesion load or brain volumes with fatigue score. CONCLUSIONS: Our results suggest that fatigue in MS is strongly correlated with an imbalance in neurometabolites but not structural brain measurements.