BackgroundObesity reportedly increases the risk for developing multiple sclerosis (MS), but little is known about its association with disability accumulation.MethodsThis nationwide longitudinal cohort study included 1066 individuals with newly diagnosed MS from the German National MS cohort. Expanded Disability Status Scale (EDSS) scores, relapse rates, MRI findings and choice of immunotherapy were compared at baseline and at years 2, 4 and 6 between obese (body mass index, BMI ≥30 kg/m2) and non-obese (BMI <30 kg/m2) patients and correlated with individual BMI values.ResultsPresence of obesity at disease onset was associated with higher disability at baseline and at 2, 4 and 6 years of follow-up (p<0.001). Median time to reach EDSS 3 was 0.99 years for patients with BMI ≥30 kg/m2 and 1.46 years for non-obese patients. Risk to reach EDSS 3 over 6 years was significantly increased in patients with BMI ≥30 kg/m2 compared with patients with BMI <30 kg/m2 after adjustment for sex, age, smoking (HR 1.87; 95% CI 1.3 to 2.6; log-rank test p<0.001) and independent of disease-modifying therapies. Obesity was not significantly associated with higher relapse rates, increased number of contrast-enhancing MRI lesions or higher MRI T2 lesion burden over 6 years of follow-up.ConclusionsObesity in newly diagnosed patients with MS is associated with higher disease severity and poorer outcome. Obesity management could improve clinical outcome of MS.
Effective connectivity (EC) is able to explore causal effects between brain areas and can depict mechanisms that underlie repair and adaptation in chronic brain diseases. Thus, the application of EC techniques in multiple sclerosis (MS) has the potential to determine directionality of neuronal interactions and may provide an imaging biomarker for disease progression. Here, serial longitudinal structural and resting-state fMRI was performed at 12-week intervals over one year in twelve MS patients. Twelve healthy subjects served as controls (HC). Two approaches for EC quantification were used: Causal Bayesian Network (CBN) and Time-resolved Partial Directed Coherence (TPDC). The EC strength was correlated with the Expanded Disability Status Scale (EDSS) and Fatigue Scale for Motor and Cognitive functions (FSMC). Our findings demonstrated a longitudinal increase in EC between specific brain regions, detected in both the CBN and TPDC analysis in MS patients. In particular, EC from the deep grey matter, frontal, prefrontal and temporal regions showed a continuous increase over the study period. No longitudinal changes in EC were attested in HC during the study. Furthermore, we observed an association between clinical performance and EC strength. In particular, the EC increase in fronto-cerebellar connections showed an inverse correlation with the EDSS and FSMC. Our data depict continuous functional reorganization between specific brain regions indicated by increasing EC over time in MS, which is not detectable in HC. In particular, fronto-cerebellar connections, which were closely related to clinical performance, may provide a marker of brain plasticity and functional reserve in MS.
Background and Purpose
Ischemic stroke causes major disability as a consequence of neuronal loss and recurrent ischemic events. Biomarkers predicting tissue damage or stroke recurrence might be useful to guide an individualized stroke therapy. NfL (neurofilament light chain) is a promising biomarker that might be used for this purpose.
Methods
We used individual data of patients with an acute ischemic stroke and clinical long term follow-up. Serum NfL (sNfL) was quantified within 24 hours after admission and after 1 year and compared with other biomarkers (GDF15 [growth differentiation factor 15], S100, NT-proBNP [N-terminal pro-B-type natriuretic peptide], ANP [atrial natriuretic peptide], and FABP [fatty acid–binding protein]). The primary end point was functional outcome after 90 days and cerebrovascular events and death (combined cardiovascular end point) within 36 months of follow-up.
Results
Two hundred eleven patients (mean age, 68.7 years; SD, ±12.6; 41.2% women) with median clinical severity on the National Institutes of Health Stroke Scale (NIHSS) score of 3 (interquartile range, 1–5) and long-term follow-up with a median of 41.8 months (interquartile range, 40.0–44.5) were prospectively included. We observed a significant correlation between sNfL and NIHSS at hospital admission (r=0.234; P<0.001). sNfL levels increased with the grade of age-related white matter changes (P<0.001) and were able to predict unfavorable clinical outcome (modified Rankin Scale score, ≥2) 90 days after stroke (odds ratio [OR], 1.562; 95% CI, 1.003–2.433; P=0.048) together with NIHSS (OR, 1.303; 95% CI, 1.164–1.458; P<0.001) and age-related white matter change rating (severe; OR, 3.326; 95% CI, 1.186–9.326; P=0.022). Similarly, sNfL was valuable for the prediction of the combined cardiovascular end point (OR, 2.002; 95% CI, 1.213–3.302; P=0.007), besides NIHSS (OR, 1.110; 95% CI, 1.000–1.232; P=0.049), diabetes mellitus (OR, 2.942; 95% CI, 1.306–6.630; P=0.005), and age-related white matter change rating (severe; OR, 4.816; 95% CI, 1.206–19.229; P=0.026) after multivariate regression analysis. Kaplan-Meier analysis revealed significantly more combined cardiovascular end points (18 [14.1%] versus 38 [45.8%], log-rank test P<0.001) during long-term follow-up in patients with elevated sNfL levels.
Conclusions
sNFL is a valuable biomarker for functional independence 90 days after ischemic stroke and predicts cardiovascular long-term outcome.
Objective
We explored the incremental value of adding multiple disease activity biomarkers in CSF and serum for distinguishing MRI-based benign from aggressive MS in early disease course.
Methods
Ninety-three patients diagnosed with clinically isolated syndrome (CIS) or early MS were divided into 3 nonoverlapping severity groups defined by objective MRI criteria. Ninety-seven patients with noninflammatory neurologic disorders and 48 patients with other inflammatory neurologic diseases served as controls. Leukocyte subsets in the CSF were analyzed by flow cytometry. CSF neurofilament light chain (NfL) and chitinase-3-like protein 1 (CHI3L1) levels were measured by ELISA. Serum NfL levels were examined using single molecule array technology.
Results
CSF CD20+/CD14+ ratios and NfL levels in CSF and serum were significantly different between high and low MRI severity groups, whereas no difference was found for CSF CHI3L1 levels. NfL levels in CSF and serum highly correlated. Receiver operating characteristic analysis demonstrated that the cumulative sums combining CSF CD20+/CD14+ ratios and NfL levels in serum or CSF considerably improved diagnostic accuracy. A composite score built from these 2 cumulative sums best distinguished MRI severity. These findings were validated by support vector machine analysis, which confirmed that the accuracy of the cumulative sums and composite score outperforms single biomarkers.
Conclusion
Patients with extreme manifestations of CIS or early MS defined by strict MRI parameters can be best distinguished by combining markers of intrathecal B-cell accumulation and axonal damage. This could stratify individual treatment decisions toward a more personalized immunotherapy.
Background
Currently, no unequivocal predictors of disease evolution exist in patients with multiple sclerosis (MS). Cortical atrophy measurements are, however, closely associated with cumulative disability.
Objective
Here, we aim to forecast longitudinal magnetic resonance imaging (MRI)-driven cortical atrophy and clinical disability from cerebrospinal fluid (CSF) markers.
Methods
We analyzed CSF fractions of albumin and immunoglobulins (Ig) A, G, and M and their CSF to serum quotients.
Results
Widespread atrophy was highly associated with increased baseline CSF concentrations and quotients of albumin and IgA. Patients with increased CSFIgA and CSFIgM showed higher functional disability at follow-up.
Conclusion
CSF markers of blood–brain barrier integrity and specific immune response forecast emerging gray matter pathology and disease progression in MS.
Diffuse inflammation in multiple sclerosis (MS) extends beyond focal lesion sites, affecting interconnected regions; however, little is known about the impact of an individual lesion affecting major white matter (WM) pathways on brain functional connectivity (FC). Here, we longitudinally assessed the effects of acute and chronic lesions on FC in relapsing-remitting MS (RRMS) patients using resting-state fMRI. 45 MRI data sets from 9 RRMS patients were recorded using 3T MR scanner over 5 time points at 8 week intervals. Patients were divided into two groups based on the presence (n = 5; MS+) and absence (n = 4; MS-) of a lesion at a predilection site for MS. While FC levels were found not to fluctuate significantly in the overall patient group, the MS+ patient group showed increased FC in the contralateral cuneus and precuneus and in the ipsilateral precuneus (p < 0.01, corrected). This can be interpreted as the recruitment of intact cortical regions to compensate for tissue damage. During the study, one patient developed an acute WM lesion in the left posterior periventricular space. A marked increase in FC in the right pre-, post-central gyrus, right superior frontal gyrus, the left cuneus, the vermis and the posterior and anterior lobes of the cerebellum was noted following the clinical relapse, which gradually decreased in subsequent follow-ups, suggesting short-term functional reorganization during the acute phase. This strongly suggests that the lesion-related network changes observed in patients with chronic lesions occur as a result of reorganization processes following the initial appearance of an acute lesion.
Background
The pathology of multiple sclerosis (MS) consists of demyelination and neuronal injury, which occur early in the disease; yet, remission phases indicate repair. Whether and how the central nervous system (CNS) maintains homeostasis to counteract clinical impairment is not known.
Objective
We analyse the structural connectivity of white matter (WM) and grey matter (GM) networks to understand the absence of clinical decline as the disease progresses.
Methods
A total of 138 relapsing–remitting MS patients (classified into six groups by disease duration) and 32 healthy controls were investigated using 3-Tesla magnetic resonance imaging (MRI). Networks were analysed using graph theoretical approaches based on connectivity patterns derived from diffusion-tensor imaging with probabilistic tractography for WM and voxel-based morphometry and regional-volume-correlation matrix for GM.
Results
In the first year after disease onset, WM networks evolved to a structure of increased modularity, strengthened local connectivity and increased local clustering while no clinical decline occurred. GM networks showed a similar dynamic of increasing modularity. This modified connectivity pattern mainly involved the cerebellum, cingulum and temporo-parietal regions. Clinical impairment was associated at later disease stages with a divergence of the network patterns.
Conclusion
Our findings suggest that network functionality in MS is maintained through structural adaptation towards increased local and modular connectivity, patterns linked to adaptability and homeostasis.
Focal demyelinated lesions, diffuse white matter (WM) damage, and gray matter (GM) atrophy influence directly the disease progression in patients with multiple sclerosis. The aim of this study was to identify specific characteristics of GM and WM structural networks in subjects with clinically isolated syndrome (CIS) in comparison to patients with early relapsing-remitting multiple sclerosis (RRMS). Twenty patients with CIS, 33 with RRMS, and 40 healthy subjects were investigated using 3 T-MRI. Diffusion tensor imaging was applied, together with probabilistic tractography and fractional anisotropy (FA) maps for WM and cortical thickness correlation analysis for GM, to determine the structural connectivity patterns. A network topology analysis with the aid of graph theoretical approaches was used to characterize the network at different community levels (modularity, clustering coefficient, global, and local efficiencies). Finally, we applied support vector machines (SVM) to automatically discriminate the two groups. In comparison to CIS subjects, patients with RRMS were found to have increased modular connectivity and higher local clustering, highlighting increased local processing in both GM and WM. Both groups presented increased modularity and clustering coefficients in comparison to healthy controls. SVM algorithms achieved 97% accuracy using the clustering coefficient as classifier derived from GM and 65% using WM from probabilistic tractography and 67% from modularity of FA maps to differentiate between CIS and RRMS patients. We demonstrate a clear increase of modular and local connectivity in patients with early RRMS in comparison to CIS and healthy subjects. Based only on a single anatomic scan and without a priori information, we developed an automated and investigator-independent paradigm that can accurately discriminate between patients with these clinically similar disease entities, and could thus complement the current dissemination-in-time criteria for clinical diagnosis.
Excitation/inhibition (E/I) balance plays important roles in mental disorders. Bioactive phospholipids like lysophosphatidic acid (LPA) are synthesized by the enzyme autotaxin (ATX) at cortical synapses and modulate glutamatergic transmission, and eventually alter E/I balance of cortical networks. Here, we analyzed functional consequences of altered E/I balance in 25 human subjects induced by genetic disruption of the synaptic lipid signaling modifier PRG-1, which were compared to 25 age and sex matched control subjects. Furthermore, we tested therapeutic options targeting ATX in a related mouse line. Using EEG combined with TMS in an instructed fear paradigm, neuropsychological analysis and an fMRI based episodic memory task, we found intermediate phenotypes of mental disorders in human carriers of a loss-of-function single nucleotide polymorphism of PRG-1 (PRG-1R345T/WT). Prg-1R346T/WT animals phenocopied human carriers showing increased anxiety, a depressive phenotype and lower stress resilience. Network analysis revealed that coherence and phase-amplitude coupling were altered by PRG-1 deficiency in memory related circuits in humans and mice alike. Brain oscillation phenotypes were restored by inhibtion of ATX in Prg-1 deficient mice indicating an interventional potential for mental disorders.
