Progressive MS trials: Lessons learned.

Up to very recently, no treatments had proved effective in progressive multiple sclerosis (MS). In 2016, four drugs, two tested in phase 3 and two in phase 2 trials, showed a beneficial effect in primary or secondary progressive MS. Although this could indicate a turning point in progressive MS treatment, most of these successes have been modest and mainly restricted to patients with active inflammation, in the context of trials with powerful anti-inflammatory agents. This paper summarises these reasons, particularly focusing on the main lessons learned for the design of future trials. First, a drug’s mechanism of action should tackle the specific pathogenic mechanisms that characterise progressive MS. Second, trial populations where new drugs are to be tested should be carefully chosen, possibly including younger patients with shorter disease durations, which have greater chances of showing active deterioration during the trial, therefore increasing the power to detect treatment effects. Third, outcome measures used in future phase 2 and phase 3 trials should be highly sensitive and be accompanied by smart trial designs

Linear brain atrophy measures in multiple sclerosis and clinically isolated syndromes: A 30-year follow-up

OBJECTIVE: To determine 30-year brain atrophy rates following clinically isolated syndromes and the relationship of atrophy in the first 5 years and clinical outcomes 25 years later. METHODS: A cohort of 132 people who presented with a clinically isolated syndrome suggestive of multiple sclerosis (MS) were recruited between 1984–1987. Clinical and MRI data were collected prospectively over 30 years. Widths of the third ventricle and the medulla oblongata were used as linear atrophy measures. RESULTS: At 30 years, 27 participants remained classified as having had a clinically isolated syndrome, 34 converted to relapsing remitting MS, 26 to secondary progressive MS and 16 had died due to MS. The mean age at baseline was 31.7 years (SD 7.5) and the mean disease duration was 30.8 years (SD 0.9). Change in medullary and third ventricular width within the first 5 years, allowing for white matter lesion accrual and Expanded Disability Status Scale increases over the same period, predicted clinical outcome measures at 30 years. 1 mm of medullary atrophy within the first 5 years increased the risk for secondary progressive MS or MS related death by 30 years by 583% (OR 5.83, 95% CI 1.74 to 19.61, p<0.005), using logistic regression. CONCLUSIONS: Our findings show that brain regional atrophy within 5 years of a clinically isolated syndrome predicts progressive MS or a related death, and disability 25 years later

Relevance of time-dependence for clinically viable diffusion imaging of the spinal cord

Purpose Time‐dependence is a key feature of the diffusion‐weighted (DW) signal, knowledge of which informs biophysical modelling. Here, we study time‐dependence in the human spinal cord, as its axonal structure is specific and different from the brain. Theory and Methods We run Monte Carlo simulations using a synthetic model of spinal cord white matter (WM) (large axons), and of brain WM (smaller axons). Furthermore, we study clinically feasible multi‐shell DW scans of the cervical spinal cord (b = 0; b = 711 s mm−2; b = 2855 s mm−2), obtained using three diffusion times (Δ of 29, 52 and 76 ms) from three volunteers. Results Both intra‐/extra‐axonal perpendicular diffusivities and kurtosis excess show time‐dependence in our synthetic spinal cord model. This time‐dependence is reflected mostly in the intra‐axonal perpendicular DW signal, which also exhibits strong decay, unlike our brain model. Time‐dependence of the total DW signal appears detectable in the presence of noise in our synthetic spinal cord model, but not in the brain. In WM in vivo, we observe time‐dependent macroscopic and microscopic diffusivities and diffusion kurtosis, NODDI and two‐compartment SMT metrics. Accounting for large axon calibers improves fitting of multi‐compartment models to a minor extent. Conclusions Time‐dependence of clinically viable DW MRI metrics can be detected in vivo in spinal cord WM, thus providing new opportunities for the non‐invasive estimation of microstructural properties. The time‐dependence of the perpendicular DW signal may feature strong intra‐axonal contributions due to large spinal axon caliber. Hence, a popular model known as “stick” (zero‐radius cylinder) may be sub‐optimal to describe signals from the largest spinal axons

High-dimensional detection of imaging response to treatment in multiple sclerosis

Changes on brain imaging may precede clinical manifestations or disclose disease progression opaque to conventional clinical measures. Where, as in multiple sclerosis, the pathological process has a complex anatomical distribution, such changes are not easily detected by low-dimensional models in common use. This hinders our ability to detect treatment effects, both in the management of individual patients and in interventional trials. Here we compared the ability of conventional models to detect an imaging response to treatment against high-dimensional models incorporating a wide multiplicity of imaging factors. We used fully-automated image analysis to extract 144 regional, longitudinal trajectories of pre- and post- treatment changes in brain volume and disconnection in a cohort of 124 natalizumab-treated patients. Low- and high-dimensional models of the relationship between treatment and the trajectories of change were built and evaluated with machine learning, quantifying performance with receiver operating characteristic curves. Simulations of randomised controlled trials enrolling varying numbers of patients were used to quantify the impact of dimensionality on statistical efficiency. Compared to existing methods, high-dimensional models were superior in treatment response detection (area under the receiver operating characteristic curve = 0.890 [95% CI = 0.885–0.895] vs. 0.686 [95% CI = 0.679–0.693], P < 0.01]) and in statistical efficiency (achieved statistical power = 0.806 [95% CI = 0.698–0.872] vs. 0.508 [95% CI = 0.403–0.593] with number of patients enrolled = 50, at α = 0.01). High-dimensional models based on routine, clinical imaging can substantially enhance the detection of the imaging response to treatment in multiple sclerosis, potentially enabling more accurate individual prediction and greater statistical efficiency of randomised controlled trials

The value of the central vein sign at 3T to differentiate MS from seropositive-NMOSD.

Objective: To assess the value of the central vein sign (CVS) on a clinical 3T scanner to distinguish between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). / Methods: Eighteen aquaporin-4-antibody-positive patients with NMOSD, 18 patients with relapsing-remitting MS, and 25 healthy controls underwent 3T MRI. The presence of a central vein in white matter lesions on susceptibility-weighted imaging, defined as a thin hypointense line or a small dot, was recorded. / Results: The proportion of lesions with the CVS was higher in MS than NMOSD (80% vs 32%, p < 0.001). A greater proportion of lesions with the CVS predicted the diagnosis of MS, rather than NMOSD (odds ratio 1.10, 95% confidence interval [CI] 1.04 to 1.16, p = 0.001), suggesting that each percent unit increase in the proportion of lesions with the CVS in an individual patient was associated with a 10% increase in the risk of the same patient having MS. If more than 54% of the lesions on any given scan show the CVS, then the patient can be given a diagnosis of MS with an accuracy of 94% (95% CIs 81.34, 99.32, p < 0.001, sensitivity/specificity 90%/100%). / Conclusion: The clinical value of the CVS in the context of the differential diagnosis between MS and NMOSD, previously suggested using 7T scanners, is now extended to clinical 3T scanners, thereby making a step towards the use of CVS in clinical practice. / Classification of evidence: This study provides Class III evidence that the CVS on 3T MRI accurately distinguishes patients with MS from those with seropositive NMOSD

Genome-wide association analysis of dementia and its clinical endophenotypes reveal novel loci associated with Alzheimer's disease and three causality networks: The GR@ACE project

Introduction: Large variability among Alzheimer's disease (AD) cases might impact genetic discoveries and complicate dissection of underlying biological pathways. Methods: Genome Research at Fundacio ACE (GR@ACE) is a genome-wide study of dementia and its clinical endophenotypes, defined based on AD's clinical certainty and vascular burden. We assessed the impact of known AD loci across endophenotypes to generate loci categories. We incorporated gene coexpression data and conducted pathway analysis per category. Finally, to evaluate the effect of heterogeneity in genetic studies, GR@ACE series were meta-analyzed with additional genome-wide association study data sets. Results: We classified known AD loci into three categories, which might reflect the disease clinical heterogeneity. Vascular processes were only detected as a causal mechanism in probable AD. The meta-analysis strategy revealed the ANKRD31-rs4704171 and NDUFAF6-rs10098778 and confirmed SCIMP-rs7225151 and CD33-rs3865444. Discussion: The regulation of vasculature is a prominent causal component of probable AD. GR@ACE meta-analysis revealed novel AD genetic signals, strongly driven by the presence of clinical heterogeneity in the AD series

Deep grey matter volume loss drives disability worsening in multiple sclerosis

Gray matter (GM) atrophy occurs in all multiple sclerosis (MS) phenotypes. We investigated whether there is a spatiotemporal pattern of GM atrophy that is associated with faster disability accumulation in MS.We analyzed 3,604 brain high-resolution T1-weighted magnetic resonance imaging scans from 1,417 participants: 1,214 MS patients (253 clinically isolated syndrome [CIS], 708 relapsing-remitting [RRMS], 128 secondary-progressive [SPMS], and 125 primary-progressive [PPMS]), over an average follow-up of 2.41 years (standard deviation [SD] = 1.97), and 203 healthy controls (HCs; average follow-up = 1.83 year; SD = 1.77), attending seven European centers. Disability was assessed with the Expanded Disability Status Scale (EDSS). We obtained volumes of the deep GM (DGM), temporal, frontal, parietal, occipital and cerebellar GM, brainstem, and cerebral white matter. Hierarchical mixed models assessed annual percentage rate of regional tissue loss and identified regional volumes associated with time-to-EDSS progression.SPMS showed the lowest baseline volumes of cortical GM and DGM. Of all baseline regional volumes, only that of the DGM predicted time-to-EDSS progression (hazard ratio = 0.73; 95% confidence interval, 0.65, 0.82; p < 0.001): for every standard deviation decrease in baseline DGM volume, the risk of presenting a shorter time to EDSS worsening during follow-up increased by 27%. Of all longitudinal measures, DGM showed the fastest annual rate of atrophy, which was faster in SPMS (-1.45%), PPMS (-1.66%), and RRMS (-1.34%) than CIS (-0.88%) and HCs (-0.94%; p < 0.01). The rate of temporal GM atrophy in SPMS (-1.21%) was significantly faster than RRMS (-0.76%), CIS (-0.75%), and HCs (-0.51%). Similarly, the rate of parietal GM atrophy in SPMS (-1.24-%) was faster than CIS (-0.63%) and HCs (-0.23%; all p values <0.05). Only the atrophy rate in DGM in patients was significantly associated with disability accumulation (beta = 0.04; p < 0.001).This large, multicenter and longitudinal study shows that DGM volume loss drives disability accumulation in MS, and that temporal cortical GM shows accelerated atrophy in SPMS than RRMS. The difference in regional GM atrophy development between phenotypes needs to be taken into account when evaluating treatment effect of therapeutic interventions