Routine CSF parameters as predictors of disease course in multiple sclerosis: an MSBase cohort study

BACKGROUND

It remains unclear whether routine cerebrospinal fluid (CSF) parameters can serve as predictors of multiple sclerosis (MS) disease course.

METHODS

This large-scale cohort study included persons with MS with CSF data documented in the MSBase registry. CSF parameters to predict time to reach confirmed Expanded Disability Status Scale (EDSS) scores 4, 6 and 7 and annualised relapse rate in the first 2 years after diagnosis (ARR2) were assessed using (cox) regression analysis.

RESULTS

In total, 11 245 participants were included of which 93.7% (n=10 533) were persons with relapsing-remitting MS (RRMS). In RRMS, the presence of CSF oligoclonal bands (OCBs) was associated with shorter time to disability milestones EDSS 4 (adjusted HR=1.272 (95% CI, 1.089 to 1.485), p=0.002), EDSS 6 (HR=1.314 (95% CI, 1.062 to 1.626), p=0.012) and EDSS 7 (HR=1.686 (95% CI, 1.111 to 2.558), p=0.014). On the other hand, the presence of CSF pleocytosis (≥5 cells/µL) increased time to moderate disability (EDSS 4) in RRMS (HR=0.774 (95% CI, 0.632 to 0.948), p=0.013). None of the CSF variables were associated with time to disability milestones in persons with primary progressive MS (PPMS). The presence of CSF pleocytosis increased ARR2 in RRMS (adjusted R2=0.036, p=0.015).

CONCLUSIONS

In RRMS, the presence of CSF OCBs predicts shorter time to disability milestones, whereas CSF pleocytosis could be protective. This could however not be found in PPMS. CSF pleocytosis is associated with short-term inflammatory disease activity in RRMS. CSF analysis provides prognostic information which could aid in clinical and therapeutic decision-making.

Effectiveness of multiple disease-modifying therapies in relapsing-remitting multiple sclerosis: causal inference to emulate a multiarm randomised trial

BACKGROUND

Simultaneous comparisons of multiple disease-modifying therapies for relapsing-remitting multiple sclerosis (RRMS) over an extended follow-up are lacking. Here we emulate a randomised trial simultaneously comparing the effectiveness of six commonly used therapies over 5 years.

METHODS

Data from 74 centres in 35 countries were sourced from MSBase. For each patient, the first eligible intervention was analysed, censoring at change/discontinuation of treatment. The compared interventions included natalizumab, fingolimod, dimethyl fumarate, teriflunomide, interferon beta, glatiramer acetate and no treatment. Marginal structural Cox models (MSMs) were used to estimate the average treatment effects (ATEs) and the average treatment effects among the treated (ATT), rebalancing the compared groups at 6-monthly intervals on age, sex, birth-year, pregnancy status, treatment, relapses, disease duration, disability and disease course. The outcomes analysed were incidence of relapses, 12-month confirmed disability worsening and improvement.

RESULTS

23 236 eligible patients were diagnosed with RRMS or clinically isolated syndrome. Compared with glatiramer acetate (reference), several therapies showed a superior ATE in reducing relapses: natalizumab (HR=0.44, 95% CI=0.40 to 0.50), fingolimod (HR=0.60, 95% CI=0.54 to 0.66) and dimethyl fumarate (HR=0.78, 95% CI=0.66 to 0.92). Further, natalizumab (HR=0.43, 95% CI=0.32 to 0.56) showed a superior ATE in reducing disability worsening and in disability improvement (HR=1.32, 95% CI=1.08 to 1.60). The pairwise ATT comparisons also showed superior effects of natalizumab followed by fingolimod on relapses and disability.

CONCLUSIONS

The effectiveness of natalizumab and fingolimod in active RRMS is superior to dimethyl fumarate, teriflunomide, glatiramer acetate and interferon beta. This study demonstrates the utility of MSM in emulating trials to compare clinical effectiveness among multiple interventions simultaneously.

The risk of secondary progressive multiple sclerosis is geographically determined but modifiable

Geographical variations in the incidence and prevalence of multiple sclerosis have been reported globally. Latitude as a surrogate for exposure to ultraviolet radiation but also other lifestyle and environmental factors are regarded as drivers of this variation. No previous studies evaluated geographical variation in the risk of secondary progressive multiple sclerosis, an advanced form of multiple sclerosis that is characterized by steady accrual of irreversible disability. We evaluated differences in the risk of secondary progressive multiple sclerosis in relation to latitude and country of residence, modified by high-to-moderate efficacy immunotherapy in a geographically diverse cohort of patients with relapsing-remitting multiple sclerosis. The study included relapsing-remitting multiple sclerosis patients from the global MSBase registry with at least one recorded assessment of disability. Secondary progressive multiple sclerosis was identified as per clinician diagnosis. Sensitivity analyses used the operationalized definition of secondary progressive multiple sclerosis and the Swedish decision tree algorithm. A proportional hazards model was used to estimate the cumulative risk of secondary progressive multiple sclerosis by country of residence (latitude), adjusted for sex, age at disease onset, time from onset to relapsing-remitting phase, disability (Multiple Sclerosis Severity Score) and relapse activity at study inclusion, national multiple sclerosis prevalence, government health expenditure, and proportion of time treated with high-to-moderate efficacy disease-modifying therapy. Geographical variation in time from relapsing-remitting phase to secondary progressive phase of multiple sclerosis was modelled through a proportional hazards model with spatially correlated frailties. We included 51 126 patients (72% female) from 27 countries. The median survival time from relapsing-remitting phase to secondary progressive multiple sclerosis among all patients was 39 (95% confidence interval: 37 to 43) years. Higher latitude [median hazard ratio = 1.21, 95% credible interval (1.16, 1.26)], higher national multiple sclerosis prevalence [1.07 (1.03, 1.11)], male sex [1.30 (1.22, 1.39)], older age at onset [1.35 (1.30, 1.39)], higher disability [2.40 (2.34, 2.47)] and frequent relapses [1.18 (1.15, 1.21)] at inclusion were associated with increased hazard of secondary progressive multiple sclerosis. Higher proportion of time on high-to-moderate efficacy therapy substantially reduced the hazard of secondary progressive multiple sclerosis [0.76 (0.73, 0.79)] and reduced the effect of latitude [interaction: 0.95 (0.92, 0.99)]. At the country-level, patients in Oman, Tunisia, Iran and Canada had higher risks of secondary progressive multiple sclerosis relative to the other studied regions. Higher latitude of residence is associated with a higher probability of developing secondary progressive multiple sclerosis. High-to-moderate efficacy immunotherapy can mitigate some of this geographically co-determined risk.

Comparative effectiveness in multiple sclerosis: A methodological comparison

BACKGROUND

In the absence of evidence from randomised controlled trials, observational data can be used to emulate clinical trials and guide clinical decisions. Observational studies are, however, susceptible to confounding and bias. Among the used techniques to reduce indication bias are propensity score matching and marginal structural models.

OBJECTIVE

To use the comparative effectiveness of fingolimod vs natalizumab to compare the results obtained with propensity score matching and marginal structural models.

METHODS

Patients with clinically isolated syndrome or relapsing remitting MS who were treated with either fingolimod or natalizumab were identified in the MSBase registry. Patients were propensity score matched, and inverse probability of treatment weighted at six monthly intervals, using the following variables: age, sex, disability, MS duration, MS course, prior relapses, and prior therapies. Studied outcomes were cumulative hazard of relapse, disability accumulation, and disability improvement.

RESULTS

4608 patients (1659 natalizumab, 2949 fingolimod) fulfilled inclusion criteria, and were propensity score matched or repeatedly reweighed with marginal structural models. Natalizumab treatment was associated with a lower probability of relapse (PS matching: HR 0.67 [95% CI 0.62-0.80]; marginal structural model: 0.71 [0.62-0.80]), and higher probability of disability improvement (PS matching: 1.21 [1.02 -1.43]; marginal structural model 1.43 1.19 -1.72]). There was no evidence of a difference in the magnitude of effect between the two methods.

CONCLUSIONS

The relative effectiveness of two therapies can be efficiently compared by either marginal structural models or propensity score matching when applied in clearly defined clinical contexts and in sufficiently powered cohorts.

Early non-disabling relapses are important predictors of disability accumulation in people with relapsing-remitting multiple sclerosis

BACKGROUND

The prognostic significance of non-disabling relapses in people with relapsing-remitting multiple sclerosis (RRMS) is unclear.

OBJECTIVE

To determine whether early non-disabling relapses predict disability accumulation in RRMS.

METHODS

We redefined mild relapses in MSBase as 'non-disabling', and moderate or severe relapses as 'disabling'. We used mixed-effects Cox models to compare 90-day confirmed disability accumulation events in people with exclusively non-disabling relapses within 2 years of RRMS diagnosis to those with no early relapses; and any early disabling relapses. Analyses were stratified by disease-modifying therapy (DMT) efficacy during follow-up.

RESULTS

People who experienced non-disabling relapses within 2 years of RRMS diagnosis accumulated more disability than those with no early relapses if they were untreated (n = 285 vs 4717; hazard ratio (HR) = 1.29, 95% confidence interval (CI) = 1.00-1.68) or given platform DMTs (n = 1074 vs 7262; HR = 1.33, 95% CI = 1.15-1.54), but not if given high-efficacy DMTs (n = 572 vs 3534; HR = 0.90, 95% CI = 0.71-1.13) during follow-up. Differences in disability accumulation between those with early non-disabling relapses and those with early disabling relapses were not confirmed statistically.

CONCLUSION

This study suggests that early non-disabling relapses are associated with a higher risk of disability accumulation than no early relapses in RRMS. This risk may be mitigated by high-efficacy DMTs. Therefore, non-disabling relapses should be considered when making treatment decisions.

Effect of the Mediterranean diet and probiotic supplementation in the management of mild cognitive impairment: Rationale, methods, and baseline characteristics

Introduction

Mild cognitive impairment (MCI) can progress to Alzheimer's disease (AD). When MCI is not properly controlled, the speed of deterioration can dramatically increase. Reduction of oxidative stress/inflammation and the modulation of the gut-brain axis could be new potential therapeutic targets for the prevention and treatment of AD. Consumption of specific nutrients, diets and probiotic supplementation have been evaluated for neurodegenerative disorders. We focus on a detailed description of the study methods and baseline characteristics of a clinical trial aiming to evaluate the efficacy of a combined nutritional intervention, i.e., a Mediterranean diet with probiotics, on cognitive capacity in a population with MCI.

Methods

In this randomized, latin-square crossover, double-blind, and controlled dietary intervention trial (clinicaltrials.gov NCT05029765), 47 MCI patients were randomized to consume three dietary interventions for 24-weeks each: (1) A Mediterranean diet supplemented with probiotics (10⁹ colony-forming units of Lactobacillus rhamnosus and Bifidobacterium longum); (2) A Mediterranean diet + placebo; and (3) A Healthy diet according to the World Health Organization (WHO) recommendations. Participants will be evaluated before and after each of the three intervention periods (each 24-weeks, with a total of 72-weeks) for adherence to the assigned diet, blood tests, cognitive performance, gut microbiota analysis and functional neuroimaging studies.

Results

Fifty patients, ≥60 years-old and diagnosed with MCI, underwent randomization. A total of 47 patients completed follow-up dietary interventions (57.4% males), with a good glycemic control (HbA1c 5.8 ± 0.1%, fasting glucose and insulin 99.7 ± 3.3 mg/dL and 10.4 ± 0.9 mU/L, respectively), elevated systolic blood pressure (136.9 ± 2.1 mmHg) and increased degree of inflammation (high-sensitivity C-reactive protein, 8.8 ± 0.9 mg/dL). Baseline adherence to the Mediterranean diet was medium (7.5 ± 0.3 points on the score that ranged from 0 to 14 points).

Conclusion

The results of this clinical study would provide more evidence on the need for dietary therapeutic strategies, for clinical and individual practice, in the management of MCI patients to reduce the risk of AD development. Targeting lifestyle modifications in high-risk populations could prevent substantial cases of cognitive decline.

Clinical trial registration

[ClinicalTrials.gov], identifier [NCT05029765].

Disease Reactivation After Cessation of Disease-Modifying Therapy in Patients With Relapsing-Remitting Multiple Sclerosis

OBJECTIVES

To evaluate the rate of return of disease activity after cessation of multiple sclerosis (MS) disease-modifying therapy.

METHODS

This was a retrospective cohort study from two large observational MS registries: MSBase and OFSEP. Patients with relapsing-remitting MS who had ceased a disease-modifying therapy and were followed up for the subsequent 12-months were included in the analysis. The primary study outcome was annualised relapse rate in the 12 months after disease-modifying therapy discontinuation stratified by patients who did, and did not, commence a subsequent therapy. The secondary endpoint was the predictors of first relapse and disability accumulation after treatment discontinuation.

RESULTS

14,213 patients, with 18,029 eligible treatment discontinuation epochs, were identified for seven therapies. Annualised rates of relapse (ARR) started to increase 2-months after natalizumab cessation (month 2-4 ARR, 95% confidence interval): 0.47, 0.43-0.51). Commencement of a subsequent therapy within 2-4 months reduced the magnitude of disease reactivation (mean ARR difference: 0.15, 0.08-0.22). After discontinuation of fingolimod, rates of relapse increased overall (month 1-2 ARR: 0.80, 0.70-0.89), and stabilised faster in patients who started a new therapy within 1-2 months (mean ARR difference: 0.14, -0.01-0.29). Magnitude of disease reactivation for other therapies was low, but reduced further by commencement of another treatment 1-10 months after treatment discontinuation. Predictors of relapse were higher relapse rate in the year before cessation, female sex, younger age and higher EDSS. Commencement of a subsequent therapy reduced both the risk of relapse (HR 0.76, 95%CI 0.72-0.81) and disability accumulation (0.73, 0.65-0.80).

CONCLUSION

The rate of disease reactivation after treatment cessation differs among MS treatments, with the peaks of relapse activity ranging from 1 to 10 months in untreated cohorts that discontinued different therapies. These results suggest that untreated intervals should be minimised after stopping anti-trafficking therapies (natalizumab and fingolimod).

CLASSIFICATION OF EVIDENCE

This study provides class III that disease reactivation occurs within months of discontinuation of multiple sclerosis disease-modifying therapies. Risk of disease activity is reduced by commencement of a subsequent therapy.

Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

BACKGROUND AND OBJECTIVES

Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem.

METHODS

We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used.

RESULTS

We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features.

CONCLUSIONS

Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS.

Determinants of therapeutic lag in multiple sclerosis

BACKGROUND

A delayed onset of treatment effect, termed therapeutic lag, may influence the assessment of treatment response in some patient subgroups.

OBJECTIVES

The objective of this study is to explore the associations of patient and disease characteristics with therapeutic lag on relapses and disability accumulation.

METHODS

Data from MSBase, a multinational multiple sclerosis (MS) registry, and OFSEP, the French MS registry, were used. Patients diagnosed with MS, minimum 1 year of exposure to MS treatment and 3 years of pre-treatment follow-up, were included in the analysis. Studied outcomes were incidence of relapses and disability accumulation. Therapeutic lag was calculated using an objective, validated method in subgroups stratified by patient and disease characteristics. Therapeutic lag under specific circumstances was then estimated in subgroups defined by combinations of clinical and demographic determinants.

RESULTS

High baseline disability scores, annualised relapse rate (ARR) ⩾ 1 and male sex were associated with longer therapeutic lag on disability progression in sufficiently populated groups: females with expanded disability status scale (EDSS) < 6 and ARR < 1 had mean lag of 26.6 weeks (95% CI = 18.2-34.9), males with EDSS < 6 and ARR < 1 31.0 weeks (95% CI = 25.3-36.8), females with EDSS < 6 and ARR ⩾ 1 44.8 weeks (95% CI = 24.5-65.1), and females with EDSS ⩾ 6 and ARR < 1 54.3 weeks (95% CI = 47.2-61.5).

CONCLUSIONS

Pre-treatment EDSS and ARR are the most important determinants of therapeutic lag.

Delay from treatment start to full effect of immunotherapies for multiple sclerosis

In multiple sclerosis, treatment start or switch is prompted by evidence of disease activity. Whilst immunomodulatory therapies reduce disease activity, the time required to attain maximal effect is unclear. In this study we aimed to develop a method that allows identification of the time to manifest fully and clinically the effect of multiple sclerosis treatments (‘therapeutic lag’) on clinical disease activity represented by relapses and progression-of-disability events. Data from two multiple sclerosis registries, MSBase (multinational) and OFSEP (French), were used. Patients diagnosed with multiple sclerosis, minimum 1-year exposure to treatment, minimum 3-year pretreatment follow-up and yearly review were included in the analysis. For analysis of disability progression, all events in the subsequent 5-year period were included. Density curves, representing incidence of relapses and 6-month confirmed progression events, were separately constructed for each sufficiently represented therapy. Monte Carlo simulations were performed to identify the first local minimum of the first derivative after treatment start; this point represented the point of stabilization of treatment effect, after the maximum treatment effect was observed. The method was developed in a discovery cohort (MSBase), and externally validated in a separate, non-overlapping cohort (OFSEP). A merged MSBase-OFSEP cohort was used for all subsequent analyses. Annualized relapse rates were compared in the time before treatment start and after the stabilization of treatment effect following commencement of each therapy. We identified 11 180 eligible treatment epochs for analysis of relapses and 4088 treatment epochs for disability progression. External validation was performed in four therapies, with no significant difference in the bootstrapped mean differences in therapeutic lag duration between registries. The duration of therapeutic lag for relapses was calculated for 10 therapies and ranged between 12 and 30 weeks. The duration of therapeutic lag for disability progression was calculated for seven therapies and ranged between 30 and 70 weeks. Significant differences in the pre- versus post-treatment annualized relapse rate were present for all therapies apart from intramuscular interferon beta-1a. In conclusion we have developed, and externally validated, a method to objectively quantify the duration of therapeutic lag on relapses and disability progression in different therapies in patients more than 3 years from multiple sclerosis onset. Objectively defined periods of expected therapeutic lag allows insights into the evaluation of treatment response in randomized clinical trials and may guide clinical decision-making in patients who experience early on-treatment disease activity. This method will subsequently be applied in studies that evaluate the effect of patient and disease characteristics on therapeutic lag.