Circulating microRNA after autologous bone marrow mononuclear cell (BM-MNC) injection in patients with ischemic stroke

Previous studies have shown the potential of microRNAs (miRNA) in the pathological process of stroke and functional recovery. Bone marrow mononuclear cell (BM-MNC) transplantation improves recovery in experimental models of ischemic stroke that might be related with miRNA modifications. However, its effect on circulating miRNA has not been described in patients with stroke. We aimed to evaluate the circulating levels of miRNAs after autologous BM-MNC transplantation in patients with stroke. We investigate the pattern of miRNA-133b and miRNA-34a expression in patients with ischemic stroke included in a multicenter randomized controlled phase IIb trial (http://www.clinicaltrials.gov; unique identifier: NCT02178657). Patients were randomized to 2 different doses of autologous intra-arterial BM-MNC injection (2×106/kg or 5×106/kg) or control group within the first 7 days after stroke onset. We evaluate plasma concentration of miRNA-113b and miRNA-34a at inclusion and 4, 7, and 90 days after treatment. Thirteen cases (8 with 2×106/kg BM-MNC dose and 5 with 5×106/kg dose) and 11 controls (BM-MNC non-treated) were consecutively included. Mean age was 64.1±12.3 with a mean National Institutes of Health Stroke Scale score at inclusion of 14.5. Basal levels of miRNA were similar in both groups. miR-34a-5p and miR-133b showed different expression patterns. There was a significant dose-dependent increase of miRNA-34a levels 4 days after BM-MNC injection (fold change 3.7, p<0.001), whereas miRNA-133b showed a significant increase in the low-dose BM-MNC group at 90 days. Intra-arterial BM-MNC transplantation in patients with ischemic stroke seems to modulate early circulating miRNA-34a levels, which have been related to precursor cell migration in stroke and smaller infarct volumes.This work has been supported by the grants PI15/01197, PI18/01414 and RD16/0019/0015 (INVICTUS+) from the Spanish Ministry of Economy and Competitiveness, cofunded by ISCIII and FEDER funds; Mutua Madrileña grant. FMa is supported by a Rio Hortega contract (CM16/00015). Andalusian Initiative for Advanced Therapies (IATA) is the sponsor of the trial

Data_Sheet_1_Effect of the Mediterranean diet and probiotic supplementation in the management of mild cognitive impairment: Rationale, methods, and baseline characteristics.docx

IntroductionMild 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.MethodsIn 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.ResultsFifty 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).ConclusionThe 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].</p

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

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. 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. 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. 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

Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180].

The authors regret an error in the pre-processing of the dataset that went unnoticed in the extensive code used for pre-processing of the study data. As a result of this error, the last EDSS overall for each patient was included as the last EDSS in the observation period. We corrected the error and repeated all analyses. [...

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

Identification of Therapeutic Lag in Multiple Sclerosis

International audienceObjective: To develop a method that allows identification of the time to full clinically manifest effect of multiple sclerosis (MS) treatments (‘therapeutic lag’) on clinical disease activity.Background: In MS, treatment start or switch is prompted by evidence of disease activity, often presenting as relapses or disability progression. Whilst immunomodulatory therapies reduce disease activity, the time required to attain maximal effect is unclear.Design/Methods: Data from MSBase, a multinational MS registry, and OFSEP, the French national registry, were used. Patients diagnosed with MS, minimum 1-year exposure to MS treatment, minimum 3-year pre-treatment 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 in the analysis. 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 represents the point of stabilisation of treatment effect, after the maximum treatment effect was observed. The method was developed using MSBase, and externally validated in OFSEP. A merged MSBase-OFSEP cohort was used for all subsequent analyses.Results: 11180 eligible treatment epochs were identified for analysis of relapses and 4088 treatment epochs for disability progression. There were no significant differences between the results of discovery and validation analyses. The duration of therapeutic lag for relapses was calculated for 10 therapies and ranged between 12–30 weeks. The duration of therapeutic lag for disability progression was calculated for 7 therapies and ranged between 30–70 weeks.Conclusions: We have developed, and externally validated, a method to objectively quantify the duration of therapeutic lag on relapses and disability progression in different therapies. This method will be applied in studies that will evaluate the effect of patient and disease characteristics on 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

Determinants of Therapeutic Lag in Multiple Sclerosis

International audienceObjective: To explore the associations of patient and disease characteristics with the duration of therapeutic lag for relapses and disability progression.Background: Therapeutic lag represents the delay from initiation of therapy to attainment of full treatment effect. Understanding the determinants of therapeutic lag provides valuable information for personalised choice of therapy in multiple sclerosis (MS).Design/Methods: Data from MSBase, a multinational MS registry, and OFSEP, the French national registry, were used. Patients diagnosed with MS, minimum 1-year exposure to MS treatment, minimum 3-year pre-treatment follow up and yearly review were included in the analysis. By studying incidence of relapses and 6-month confirmed disability progression, the duration of therapeutic lag was calculated by identifying the first local minimum of the first derivative after treatment start in subgroups stratified by patient and disease characteristics. Pairwise analyses of univariate predictors were performed. Combinations of determinants that consistently drove differences in therapeutic lag in pair by pair analyses were included in the final model.Results: Baseline EDSS, ARR and sex were associated with duration of therapeutic lag on disability progression in univariate and pairwise bivariable analyses. In the final model, therapeutic lag was 27.8 weeks shorter in females with ARR6 compared to those with EDSS>=6 (26.6, 18.2–34.9 vs 54.3, 47.2–61.5). Baseline EDSS, ARR, sex and MS phenotype were associated with duration of therapeutic lag on relapses in univariate analyses. Pairwise bivariable analyses of the pairs of determinants suggested ependently associated with therapeutic lag. In the final model, therapeutic lag was shortest in those with RRMS and EDSS<6 compared to the other represented groups.Conclusions: We have utilised a novel method for the quantification of therapeutic lag in different patient groups. Baseline EDSS and ARR are the most important determinants of therapeutic lag for both disability progression and relapses

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