In North African populations, G2019S mutation in LRRK2 gene, encoding for the leucine-rich repeat kinase 2, is the most prevalent mutation linked to familial and sporadic Parkinson’s disease (PD). Early detection of G2019S by fast genetic testing is very important to guide PD’s diagnosis and support patients and their family caregivers for better management of their life according to disease’s evolution. In our study, a genetic PD’s diagnosis tool was developed for large scale genotyping using Kompetitive Allele Specific PCR (KASP) technology. We investigated G2019S’s frequency in 250 Tunisian PD patients and 218 controls. We found that 33.6% of patients and 1.3% of controls were carriers. Demographic characteristics of patients with G2019S had no differences compared with non-carrier patients. Thereby, we could emphasize the implication of G2019S in PD without any distinctive demographic factors in the studied cohort. Sixty patients out of 250 were genotyped using Taqman assay and Sanger sequencing. The genotyping results were found to be concordant with KASP assay. The G2019S mutation frequency in our cohort was similar to that reported in previous studies. Comparing to Taqman assay and Sanger sequencing, KASP was shown to be a reliable, time and cost effective genotyping assay for routine G2019S screening in genetic testing laboratories.

Patients with pediatric-onset multiple sclerosis (POMS) typically experience higher levels of inflammation with more frequent relapses, and though patients with POMS usually recover from relapses better than adults, patients with POMS reach irreversible disability at a younger age than adult-onset patients. There have been few randomized, placebo-controlled clinical trials of multiple sclerosis (MS) disease-modifying therapies (DMTs) in patients with POMS, and most available data are based on observational studies of off-label use of DMTs approved for adults. We assessed the effectiveness of natalizumab compared with fingolimod using injectable platform therapies as a reference in pediatric patients in the global MSBase registry. This retrospective study included patients with POMS who initiated treatment with an injectable DMT, natalizumab, or fingolimod between January 1, 2006, and May 3, 2021. Patients were matched using inverse probability treatment weighting. The primary outcome was time to first relapse from index therapy initiation. Secondary study outcomes included annualized relapse rate; proportions of relapse-free patients at 1, 2, and 5 years; time to treatment discontinuation; and times to 24-week confirmed disability worsening and confirmed disability improvement. A total of 1,218 patients with POMS were included in this analysis. Patients treated with fingolimod had a significantly lower risk of relapse than patients treated with injectable DMTs (hazard ratio [HR], 0.49; 95% confidence interval [CI], 0.29-0.83; p = 0.008). After adjustment for prior DMT experience in the unmatched sample, patients treated with natalizumab had a significantly lower risk of relapse than patients treated either with injectable DMTs (HR, 0.15; 95% CI 0.07-0.31; p < 0.001) or fingolimod (HR, 0.37; 95% CI 0.14-1.00; p = 0.049). The adjusted secondary study outcomes were generally consistent with the primary outcome or with previous observations. The findings in the inverse probability treatment weighting-adjusted patient populations were confirmed in multiple sensitivity analyses. Our analyses of relapse risk suggest that natalizumab is more effective than fingolimod in the control of relapses in this population with high rates of new inflammatory activity, consistent with previous studies of natalizumab and fingolimod in adult-onset patients and POMS. In addition, both fingolimod and natalizumab were more effective than first-line injectable therapies. This study provides Class II evidence that patients with POMS treated with natalizumab had a lower risk of relapse than those with fingolimod.

Objective: To describe the phenotypic heterogeneity of amyotrophic lateral sclerosis (ALS) in Tunisian patients, and to define the sociodemographic features, treatments, and survival rate with ALS. Methodology: The study included 210 patients with ALS diagnosed between 2003 and 2019 in The Neurology department, Razi Hospital Tunisia. ALS patients were phenotyped and followed until their death. Results: median age of ALS onset was 54.93 ± 14.08 years (men = 56.21 ± 12.58, women = 52.36 ± 16.49). The sex-ratio was 2.0 with obvious male predominance. Juvenile ALS form was found in 5.71% of our cohort and 94, 76% of the patients had the classic ALS form. In the latter, 76.3% had spinal onset whereas bulbar onset was seen in 20.1% of patients. Spinal onset was most frequent in Juvenile ALS patients. Approximately half of the ALS patients used Riluzole (58.5%). Median survival rate was 60 months (5 years). According to univariate analysis, the factors related to survival rate of ALS patient were: age at onset, diagnostic delay, site of onset, phenotype and treatment use. The multivariate analysis revealed that age at onset, gender, diagnostic delay, superior muscles atrophy, treatment use, consanguinity, cognitive signs, dysautonomia, and ALSFRS-R score were independent prognostic factors of survival among ALS patients. Conclusion: Our Tunisian cohort was characterized by a slower disease progression and a better prognosis. Juvenile ALS patients were more common. Initial ALSFRS-R scores were higher in our population. Age at onset of Bulbar ALS was younger. Our study highlights the possible presence of genetic and environmental factors that may influence the clinical phenotype of ALS in Tunisia.

Background: Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. Methods: Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. Findings: Machine learning models achieved a ROC-AUC of 0⋅71 ± 0⋅01, an AUC-PR of 0⋅26 ± 0⋅02, a Brier score of 0⋅1 ± 0⋅01 and an expected calibration error of 0⋅07 ± 0⋅04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. Conclusions: Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.