Definitions of primary-progressive multiple sclerosis trajectories by rate of clinical disability progression

Machine learning-based prediction of disease progression in primary progressive multiple sclerosis

Primary progressive multiple sclerosis (PPMS) affects 10-15% of multiple sclerosis patients and presents significant variability in the rate of disability progression. Identifying key biological features and patients at higher risk for fast progression is crucial to develop and optimize treatment strategies. Peripheral blood cell transcriptome has the potential to provide valuable information to predict patients' outcomes. In this study, we utilized a machine learning framework applied to the baseline blood transcriptional profiles and brain MRI radiological enumerations to develop prognostic models. These models aim to identify PPMS patients likely to experience significant disease progression and who could benefit from early treatment intervention. RNA-sequence analysis was performed on total RNA extracted from peripheral blood mononuclear cells of PPMS patients in the placebo arm of the ORATORIO clinical trial (NCT01412333), using Illumina NovaSeq S2. Cross-validation algorithms from Partek Genome Suite (www.partek.com) were applied to predict disability progression and brain volume loss over 120 weeks. For disability progression prediction, we analysed blood RNA samples from 135 PPMS patients (61 females and 74 males) with a mean ± standard error age of 44.0 ± 0.7 years, disease duration of 5.9 ± 0.32 years and a median baseline Expanded Disability Status Scale (EDSS) score of 4.3 (range 3.5-6.5). Over the 120-week study, 39.3% (53/135) of patients reached the disability progression end-point, with an average EDSS score increase of 1.3 ± 0.16. For brain volume loss prediction, blood RNA samples from 94 PPMS patients (41 females and 53 males), mean ± standard error age of 43.7 ± 0.7 years and a median baseline EDSS of 4.0 (range 3.0-6.5) were used. Sixty-seven per cent (63/94) experienced significant brain volume loss. For the prediction of disability progression, we developed a two-level procedure. In the first level, a 10-gene predictor achieved a classification accuracy of 70.9 ± 4.5% in identifying patients reaching the disability end-point within 120 weeks. In the second level, a four-gene classifier distinguished between fast and slow disability progression with a 506-day cut-off, achieving 74.1 ± 5.2% accuracy. For brain volume loss prediction, a 12-gene classifier reached an accuracy of 70.2 ± 6.7%, which improved to 74.1 ± 5.2% when combined with baseline brain MRI measurements. In conclusion, our study demonstrates that blood transcriptome data, alone or combined with baseline brain MRI metrics, can effectively predict disability progression and brain volume loss in PPMS patients.

Clinical impact of gender and age at onset on disease trajectory in primary progressive multiple sclerosis patients

BACKGROUND AND OBJECTIVES

Primary-progressive multiple sclerosis (PPMS) is characterized by gradual neurological deterioration without relapses. This study aimed to investigate the clinical impact of gender and age at disease onset on disease progression and disability accumulation in patients with this disease phenotype.

METHODS

Secondary data from the RelevarEM registry, a longitudinal database in Argentina, were analyzed. The cohort comprised patients with PPMS who met inclusion criteria. Statistical analysis with multilevel Bayesian robust regression modeling was conducted to assess the associations between gender, age at onset, and Expanded Disability Status Scale (EDSS) score trajectories.

RESULTS

We identified 125 patients with a confirmed diagnosis of PPMS encompassing a total of 464 observations. We found no significant differences in EDSS scores after 10 years of disease progression between genders (-0.08; credible interval (CI): -0.60, 0.42). A 20-year difference in age at onset did not show significant differences in EDSS score after 10 years of disease progression (0.281; CI: -0.251, 0.814). Finally, we also did not find any clinically relevant difference between gender EDSS score with a difference of 20 years in age at onset (-0.021; CI: -0.371, 0.319).

CONCLUSION

Biological plausibility of gender and age effects does not correlate with clinical impact measured by EDSS score.

Current knowledge on multiple sclerosis pathophysiology, disability progression assessment and treatment options, and the role of autologous hematopoietic stem cell transplantation

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that affects nearly 2.8 million people each year. MS distinguishes three main types: relapsing-remitting MS (RRMS), secondary progressive MS (SPMS) and primary progressive MS (PPMS). RRMS is the most common type, with the majority of patients eventually progressing to SPMS, in which neurological development is constant, whereas PPMS is characterized by a progressive course from disease onset. New or additional insights into the role of effector and regulatory cells of the immune and CNS systems, Epstein-Barr virus (EBV) infection, and the microbiome in the pathophysiology of MS have emerged, which may lead to the development of more targeted therapies that can halt or reverse neurodegeneration. Depending on the type and severity of the disease, various disease-modifying therapies (DMTs) are currently used for RRMS/SPMS and PPMS. As a last resort, and especially in highly active RRMS that does not respond to DMTs, autologous hematopoietic stem cell transplantation (AHSCT) is performed and has shown good results in reducing neuroinflammation. Nevertheless, the question of its potential role in preventing disability progression remains open. The aim of this review is to provide a comprehensive update on MS pathophysiology, assessment of MS disability progression and current treatments, and to examine the potential role of AHSCT in preventing disability progression.