Dataset Information

Disease phenotype prediction in multiple sclerosis.

ABSTRACT: Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.

SUBMITTER: Herman S

PROVIDER: S-EPMC10275960 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Disease phenotype prediction in multiple sclerosis.

Herman Stephanie S Arvidsson McShane Staffan S Zjukovskaja Christina C Khoonsari Payam Emami PE Svenningsson Anders A Burman Joachim J Spjuth Ola O Kultima Kim K

iScience 20230519 6

Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three ...[more]

PMID: 37332601

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Project description:Currently, there are limited therapeutic options for patients with non-active secondary progressive multiple sclerosis. Therefore, real-world studies have investigated differences between patients with relapsing-remitting multiple sclerosis, non-active secondary progressive multiple sclerosis and active secondary progressive multiple sclerosis. Here, we explore patterns and predictors of transitioning between these phenotypes. We performed a cohort study using data from The Danish Multiple Sclerosis Registry. We included patients with a relapsing-remitting phenotype, registered changes to secondary progressive multiple sclerosis and subsequent transitions between relapsing and non-relapsing secondary progressive multiple sclerosis, which was defined by the presence of relapses in the previous 2 years. We analysed predictors of transitioning from relapsing-remitting multiple sclerosis to relapsing and non-relapsing secondary progressive multiple sclerosis, as well as between the secondary progressive states using a multi-state Markov model. We included 4413 patients with relapsing-remitting multiple sclerosis. Within a median follow-up of 16.2 years, 962 were diagnosed with secondary progressive multiple sclerosis by their treating physician. Of these, we classified 729 as non-relapsing and 233 as relapsing secondary progressive multiple sclerosis. The risk of transitioning from relapsing-remitting to non-relapsing secondary progressive multiple sclerosis included older age (hazard ratio per increase of 1 year in age: 1.044, 95% confidence interval: 1.035-1.053), male sex (hazard ratio for female: 0.735, 95% confidence interval: 0.619-0.874), fewer relapses (hazard ratio per each additional relapse: 0.863, 95% confidence interval: 0.823-0.906), higher expanded disability status scale (hazard ratio per each additional point: 1.522, 95% confidence interval: 1.458-1.590) and longer time on disease-modifying therapies (hazard ratio per increase of 1 year in treatment, high-efficacy disease-modifying therapy: 1.095, 95% confidence interval: 1.051-1.141; hazard ratio, moderate-efficacy disease-modifying therapy: 1.073, 95% confidence interval: 1.051-1.095). We did not find significant predictors associated with the transition from relapsing secondary progressive multiple sclerosis to non-relapsing secondary progressive multiple sclerosis, whereas older age (hazard ratio per increase of 1 year in age: 0.956, 95% confidence interval: 0.942-0.971) prevented the transition from non-relapsing secondary progressive multiple sclerosis to relapsing secondary progressive multiple sclerosis. Our study suggests that transitioning from relapsing-remitting multiple sclerosis to non-relapsing secondary progressive multiple sclerosis depends on well-known factors affecting diagnosing secondary progressive multiple sclerosis. Further transitions between non-relapsing and relapsing secondary progressive multiple sclerosis are only affected by age. These findings add to the knowledge of non-active secondary progressive multiple sclerosis, a patient group with unmet needs in terms of therapies.

Project description:OBJECTIVE:To summarize major clinical trials which evaluate the efficacy and safety data of approved disease modifying agents for the treatment of various types of multiple sclerosis. DATA SOURCES:A MEDLINE (1966 to August 2008) search of clinical trials using the terms multiple sclerosis, interferon, glatiramer, mitoxantrone and natalizumab was performed. A manual bibliographic search was also conducted. English-language articles identified from the searches were evaluated. New agents under investigation in phase 3 clinical trials were identified using www.clinicaltrials.gov. STUDY SELECTION #ENTITYSTARTX00026; DATA EXTRACTION:Relevant information was identified and selected based on clinical relevance and evidence-based strength. Prescribing information leaflets were used to provide usual dosage, contraindications, precautions, monitoring parameters and other relevant drug-specific information. DATA SYNTHESIS:Interferon beta products are more efficacious for the treatment of relapsing-remitting multiple sclerosis. Interferon beta 1-b also delayed the time to diagnosis of definite multiple sclerosis and reduced brain lesion burden in patients with clinical isolated syndrome. Glatiramer and natalizumab have both established efficacy in relapsing forms of multiple sclerosis; whereas mitoxantrone is more commonly used in patients with advanced disease. There are limited data the comparative efficacy among different disease modifying agents. New agents currently under investigation have showed promising results and may offer more treatment options in the future. CONCLUSIONS:MS is a complex and devastating disease with challenging treatment considerations and approaches. Interferon beta products continue to be the mainstay of therapy in many patients, however, other treatments are proving to be at least as effective in the management of various types of MS. Newer compounds are being developed and studied with much anticipation and promise for the clinical management of the disease.

Dataset Information

Disease phenotype prediction in multiple sclerosis.

Publications

Disease phenotype prediction in multiple sclerosis.

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