Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

Lorefice, Lorena; Pitzalis, Maristella; Murgia, Federica; Fenu, Giuseppe; Atzori, Luigi; Cocco, Eleonora

doi:10.3389/fgene.2023.1076421

Cited by 7 publications

(3 citation statements)

References 139 publications

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“…The novel approach proposes integrative analytical methods that combine clinical characteristics, MRI findings and information from different omics approaches. The omics sciences include genomics, transcriptomics, proteomics and metabolomics and their joined use are promising direction for future research on potential biomarkers for diagnosis, prediction of disability and disease progression, as well as follow up the efficacy and safety of administered IMT in pwMS (Lorena et al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Biomarkers in Multiple Sclerosis: Analysis of the Present Advantages and Look to the Future

2023

J Psychol Neurosci

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MS is a chronic heterogeneous demyelinating disease of the CNS among the young population, manifested by unpredictable attacks and subsequent remissions (McGinley et al., 2021; Lublin et al., 2022). The disease develops as a result of an interaction between genetic and environmental factors (Dobson et al., 2019). The most important genetic risk factor are the alleles of genes encoding human leucocyte antigens (HLAs), especially HLA-DRB1*1501 (Hollenbach et al., 2015). The main exogenous noxes that have the potential to trigger the illness are Epstein Barr Virus (EBV) infection, tobacco use, obesity since childhood, low vitamin D levels. Inflammatory infiltrates within the brain lesions contain CD4 and CD8 T-lymphocytes, activated monocytes and B-lymphocytes which lead to disruption of the myelin sheaths covering the nerves (Housley et al., 2015). It is considered that EBV infection contributes to production of B cells that provokes the activation of CNS inflammatory processes (Leffler et al., 2022). A relationship between gut microbiome-derived short-chain fatty acids (SCFAs) and immune dysfunction in patients with early MS was proposed (Trend et al., 2021). According to a recent hypothesis the EBV infection and B-cell dysfunction connect with gut-associated lymphoid tissue leading to aberrant B-cell responses that guide pathogenic T-cell responses in the CNS (Leffler et al., 2022).

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Section: Discussionmentioning

confidence: 99%

Biomarkers in Multiple Sclerosis: Analysis of the Present Advantages and Look to the Future

2023

J Psychol Neurosci

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“…For instance, tryptophan has been found increased in polleninduced AR and food allergy [19,178,179] and associated with disease severity in MS [180,181]. Histidine reduction, observed in asthma [182] and IBD [183], correlates with chronic inflammation and disease severity.…”

Section: Amino Acidsmentioning

confidence: 99%

Severe Allergy as a Chronic Inflammatory Condition From a Systems Biology Perspective

Delgado Dolset,

Pablo‐Torres,

Contreras

et al. 2024

Clin Experimental Allergy

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Persistent and unresolved inflammation is a common underlying factor observed in several and seemingly unrelated human diseases, including cardiovascular and neurodegenerative diseases. Particularly, in atopic conditions, acute inflammatory responses such as those triggered by insect venom, food or drug allergies possess also a life‐threatening potential. However, respiratory allergies predominantly exhibit late immune responses associated with chronic inflammation, that can eventually progress into a severe phenotype displaying similar features as those observed in other chronic inflammatory diseases, as is the case of uncontrolled severe asthma. This review aims to explore the different facets and systems involved in chronic allergic inflammation, including processes such as tissue remodelling and immune cell dysregulation, as well as genetic, metabolic and microbiota alterations, which are common to other inflammatory conditions. Our goal here was to deepen on the understanding of an entangled disease as is chronic allergic inflammation and expose potential avenues for the development of better diagnostic and intervention strategies.

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“…10 Many of these models use "systems biology" approaches (Omics data) to predict various disease markers and outcomes. 11 Recent imaging research into predictive modeling for MS includes applications in cognitive decline 12 and deep learning approaches to disease progression. 13 Radiomics is a field of research in which higher-order image features such as texture or intensity distribution are investigated (often using AI and machine learning models to glean deeper information from radiologic images).…”

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confidence: 99%

A Radiomic “Warning Sign” of Progression on Brain MRI in Individuals with MS

Kelly,

Mathur,

McGuinness

et al. 2024

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: MS is a chronic progressive, idiopathic, demyelinating disorder whose diagnosis is contingent on the interpretation of MR imaging. New MR imaging lesions are an early biomarker of disease progression. We aimed to evaluate a machine learning model based on radiomics features in predicting progression on MR imaging of the brain in individuals with MS. MATERIALS AND METHODS:This retrospective cohort study with external validation on open-access data obtained full ethics approval. Longitudinal MR imaging data for patients with MS were collected and processed for machine learning. Radiomics features were extracted at the future location of a new lesion in the patients' prior MR imaging ("prelesion"). Additionally, "control" samples were obtained from the normal-appearing white matter for each participant. Machine learning models for binary classification were trained and tested and then evaluated the external data of the model. RESULTS:The total number of participants was 167. Of the 147 in the training/test set, 102 were women and 45 were men. The average age was 42 (range, 21-74 years). The best-performing radiomics-based model was XGBoost, with accuracy, precision, recall, and F1-score of 0.91, 0.91, 0.91, and 0.91 on the test set, and 0.74, 0.74, 0.74, and 0.70 on the external validation set. The 5 most important radiomics features to the XGBoost model were associated with the overall heterogeneity and low gray-level emphasis of the segmented regions. Probability maps were produced to illustrate potential future clinical applications. CONCLUSIONS:Our machine learning model based on radiomics features successfully differentiated prelesions from normalappearing white matter. This outcome suggests that radiomics features from normal-appearing white matter could serve as an imaging biomarker for progression of MS on MR imaging.

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Omics approaches to understanding the efficacy and safety of disease-modifying treatments in multiple sclerosis

Cited by 7 publications

References 139 publications

Biomarkers in Multiple Sclerosis: Analysis of the Present Advantages and Look to the Future

Biomarkers in Multiple Sclerosis: Analysis of the Present Advantages and Look to the Future

Severe Allergy as a Chronic Inflammatory Condition From a Systems Biology Perspective

A Radiomic “Warning Sign” of Progression on Brain MRI in Individuals with MS

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