Multiple Sclerosis (MS) is an auto-immune demyelinating and neurodegenerative disease of the central nervous system (CNS) characterized by a wide range of neurological symptoms and accumulating disability during the disease course. High inter-individual variability, related to the severity and duration of symptoms, mainly characterizes the prognosis of MS. At individual patient-level, it is important to identify demographic, clinical, neuroimaging and biological markers associated with the risk of experiencing the progressive phase of MS, which eventually leads to the irreversible accumulation of disability. Therefore, the estimation of risk factors most involved in disease progression over time, especially if prompted identified, provide support to the medical decision making. The proposed aim of this thesis was to understand better the underlying mechanism of disease progression as well as disease activity providing reliable and accurate tools that may assist the clinician in the optimization of treatment choice. Firstly, by exploiting the flexibility of machine learning approach, the Secondary Progressive-Risk Score (SP-RiSc) was developed. This integrated demographic, clinical and imaging data collected during the first 2-years after the disease diagnosis, discriminating those patients with a higher probability of progression over time. The proposed tool highlighted the early accumulation of focal and diffuse grey matter (GM) damage as the most important determinants of conversion to the progressive phase, supporting the pivotal role of the cortical pathology in the occurrence of the late severe disability. Secondly, a specific Cerebral Spinal Fluid (CSF) inflammatory profile at diagnosis was identified to be associated with cortical damage (focal and global) over time and poor clinical outcome. The analysis revealed that high CSF levels of the B-cell related cytokines and pro-inflammatory and monocyte activity molecules represent a potential tool to select patients at high risk of experiencing more severe disease activity, increased GM damage, and disability progression in the early phase of the disease. Finally, an explorative preliminary analysis was performed on an extensive CSF protein assessment to investigate the potential predictive value of inflammatory cytokine and chemokine mediators in MS. 6 In line with recent findings, these results, even if preliminary, underlined that the mechanism of disease and disability progression is characterized mainly by a focal and diffuse cortical pathology predominant since the early stage of the disease and that the CSF cytokine and chemokine mediators may reflect the intrathecal inflammation and have a potential predictive role in MS. Moreover, by the use of advanced methods, such as machine and statistical learning models, which overcome the limitations of conventional statistical analysis more accurate results were obtained. In conclusion, a step forward has done to provide reliable prognostic tools, based on as detailed as possible patients' profile at diagnosis, including clinical, neuroradiological and CSF characteristics, which may assist the clinician in optimizing the therapeutic approach moving closer to personalized medicine.

Advanced analyses for the identification of prognostic markers in Multiple Sclerosis

Anna Isabella Pisani
2021

Abstract

Multiple Sclerosis (MS) is an auto-immune demyelinating and neurodegenerative disease of the central nervous system (CNS) characterized by a wide range of neurological symptoms and accumulating disability during the disease course. High inter-individual variability, related to the severity and duration of symptoms, mainly characterizes the prognosis of MS. At individual patient-level, it is important to identify demographic, clinical, neuroimaging and biological markers associated with the risk of experiencing the progressive phase of MS, which eventually leads to the irreversible accumulation of disability. Therefore, the estimation of risk factors most involved in disease progression over time, especially if prompted identified, provide support to the medical decision making. The proposed aim of this thesis was to understand better the underlying mechanism of disease progression as well as disease activity providing reliable and accurate tools that may assist the clinician in the optimization of treatment choice. Firstly, by exploiting the flexibility of machine learning approach, the Secondary Progressive-Risk Score (SP-RiSc) was developed. This integrated demographic, clinical and imaging data collected during the first 2-years after the disease diagnosis, discriminating those patients with a higher probability of progression over time. The proposed tool highlighted the early accumulation of focal and diffuse grey matter (GM) damage as the most important determinants of conversion to the progressive phase, supporting the pivotal role of the cortical pathology in the occurrence of the late severe disability. Secondly, a specific Cerebral Spinal Fluid (CSF) inflammatory profile at diagnosis was identified to be associated with cortical damage (focal and global) over time and poor clinical outcome. The analysis revealed that high CSF levels of the B-cell related cytokines and pro-inflammatory and monocyte activity molecules represent a potential tool to select patients at high risk of experiencing more severe disease activity, increased GM damage, and disability progression in the early phase of the disease. Finally, an explorative preliminary analysis was performed on an extensive CSF protein assessment to investigate the potential predictive value of inflammatory cytokine and chemokine mediators in MS. 6 In line with recent findings, these results, even if preliminary, underlined that the mechanism of disease and disability progression is characterized mainly by a focal and diffuse cortical pathology predominant since the early stage of the disease and that the CSF cytokine and chemokine mediators may reflect the intrathecal inflammation and have a potential predictive role in MS. Moreover, by the use of advanced methods, such as machine and statistical learning models, which overcome the limitations of conventional statistical analysis more accurate results were obtained. In conclusion, a step forward has done to provide reliable prognostic tools, based on as detailed as possible patients' profile at diagnosis, including clinical, neuroradiological and CSF characteristics, which may assist the clinician in optimizing the therapeutic approach moving closer to personalized medicine.
Multiple Sclerosis
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11562/1051104
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