147625 20241220131256.0 doi 10.1016/j.compbiomed.2019.103357 sideral 114977 ART-2019-114977 eng Montolío, Alberto Universidad de Zaragoza (orcid)0000-0001-7248-4399 A mathematical model to predict the evolution of retinal nerve fiber layer thinning in multiple sclerosis patients 2019 Multiple sclerosis (MS) is a neurodegenerative disease of the central nervous system (CNS). Many studies of MS patients have described axonal loss in the optic nerve of the retina, and specifically progressive thinning of the retinal nerve fiber layer (RNFL). We hypothesize that RNFL thinning involves the participation of 2 processes that cause CNS damage: autoimmune inflammation and axonal degeneration. To test this hypothesis, we developed a mathematical model based on ordinary differential equations to relate the evolution of RNFL thickness (measured by optical coherence tomography [OCT]) with that of the Expanded Disability Status Scale (EDSS) score in MS patients. Data were obtained from a longitudinal study of 114 MS patients who were followed-up for 10 years. After adjusting the parameters using a genetic algorithm, the model''s prediction of the evolution of RNFL thickness accurately reflected the progression revealed by the 10-year clinical data. Our findings suggest that differences in the relative contributions of autoimmune inflammation and axonal degeneration can account for the complex dynamics of MS, which vary from one patient to the next. Moreover, our results show that CNS damage occurs cumulatively from the onset of MS and that most RNFL thinning occurs before the appearance of significant disability. RNFL thickness could therefore serve as a reliable biomarker of MS disease course. Our proposed methodology would enable the use of OCT data from new MS patients to predict the evolution of RNFL thinning and hence the progression of MS in individual patients, and to facilitate the selection of patient-specific therapies. Access copy available to the general public Unrestricted info:eu-repo/grantAgreement/ES/ISCIII/PI17-01726 info:eu-repo/grantAgreement/ES/MICIU/BES-2017-080384 info:eu-repo/grantAgreement/ES/MINECO/DPI2016-79302-R info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 3.434 2019 BIOLOGY 22 / 92 = 0.239 2019 Q1 T1 MATHEMATICAL & COMPUTATIONAL BIOLOGY 8 / 59 = 0.136 2019 Q1 T1 ENGINEERING, BIOMEDICAL 26 / 87 = 0.299 2019 Q2 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 33 / 109 = 0.303 2019 Q2 T1 0.834 2019 Health Informatics 2019 Q2 Computer Science Applications 2019 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Cegoñino, José Universidad de Zaragoza (orcid)0000-0002-2967-6747 Orduna, Elvira Universidad de Zaragoza (orcid)0000-0003-2710-1875 Sebastian, Berta Garcia-Martin, Elena Universidad de Zaragoza (orcid)0000-0001-6258-2489 Pérez del Palomar, Amaya Universidad de Zaragoza (orcid)0000-0003-0669-777X 1004 646 Universidad de Zaragoza Dpto. Cirugía,Ginecol.Obstetr. Área Oftalmología 5004 605 Universidad de Zaragoza Dpto. Ingeniería Mecánica Área Mec.Med.Cont. y Teor.Est. 111 (2019), 103357 1-11 Comput. biol. med. Computers in biology and medicine 0010-4825 1513637 http://zaguan.unizar.es/record/147625/files/texto_completo.pdf Postprint 852623 http://zaguan.unizar.es/record/147625/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:147625 articulos driver 2024-12-20-12:00:41 ARTICLE