Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis
ObjectiveEstablishing biomarkers to predict multiple sclerosis (MS) disability accrual has been challenging using a single biomarker approach, likely due to the complex interplay of neuroinflammation and neurodegeneration. Here, we aimed to investigate the prognostic value of single and multimodal b...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1532660/full |
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| author | Vinzenz Fleischer Tobias Brummer Muthuraman Muthuraman Muthuraman Muthuraman Falk Steffen Milena Heldt Maria Protopapa Muriel Schraad Gabriel Gonzalez-Escamilla Sergiu Groppa Stefan Bittner Frauke Zipp |
| author_facet | Vinzenz Fleischer Tobias Brummer Muthuraman Muthuraman Muthuraman Muthuraman Falk Steffen Milena Heldt Maria Protopapa Muriel Schraad Gabriel Gonzalez-Escamilla Sergiu Groppa Stefan Bittner Frauke Zipp |
| author_sort | Vinzenz Fleischer |
| collection | DOAJ |
| description | ObjectiveEstablishing biomarkers to predict multiple sclerosis (MS) disability accrual has been challenging using a single biomarker approach, likely due to the complex interplay of neuroinflammation and neurodegeneration. Here, we aimed to investigate the prognostic value of single and multimodal biomarker combinations to predict four-year disability progression in patients with MS.MethodsIn total, 111 MS patients were followed up for four years to track disability accumulation based on the Expanded Disability Status Scale (EDSS). Three clinically relevant modalities (MRI, OCT and blood serum) served as sources of potential predictors for disease worsening. Two key measures from each modality were determined and related to subsequent disability progression: lesion volume (LV), gray matter volume (GMV), retinal nerve fiber layer, ganglion cell-inner plexiform layer, serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein. First, receiver operator characteristic (ROC) analyses were performed to identify the discriminative power of individual biomarkers and their combinations. Second, we applied structural equation modeling (SEM) to the single biomarkers in order to determine their causal inter-relationships.ResultsBaseline GMV on its own allowed identification of subsequent EDSS progression based on ROC analysis. All other individual baseline biomarkers were unable to discriminate between progressive and non-progressive patients on their own. When comparing all possible biomarker combinations, the tripartite combination of MRI, OCT and blood biomarkers achieved the highest discriminative accuracy. Finally, predictive causal modeling identified that LV mediates significant parts of the effect of GMV and sNfL on disability progression.ConclusionMultimodal biomarkers, i.e. different major surrogates for pathology derived from MRI, OCT and blood, inform about different parts of the disease pathology leading to clinical progression. |
| format | Article |
| id | doaj-art-ea8cb17f3b824754a7273df427e1e4c3 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-ea8cb17f3b824754a7273df427e1e4c32025-01-31T05:10:18ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011610.3389/fimmu.2025.15326601532660Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosisVinzenz Fleischer0Tobias Brummer1Muthuraman Muthuraman2Muthuraman Muthuraman3Falk Steffen4Milena Heldt5Maria Protopapa6Muriel Schraad7Gabriel Gonzalez-Escamilla8Sergiu Groppa9Stefan Bittner10Frauke Zipp11Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Section of Neural Engineering with Signal Analytics and Artificial Intelligence, University Hospital Würzburg, Würzburg, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyDepartment of Neurology, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, GermanyObjectiveEstablishing biomarkers to predict multiple sclerosis (MS) disability accrual has been challenging using a single biomarker approach, likely due to the complex interplay of neuroinflammation and neurodegeneration. Here, we aimed to investigate the prognostic value of single and multimodal biomarker combinations to predict four-year disability progression in patients with MS.MethodsIn total, 111 MS patients were followed up for four years to track disability accumulation based on the Expanded Disability Status Scale (EDSS). Three clinically relevant modalities (MRI, OCT and blood serum) served as sources of potential predictors for disease worsening. Two key measures from each modality were determined and related to subsequent disability progression: lesion volume (LV), gray matter volume (GMV), retinal nerve fiber layer, ganglion cell-inner plexiform layer, serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein. First, receiver operator characteristic (ROC) analyses were performed to identify the discriminative power of individual biomarkers and their combinations. Second, we applied structural equation modeling (SEM) to the single biomarkers in order to determine their causal inter-relationships.ResultsBaseline GMV on its own allowed identification of subsequent EDSS progression based on ROC analysis. All other individual baseline biomarkers were unable to discriminate between progressive and non-progressive patients on their own. When comparing all possible biomarker combinations, the tripartite combination of MRI, OCT and blood biomarkers achieved the highest discriminative accuracy. Finally, predictive causal modeling identified that LV mediates significant parts of the effect of GMV and sNfL on disability progression.ConclusionMultimodal biomarkers, i.e. different major surrogates for pathology derived from MRI, OCT and blood, inform about different parts of the disease pathology leading to clinical progression.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1532660/fullmultiple sclerosisbiomarkermagnetic resonance imagingneurofilamentoptical coherence tomographydisease progression |
| spellingShingle | Vinzenz Fleischer Tobias Brummer Muthuraman Muthuraman Muthuraman Muthuraman Falk Steffen Milena Heldt Maria Protopapa Muriel Schraad Gabriel Gonzalez-Escamilla Sergiu Groppa Stefan Bittner Frauke Zipp Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis Frontiers in Immunology multiple sclerosis biomarker magnetic resonance imaging neurofilament optical coherence tomography disease progression |
| title | Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis |
| title_full | Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis |
| title_fullStr | Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis |
| title_full_unstemmed | Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis |
| title_short | Biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis |
| title_sort | biomarker combinations from different modalities predict early disability accumulation in multiple sclerosis |
| topic | multiple sclerosis biomarker magnetic resonance imaging neurofilament optical coherence tomography disease progression |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1532660/full |
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