Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data
Abstract Background Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer’s disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, usin...
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2025-06-01
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| Series: | Alzheimer’s Research & Therapy |
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| Online Access: | https://doi.org/10.1186/s13195-025-01788-6 |
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| author | Mandy M. J. Wittens Diana M. Sima Arne Brys Hanne Struyfs Ellis Niemantsverdriet Ellen De Roeck Christine Bastin Florence Benoit Bruno Bergmans Jean-Christophe Bier Peter Paul de Deyn Olivier Deryck Bernard Hanseeuw Adrian Ivanoiu Gaëtane Picard Eric Salmon Kurt Segers Anne Sieben Evert Thiery Jos Tournoy Anne-Marie van Binst Jan Versijpt Dirk Smeets Maria Bjerke Maura Bellio Neil P. Oxtoby Daniel C. Alexander Annemie Ribbens Sebastiaan Engelborghs |
| author_facet | Mandy M. J. Wittens Diana M. Sima Arne Brys Hanne Struyfs Ellis Niemantsverdriet Ellen De Roeck Christine Bastin Florence Benoit Bruno Bergmans Jean-Christophe Bier Peter Paul de Deyn Olivier Deryck Bernard Hanseeuw Adrian Ivanoiu Gaëtane Picard Eric Salmon Kurt Segers Anne Sieben Evert Thiery Jos Tournoy Anne-Marie van Binst Jan Versijpt Dirk Smeets Maria Bjerke Maura Bellio Neil P. Oxtoby Daniel C. Alexander Annemie Ribbens Sebastiaan Engelborghs |
| author_sort | Mandy M. J. Wittens |
| collection | DOAJ |
| description | Abstract Background Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer’s disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials. Methods The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ1−42, P-tau181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model’s effectiveness at patient level was evaluated. Results The research cohort’s maximum likelihood event sequence comprised CSF Aβ1-42, P-tau181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort’s order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment. Conclusions This study highlights the generalizability of EuroPOND’s AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening. |
| format | Article |
| id | doaj-art-4ac244a8e3514ea89eac381bed6bba63 |
| institution | Kabale University |
| issn | 1758-9193 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Alzheimer’s Research & Therapy |
| spelling | doaj-art-4ac244a8e3514ea89eac381bed6bba632025-08-20T03:46:07ZengBMCAlzheimer’s Research & Therapy1758-91932025-06-0117111510.1186/s13195-025-01788-6Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical dataMandy M. J. Wittens0Diana M. Sima1Arne Brys2Hanne Struyfs3Ellis Niemantsverdriet4Ellen De Roeck5Christine Bastin6Florence Benoit7Bruno Bergmans8Jean-Christophe Bier9Peter Paul de Deyn10Olivier Deryck11Bernard Hanseeuw12Adrian Ivanoiu13Gaëtane Picard14Eric Salmon15Kurt Segers16Anne Sieben17Evert Thiery18Jos Tournoy19Anne-Marie van Binst20Jan Versijpt21Dirk Smeets22Maria Bjerke23Maura Bellio24Neil P. Oxtoby25Daniel C. Alexander26Annemie Ribbens27Sebastiaan Engelborghs28Dep. of Biomedical Sciences, University of AntwerpicometrixicometrixDep. of Biomedical Sciences, University of AntwerpDep. of Biomedical Sciences, University of AntwerpDep. of Biomedical Sciences, University of AntwerpCRC Human Imaging , GIGA Research, University of LiègeGeriatrics Department, Brugmann University Hospital, Université Libre de BruxellesNeurology Department, AZ St-Jan Brugge, Ghent University and Ghent University HospitalNeurology Department, H. U. B. - Erasme Hospital, Université Libre de Bruxelles (ULB)Laboratory of Neurochemistry and Behaviour, Experimental Neurobiology unit, Department of Biomedical Sciences, University of AntwerpDepartment of Neurology, AZ Sint-LucasWELBIO department, WEL Research InstituteInstitute of Neuroscience, Université Catholique de LouvainDepartment of Neurology, Clinique Saint-PierreGIGA Cyclotron Research Centre, University of LiegeNeurology & Geriatrics Dpt, Brugmann University HospitalNeuropathology lab, IBB-NeuroBiobank BB190113, Born Bunge InstituteDepartment of Neurology, University Hospital Ghent, Ghent UniversityGerontology & Geriatrics, Department of Public Health and Primary Care, KU LeuvenRadiology department, Universitair Ziekenhuis Brussel (UZ Brussel)Dep. of Neurology, Universitair Ziekenhuis Brussel (UZ Brussel)icometrixDep. of Biomedical Sciences, University of AntwerpDepartment of Computer Science, UCL Hawkes Institute (Centre for Medical Image Computing), University College LondonDepartment of Computer Science, UCL Hawkes Institute (Centre for Medical Image Computing), University College LondonDepartment of Computer Science, UCL Hawkes Institute (Centre for Medical Image Computing), University College LondonicometrixDep. of Biomedical Sciences, University of AntwerpAbstract Background Event-based modeling (EBM) traces sequential progression of events in complex processes like neurodegenerative diseases, adept at handling uncertainties. This study validated an EBM for Alzheimer’s disease (AD) staging designed by EuroPOND, an EU-funded Horizon 2020 project, using research and real-world datasets, a crucial step towards application in multi-center trials. Methods The training dataset comprised 1737 subjects from ADNI-1/GO/2, using the EuroPOND EBM toolbox. Testing datasets included a research cohort from University of Antwerp (controls, CN (n = 46), subjective cognitive decline, SCD (n = 10), mild cognitive impairment, MCI (n = 47), AD dementia, ADD (n = 16)) and a real-world cohort from 9 Belgian Dementia Council memory clinics (CN (n = 91), SCD (n = 66), (non-amnestic) naMCI (n = 54), aMCI (n = 255), and ADD (n = 220). Biomarkers included: 2 clinical scores (Mini Mental State Examination (MMSE), Rey Auditory Verbal Learning Test (RAVLT)); 3 CSF-biomarkers (Aβ1−42, P-tau181, total-Tau); and 4 magnetic resonance imaging (MRI) biomarkers (volumes of the hippocampi, temporal, parietal, and frontal cortices) computed with icobrain dm. The naMCI and aMCI groups were compared by EBM stage proportions, and the model’s effectiveness at patient level was evaluated. Results The research cohort’s maximum likelihood event sequence comprised CSF Aβ1-42, P-tau181, T-tau, RAVLT, MMSE, and cortical volumes. The clinical cohort’s order was frontal cortex volume, MMSE, and remaining cortical regions. aMCI subjects showed higher staging than naMCI, with 54% in the two most advanced stages compared to 38% in naMCI. In the research cohort, 10 outliers were identified with potential mismatches between assigned stages and clinical or biomarker profiles, with CN (n = 4) and SCD (n = 2) subjects assigned in stage 4, one control in stage 9 with abnormal imaging, and three aMCI cases in stage 0 despite clinical or volumetric signs of impairment. Conclusions This study highlights the generalizability of EuroPOND’s AD EBM model across research and real-world clinical datasets, supporting its use in multi-center trials. aMCI subjects generally reside in more advanced stages than naMCI, who may not necessarily have AD, demonstrating utility for precision recruitment/screening.https://doi.org/10.1186/s13195-025-01788-6Alzheimer’s diseaseBiomarkersMagnetic resonance imagingAutomated volumetryEvent-based modelling |
| spellingShingle | Mandy M. J. Wittens Diana M. Sima Arne Brys Hanne Struyfs Ellis Niemantsverdriet Ellen De Roeck Christine Bastin Florence Benoit Bruno Bergmans Jean-Christophe Bier Peter Paul de Deyn Olivier Deryck Bernard Hanseeuw Adrian Ivanoiu Gaëtane Picard Eric Salmon Kurt Segers Anne Sieben Evert Thiery Jos Tournoy Anne-Marie van Binst Jan Versijpt Dirk Smeets Maria Bjerke Maura Bellio Neil P. Oxtoby Daniel C. Alexander Annemie Ribbens Sebastiaan Engelborghs Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data Alzheimer’s Research & Therapy Alzheimer’s disease Biomarkers Magnetic resonance imaging Automated volumetry Event-based modelling |
| title | Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data |
| title_full | Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data |
| title_fullStr | Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data |
| title_full_unstemmed | Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data |
| title_short | Independent validation and outlier analysis of EuroPOND Alzheimer’s disease staging model using ADNI and real-world clinical data |
| title_sort | independent validation and outlier analysis of europond alzheimer s disease staging model using adni and real world clinical data |
| topic | Alzheimer’s disease Biomarkers Magnetic resonance imaging Automated volumetry Event-based modelling |
| url | https://doi.org/10.1186/s13195-025-01788-6 |
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