Abstract
Original language | English |
---|---|
Article number | 101954 |
Journal | NeuroImage Clin. |
Volume | 24 |
DOIs | |
Publication status | Published - 2019 |
Keywords
- Alzheimer's disease
- Biomarkers progression
- Event-based models
- Inter-cohort validation
- Patient staging
- amyloid beta protein[1-42]
- apolipoprotein E
- apolipoprotein E epsilon4
- biological marker
- tau protein
- unclassified drug
- adult
- aged
- algorithm
- Alzheimer disease
- Article
- cerebrospinal fluid
- clinical feature
- cognition
- cohort analysis
- controlled study
- correlation analysis
- dementia
- discriminant analysis
- discriminative event based model
- disease classification
- disease course
- entorhinal cortex
- event based model
- external validity
- female
- fusiform gyrus
- hippocampus
- human
- linear regression analysis
- major clinical study
- male
- mathematical model
- middle aged
- middle temporal gyrus
- mild cognitive impairment
- Mini Mental State Examination
- neuroanatomy
- neuroimaging
- nuclear magnetic resonance imaging
- precuneus
- prediction
- priority journal
- statistical significance
- validation study
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Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease : NeuroImage: Clinical. / Archetti, D.; Ingala, S.; Venkatraghavan, V. et al.
In: NeuroImage Clin., Vol. 24, 101954, 2019.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer's disease
T2 - NeuroImage: Clinical
AU - Archetti, D.
AU - Ingala, S.
AU - Venkatraghavan, V.
AU - Wottschel, V.
AU - Young, A.L.
AU - Bellio, M.
AU - Bron, E.E.
AU - Klein, S.
AU - Barkhof, F.
AU - Alexander, D.C.
AU - Oxtoby, N.P.
AU - Frisoni, G.B.
AU - Redolfi, A.
AU - Initiative, for the Alzheimer's Disease Neuroimaging
AU - Consortium, for EuroPOND
N1 - Export Date: 10 February 2020 Correspondence Address: Archetti, D.Via Pilastroni 4, Italy; email: darchetti@fatebenefratelli.eu Funding details: Takeda Pharmaceutical Company Funding details: National Institute on Aging, NIA Funding details: GE Healthcare Funding details: Eli Lilly and Company Funding details: H. Lundbeck A/S Funding details: 666992, 634541 Funding details: European Commission, EC, R01 AG021910, P01 AG03991, R01 MH56584, P50 MH071616, P50 AG05681, U24 RR021382 Funding details: F. Hoffmann-La Roche Funding details: U.S. Department of Defense, DOD, W81XWH-12-2-0012 Funding details: Servier Funding details: University College London Hospitals NHS Foundation Trust, UCLH Funding details: Johnson and Johnson Pharmaceutical Research and Development, J&JPRD Funding details: Alzheimer's Drug Discovery Foundation, ADDF Funding details: NIHR Imperial Biomedical Research Centre, BRC Funding details: Pfizer Funding details: European Federation of Pharmaceutical Industries and Associations, EFPIA Funding details: National Institute of Biomedical Imaging and Bioengineering, NIBIB Funding details: Innovative Medicines Initiative, IMI, 115736, 115952 Funding details: BioClinica Funding details: Novartis Pharmaceuticals Corporation, NPC Funding details: Eisai Funding details: Alzheimer's Disease Neuroimaging Initiative, ADNI Funding details: AbbVie Funding details: Foundation for the National Institutes of Health, FNIH, 283562, http://www.neugrid4you.eu Funding details: National Institutes of Health, NIH, U01 AG024904 Funding details: Fujirebio Europe Funding details: Genentech Funding details: Biogen Funding details: Stichting Retina Fonds Funding details: Alzheimer's Association, AA Funding details: Canadian Institutes of Health Research, CIHR Funding text 1: This project has received funding from the European Union 's Horizon 2020 research and innovation programme under grant agreement No. 666992 . This project has received funding from the European Union 's Horizon 2020 research and innovation programme under grant agreement No. 634541 . ADNI data were funded by the Alzheimer's Disease Neuroimaging Initiative ( National Institutes of Health grant U01 AG024904 ) and Department of Defense Alzheimer's Disease Neuroimaging Initiative (Department of Defense award W81XWH-12-2-0012 ). The Alzheimer's Disease Neuroimaging Initiative is funded by the National Institute on Aging , the National Institute of Biomedical Imaging and Bioengineering , and through contributions from the following: AbbVie , Alzheimer's Association ; Alzheimer's Drug Discovery Foundation ; Araclon Biotech ; BioClinica, Inc. ; Biogen ; Bristol- Myers Squibb Company ; CereSpir Inc. ; Cogstate ; Eisai Inc. ; Elan Pharmaceuticals Inc. ; Eli Lilly and Company ; EuroImmun ; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech Inc. ; Fujirebio ; GE Healthcare ; IXICO Ltd. ; Janssen Alzheimer Immunotherapy Research and Development LLC ; Johnson & Johnson Pharmaceutical Research & Development LLC ; Lumosity ; Lundbeck ; Merck and Co Inc. ; Meso Scale Diagnostics LLC ; NeuroRx Research ; Neuro-track Technologies ; Novartis Pharmaceuticals Corporation ; Pfizer Inc. ; Piramal Imaging ; Servier ; Takeda Pharmaceutical Company ; and Transition Therapeutics . The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health. The grantee organization is the Northern California Institute for Research and Education , and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. Alzheimer's Disease Neuroimaging Initiative data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. The investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf . ARWiBo, EDSD, ViTA, and PharmaCog (alias E-ADNI) data used in the preparation of this article were obtained from NeuGRID4You initiative ( http://www.neugrid4you.eu ) funded by grant 283562 from the European Commission . OASIS was funded by grant P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584. ADC was obtained from the VUmc Alzheimer centre which is part of the neurodegeneration research program of Amsterdam Neuroscience ( http://www.amsterdamresearch.org ). The ADC was supported by Innovatie Fonds Ziektekostenverzekeraars , Stichting Diorapthe and Stichting VUmc fonds. This project has received funding from the Innovative Medicines Initiative 2 Joint undertaking under grant agreement No 115736 (EPAD) and 115952 (AMYPAD). This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA . FB is supported by the NIHR biomedical research centre at UCLH . Appendix A References: Aisen, P.S., Petersen, R.C., Donohue, M.C., Gamst, A., Raman, R., Alzheimer's Disease Neuroimaging Initiative. 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PY - 2019
Y1 - 2019
N2 - Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2 EBM = 0.866; R2 DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain. © 2019 The Authors
AB - Understanding the sequence of biological and clinical events along the course of Alzheimer's disease provides insights into dementia pathophysiology and can help participant selection in clinical trials. Our objective is to train two data-driven computational models for sequencing these events, the Event Based Model (EBM) and discriminative-EBM (DEBM), on the basis of well-characterized research data, then validate the trained models on subjects from clinical cohorts characterized by less-structured data-acquisition protocols. Seven independent data cohorts were considered totalling 2389 cognitively normal (CN), 1424 mild cognitive impairment (MCI) and 743 Alzheimer's disease (AD) patients. The Alzheimer's Disease Neuroimaging Initiative (ADNI) data set was used as training set for the constriction of disease models while a collection of multi-centric data cohorts was used as test set for validation. Cross-sectional information related to clinical, cognitive, imaging and cerebrospinal fluid (CSF) biomarkers was used. Event sequences obtained with EBM and DEBM showed differences in the ordering of single biomarkers but according to both the first biomarkers to become abnormal were those related to CSF, followed by cognitive scores, while structural imaging showed significant volumetric decreases at later stages of the disease progression. Staging of test set subjects based on sequences obtained with both models showed good linear correlation with the Mini Mental State Examination score (R2 EBM = 0.866; R2 DEBM = 0.906). In discriminant analyses, significant differences (p-value ≤ 0.05) between the staging of subjects from training and test sets were observed in both models. No significant difference between the staging of subjects from the training and test was observed (p-value > 0.05) when considering a subset composed by 562 subjects for which all biomarker families (cognitive, imaging and CSF) are available. Event sequence obtained with DEBM recapitulates the heuristic models in a data-driven fashion and is clinically plausible. We demonstrated inter-cohort transferability of two disease progression models and their robustness in detecting AD phases. This is an important step towards the adoption of data-driven statistical models into clinical domain. © 2019 The Authors
KW - Alzheimer's disease
KW - Biomarkers progression
KW - Event-based models
KW - Inter-cohort validation
KW - Patient staging
KW - amyloid beta protein[1-42]
KW - apolipoprotein E
KW - apolipoprotein E epsilon4
KW - biological marker
KW - tau protein
KW - unclassified drug
KW - adult
KW - aged
KW - algorithm
KW - Alzheimer disease
KW - Article
KW - cerebrospinal fluid
KW - clinical feature
KW - cognition
KW - cohort analysis
KW - controlled study
KW - correlation analysis
KW - dementia
KW - discriminant analysis
KW - discriminative event based model
KW - disease classification
KW - disease course
KW - entorhinal cortex
KW - event based model
KW - external validity
KW - female
KW - fusiform gyrus
KW - hippocampus
KW - human
KW - linear regression analysis
KW - major clinical study
KW - male
KW - mathematical model
KW - middle aged
KW - middle temporal gyrus
KW - mild cognitive impairment
KW - Mini Mental State Examination
KW - neuroanatomy
KW - neuroimaging
KW - nuclear magnetic resonance imaging
KW - precuneus
KW - prediction
KW - priority journal
KW - statistical significance
KW - validation study
U2 - 10.1016/j.nicl.2019.101954
DO - 10.1016/j.nicl.2019.101954
M3 - Article
VL - 24
JO - NeuroImage Clin.
JF - NeuroImage Clin.
SN - 2213-1582
M1 - 101954
ER -