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A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort
- Source :
- Alzheimer's and Dementia: Translational Research and Clinical Interventions, 5, 933-938. Elsevier Inc., Alzheimer's and Dementia: Translational Research and Clinical Interventions, 5(1), 933-938. Elsevier Inc., Alzheimer's & Dementia : Translational Research & Clinical Interventions, Alzheimer's & dementia, Vol. 5 (2019) pp. 933-938, Alzheimer's and Dementia: Translational Research and Clinical Interventions, Stamate, D, Kim, M, Proitsi, P, Westwood, S, Baird, A, Nevado-Holgado, A, Hye, A, Bos, I, Vos, S J B, Vandenberghe, R, Teunissen, C E, Kate, M T, Scheltens, P, Gabel, S, Meersmans, K, Blin, O, Richardson, J, de Roeck, E, Engelborghs, S, Sleegers, K, Bordet, R G, Ramit, L, Kettunen, P, Tsolaki, M, Verhey, F, Alcolea, D, Lléo, A, Peyratout, G, Tainta, M, Johannsen, P, Freund-Levi, Y, Frölich, L, Dobricic, V, Frisoni, G B, Molinuevo, J L, Wallin, A, Popp, J, Martinez-Lage, P, Bertram, L, Blennow, K, Zetterberg, H, Streffer, J, Visser, P J, Lovestone, S & Legido-Quigley, C 2019, ' A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort ', Alzheimer's and Dementia: Translational Research and Clinical Interventions, vol. 5, pp. 933-938 . https://doi.org/10.1016/j.trci.2019.11.001, Alzheimer's & dementia, vol. 5, pp. 933-938
- Publication Year :
- 2019
-
Abstract
- Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer Disease (AD). Here we set out to test the performance of metabolites in blood to categorize AD when compared to CSF biomarkers. This study analyzed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n = 883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV). On the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively. This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders. The present study was conducted as part of the EMIF-AD project, which has received support from the Innovative Medicines Initiative Joint Undertaking under EMIF grant agreement no. 115372, resources of which are composed of financial contribution from the European Union’s Seventh Framework Program (FP7/2007–2013) and EFPIA companies’ in-kind contribution. The DESCRIPA study was funded by the European Commission within the fifth framework program (QLRT-2001-2455). The EDAR study was funded by the European Commission within the fifth framework program (contract no. 37670). The San Sebastian GAP study is partially funded by the Department of Health of the Basque Government (allocation 17.0.1.08.12.0000.2.454.01. 41142.001.H). Kristel Sleegers is supported by the Research Fund of the University of Antwerp. Daniel Stamate is supported by the Alzheimer’s Research UK (ARUK-PRRF2017-012).
- Subjects :
- 0301 basic medicine
medicine.medical_specialty
Metabolite
Clinical Neurology
Short Report
Machine-Learning
Medical information
Alzheimer type dementia
Bioinformatics
ddc:616.89
03 medical and health sciences
chemistry.chemical_compound
0302 clinical medicine
Metabolomics
Medicine
Biomarker discovery
Biology
Medicine(all)
Geriatrics
Science & Technology
business.industry
Neurosciences
EMIF-AD
Alzheimer's disease
medicine.disease
3. Good health
Psychiatry and Mental health
030104 developmental biology
chemistry
Cohort
Biomarkers
Human medicine
Neurosciences & Neurology
Neurology (clinical)
business
Life Sciences & Biomedicine
030217 neurology & neurosurgery
Subjects
Details
- Language :
- English
- ISSN :
- 23528737
- Database :
- OpenAIRE
- Journal :
- Alzheimer's and Dementia: Translational Research and Clinical Interventions, 5, 933-938. Elsevier Inc., Alzheimer's and Dementia: Translational Research and Clinical Interventions, 5(1), 933-938. Elsevier Inc., Alzheimer's & Dementia : Translational Research & Clinical Interventions, Alzheimer's & dementia, Vol. 5 (2019) pp. 933-938, Alzheimer's and Dementia: Translational Research and Clinical Interventions, Stamate, D, Kim, M, Proitsi, P, Westwood, S, Baird, A, Nevado-Holgado, A, Hye, A, Bos, I, Vos, S J B, Vandenberghe, R, Teunissen, C E, Kate, M T, Scheltens, P, Gabel, S, Meersmans, K, Blin, O, Richardson, J, de Roeck, E, Engelborghs, S, Sleegers, K, Bordet, R G, Ramit, L, Kettunen, P, Tsolaki, M, Verhey, F, Alcolea, D, Lléo, A, Peyratout, G, Tainta, M, Johannsen, P, Freund-Levi, Y, Frölich, L, Dobricic, V, Frisoni, G B, Molinuevo, J L, Wallin, A, Popp, J, Martinez-Lage, P, Bertram, L, Blennow, K, Zetterberg, H, Streffer, J, Visser, P J, Lovestone, S & Legido-Quigley, C 2019, ' A metabolite-based machine learning approach to diagnose Alzheimer-type dementia in blood: Results from the European Medical Information Framework for Alzheimer disease biomarker discovery cohort ', Alzheimer's and Dementia: Translational Research and Clinical Interventions, vol. 5, pp. 933-938 . https://doi.org/10.1016/j.trci.2019.11.001, Alzheimer's & dementia, vol. 5, pp. 933-938
- Accession number :
- edsair.doi.dedup.....a8c86dc998661b100ef00cac7369e716