Biological brain age prediction using machine learning on structural neuroimaging data: Multi-cohort validation against biomarkers of Alzheimer’s disease and neurodegeneration

Autor: Cumplido Mayoral, Irene, García Prat, Marina, Operto, Grégory, Falcón Falcón, Carles, Shekari, Mahnaz, Cacciaglia, Raffaele, Milà Alomà, Marta, Lorenzini, Luigi, Ingala, Silvia, Vilaplana Besler, Verónica
Přispěvatelé: Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Popis: Background: Brain-age can be inferred from structural neuroimaging and compared to chronological age (brain-age delta), as a marker of accelerated/decelerated biological brain aging. Accelerated biological aging has been found in Alzheimer’s disease (AD), but validation against biomarkers of AD and neurodegeneration is lacking. We studied the association between brain-age delta vs biomarkers and risk factors for AD, neurodegeneration, and cerebrovascular disease in non-demented individuals. Furthermore, between-sex differences in the brain areas that better predicted age were sought. Method: We trained XGBoost regressor models to predict brain-age separately for females and males using volumes and cortical thickness in regions of the Desikan-Kiliany atlas (obtained with Freesurfer 6.0) from the UKBioBank cohort (n=22,661). Using this trained model, we estimated brain-age delta in cognitively unimpaired (CU) and mild cognitive impaired (MCI) individuals four independent cohorts: ALFA+ (nCU=380), ADNI (nCU=253, nMCI=498), EPAD (nCU=653, nMCI=155) and OASIS (nCU=407). Chronological age, sex, MMSE and APOE categories were available for all subjects. ALFA+, ADNI and EPAD cohorts included data for AD CSF biomarkers (Aß42 and p-tau) and amyloid-b/tau (AT) staging was performed using pre-established cut-off values, whereas for OASIS amyloid-b was determined by PET. White Matter Hyperintensities (WMH) were available as a marker of small vessel disease and plasma (ALFA+ and ADNI) neurofilament light (NfL) as of neurodegeneration. Linear regression models, including chronological age and sex as covariates were used to identify associations between brain-age delta and biomarkers. We identified the individuals at the 10th and 90th deciles to select those with higher (accelerated) and lower (decelerated) brain-age delta and tested for interactions between age and all the variables on brain-age delta. Result: Between-sex differences were found in the most predictive brain regions (Figure 1). Brain-age delta was positively associated with abnormal amyloid-ß status, advanced AT stages and APOE-e4 carriership. Furthermore, brain-age delta was positively associated with plasma NfL in MCI patients and an interaction between age and plasma NfL was found on brain-age delta of CU individuals (Figure 2). Conclusion: Biological brain-age can be estimated from structural neuroimaging and is associated with biomarkers and risk factors of AD pathology and neurodegeneration in non-demented individuals. This project has received support from European Prevention of Alzheimer’s Dementia (EPAD) grant no. 115736, Edinburgh, United Kingdom. Peer Reviewed La publicació està signada per 27 autors/autores: Irene Cumplido-Mayoral 1,2; Marina Garcia 1; Grégory Operto 1,3,4; Carles Falcon 1,3,5; Mahnaz Shekari 1,2,6; Raffaele Cacciaglia 1,3,4; Marta Milà-Alomà 1,2,3,4; Luigi Lorenzini 7; Silvia Ingala 7; Alle Meije Wink 7; Henk-Jan Mutsaerts 7; Carolina Minguillón 1,3,4; Karine Fauria 1,4; Jose Luis Molinuevo 1,3,4,8; Sven Haller 9; Gael Chetelat 10; Adam Waldman 11; Adam J. Schwarz 12; Frederik Barkhof 7,13; Gwendlyn Kollmorgen 14; Ivonne Suridjan 14; Norbert Wild 14; Henrik Zetterberg 15,16,17,18,19; Kaj Blennow 15,19; Marc Suárez-Calvet 1,3,4,20; Verónica Vilaplana 21; Juan Domingo Gispert 1,3,5; ALFA study 22; ADNI study 23 on Behalf Of The EPAD Consortium 24 // 1 Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; 2 Universitat Pompeu Fabra, Barcelona, Spain; 3 IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; 4 Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain; 5 Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain; 6 Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain; 7 Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands; 8 Lundbeck A/S, Copenhagen, Denmark; 9 CIRD Centre d’Imagerie Rive Droite, Geneva, Switzerland; 10 Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France; 11 Centre for Dementia Prevention, Edinburgh Imaging, and UK Dementia Research Institute at The University of Edinburgh, Edinburgh, United Kingdom; 12 Takeda Pharmaceutical Company Ltd, Cambridge, MA, USA; 13 Institutes of Neurology and Healthcare Engineering, University College London, London, United Kingdom; 14 Roche Diagnostics International Ltd, Rotkreuz, Switzerland; 15 Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; 16 UK Dementia Research Institute at UCL, London, United Kingdom; 17 Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong; 18 Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; 19 Institute of Neuroscience and Physiology, University of Gothenburg, Mölndal, Sweden; 20 Servei de Neurologia, Hospital del Mar, Barcelona, Spain; 21 Department of Signal Theory and Communications, Universitat Politècnica de Catalunya, Barcelona, Spain; 22 BarcelonaBeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain; 23 Laboratory of Neuroimaging (LONI), University of Southern California, Los Angeles, CA, USA.
Databáze: OpenAIRE
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