Biological Brain Age Prediction Using Machine Learning on Structural Neuroimaging Data: Multi‐Cohort Validation Against Biomarkers of Alzheimer's Disease and Neurodegeneration. (20th December 2022)

• Record Type: Journal Article
• Authors: Cumplido‐Mayoral, Irene; Garcia, Marina; Operto, Grégory; Falcon, Carles; Shekari, Mahnaz; Cacciaglia, Raffaele; Milà‐Alomà, Marta; Lorenzini, Luigi; Ingala, Silvia; Wink, Alle Meije; Mutsaerts, Henk‐Jan; Minguillón, Carolina; Fauria, Karine; Molinuevo, Jose Luis; Haller, Sven; Chetelat, Gael; Waldman, Adam; Schwarz, Adam J.; Barkhof, Frederik; Kollmorgen, Gwendlyn; Suridjan, Ivonne; Wild, Norbert; Zetterberg, Henrik; Blennow, Kaj; Suárez‐Calvet, Marc; Vilaplana, Verónica; Gispert, Juan Domingo
• Is Part Of: Alzheimer's & dementia. Volume 18(2022)Supplement 5
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-12-20
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.064047
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: 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 (Aβ42 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 10 th and 90 th 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‐β 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.