Brain age prediction improves the early detection of Alzheimer’s disease in East Asian elderly

Introduction Brain age prediction is used to quantify the pathological and cognitive changes associated with brain aging. However, the predicted age derived from certain models can result in biased estimation and the concealment of inherent aged brain function. Methods We constructed a brain age prediction model for the East Asian elderly brain using the brain volume and cortical thickness features from cognitively normal (CN) brains. Furthermore, our model was used to estimate different diagnoses and to construct a classification model of mild cognitive impairment (MCI) conversion and Alzheimer’s disease (AD) conversion. Results Our model showed a strong association of the brain age difference (BAD) with three diagnosis groups. In addition, the classification models of MCI conversion and AD conversion showed acceptable and robust performances, respectively (area under the curve [AUC] = 0.66, AUC = 0.76). Discussion We believe that our model can be used to estimate the predicted status of an East Asian elderly brain. Moreover, the MCI conversion model has the potential to prevent severe cognitive impairment and can be used for the early detection of AD. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was supported by the KBRI Basic Research Program through the Korea Brain Research Institute funded by the Ministry of Science and ICT (22-BR-03-05), the Korea National Institute of Health Research Project (project No. 2021-ER1007-01), and the 'Creative KMEDI hub' in 2022. [No. B-C-N-22-10]. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This study was approved by the Institutional Review Boards of Chosun University Hospital and Chonnam National University. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors