한빛사 논문
Helmet T. Karim1,2, Howard J. Aizenstein1,2, Akiko Mizuno1, Maria Ly1, Carmen Andreescu1, Minjie Wu1, Chang Hyung Hong3, Hyun Woong Roh3, Bumhee Park4,5, Heirim Lee4,5, Na-Rae Kim4, Jin Wook Choi6, Sang Won Seo7, Seong Hye Choi8, Eun-Joo Kim9, Byeong C. Kim10, Jae Youn Cheong11,12, Eunyoung Lee4,5, Dong-gi Lee3, Yong Hyuk Cho3, So Young Moon13 and Sang Joon Son1,3,*
1Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. 2Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA. 3Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea. 4Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea. 5Office of Biostatistics, Medical Research Collaborating Centre, Ajou Research Institute for Innovative Medicine, Ajou University Medical Centre, Suwon, Republic of Korea. 6Department of Radiology, Ajou University School of Medicine, Suwon, Republic of Korea. 7Department of Neurology, Samsung Medical Centre, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. 8Department of Neurology, Inha University College of Medicine, Incheon, Republic of Korea. 9Department of Neurology, Pusan National University Hospital, Pusan National University School of Medicine and Biomedical research institute, Busan, Republic of Korea. 10Department of Neurology, Chonnam National University Medical School, Gwangju, Republic of Korea. 11Department of Gastroenterology, Ajou University School of Medicine, Suwon, Republic of Korea. 12Human Genome Research and Bio-Resource Centre, Ajou University Medical Centre, Suwon, Republic of Korea. 13Department of Neurology, Ajou University School of Medicine, Suwon, Republic of Korea.
*Corresponding author.
Abstract
We previously developed a novel machine-learning-based brain age model that was sensitive to amyloid. We aimed to independently validate it and to demonstrate its utility using independent clinical data. We recruited 650 participants from South Korean memory clinics to undergo magnetic resonance imaging and clinical assessments. We employed a pretrained brain age model that used data from an independent set of largely Caucasian individuals (n = 757) who had no or relatively low levels of amyloid as confirmed by positron emission tomography (PET). We investigated the association between brain age residual and cognitive decline. We found that our pretrained brain age model was able to reliably estimate brain age (mean absolute error = 5.68 years, r(650) = 0.47, age range = 49–89 year) in the sample with 71 participants with subjective cognitive decline (SCD), 375 with mild cognitive impairment (MCI), and 204 with dementia. Greater brain age was associated with greater amyloid and worse cognitive function [Odds Ratio, (95% Confidence Interval {CI}): 1.28 (1.06–1.55), p = 0.030 for amyloid PET positivity; 2.52 (1.76–3.61), p < 0.001 for dementia]. Baseline brain age residual was predictive of future cognitive worsening even after adjusting for apolipoprotein E e4 and amyloid status [Hazard Ratio, (95% CI): 1.94 (1.33–2.81), p = 0.001 for total 336 follow-up sample; 2.31 (1.44–3.71), p = 0.001 for 284 subsample with baseline Clinical Dementia Rating ≤ 0.5; 2.40 (1.43–4.03), p = 0.001 for 240 subsample with baseline SCD or MCI]. In independent data set, these results replicate our previous findings using this model, which was able to delineate significant differences in brain age according to the diagnostic stages of dementia as well as amyloid deposition status. Brain age models may offer benefits in discriminating and tracking cognitive impairment in older adults.
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