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.

我们之前开发了一种新的基于机器学习的脑年龄模型，该模型对淀粉样蛋白敏感。我们旨在独立验证它并使用独立的临床数据证明其实用性。我们从韩国记忆诊所招募了 650 名参与者进行磁共振成像和临床评估。我们采用了一个预训练的大脑年龄模型，该模型使用来自一组独立的主要是白人个体 ( n  = 757) 的数据，这些人没有或相对较低水平的淀粉样蛋白，正如正电子发射断层扫描 (PET) 所证实的那样。我们调查了脑残龄与认知能力下降之间的关系。我们发现我们的预训练大脑年龄模型能够可靠地估计大脑年龄（平均绝对误差 = 5.68 岁，r(650) = 0.47，年龄范围 = 49-89 岁）样本中有 71 名参与者有主观认知衰退 (SCD)，375 名有轻度认知障碍 (MCI)，204 名有痴呆症。更大的大脑年龄与更大的淀粉样蛋白和更差的认知功能相关 [优势比，（95% 置信区间 {CI}）：1.28 (1.06–1.55)，淀粉样蛋白 PET 阳性p  = 0.030；2.52 (1.76–3.61)， 痴呆症p < 0.001]。即使在调整载脂蛋白 E e4 和淀粉样蛋白状态后，基线脑年龄残差仍可预测未来的认知恶化 [风险比，(95% CI)：1.94 (1.33–2.81)，p = 0.001，总共 336 个随访样本 ；2.31 (1.44–3.71)，p  = 0.001 对于基线临床痴呆评分 ≤ 0.5 的 284 个子样本；2.40 (1.43–4.03), 对于具有基线 SCD 或 MCI 的 240 个子样本，p = 0.001]。在独立数据集中，这些结果复制了我们之前使用该模型的发现，该模型能够根据痴呆症的诊断阶段以及淀粉样蛋白沉积状态描绘大脑年龄的显着差异。脑龄模型可能有助于区分和跟踪老年人的认知障碍。