Chen Han, Cao Zhi, Zhang Jing, Li Dun, Wang Yaogang, Xu Chenjie
School of Public Health, Hangzhou Normal University, Hangzhou, China.
Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Health Data Sci. 2025 May 2;5:0257. doi: 10.34133/hds.0257. eCollection 2025.
A neuroimaging-derived biomarker termed the brain age is considered to capture the degree and diversity in the aging process of the brain, serving as a robust indicator of overall brain health. The impact of different levels of physical activity (PA) intensities on brain age is still not fully understood. This study aimed to investigate the associations between accelerometer-measured PA and brain age. A total of 16,972 eligible participants with both valid -weighted neuroimaging and accelerometer data from the UK Biobank was included. Brain age was estimated using an ensemble learning approach called Light Gradient-Boosting Machine (LightGBM). Over 1,400 image-derived phenotypes (IDPs) were initially chosen to undergo data-driven feature selection for brain age prediction. A measure of accelerated brain aging, the brain age gap (BAG) can be derived by subtracting the chronological age from the estimated brain age. A positive BAG indicates accelerated brain aging. PA was measured over a 7-day period using wrist-worn accelerometers, and time spent on light-intensity PA (LPA), moderate-intensity PA (MPA), vigorous-intensity PA (VPA), and moderate- to vigorous-intensity PA (MVPA) was extracted. The generalized additive model was applied to examine the nonlinear association between PA and BAG after adjusting for potential confounders. The brain age estimated by LightGBM achieved an appreciable performance ( = 0.81, mean absolute error [MAE] = 3.65), which was further improved by age bias correction ( = 0.90, MAE = 3.03). We found that LPA ( = 2.47, = 0.04), MPA ( = 6.49, < 1 × 10), VPA ( = 4.92, = 2.58 × 10), and MVPA ( = 6.45, < 1 × 10) exhibited an approximate U-shaped relationship with BAG, demonstrating that both insufficient and excessive PA levels adversely impact brain aging. Furthermore, mediation analysis suggested that BAG partially mediated the associations between PA and cognitive functions as well as brain-related disorders. Our study revealed a U-shaped association between accelerometer-measured PA and BAG, highlighting that advanced brain health may be attainable through engaging in moderate amounts of objectively measured PA irrespectively of intensities.
一种源自神经影像学的生物标志物——脑龄,被认为可以反映大脑衰老过程中的程度和多样性,是整体大脑健康的有力指标。不同强度的身体活动(PA)对脑龄的影响仍未完全明确。本研究旨在探讨通过加速度计测量的PA与脑龄之间的关联。研究共纳入了来自英国生物银行的16972名符合条件的参与者,他们同时拥有有效的加权神经影像学数据和加速度计数据。使用一种名为Light梯度提升机(LightGBM)的集成学习方法来估计脑龄。最初选择了1400多个源自图像的表型(IDP)进行数据驱动的特征选择,以用于脑龄预测。通过从估计的脑龄中减去实际年龄,可以得出脑龄加速的指标——脑龄差距(BAG)。正的BAG表明脑龄加速。使用腕部佩戴的加速度计在7天内测量PA,并提取在轻度强度PA(LPA)、中度强度PA(MPA)、剧烈强度PA(VPA)以及中度至剧烈强度PA(MVPA)上花费的时间。在调整潜在混杂因素后,应用广义相加模型来检验PA与BAG之间的非线性关联。通过LightGBM估计的脑龄表现出可观的性能(=0.81,平均绝对误差[MAE]=3.65),通过年龄偏差校正后进一步提高(=0.90,MAE=3.03)。我们发现,LPA(=2.47,=0.04)、MPA(=6.49,<1×10)、VPA(=4.92,=2.58×10)和MVPA(=6.45,<1×10)与BAG呈现近似U形关系,表明PA水平不足和过高都会对脑衰老产生不利影响。此外,中介分析表明,BAG部分介导了PA与认知功能以及脑相关疾病之间的关联。我们的研究揭示了通过加速度计测量的PA与BAG之间的U形关联,强调通过进行适量的客观测量的PA(无论强度如何)可能实现良好的脑健康。