Accelerometer-Measured Physical Activity and Neuroimaging-Driven Brain Age.

Health data science Pub Date : 2025-05-02 eCollection Date: 2025-01-01 DOI:10.34133/hds.0257
Han Chen, Zhi Cao, Jing Zhang, Dun Li, Yaogang Wang, Chenjie Xu
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Abstract

Background: 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. Methods: A total of 16,972 eligible participants with both valid T 1-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. Results: The brain age estimated by LightGBM achieved an appreciable performance (r = 0.81, mean absolute error [MAE] = 3.65), which was further improved by age bias correction (r = 0.90, MAE = 3.03). We found that LPA (F = 2.47, P = 0.04), MPA (F = 6.49, P < 1 × 10-300), VPA (F = 4.92, P = 2.58 × 10-5), and MVPA (F = 6.45, P < 1 × 10-300) 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. Conclusions: 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.

加速度计测量的身体活动和神经成像驱动的大脑年龄。
背景:一种被称为脑年龄的神经成像衍生生物标志物被认为可以捕捉大脑衰老过程的程度和多样性,作为整体大脑健康的有力指标。不同水平的体育活动(PA)强度对脑年龄的影响仍未完全了解。本研究旨在探讨加速度计测量的PA与脑年龄之间的关系。方法:共纳入16,972名符合条件的参与者,他们具有有效的t1加权神经成像和来自UK Biobank的加速度计数据。脑年龄是使用一种称为光梯度增强机(LightGBM)的集成学习方法来估计的。最初选择了1400多个图像衍生表型(IDPs)进行数据驱动的特征选择,以预测大脑年龄。脑年龄差距(BAG)是衡量大脑加速老化的一个指标,可以通过从估计的脑年龄减去实际年龄得出。BAG阳性表明大脑老化加速。在7天的时间内,使用腕带加速度计测量PA,并提取光强度PA (LPA)、中强度PA (MPA)、强强度PA (VPA)和中强至强强度PA (MVPA)的时间。在调整潜在混杂因素后,应用广义加性模型检验了PA和BAG之间的非线性关联。结果:LightGBM估计的脑年龄取得了较好的效果(r = 0.81,平均绝对误差[MAE] = 3.65),年龄偏差校正进一步改善了这一效果(r = 0.90, MAE = 3.03)。我们发现LPA (F = 2.47, P = 0.04)、MPA (F = 6.49, P < 1 × 10-300)、VPA (F = 4.92, P = 2.58 × 10-5)和MVPA (F = 6.45, P < 1 × 10-300)与BAG呈近似u型关系,表明PA水平不足和过高都会对脑衰老产生不利影响。此外,BAG在PA与认知功能和脑相关疾病的关系中起到部分中介作用。结论:我们的研究揭示了加速度计测量的PA和BAG之间的u型关联,强调了通过参与适度的客观测量的PA,无论强度如何,都可以实现高级大脑健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
3.70
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