Deatsch Alison, McKenna Michael, Palumbo Jonathan, Tian Qu, Simonsick Eleanor, Ferrucci Luigi, Jeraj Robert, Spencer Richard G
Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America.
Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America.
PLoS One. 2025 Jun 17;20(6):e0325172. doi: 10.1371/journal.pone.0325172. eCollection 2025.
Identification of accelerated aging and its biomarkers can lead to more timely therapeutic interventions and decision-making. Therefore, we sought to predict aging-related slow gait, a known predictor of accelerated aging, and its determinants.
We applied a deep learning neural network (NN) and compared it to conventional logistic regression (LR) analysis. We incorporated 1,363 participants from the Baltimore Longitudinal Study of Aging to predict current and future slow gait at 6-year and 10-year follow-up using two clinically-relevant cut-points.
Our NN achieved a maximum sensitivity (specificity) of 81.2% (87.9%), for a 10-year prediction with 0.8 m/s cut-point. We demonstrated the necessity of class balancing and found the NN to perform comparably to or in some cases, better than, LR which achieved a maximum sensitivity and specificity of 84.5% and 86.3%, respectively. Sobol index analysis identified the strongest determinants to be age, BMI, sleep, and grip strength.
The novel use of a NN for this purpose, and successful benchmarking against conventional techniques, justifies further exploration and expansion of this model.
识别加速衰老及其生物标志物可实现更及时的治疗干预和决策。因此，我们试图预测与衰老相关的步态缓慢（一种已知的加速衰老预测指标）及其决定因素。
我们应用了深度学习神经网络（NN），并将其与传统逻辑回归（LR）分析进行比较。我们纳入了巴尔的摩衰老纵向研究中的1363名参与者，使用两个临床相关切点预测6年和10年随访时当前和未来的步态缓慢情况。
我们的神经网络在以0.8米/秒为切点进行10年预测时，最大灵敏度（特异性）达到81.2%（87.9%）。我们证明了类别平衡的必要性，并发现神经网络的表现与逻辑回归相当，在某些情况下甚至更好，逻辑回归的最大灵敏度和特异性分别为84.5%和86.3%。索博尔指数分析确定最强的决定因素为年龄、体重指数、睡眠和握力。
将神经网络用于此目的的新方法，以及与传统技术的成功对比，证明了对该模型进行进一步探索和扩展的合理性。
