Ly Maria, Yu Gary, Son Sang Joon, Pascoal Tharick, Karim Helmet T
Department of Internal Medicine, Allegheny General Hospital, Pittsburgh, PA, United States.
Department of Psychiatry, Ajou University School of Medicine, Suwon, Republic of Korea.
Front Aging Neurosci. 2024 Oct 22;16:1433426. doi: 10.3389/fnagi.2024.1433426. eCollection 2024.
Brain age is a machine learning-derived estimate that captures lower brain volume. Previous studies have found that brain age is significantly higher in mild cognitive impairment and Alzheimer's disease (AD) compared to healthy controls. Few studies have investigated changes in brain age longitudinally in MCI and AD. We hypothesized that individuals with MCI and AD would show heightened brain age over time and across the lifespan. We also hypothesized that both MCI and AD would show faster rates of brain aging (higher slopes) over time compared to healthy controls.
We utilized data from an archival dataset, mainly Alzheimer's disease Neuroimaging Initiative (ADNI) 1 with 3Tesla (3 T) data which totaled 677 scans from 183 participants. This constitutes a secondary data analysis on existing data. We included control participants (healthy controls or HC), individuals with MCI, and individuals with AD. We predicted brain age using a pre-trained model and tested for accuracy. We investigated cross-sectional differences in brain age by group [healthy controls or HC, mild cognitive impairment (MCI), and AD]. We conducted longitudinal modeling of age and brain age by group using time from baseline in one model and chronological age in another model.
We predicted brain age with a mean absolute error (MAE) < 5 years. Brain age was associated with age across the study and individuals with MCI and AD had greater brain age on average. We found that the MCI group had significantly higher rates of change in brain age over time compared to the HC group regardless of individual chronologic age, while the AD group did not differ in rate of brain age change.
We replicated past studies that showed that MCI and AD had greater brain age than HC. We additionally found that this was true over time, both groups showed higher brain age longitudinally. Contrary to our hypothesis, we found that the MCI, but not the AD group, showed faster rates of brain aging. We essentially found that while the MCI group was actively experiencing faster rates of brain aging, the AD group may have already experienced this acceleration (as they show higher brain age). Individuals with MCI may experience higher rates of brain aging than AD and controls. AD may represent a homeostatic endpoint after significant neurodegeneration. Future work may focus on individuals with MCI as one potential therapeutic option is to alter rates of brain aging, which ultimately may slow cognitive decline in the long-term.
脑龄是一种通过机器学习得出的估计值，反映了较低的脑容量。先前的研究发现，与健康对照相比，轻度认知障碍和阿尔茨海默病（AD）患者的脑龄显著更高。很少有研究纵向调查MCI和AD患者脑龄的变化。我们假设，MCI和AD患者在整个生命周期中脑龄会随着时间的推移而增加。我们还假设，与健康对照相比，MCI和AD患者随着时间的推移脑老化速度会更快（斜率更高）。
我们利用了一个存档数据集的数据，主要是阿尔茨海默病神经影像学倡议（ADNI）1中3特斯拉（3T）的数据，共有来自183名参与者的677次扫描。这是对现有数据的二次数据分析。我们纳入了对照参与者（健康对照或HC）、MCI患者和AD患者。我们使用预训练模型预测脑龄并测试其准确性。我们按组[健康对照或HC、轻度认知障碍（MCI）和AD]调查脑龄的横断面差异。我们通过组对年龄和脑龄进行纵向建模，在一个模型中使用从基线开始的时间，在另一个模型中使用实际年龄。
我们预测脑龄的平均绝对误差（MAE）<5岁。在整个研究中，脑龄与年龄相关，MCI和AD患者的平均脑龄更大。我们发现，无论个体实际年龄如何，与HC组相比，MCI组脑龄随时间的变化率显著更高，而AD组脑龄变化率无差异。
我们重复了过去的研究，即MCI和AD患者的脑龄高于HC患者。我们还发现，随着时间的推移情况确实如此，两组纵向脑龄均更高。与我们的假设相反，我们发现MCI组而非AD组脑老化速度更快。我们基本上发现，虽然MCI组正在积极经历更快的脑老化速度，但AD组可能已经经历了这种加速（因为他们的脑龄更高）。MCI患者可能比AD患者和对照经历更高的脑老化速度。AD可能代表了显著神经退行性变后的稳态终点。未来的工作可能聚焦于MCI患者，因为一种潜在的治疗选择是改变脑老化速度，这最终可能长期减缓认知衰退。
