Schalkamp Ann-Kathrin, Lerche Stefanie, Wurster Isabel, Roeben Benjamin, Zimmermann Milan, Fries Franca, von Thaler Anna-Katharina, Eschweiler Gerhard, Maetzler Walter, Berg Daniela, Sinz Fabian H, Brockmann Kathrin
Department of Psychological Medicine and Clinical Neuroscience, UK Dementia Research Institute, Cardiff University, Cardiff, United Kingdom.
Department of Neurodegeneration and Hertie-Institute for Clinical Brain Research, Center of Neurology, University of Tübingen, Tübingen, Germany.
Front Aging Neurosci. 2024 Oct 15;16:1447944. doi: 10.3389/fnagi.2024.1447944. eCollection 2024.
With age, sensory, cognitive, and motor abilities decline, and the risk for neurodegenerative disorders increases. These impairments influence the quality of life and increase the need for care, thus putting a high burden on society, the economy, and the healthcare system. Therefore, it is important to identify factors that influence healthy aging, particularly ones that are potentially modifiable through lifestyle choices. However, large-scale studies investigating the influence of multi-modal factors on a global description of healthy aging measured by multiple clinical assessments are sparse.
We propose a machine learning model that simultaneously predicts multiple cognitive and motor outcome measurements on a personalized level recorded from one learned composite score. This personalized composite score is derived from a large set of multi-modal components from the TREND cohort, including genetic, biofluid, clinical, demographic, and lifestyle factors.
We found that a model based on a single composite score was able to predict cognitive and motor abilities almost as well as a classical flexible regression model specifically trained for each single clinical score. In contrast to the flexible regression model, our composite score model is able to identify factors that globally influence cognitive and motoric abilities as measured by multiple clinical scores. The model identified several risk and protective factors for healthy aging and recovered physical exercise as a major, modifiable, protective factor.
We conclude that our low parametric modeling approach successfully recovered known risk and protective factors of healthy aging on a personalized level while providing an interpretable composite score. We suggest validating this modeling approach in other cohorts.
随着年龄增长，感觉、认知和运动能力会下降，神经退行性疾病的风险也会增加。这些损伤会影响生活质量，增加护理需求，从而给社会、经济和医疗保健系统带来沉重负担。因此，识别影响健康老龄化的因素非常重要，尤其是那些可以通过生活方式选择进行潜在改变的因素。然而，通过多项临床评估对多模式因素对健康老龄化的全球描述的影响进行调查的大规模研究却很少。
我们提出了一种机器学习模型，该模型可以根据一个学习到的综合评分，在个性化层面上同时预测多项认知和运动结果测量值。这个个性化的综合评分来自于TREND队列的大量多模式成分，包括基因、生物流体、临床、人口统计学和生活方式因素。
我们发现，基于单一综合评分的模型能够预测认知和运动能力，几乎与专门为每个单一临床评分训练的经典灵活回归模型一样好。与灵活回归模型不同，我们的综合评分模型能够识别通过多项临床评分衡量的对认知和运动能力有全球影响的因素。该模型确定了健康老龄化的几个风险和保护因素，并恢复了体育锻炼作为一个主要的、可改变的保护因素。
我们得出结论，我们的低参数建模方法在个性化层面上成功地恢复了健康老龄化的已知风险和保护因素，同时提供了一个可解释的综合评分。我们建议在其他队列中验证这种建模方法。
