Institute of Gerontology, The University of Tokyo, Tokyo, Japan.
Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan.
Adv Exp Med Biol. 2024;1463:263-269. doi: 10.1007/978-3-031-67458-7_44.
Recent research has linked systemic metabolic disorders to cognitive decline and dementia risk, including Alzheimer's. This is suspected to be due to lifestyle-related vascular impairments from atherosclerosis and other factors, such as malnutrition and anaemia. Applying deep learning using 2897 cases from a rehabilitation hospital and health screenings, we trained a model to predict cognitive function [mini-mental state examination (MMSE) scores] and brain atrophy [Brain Healthcare Quotient (BHQ) scores] from basic blood tests and age. The deep learning model accurately estimated MMSE and BHQ from these inputs, with age, nutritional information, and organ function indicators being top predictors. These findings highlight the relationship of dementia with systemic metabolic disorders and suggest the potential of using routine blood tests for dementia risk assessment. Furthermore, personalised dietary interventions could be tailored based on blood test anomalies. This holistic view mirrors traditional Chinese medicine, which considers brain disorders systemic, that is related to vital organs but not the brain itself.
最近的研究将系统性代谢紊乱与认知能力下降和痴呆风险（包括阿尔茨海默病）联系起来。这被怀疑是由于动脉粥样硬化和其他因素（如营养不良和贫血）引起的与生活方式相关的血管损伤所致。我们应用深度学习，使用康复医院和健康筛查的 2897 例病例，训练了一个模型，以从基本血液检查和年龄预测认知功能[迷你精神状态检查（MMSE）评分]和脑萎缩[脑保健商数（BHQ）评分]。该深度学习模型能够准确地从这些输入中估算 MMSE 和 BHQ，年龄、营养信息和器官功能指标是最重要的预测因素。这些发现强调了痴呆与系统性代谢紊乱的关系，并表明可以使用常规血液检查来评估痴呆风险。此外，还可以根据血液检查异常情况来制定个性化的饮食干预措施。这种整体观点反映了中医的理念，中医认为脑障碍是全身性的，与重要器官有关，但与大脑本身无关。
