Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Bunkyo City, Tokyo, Japan.
Department of Hospital Quality and Safety Management, Osaka Medical and Pharmaceutical University Hospital, Takatsuki, Japan.
Adv Exp Med Biol. 2024;1463:251-255. doi: 10.1007/978-3-031-67458-7_42.
Recently, a method using deep learning has been developed to estimate the risk of developing dementia. This method uses general blood test data from routine health examinations that reveal lifestyle-related diseases, which can lead to vascular cognitive impairment via arteriosclerosis, as well as systemic metabolic disorders that are unrelated to lifestyle, such as nutritional disorders. In this study, we investigated the differences in the accuracy of estimating the risk of dementia based on the presence or the absence of blood test parameters reflecting nutritional disorders while focusing on the association between malnutrition and the risk of dementia in frail, elderly individuals.
The objective of this study was to evaluate the impact of including or excluding serum albumin, which reflects nutritional status, on the accuracy of predicting cognitive function in older adults using blood test data.
We estimated cognitive function, as measured by the Mini-Mental State Examination (MMSE), using the deep learning model (DLM). The estimation was performed based on general blood test data, including complete blood tests and basic metabolic panels, obtained from a selection of 1287 patients admitted to Osaka Medical and Pharmaceutical University Hospital. The data were divided into two groups: individuals aged 65 and above and those aged below 65. The impact of including or excluding serum albumin on the predictive performance of MMSE was examined within each group.
In those aged below 65, the mean squared error (MSE) of the DLM was 5.33 without albumin and 4.62 with albumin, showing a -0.71 improvement with albumin. In those aged 65 and above, the MSE of the DLM was 6.38 without albumin and 6.28 with albumin, showing a -0.1 improvement with albumin.
The present study demonstrated that including serum albumin in the input data resulted in lower estimation errors for MMSE across all applied algorithms in the group aged 65 and above. This is consistent with previously reported studies that have shown the adverse effects of malnutrition on cognitive function in older adults.
This study highlighted the significance of serum albumin, which reflects nutritional status, as an important assessment variable for estimating MMSE from blood test data, particularly in individuals aged 65 and above.
最近，一种使用深度学习的方法已经被开发出来，用于估计痴呆症的发病风险。这种方法使用来自常规健康检查的一般血液测试数据，这些数据揭示了与生活方式相关的疾病，这些疾病可以通过动脉硬化导致血管性认知障碍，以及与生活方式无关的全身代谢紊乱，如营养紊乱。在这项研究中，我们研究了在考虑营养不良与虚弱老年人痴呆风险之间的关联的同时，基于是否存在反映营养紊乱的血液测试参数，估计痴呆风险的准确性的差异。
本研究旨在评估在使用血液测试数据预测老年人认知功能时，包含或排除反映营养状况的血清白蛋白对预测准确性的影响。
我们使用深度学习模型（DLM）估计了认知功能，用简易精神状态检查（MMSE）来衡量。该估计是基于从大阪医科药科大学医院收治的 1287 名患者中选择的一般血液测试数据进行的，包括全血细胞计数和基本代谢组。数据被分为两组：65 岁及以上和 65 岁以下。在每组中，检查了包含或排除血清白蛋白对 MMSE 预测性能的影响。
在 65 岁以下的人群中，没有白蛋白的 DLM 的均方误差（MSE）为 5.33，有白蛋白的为 4.62，有 0.71 的改善。在 65 岁及以上的人群中，没有白蛋白的 DLM 的 MSE 为 6.38，有白蛋白的为 6.28，有 0.1 的改善。
本研究表明，在所有应用于 65 岁及以上人群的算法中，将血清白蛋白纳入输入数据会导致 MMSE 的估计误差降低。这与先前的研究一致，这些研究表明，营养不良对老年人的认知功能有不利影响。
本研究强调了血清白蛋白（反映营养状况）作为从血液测试数据估计 MMSE 的重要评估变量的重要性，特别是在 65 岁及以上的人群中。
