Karako Kenji, Hata Takeo, Inoue Atsushi, Oyama Katsunori, Ueda Eiichiro, Sakatani Kaoru
Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan.
Department of Hospital Quality and Safety Management, Osaka Medical and Pharmaceutical University Hospital, Osaka, Japan.
Front Neurol. 2024 Jul 24;15:1362560. doi: 10.3389/fneur.2024.1362560. eCollection 2024.
In this study, we investigated the correlation between serum albumin levels and cognitive function, and examined the impact of including serum albumin values in the input layer on the prediction accuracy when forecasting cognitive function using deep learning and other machine learning models.
We analyzed the electronic health record data from Osaka Medical and Pharmaceutical University Hospital between 2014 and 2021. The study included patients who underwent cognitive function tests during this period; however, patients from whom blood test data was not obtained up to 30 days before the cognitive function tests and those with values due to measurement error in blood test results were excluded. The Mini-Mental State Examination (MMSE) was used as the cognitive function test, and albumin levels were examined as the explanatory variable. Furthermore, we estimated MMSE scores from blood test data using deep learning models (DLM), linear regression models, support vector machines (SVM), decision trees, random forests, extreme gradient boosting (XGBoost), and light gradient boosting machines (LightGBM).
Out of 5,017 patients who underwent cognitive function tests, 3,663 patients from whom blood test data had not been obtained recently and two patients with values due to measurement error were excluded. The final study population included 1,352 patients, with 114 patients (8.4%) aged below 65 and 1,238 patients (91.6%) aged 65 and above. In patients aged 65 and above, the age and male sex showed significant associations with MMSE scores of less than 24, while albumin and potassium levels showed negative associations with MMSE scores of less than 24. Comparing MMSE estimation performance, in those aged below 65, the mean squared error (MSE) of DLM was improved with the inclusion of albumin. Similarly, the MSE improved when using SVM, random forest and XGBoost. In those aged 65 and above, the MSE improved in all models.
Our study results indicated a positive correlation between serum albumin levels and cognitive function, suggesting a positive correlation between nutritional status and cognitive function in the elderly. Serum albumin levels were shown to be an important explanatory variable in the estimation of cognitive function for individuals aged 65 and above.
在本研究中，我们调查了血清白蛋白水平与认知功能之间的相关性，并研究了在使用深度学习和其他机器学习模型预测认知功能时，将血清白蛋白值纳入输入层对预测准确性的影响。
我们分析了大阪医科药科大学医院2014年至2021年的电子健康记录数据。该研究纳入了在此期间接受认知功能测试的患者；然而，排除了在认知功能测试前30天内未获得血液检测数据的患者以及因血液检测结果测量误差而有异常值的患者。简易精神状态检查表（MMSE）用作认知功能测试，白蛋白水平作为解释变量进行检测。此外，我们使用深度学习模型（DLM）、线性回归模型、支持向量机（SVM）、决策树、随机森林、极端梯度提升（XGBoost）和轻梯度提升机（LightGBM）从血液检测数据中估计MMSE分数。
在5017名接受认知功能测试的患者中，排除了3663名近期未获得血液检测数据的患者和2名因测量误差而有异常值的患者。最终研究人群包括1352名患者，其中114名患者（8.4%）年龄在65岁以下，1238名患者（91.6%）年龄在65岁及以上。在65岁及以上的患者中，年龄和男性与MMSE分数低于24显著相关，而白蛋白和钾水平与MMSE分数低于24呈负相关。比较MMSE估计性能，在65岁以下的人群中，纳入白蛋白后DLM的均方误差（MSE）有所改善。同样，使用SVM、随机森林和XGBoost时MSE也有所改善。在65岁及以上的人群中，所有模型的MSE均有所改善。
我们的研究结果表明血清白蛋白水平与认知功能呈正相关，提示老年人营养状况与认知功能之间存在正相关。血清白蛋白水平被证明是65岁及以上个体认知功能估计中的一个重要解释变量。
