Gomes Karina Braga, Pereira Ramon Gonçalves, Braga Alexandre Alberto, Guimarães Henrique Cerqueira, Resende Elisa de Paula França, Teixeira Antônio Lúcio, Barbosa Maira Tonidandel, Junior Wagner Meira, Carvalho Maria das Graças, Caramelli Paulo
Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil.
Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, MG, Brazil.
Brain Sci. 2023 Apr 20;13(4):690. doi: 10.3390/brainsci13040690.
Cognitive and functional decline are common problems in older adults, especially in those 75+ years old. Currently, there is no specific plasma biomarker able to predict this decline in healthy old-age people. Machine learning (ML) is a subarea of artificial intelligence (AI), which can be used to predict outcomes Aim: This study aimed to evaluate routine laboratory variables able to predict cognitive and functional impairment, using ML algorithms, in a cohort aged 75+ years, in a one-year follow-up study.
One hundred and thirty-two older adults aged 75+ years were selected through a community-health public program or from long-term-care institutions. Their functional and cognitive performances were evaluated at baseline and one year later using a functional activities questionnaire, Mini-Mental State Examination, and the Brief Cognitive Screening Battery. Routine laboratory tests were performed at baseline. ML algorithms-random forest, support vector machine (SVM), and XGBoost-were applied in order to describe the best model able to predict cognitive and functional decline using routine tests as features.
The random forest model showed better accuracy than other algorithms and included triglycerides, glucose, hematocrit, red cell distribution width (RDW), albumin, hemoglobin, globulin, high-density lipoprotein cholesterol (HDL-c), thyroid-stimulating hormone (TSH), creatinine, lymphocyte, erythrocyte, platelet/leucocyte (PLR), and neutrophil/leucocyte (NLR) ratios, and alanine transaminase (ALT), leukocyte, low-density lipoprotein cholesterol (LDL-c), cortisol, gamma-glutamyl transferase (GGT), and eosinophil as features to predict cognitive decline (accuracy = 0.79). For functional decline, the most important features were platelet, PLR and NLR, hemoglobin, globulin, cortisol, RDW, glucose, basophil, B12 vitamin, creatinine, GGT, ALT, aspartate transferase (AST), eosinophil, hematocrit, erythrocyte, triglycerides, HDL-c, and monocyte (accuracy = 0.92).
Routine laboratory variables could be applied to predict cognitive and functional decline in oldest-old populations using ML algorithms.
认知和功能衰退是老年人常见的问题，尤其是75岁及以上的老年人。目前，尚无特定的血浆生物标志物能够预测健康老年人的这种衰退。机器学习（ML）是人工智能（AI）的一个子领域，可用于预测结果。目的：本研究旨在通过机器学习算法，在一项为期一年的随访研究中，评估能够预测75岁及以上队列中认知和功能障碍的常规实验室变量。
通过社区健康公共项目或从长期护理机构中选取132名75岁及以上的老年人。在基线和一年后，使用功能活动问卷、简易精神状态检查表和简易认知筛查量表对他们的功能和认知表现进行评估。在基线时进行常规实验室检查。应用机器学习算法——随机森林、支持向量机（SVM）和XGBoost——以描述使用常规检查作为特征能够预测认知和功能衰退的最佳模型。
随机森林模型显示出比其他算法更高的准确性，其预测认知衰退的特征包括甘油三酯、葡萄糖、血细胞比容、红细胞分布宽度（RDW）、白蛋白、血红蛋白、球蛋白、高密度脂蛋白胆固醇（HDL-c）、促甲状腺激素（TSH）、肌酐、淋巴细胞、红细胞、血小板/白细胞（PLR）和中性粒细胞/白细胞（NLR）比值，以及丙氨酸转氨酶（ALT）、白细胞、低密度脂蛋白胆固醇（LDL-c）、皮质醇、γ-谷氨酰转移酶（GGT）和嗜酸性粒细胞（准确率=0.79）。对于功能衰退，最重要的特征是血小板、PLR和NLR、血红蛋白、球蛋白、皮质醇、RDW、葡萄糖、嗜碱性粒细胞、维生素B12、肌酐、GGT、ALT、天冬氨酸转氨酶（AST）、嗜酸性粒细胞、血细胞比容、红细胞、甘油三酯、HDL-c和单核细胞（准确率=0.92）。
常规实验室变量可用于通过机器学习算法预测高龄人群的认知和功能衰退。
