轻度认知障碍患者d -氨基酸谱与认知功能之间关系的研究:机器学习方法。

IF 4.5 2区 医学 Q1 CLINICAL NEUROLOGY
Sou Sugiki, Shigeki Tsuchiya, Ren Kimura, Shun Katada, Koichi Misawa, Hisashi Tsujimura, Masanobu Hibi
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引用次数: 0

摘要

背景:痴呆症的全球患病率正在显著增加。早期发现和预防策略,特别是轻度认知障碍(MCI),是至关重要的,但目前由于缺乏既定的生物标志物而受到阻碍。在这里,我们的目标是通过使用非线性机器学习算法将外周血样本的d -氨基酸谱与非侵入性受试者信息相结合,开发一种高精度的MCI筛选方法。方法:采用横断面研究方法,选取年龄50 ~ 89岁的200例受试者,根据Mini Mental State Examination评分分为认知正常组和疑似mci组。采用高通量技术分析外周血中d -氨基酸谱,特别是d -丙氨酸(%)和d -脯氨酸(%)。对静脉和指尖血d -氨基酸水平进行相关性分析。比较了各种机器学习模型的预测性能,包括逻辑回归、随机森林(RF)、核支持向量机(SVM)和人工神经网络(ANN)。结果:d -氨基酸谱与受试者信息相结合的非线性模型(核支持向量机和神经网络)曲线下面积最大,分别为0.78和0.79,表明d -氨基酸谱与非侵入性受试者信息相结合是检测MCI的有效方法。结论:利用非线性机器学习模型,特别是核支持向量机和神经网络,将d -氨基酸谱与非侵入性受试者信息相结合,有望成为MCI的高精度筛选工具。这种方法可以作为更具侵入性和昂贵的诊断测试之前的一种具有成本效益的初步筛查方法,并显著有助于早期发现和制定痴呆症的干预策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Examination of the relationship between D-amino acid profiles and cognitive function in individuals with mild cognitive impairment: A machine learning approach.

Background: The global prevalence of dementia is significantly increasing. Early detection and prevention strategies, particularly for mild cognitive impairment (MCI), are crucial but currently hindered by the lack of established biomarkers. Here, we aimed to develop a high-precision screening method for MCI by combining D-amino acid profiles from peripheral blood samples with non-invasive subject information using nonlinear machine learning algorithms.

Methods: A cross-sectional study was conducted with 200 participants aged 50-89 years, classified into cognitively normal and MCI-suspected groups based on Mini Mental State Examination scores. High-throughput techniques were used to analyze the D-amino acid profiles, specifically D-alanine (%) and D-proline (%), in peripheral blood. Correlation analysis was performed between D-amino acid levels in venous and fingertip blood. The predictive performance of various machine learning models, including Logistic Regression, Random Forest (RF), kernel Support Vector Machine (SVM), and Artificial Neural Network (ANN), was compared.

Results: Nonlinear models (kernel SVM and ANN) that combined D-amino acid profiles with subject information achieved the highest area under the curve values of 0.78 and 0.79, respectively, demonstrating that the combination of D-amino acid profiles and non-invasive subject information is effective in detecting MCI.

Conclusion: Combining D-amino acid profiles with non-invasive subject information using nonlinear machine learning models, particularly kernel SVM and ANN, shows promise as a high-precision screening tool for MCI. This approach could serve as a cost-effective preliminary screening method before more invasive and expensive diagnostic tests and significantly contribute to the early detection and development of intervention strategies for dementia.

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来源期刊
CiteScore
8.40
自引率
2.10%
发文量
230
审稿时长
4-8 weeks
期刊介绍: The central focus of the journal is on research that advances understanding of existing and new neuropsychopharmacological agents including their mode of action and clinical application or provides insights into the biological basis of psychiatric disorders and thereby advances their pharmacological treatment. Such research may derive from the full spectrum of biological and psychological fields of inquiry encompassing classical and novel techniques in neuropsychopharmacology as well as strategies such as neuroimaging, genetics, psychoneuroendocrinology and neuropsychology.
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