Demographic data of the study population.

Inter-individual cognitive variability, influenced by genetic and environmental factors, is crucial for understanding typical cognition and identifying early cognitive disorders. This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. We included 199 subjects, aged 20 to 70, and measured cognitive performance with the Wisconsin Card Sorting Test. Differentially expressed proteins (DEPs) were identified using label-free proteomics and analyzed with the Linear Model for Microarray Data. We discovered 213 DEPs between lower and higher cognition groups, with 155 upregulated in the lower cognition group and enriched in the IL-17 signaling pathway. Subsequent bioinformatic analysis linked these DEPs to neuroinflammation-related cognitive impairment. A random forest model classified cognitive ability groups with an accuracy of 81.5%, sensitivity of 65%, specificity of 85.9%, and an AUC of 0.79. By targeting a specific Thai cohort, this research provides novel insights into the link between neuroinflammation and cognitive performance, advancing our understanding of cognitive variability, highlighting the role of biological markers in cognitive function, and contributing to developing more accurate machine learning models for diverse populations.

受遗传与环境因素共同影响的个体间认知变异，对于理解典型认知特征及早期识别认知障碍具有关键意义。本研究针对健康泰国人群队列，采用机器学习算法探究了血清蛋白质表达谱与认知变异之间的关联。本研究纳入199名年龄介于20至70岁之间的受试者，采用威斯康星卡片分类测验（Wisconsin Card Sorting Test）评估其认知表现。通过无标记蛋白质组学（label-free proteomics）鉴定差异表达蛋白质（differentially expressed proteins, DEPs），并采用微阵列数据线性模型（Linear Model for Microarray Data）进行分析。本研究在高、低认知能力组间共鉴定出213个差异表达蛋白质，其中155个在低认知能力组中呈上调表达，且富集于IL-17信号通路。后续生物信息学分析将上述差异表达蛋白质与神经炎症相关认知损害建立了关联。随机森林（random forest）模型对认知能力组别的分类准确率达81.5%，灵敏度为65%，特异性为85.9%，受试者工作特征曲线下面积（Area Under Curve, AUC）为0.79。本研究聚焦特定泰国人群队列，为神经炎症与认知表现之间的关联提供了全新见解，加深了我们对认知变异的理解，凸显了生物标志物在认知功能中的作用，并为面向不同人群的高精度机器学习认知分类模型开发提供了重要参考。