Chen Chen, Khanthiyong Bupachad, Thaweetee-Sukjai Benjamard, Charoenlappanit Sawanya, Roytrakul Sittiruk, Surit Phrutthinun, Phoungpetchara Ittipon, Thanoi Samur, Reynolds Gavin P, Nudmamud-Thanoi Sutisa
Faculty of Medical Science, Medical Science graduate program, Naresuan University, Phitsanulok, Thailand.
Faculty of Medicine, Bangkokthonburi University, Bangkok, Thailand.
PLoS One. 2025 Feb 20;20(2):e0313365. doi: 10.1371/journal.pone.0313365. eCollection 2025.
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.
受遗传和环境因素影响的个体间认知变异性，对于理解典型认知和识别早期认知障碍至关重要。本研究使用机器学习算法，调查了泰国健康人群血清蛋白表达谱与认知变异性之间的关联。我们纳入了199名年龄在20至70岁之间的受试者，并使用威斯康星卡片分类测试测量认知表现。使用无标记蛋白质组学鉴定差异表达蛋白（DEPs），并采用微阵列数据线性模型进行分析。我们在低认知组和高认知组之间发现了213个DEPs，其中155个在低认知组中上调，并在IL-17信号通路中富集。随后的生物信息学分析将这些DEPs与神经炎症相关的认知障碍联系起来。一个随机森林模型对认知能力组进行分类，准确率为81.5%，灵敏度为65%，特异性为85.9%，曲线下面积为0.79。通过针对特定的泰国队列，本研究为神经炎症与认知表现之间的联系提供了新的见解，推进了我们对认知变异性的理解，突出了生物标志物在认知功能中的作用，并有助于为不同人群开发更准确的机器学习模型。
