Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population - A machine learning approach

Aug 6, 2026

Inter-individual cognitive variability, shaped by genetic and environmental influences, is essential for understanding typical cognition and identifying early cognitive disorders. This study examines how serum protein expression profiles relate to cognitive variability in a healthy Thai cohort. Using machine learning, 199 participants aged 20–70 were assessed with the Wisconsin Card Sorting Test and label-free proteomics, followed by bioinformatic pathway and classification analyses.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages18

Reading Time45 min

AccessFree to download

Abstract

Study design and cohort

Proteomics and differential analysis

Bioinformatic interpretation

Machine learning classification

Introduction

Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach CHEN, Chen, KHANTHIYONG, Bupachad, THAWEETEE-SUKJAI, Benjamard, CHAROENLAPPANIT, Sawanya, ROYTRAKUL, Sittiruk, SURIT, Phrutthinun, PHOUNGPETCHARA, Ittipon, THANOI, Samur, REYNOLDS, Gavin < [http://orcid.org/0000-0001-9026-7726](http://orcid.org/0000-0001-9026-7726)> and NUDMAMUD-THANOI, Sutisa Available from Sheffield Hallam University Research Archive (SHURA) at: [https://shura.shu.ac.uk/34925/](https://shura.shu.ac.uk/34925/) This document is the Published Version [VoR] Citation: CHEN, Chen, KHANTHIYONG, Bupachad, THAWEETEE-SUKJAI, Benjamard, CHAROENLAPPANIT, Sawanya, ROYTRAKUL, Sittiruk, SURIT, Phrutthinun, PHOUNGPETCHARA, Ittipon, THANOI, Samur, REYNOLDS, Gavin and NUDMAMUD-THANOI, Sutisa (2025) . Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach. PLOS ONE, 20 (2) . [Article] Copyright and re-use policy See [http://shura.shu.ac.uk/information.html](http://shura.shu.ac.uk/information.html) Sheffield Hallam University Research Archive [http://shura.shu.ac.uk](http://shura.shu.ac.uk) OPEN ACCESS Citation: Chen C, Khanthiyong B, ThaweeteeSukjai B, Charoenlappanit S, Roytrakul S, Surit P, et al. (2025) Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach. PLoSONE 20(2): e0313365 . [https://doi.org/10.1371/](https://doi.org/10.1371/)[ ](https://doi.org/10.1371/)[journal.pone.0313365](journal.pone.0313365) Editor: Nafisa M. Jadavji, Southern Illinois University Carbondale, UNITED STATES MINOR OUTLYING ISLANDS Received: October 23, 2024 Accepted: January 21, 2025 Published: February 20, 2025 Peer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here: [https://doi.org/10.1371/journal](https://doi.org/10.1371/journal). pone.0313365 Copyright: © 2025 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, RESEARCH ARTICLE Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach Chen Chen1*, Bupachad Khanthiyong2, Benjamard Thaweetee-Sukjai3, Sawanya Charoenlappanit4, Sittiruk Roytrakul4, Phrutthinun Surit5, Ittipon Phoungpetchara6,7, SamurThanoi8, Gavin P. Reynolds9,7, Sutisa Nudmamud-Thanoi6,7* 1 Faculty of Medical Science, Medical Science graduate program, Naresuan University, Phitsanulok, Thailand, 2 Faculty of Medicine, Bangkokthonburi University, Bangkok, Thailand, 3 School of Medicine, Mae Fah Luang University, Chiang Rai, Thailand, 4 National Centre for Genetic Engineering and Biotechnology, National Science and Technology Development Agency, Pathum Thani, Thailand, 5 Department of Biochemistry, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand, 6 Department of Anatomy, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand, 7 Centre of Excellence in Medical Biotechnology, Faculty of Medical Science, Naresuan University, Phitsanulok, Thailand, 8 School of Medical Sciences, University of Phayao, Phayao, Thailand, 9 Biomolecular Sciences Research Centre, Sheffield Hallam University, Sheffield, United Kingdom * [wzhnky@hotmail.com](wzhnky@hotmail.com) (CC); [sutisat@nu.ac.th](sutisat@nu.ac.th) (SN-T) Abstract Inter-individual cognitive variability, influenced by genetic and environmental factors, is crucial for understanding typical cognition and identifying early co

FAQs

What cognitive measure and test were used in the study?

Cognitive variability was assessed using the Wisconsin Card Sorting Test. Participants were grouped based on performance levels to compare cognitive ability.

How were serum protein differences identified between cognition groups?

Serum proteins were profiled using label-free proteomics. Differentially expressed proteins were identified between lower and higher cognition groups using statistical analysis.

What machine learning result was reported for cognitive classification?

A random forest model classified cognitive ability groups with 81.5% accuracy, 65% sensitivity, 85.9% specificity, and an AUC of 0.79.

Aug 6, 2026

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