Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation
Abstract Background The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue in terms of clinical utility for psychiatric disorders. In this study, our objective was to identify the methylation-based biomarkers for anxie...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-02-01
|
| Series: | Clinical Epigenetics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13148-025-01819-x |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849715666788024320 |
|---|---|
| author | Yoonsung Kwon Asta Blazyte Yeonsu Jeon Yeo Jin Kim Kyungwhan An Sungwon Jeon Hyojung Ryu Dong-Hyun Shin Jihye Ahn Hyojin Um Younghui Kang Hyebin Bak Byoung-Chul Kim Semin Lee Hyung-Tae Jung Eun-Seok Shin Jong Bhak |
| author_facet | Yoonsung Kwon Asta Blazyte Yeonsu Jeon Yeo Jin Kim Kyungwhan An Sungwon Jeon Hyojung Ryu Dong-Hyun Shin Jihye Ahn Hyojin Um Younghui Kang Hyebin Bak Byoung-Chul Kim Semin Lee Hyung-Tae Jung Eun-Seok Shin Jong Bhak |
| author_sort | Yoonsung Kwon |
| collection | DOAJ |
| description | Abstract Background The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue in terms of clinical utility for psychiatric disorders. In this study, our objective was to identify the methylation-based biomarkers for anxiety disorders and subsequently validate their reliability. Methods A comparative differential methylation analysis was performed on whole blood samples from 94 anxiety disorder patients and 296 control samples using targeted bisulfite sequencing. Subsequent validation of identified biomarkers employed an artificial intelligence-based risk prediction models: a linear calculation-based methylation risk score model and two tree-based machine learning models: Random Forest and XGBoost. Results Seventeen novel epigenetic methylation biomarkers were identified to be associated with anxiety disorders. These biomarkers were predominantly localized near CpG islands, and they were associated with two distinct biological processes: 1) cell apoptosis and mitochondrial dysfunction and 2) the regulation of neurosignaling. We further developed a robust diagnostic risk prediction system to classify anxiety disorders from healthy controls using the 17 biomarkers. Machine learning validation confirmed the robustness of our biomarker set, with XGBoost as the best-performing algorithm, an area under the curve of 0.876. Conclusion Our findings support the potential of blood liquid biopsy in enhancing the clinical utility of anxiety disorder diagnostics. This unique set of epigenetic biomarkers holds the potential for early diagnosis, prediction of treatment efficacy, continuous monitoring, health screening, and the delivery of personalized therapeutic interventions for individuals affected by anxiety disorders. |
| format | Article |
| id | doaj-art-da2b2ae97a924e98aed06178b89ce60b |
| institution | DOAJ |
| issn | 1868-7083 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Clinical Epigenetics |
| spelling | doaj-art-da2b2ae97a924e98aed06178b89ce60b2025-08-20T03:13:15ZengBMCClinical Epigenetics1868-70832025-02-0117111310.1186/s13148-025-01819-xIdentification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validationYoonsung Kwon0Asta Blazyte1Yeonsu Jeon2Yeo Jin Kim3Kyungwhan An4Sungwon Jeon5Hyojung Ryu6Dong-Hyun Shin7Jihye Ahn8Hyojin Um9Younghui Kang10Hyebin Bak11Byoung-Chul Kim12Semin Lee13Hyung-Tae Jung14Eun-Seok Shin15Jong Bhak16Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST)Korean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST)Clinomics IncClinomics IncKorean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST)Clinomics IncClinomics IncKorean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST)Clinomics IncClinomics IncClinomics IncClinomics IncClinomics IncKorean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST)Department of Psychiatry, Ulsan Medical CenterDepartment of Cardiology, Ulsan University Hospital, University of Ulsan College of MedicineKorean Genomics Center (KOGIC), Ulsan National Institute of Science and Technology (UNIST)Abstract Background The changes in DNA methylation patterns may reflect both physical and mental well-being, the latter being a relatively unexplored avenue in terms of clinical utility for psychiatric disorders. In this study, our objective was to identify the methylation-based biomarkers for anxiety disorders and subsequently validate their reliability. Methods A comparative differential methylation analysis was performed on whole blood samples from 94 anxiety disorder patients and 296 control samples using targeted bisulfite sequencing. Subsequent validation of identified biomarkers employed an artificial intelligence-based risk prediction models: a linear calculation-based methylation risk score model and two tree-based machine learning models: Random Forest and XGBoost. Results Seventeen novel epigenetic methylation biomarkers were identified to be associated with anxiety disorders. These biomarkers were predominantly localized near CpG islands, and they were associated with two distinct biological processes: 1) cell apoptosis and mitochondrial dysfunction and 2) the regulation of neurosignaling. We further developed a robust diagnostic risk prediction system to classify anxiety disorders from healthy controls using the 17 biomarkers. Machine learning validation confirmed the robustness of our biomarker set, with XGBoost as the best-performing algorithm, an area under the curve of 0.876. Conclusion Our findings support the potential of blood liquid biopsy in enhancing the clinical utility of anxiety disorder diagnostics. This unique set of epigenetic biomarkers holds the potential for early diagnosis, prediction of treatment efficacy, continuous monitoring, health screening, and the delivery of personalized therapeutic interventions for individuals affected by anxiety disorders.https://doi.org/10.1186/s13148-025-01819-xAnxiety disorderMethylation risk scoreMachine learningEpigenetic biomarkerLiquid biopsy |
| spellingShingle | Yoonsung Kwon Asta Blazyte Yeonsu Jeon Yeo Jin Kim Kyungwhan An Sungwon Jeon Hyojung Ryu Dong-Hyun Shin Jihye Ahn Hyojin Um Younghui Kang Hyebin Bak Byoung-Chul Kim Semin Lee Hyung-Tae Jung Eun-Seok Shin Jong Bhak Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation Clinical Epigenetics Anxiety disorder Methylation risk score Machine learning Epigenetic biomarker Liquid biopsy |
| title | Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation |
| title_full | Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation |
| title_fullStr | Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation |
| title_full_unstemmed | Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation |
| title_short | Identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning-based validation |
| title_sort | identification of 17 novel epigenetic biomarkers associated with anxiety disorders using differential methylation analysis followed by machine learning based validation |
| topic | Anxiety disorder Methylation risk score Machine learning Epigenetic biomarker Liquid biopsy |
| url | https://doi.org/10.1186/s13148-025-01819-x |
| work_keys_str_mv | AT yoonsungkwon identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT astablazyte identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT yeonsujeon identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT yeojinkim identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT kyungwhanan identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT sungwonjeon identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT hyojungryu identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT donghyunshin identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT jihyeahn identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT hyojinum identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT younghuikang identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT hyebinbak identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT byoungchulkim identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT seminlee identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT hyungtaejung identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT eunseokshin identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation AT jongbhak identificationof17novelepigeneticbiomarkersassociatedwithanxietydisordersusingdifferentialmethylationanalysisfollowedbymachinelearningbasedvalidation |