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Peer-reviewedCC BY-NC 4.0Journal of Neurogastroenterology and Motility

Psychological Predictors of Symptom Severity in Disorders of Gut–Brain Interaction: A Machine Learning Approach

  • Sung-Hoon Yoon1,2
  • Dawoon Jung2
  • Dae-Jin Kim1
  • Chan-mo Yang1,2
  • Young Hyeon Ahn3
  • June-Woo Kim4
  • Yong-Sung Kim5,6
  • Suck-Chei Choi7
  • Sang-Yeol Lee1,2
7 affiliations
  1. 1Department of Psychiatry, Wonkwang University Hospital Iksan Korea
  2. 2Department of Psychiatry, School of Medicine, Wonkwang University Iksan Korea
  3. 3Department of Gastroenterology, Soonchunhyang University Cheonan Hospital Cheonan Korea
  4. 4GIST InnoCORE AI-Nano Convergence Institute for Early Detection of Neurodegenerative Disease, Gwangju Institute of Science and Technology Gwangju Korea
  5. 5Digestive Disease Research Institute, School of Medicine, Wonkwang University Iksan Korea
  6. 6Kim’s GutEase Internal Medicine Gunpo Korea
  7. 7Department of Gastroenterology, School of Medicine, Wonkwang University Iksan Korea
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Abstract

Background/Aims: Disorders of gut–brain interaction (DGBI) exhibit heterogeneous symptom burdens and psychosocial variability. We aimed to identify severity-based phenotypes via unsupervised clustering and evaluate the psychological factors independently associated with symptom severity using machine learning.

Methods: We analyzed 480 patients with DGBI who completed validated gastrointestinal symptom scales and a psychological battery assessing anxiety, depression, childhood trauma, social support, resilience, and Type D personality. K-means clustering (k = 2) yielded low-severity groups (n = 281) and high-severity groups (n = 199). Between-group differences were examined using Welch’s t-test with false discovery rate (FDR) correction and Cohen’s d. Five logistic regression models with different feature sets were compared under stratified 5-fold cross-validation. Feature importance was assessed via bootstrap odds ratios with 95% confidence intervals.

Results: The high-severity group showed significantly elevated anxiety and depressive symptoms (Cohen’s d = 0.55–0.92 for the Beck Anxiety Inventory [BAI]; d = 0.56–0.63 for the Beck Depression Inventory) and higher childhood trauma scores on physical abuse, sexual abuse, and physical neglect subscales after FDR correction. The BAI-only model (area under the receiver operating characteristic curve [AUC] = 0.750) nearly matched the full 17-feature model (AUC = 0.753; accuracy = 71.3%; balanced accuracy = 69.9%), suggesting that the incremental contribution of multidimensional psychological profiling beyond anxiety assessment alone was limited.

Conclusions: Anxiety symptoms, particularly somatic and physiological dimensions, are the primary psychological determinants of DGBI severity. Childhood trauma associations are likely to operate through anxiety and depression pathways. These findings support routine somatic anxiety screening in gastroenterology to identify candidates for brain–gut behavioral therapies.

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Cite

BibTeXRIS
BibTeX
@article{yoon2026psychological,
  title = {{Psychological Predictors of Symptom Severity in Disorders of Gut–Brain Interaction: A Machine Learning Approach}},
  author = {Sung-Hoon Yoon and Dawoon Jung and Dae-Jin Kim and Chan-mo Yang and Young Hyeon Ahn and June-Woo Kim and Yong-Sung Kim and Suck-Chei Choi and Sang-Yeol Lee},
  journal = {Journal of Neurogastroenterology and Motility},
  year = {2026},
  month = oct,
  doi = {10.5056/jnm25246},
  url = {https://doi.org/10.5056/jnm25246},
  note = {CC BY-NC 4.0},
}
RIS
TY  - JOUR
TI  - Psychological Predictors of Symptom Severity in Disorders of Gut–Brain Interaction: A Machine Learning Approach
AU  - Sung-Hoon Yoon
AU  - Dawoon Jung
AU  - Dae-Jin Kim
AU  - Chan-mo Yang
AU  - Young Hyeon Ahn
AU  - June-Woo Kim
AU  - Yong-Sung Kim
AU  - Suck-Chei Choi
AU  - Sang-Yeol Lee
PY  - 2026
DA  - 2026/10/31
JO  - Journal of Neurogastroenterology and Motility
DO  - 10.5056/jnm25246
UR  - https://doi.org/10.5056/jnm25246
AB  - Background/Aims: Disorders of gut–brain interaction (DGBI) exhibit heterogeneous symptom burdens and psychosocial variability. We aimed to identify severity-based phenotypes via unsupervised clustering and evaluate the psychological factors independently associated with symptom severity using machine learning.  Methods: We analyzed 480 patients with DGBI who completed validated gastrointestinal symptom scales and a psychological battery assessing anxiety, depression, childhood trauma, social support, resilience, and Type D personality. K-means clustering (k = 2) yielded low-severity groups (n = 281) and high-severity groups (n = 199). Between-group differences were examined using Welch’s t-test with false discovery rate (FDR) correction and Cohen’s d. Five logistic regression models with different feature sets were compared under stratified 5-fold cross-validation. Feature importance was assessed via bootstrap odds ratios with 95% confidence intervals.  Results: The high-severity group showed significantly elevated anxiety and depressive symptoms (Cohen’s d = 0.55–0.92 for the Beck Anxiety Inventory [BAI]; d = 0.56–0.63 for the Beck Depression Inventory) and higher childhood trauma scores on physical abuse, sexual abuse, and physical neglect subscales after FDR correction. The BAI-only model (area under the receiver operating characteristic curve [AUC] = 0.750) nearly matched the full 17-feature model (AUC = 0.753; accuracy = 71.3%; balanced accuracy = 69.9%), suggesting that the incremental contribution of multidimensional psychological profiling beyond anxiety assessment alone was limited.  Conclusions: Anxiety symptoms, particularly somatic and physiological dimensions, are the primary psychological determinants of DGBI severity. Childhood trauma associations are likely to operate through anxiety and depression pathways. These findings support routine somatic anxiety screening in gastroenterology to identify candidates for brain–gut behavioral therapies.
SN  - 2093-0879
SN  - 2093-0887
ER  -