Nutrient-, Food-Group-, and Food-Item-Level Dietary Signatures of Psoriasis Comorbidity Among Adults With Anxiety Disorder: An Explainable Machine Learning Study.
- 1The First Affiliated Hospital of Xi'an Jiaotong University Xi'an China.
- 2Department of Anesthesiology (Xiamen Anesthesia Quality Control Center), The First Affiliated Hospital of Xiamen University, School of Medicine Xiamen University Xiamen China.
Abstract
Psoriasis and anxiety disorders frequently co-occur, yet the dietary features associated with this comorbidity remain poorly characterized. This cross-sectional study compared nutrient-, food-group-, and food-item-level dietary representations among adults with medication-defined anxiety disorder using linked National Health and Nutrition Examination Survey (NHANES), Food Patterns Equivalents Database/MyPyramid Equivalents Database (FPED/MPED), and What We Eat in America (WWEIA) data. Three analytic datasets were constructed at the nutrient level (n = 1016; 37 psoriasis-comorbidity cases), food-group level (n = 1215; 46 cases), and food-item level (n = 1257; 46 cases). Each dataset was divided using a fixed stratified 7:3 training-validation split. Correlation filtering, shadow-feature selection, scaling, class-imbalance handling, and hyperparameter tuning were confined to the training workflow, with five-fold stratified cross-validation and an untouched validation set. Seven models, including conventional multivariable logistic regression, were compared. In locked validation, the nutrient-level logistic regression model showed the strongest validation signal (area under the receiver operating characteristic curve [AUC-ROC] 0.645, 95% confidence interval [CI] 0.464-0.811; area under the precision-recall curve [AUC-PR] 0.063; Brier score 0.176). The food-group logistic regression model (AUC-ROC 0.396, 95% CI 0.256-0.539) and the food-item extreme gradient boosting (XGBoost) model (AUC-ROC 0.401, 95% CI 0.273-0.537) provided more limited validation signals in the present sample. SHapley Additive exPlanations (SHAP) analyses of the cross-validation-selected models identified niacin, carbohydrate intake, β-cryptoxanthin, fat-related variables, hypertension, body mass index, and selected food categories as influential model features. These contributions are best interpreted as model-derived signals that prioritize candidate dietary and clinical features for further study. Together, the findings highlight nutrient-level dietary information as the most promising representation for subsequent development of nutrition-informed risk assessment in adults with anxiety. Larger prospective cohorts and external validation may further strengthen the clinical applicability of these signals.
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BibTeX
@article{gao2026nutrient,
title = {{Nutrient-, Food-Group-, and Food-Item-Level Dietary Signatures of Psoriasis Comorbidity Among Adults With Anxiety Disorder: An Explainable Machine Learning Study.}},
author = {Canni Gao and Xinxin Duan and Mei Luan and Weijie Huang and Guangdong Wang and Zhihong Xu},
journal = {Food science \& nutrition},
year = {2026},
month = oct,
doi = {10.1002/fsn3.72482},
url = {https://europepmc.org/article/MED/42909865},
note = {CC BY},
}
RIS
TY - JOUR
TI - Nutrient-, Food-Group-, and Food-Item-Level Dietary Signatures of Psoriasis Comorbidity Among Adults With Anxiety Disorder: An Explainable Machine Learning Study.
AU - Canni Gao
AU - Xinxin Duan
AU - Mei Luan
AU - Weijie Huang
AU - Guangdong Wang
AU - Zhihong Xu
PY - 2026
DA - 2026/10/21
JO - Food science & nutrition
DO - 10.1002/fsn3.72482
UR - https://europepmc.org/article/MED/42909865
AB - Psoriasis and anxiety disorders frequently co-occur, yet the dietary features associated with this comorbidity remain poorly characterized. This cross-sectional study compared nutrient-, food-group-, and food-item-level dietary representations among adults with medication-defined anxiety disorder using linked National Health and Nutrition Examination Survey (NHANES), Food Patterns Equivalents Database/MyPyramid Equivalents Database (FPED/MPED), and What We Eat in America (WWEIA) data. Three analytic datasets were constructed at the nutrient level (n = 1016; 37 psoriasis-comorbidity cases), food-group level (n = 1215; 46 cases), and food-item level (n = 1257; 46 cases). Each dataset was divided using a fixed stratified 7:3 training-validation split. Correlation filtering, shadow-feature selection, scaling, class-imbalance handling, and hyperparameter tuning were confined to the training workflow, with five-fold stratified cross-validation and an untouched validation set. Seven models, including conventional multivariable logistic regression, were compared. In locked validation, the nutrient-level logistic regression model showed the strongest validation signal (area under the receiver operating characteristic curve [AUC-ROC] 0.645, 95% confidence interval [CI] 0.464-0.811; area under the precision-recall curve [AUC-PR] 0.063; Brier score 0.176). The food-group logistic regression model (AUC-ROC 0.396, 95% CI 0.256-0.539) and the food-item extreme gradient boosting (XGBoost) model (AUC-ROC 0.401, 95% CI 0.273-0.537) provided more limited validation signals in the present sample. SHapley Additive exPlanations (SHAP) analyses of the cross-validation-selected models identified niacin, carbohydrate intake, β-cryptoxanthin, fat-related variables, hypertension, body mass index, and selected food categories as influential model features. These contributions are best interpreted as model-derived signals that prioritize candidate dietary and clinical features for further study. Together, the findings highlight nutrient-level dietary information as the most promising representation for subsequent development of nutrition-informed risk assessment in adults with anxiety. Larger prospective cohorts and external validation may further strengthen the clinical applicability of these signals.
SN - 2048-7177
ER -