高齢者の神経精神症状に対する地域スクリーニングツール：機械学習によるコルチゾール、マイクロバイオーム、社会的因子の統合

Neuropsychiatric symptoms (NPS) are early indicators of cognitive decline due to neurodegenerative diseases, and their timely detection is of the utmost importance. We aimed to develop and validate methods for large-scale NPS screening among elderly individuals and explore underlying metabolic mechanisms. This observational, cross-section study involved 138 and 200 participants in the modeling and external validation cohorts, respectively, chosen from community healthcare centers in Chongqing, China. Data collection involved demographic questionnaires, saliva samples for oral microbiome analysis, and assays for other biomarkers (IL-6, IL-1β, TNF-α, Cath-B and cortisol). EXtreme gradient boosting(XGBoost), support vector machine(SVM), and logistic regression(LR) were developed with RFE and LASSO. The models were primarily evaluated using AUROC and F1 scores. The best model was interpreted using SHAP values, while the LR model was transformed into a nomogram. Additionally, BioCyc function pathway analysis was used to predict the functional shift of biomarkers. The genus-augmented XGBoost model achieved the highest performance, with an AUROC of 0.936 and an F1 score of 0.864, outperforming other models. The LR model was converted into a nomogram to facilitate NPS-risk assessment in community settings. The external validation confirmed the strong predictive power (AUROC = 0.986, F1 score = 0.944). Enrichment and correlation analyses revealed cortisol and microbial interactions with pathways such as the pentose phosphate pathway and enterobacterial common antigen biosynthesis. The XGBoost-augmented model and nomogram offer promising tools for community-based NPS screening, while enrichment analysis provides insights into biological mechanisms.