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"... Researchers at Chongqing Medical University and the Chongqing Key Laboratory of Oral Diseases recently explored the potential of a new approach to predict the onset of cognitive decline, which combines biological samples with machine learning.
Their paper, published in Translational Psychiatry, highlights the potential of this approach for the large-scale screening of older adults and the identification of people who are more at risk of developing neuropsychiatric disorders or neurodegenerative diseases. ..."
From the abstract:
"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."
Fig. 2: The differences of salivary microbiota in elderly with NPS and healthy controls according to the 16S rRNA data.
Fig. 4: Interactions between oral microbiota, inflammatory factors, and metabolic pathways in NPS and healthy controls.
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