Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder increasingly associated with peripheral inflammatory conditions such as chronic periodontitis (CP); however, the molecular mechanisms linking these conditions remain poorly understood. Here, we investigated the therapeutic effects of Huanglian Jieddu Decoction (HLJDD) on CP-induced AD using an integrative machine learning-guided multi-omics approach. Analysis of public single-cell RNA-sequencing data revealed pronounced inflammatory activation in microglia from AD samples. We further established a CP-induced AD rat model and performed hippocampal transcriptomic profiling. Multiple complementary machine learning strategies, including Random Forest-based feature selection, support vector machine-based refinement, network modeling, and interpretable model analysis, were applied to prioritize disease-relevant pathways from high-dimensional transcriptomic data. Across models, components of the cGAS–STING signaling pathway consistently exhibited strong and directional contributions to CP–AD pathology, indicating a central inflammatory axis linking peripheral infection to neurodegeneration. Guided by these data-driven insights, in vivo and in vitro experiments demonstrated that HLJDD suppressed cGAS–STING activation, attenuated neuroinflammation, and improved cognitive function in CP-induced AD models. Collectively, this study highlights the value of machine learning-assisted transcriptomic interpretation for mechanistic prioritization and identifies HLJDD as a multitarget therapeutic strategy for CP-induced AD.
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Data availability
The single-cell RNA sequencing data analyzed in this study were retrieved from the GEO database under accession number GSE157827. Bulk RNA sequencing data for periodontitis were obtained from GEO under accession number GSE23586. The rat hippocampal RNA sequencing data generated in this study have been deposited in the NCBI Sequence Read Archive (SRA) under accession number PRJNA1394501. All other data supporting the findings of this study are available from the corresponding authors upon reasonable request.
Code availability
All custom code used for data preprocessing, statistical analysis, and machine learning modeling in this study was written in R (v4.1.0). Analyses were performed using established R packages including Seurat (v4.3.0), ggplot2 (v3.4.0), clusterProfiler, randomForest, Boruta, e1071, caret, neuralnet, bnlearn, ropls, and UpSetR. The code is available from the corresponding authors upon reasonable request.
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Acknowledgements
This work was supported by grants from the National Natural Science Foundation of China (No. 82174358) and the 2024 Joint Innovation Foundation of Chengdu University of Traditional Chinese Medicine (Young Leading Talent Program) (No. WXLH20240302).
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All the authors read and approved the manuscript. Wenbin Wu, Guohua Zhao, Yuzhen Xu, and Jie Li designed and supervised the studies. Jie Li and Mingqi Chen performed the experiments with the help of Pan Ren, Furong Zhong, Guangming Sun, Yue Zhu, Yiran Fan, Jinxin Chen, Manru Xu, Mengyuan Qiao, and Ganggang Li. Jie Li and Guangming Sun wrote the manuscript.
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Li, J., Chen, M., Ren, P. et al. Machine learning–guided Huanglian Jiedu decoction targets STING in periodontitis-induced Alzheimer’s Disease. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02468-x
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DOI: https://doi.org/10.1038/s41746-026-02468-x
