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<p>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.</p>

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Machine learning–guided Huanglian Jiedu decoction targets STING in periodontitis-induced Alzheimer’s Disease

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月27日

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  • Jie Li,
  • Mingqi Chen,
  • Pan Ren,
  • Guangming Sun,
  • Furong Zhong,
  • Yue Zhu,
  • Ganggang Li,
  • Yiran Fan,
  • Jinxin Chen,
  • Manru Xu,
  • Mengyuan Qiao,
  • Guohua Zhao,
  • Yuzhen Xu,
  • Wenbin Wu

摘要

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.