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<p>Although chronic rhinosinusitis (CRS) and chronic obstructive pulmonary disease (COPD) are both common chronic inflammatory diseases, the interaction between their comorbidities remains poorly understood within the unified airway disease framework. This study, for the first time, integrated multi-omics analysis and large-scale epidemiological data to explore their common mechanisms and clinical significance. The NHANES database was used for analysis, and multivariate logistic regression was employed to assess the comorbidity risk of CRS and COPD. Machine-learning models (glmnet, ranger, and xgboost) were used to analyze the NHANES data to determine the best model. Subsequently, Mendelian randomization(MR) was applied to explore relevant associations. Additionally, CRS and COPD datasets from GEO were further analyzed to identify potential targets. The NHANES analysis showed a significant association between CRS and COPD, with MR results indicating that CRS significantly increased the risk of COPD. Multi-omics integration revealed that C3 and CD163 are core targets in CRS/COPD patients. The ranger model was identified as the most suitable in this study. This study provides new evidence that CRS is an independent risk factor for COPD and establishes a unified airway mechanism centered on C3-CD163-mediated inflammation. These findings advance the “one airway, one disease” paradigm and support a dual-target therapeutic strategy.</p>

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Exploring the comorbidity association and biological mechanisms of chronic rhinosinusitis and chronic obstructive pulmonary disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月22日

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  • Shihan Liu,
  • Jinxiong Yang,
  • Yiyi Lin,
  • Lingli Zhang,
  • Wenlong Luo

摘要

Although chronic rhinosinusitis (CRS) and chronic obstructive pulmonary disease (COPD) are both common chronic inflammatory diseases, the interaction between their comorbidities remains poorly understood within the unified airway disease framework. This study, for the first time, integrated multi-omics analysis and large-scale epidemiological data to explore their common mechanisms and clinical significance. The NHANES database was used for analysis, and multivariate logistic regression was employed to assess the comorbidity risk of CRS and COPD. Machine-learning models (glmnet, ranger, and xgboost) were used to analyze the NHANES data to determine the best model. Subsequently, Mendelian randomization(MR) was applied to explore relevant associations. Additionally, CRS and COPD datasets from GEO were further analyzed to identify potential targets. The NHANES analysis showed a significant association between CRS and COPD, with MR results indicating that CRS significantly increased the risk of COPD. Multi-omics integration revealed that C3 and CD163 are core targets in CRS/COPD patients. The ranger model was identified as the most suitable in this study. This study provides new evidence that CRS is an independent risk factor for COPD and establishes a unified airway mechanism centered on C3-CD163-mediated inflammation. These findings advance the “one airway, one disease” paradigm and support a dual-target therapeutic strategy.