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已发表论文
阈值触发的免疫失调先于临床慢性阻塞性肺疾病：结合细胞因子谱分析和淋巴细胞表型分析的阶段特异性诊断模型
Authors Wu B, Li G, Hu X, Chen Q, Yong L
Received 28 April 2025
Accepted for publication 4 November 2025
Published 11 November 2025 Volume 2025:18 Pages 6899—6908
DOI https://doi.org/10.2147/IJGM.S527733
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Dr Woon-Man Kung
Biying Wu,1,2 Guyanan Li,3 Xiaoying Hu,4 Qiudan Chen,5 Lin Yong1,2,5
1Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, People’s Republic of China; 2Department of Clinical Laboratory Medicine, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, People’s Republic of China; 3College of Public Health, Shanghai University of Medicine and Health Sciences, Shanghai, People’s Republic of China; 4Department of General Practice, Jiangchuan Community Health Service Center of Minhang District, Shanghai, People’s Republic of China; 5Department of Clinical Laboratory, Central Laboratory, Jing’an District Center Hospital of Shanghai, Fudan University, Shanghai, People’s Republic of China
Correspondence: Lin Yong, Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, People’s Republic of China, Tel +86-021-13916553198, Email linyong7007@163.com
Purpose: To establish a stage-specific diagnostic model for chronic obstructive pulmonary disease (COPD) high-risk individuals by characterizing immune dysregulation through integrated cytokine and lymphocyte profiling.
Methods: In this cross-sectional study, 116 participants (34 healthy controls, 56 high-risk individuals, and 26 stable COPD patients) underwent comprehensive immunological evaluation. Peripheral blood cytokine levels (IL-6, IL-8, TNF-α) and lymphocyte subsets—including programmed cell death protein 1-positive (PD-1+) CD4+ T cells and effector memory regulatory T cells (emTregs)—were quantified via flow cytometry and multiplex immunoassays. A multivariable logistic regression model was developed to identify predictors of high-risk COPD, incorporating variables selected through hierarchical likelihood ratio testing and variance inflation factor (VIF)-based multicollinearity adjustment. Statistical validation included receiver operating characteristic (ROC) curve analysis, sensitivity-specificity assessment, and effect size calculations (Cohen’s f).
Results: Threshold-driven immunological alterations were identified in high-risk individuals, marked by a 7.8-fold elevation in PD-1+CD4+ T cells (p < 0.001) and increased IL-6 levels (median difference: 1.064 pg/mL, p < 0.001). Effector memory Tregs exhibited progressive depletion from healthy to stable COPD stages (p < 0.001). The final regression model—incorporating PD-1+CD4+ T cells, age, and emTregs-demonstrated robust diagnostic accuracy (AUC = 0.912; 95% CI: 0.848– 0.975), with 80.9% sensitivity and 79.3% specificity. PD-1+CD4+ T cells and age independently predicted high-risk status (adjusted odds ratio = 1.17, 95% CI: 1.05– 1.30, p = 0.005; adjusted odds ratio = 1.11, 95% CI: 1.03– 1.20, p = 0.007).
Conclusion: This study delineates a threshold-triggered immune signature preceding clinical COPD, providing a validated diagnostic framework for early detection. By integrating lymphocyte exhaustion markers and cytokine dynamics, the model bridges a critical gap in identifying subclinical immune dysfunction, enabling targeted interventions prior to irreversible lung damage.
Keywords: chronic obstructive pulmonary disease, immune dysregulation, T-cell exhaustion, diagnostic model, biomarker discovery
1Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, People’s Republic of China; 2Department of Clinical Laboratory Medicine, Shanghai Fifth People’s Hospital, Fudan University, Shanghai, People’s Republic of China; 3College of Public Health, Shanghai University of Medicine and Health Sciences, Shanghai, People’s Republic of China; 4Department of General Practice, Jiangchuan Community Health Service Center of Minhang District, Shanghai, People’s Republic of China; 5Department of Clinical Laboratory, Central Laboratory, Jing’an District Center Hospital of Shanghai, Fudan University, Shanghai, People’s Republic of China
Correspondence: Lin Yong, Department of Laboratory Medicine, Huashan Hospital, Fudan University, Shanghai, People’s Republic of China, Tel +86-021-13916553198, Email linyong7007@163.com
Purpose: To establish a stage-specific diagnostic model for chronic obstructive pulmonary disease (COPD) high-risk individuals by characterizing immune dysregulation through integrated cytokine and lymphocyte profiling.
Methods: In this cross-sectional study, 116 participants (34 healthy controls, 56 high-risk individuals, and 26 stable COPD patients) underwent comprehensive immunological evaluation. Peripheral blood cytokine levels (IL-6, IL-8, TNF-α) and lymphocyte subsets—including programmed cell death protein 1-positive (PD-1+) CD4+ T cells and effector memory regulatory T cells (emTregs)—were quantified via flow cytometry and multiplex immunoassays. A multivariable logistic regression model was developed to identify predictors of high-risk COPD, incorporating variables selected through hierarchical likelihood ratio testing and variance inflation factor (VIF)-based multicollinearity adjustment. Statistical validation included receiver operating characteristic (ROC) curve analysis, sensitivity-specificity assessment, and effect size calculations (Cohen’s f).
Results: Threshold-driven immunological alterations were identified in high-risk individuals, marked by a 7.8-fold elevation in PD-1+CD4+ T cells (p < 0.001) and increased IL-6 levels (median difference: 1.064 pg/mL, p < 0.001). Effector memory Tregs exhibited progressive depletion from healthy to stable COPD stages (p < 0.001). The final regression model—incorporating PD-1+CD4+ T cells, age, and emTregs-demonstrated robust diagnostic accuracy (AUC = 0.912; 95% CI: 0.848– 0.975), with 80.9% sensitivity and 79.3% specificity. PD-1+CD4+ T cells and age independently predicted high-risk status (adjusted odds ratio = 1.17, 95% CI: 1.05– 1.30, p = 0.005; adjusted odds ratio = 1.11, 95% CI: 1.03– 1.20, p = 0.007).
Conclusion: This study delineates a threshold-triggered immune signature preceding clinical COPD, providing a validated diagnostic framework for early detection. By integrating lymphocyte exhaustion markers and cytokine dynamics, the model bridges a critical gap in identifying subclinical immune dysfunction, enabling targeted interventions prior to irreversible lung damage.
Keywords: chronic obstructive pulmonary disease, immune dysregulation, T-cell exhaustion, diagnostic model, biomarker discovery