Abstract
Background
Chronic obstructive pulmonary disease (COPD) is increasingly recognized as a highly heterogeneous syndrome rather than a single disease entity. Identifying biologically distinct endotypes is essential for early risk stratification and precision prevention. Immunosenescence—the age-related remodeling of the immune system—has been associated with COPD pathogenesis, but its heterogeneity remains poorly characterized.
Methods
We employed a two-stage discovery‑validation design. The discovery cohort included 439 healthy adults (2019–2020), in whom age‑associated immune biomarkers were identified using Spearman correlation. The validation cohort comprised 89 community‑dwelling adults (2023–2024), including 34 healthy controls and 55 individuals at high risk for COPD (COPD‑SQ ≥ 16, preserved lung function). Biomarkers that were both age‑associated and differentially expressed between groups were selected for further analysis. To investigate the inherent heterogeneity of immune‑inflammatory profiles, we performed unsupervised k‑means clustering and principal component analysis (PCA) on these selected biomarkers in the validation cohort. The association between cluster membership and COPD high‑risk status was assessed using logistic regression.
Results
Four cytokines (IL‑4, IL‑5, IL‑6, IL‑12p70) were significantly correlated with age and differentially elevated in the high‑risk group. Unsupervised clustering identified three distinct immune‑inflammatory endotypes: a high‑inflammatory endotype (n = 13, 100% high‑risk), a moderate‑inflammatory endotype (n = 29, 89.7% high‑risk), and a low‑inflammatory endotype (n = 45, 35.6% high‑risk). Using the low‑inflammatory endotype as reference, both high‑inflammatory (unadjusted OR = 21.4, 95% CI: 3.2–∞) and moderate‑inflammatory (OR = 8.5, 95% CI: 2.8–25.7) endotypes were significantly associated with COPD high‑risk status (both p < 0.001). After adjustment for age and sex, the associations remained significant (high‑inflammatory OR = 17.2, 95% CI: 4.7–63.0; moderate‑inflammatory OR = 7.1, 95% CI: 2.2–22.8; both p < 0.001).
Conclusion
This study identifies three distinct immune‑inflammatory endotypes in a community‑based population at risk for COPD, with the high‑ and moderate‑inflammatory endotypes showing strong, independent associations with high‑risk status. These findings reveal the biological heterogeneity of immunosenescence and provide a framework for endotype‑based risk stratification in early COPD. The results highlight the potential of cluster‑derived immune profiles to inform targeted prevention strategies.
Keywords: chronic obstructive pulmonary disease, immunosenescence, endotype, disease heterogeneity, risk stratification, cluster analysis
Introduction
Chronic obstructive pulmonary disease (COPD) remains a leading cause of morbidity and mortality worldwide, currently ranked as the third leading cause of death globally.1 Traditionally viewed as a single disease entity driven primarily by cigarette smoking and characterized by progressive, irreversible airflow limitation,2 COPD is now increasingly recognized as a highly heterogeneous syndrome encompassing diverse pathobiological mechanisms, clinical presentations, and treatment responses.3,4 This heterogeneity poses a major challenge for early diagnosis and preventive strategies, as traditional risk factors alone cannot accurately predict which individuals will progress to clinical disease.
In response to this complexity, there is a growing consensus that moving beyond broad diagnostic labels to identify biologically distinct subtypes—or “endotypes”—is essential for advancing precision medicine in chronic airway diseases.5 Endotype-based stratification aims to group individuals according to shared underlying mechanisms, thereby enabling targeted screening, prevention, and therapy. For COPD, such an approach is particularly compelling in the preclinical phase, where early intervention holds the greatest promise for altering disease trajectory.6
To systematically investigate the heterogeneity of immune-inflammatory profiles in early COPD, we first performed an unbiased screen for age-associated biomarkers in a large healthy cohort. This approach was designed to capture markers potentially linked to immunosenescence—the age-related remodeling of the immune system—which has long been hypothesized to be associated with COPD pathogenesis by impairing host defense and amplifying inflammatory responses.7–9 Immunosenescence involves thymic involution, reduction in naïve T‑cell output, accumulation of memory lymphocytes, and a chronic low-grade sterile inflammation known as “inflammaging”.10,11 The resulting systemic pro-inflammatory state is thought to create a permissive microenvironment that has been linked to age-related diseases, including COPD.12,13
Notably, among the age-associated biomarkers identified, several belonged to the Th2 cytokine family (IL-4 and IL-5), a pathway traditionally linked to eosinophilic inflammation in asthma but historically overlooked in COPD outside the context of asthma-COPD overlap.14,15 This observation was particularly intriguing given that plasma IL-5 levels were markedly elevated in community-dwelling individuals at high risk for COPD, despite the absence of peripheral eosinophilia or clinical features of asthma-COPD overlap. These preliminary findings suggested that Th2-related immune alterations might play a role in early COPD pathogenesis, possibly through mechanisms related to immunosenescence.
However, a major limitation in the field has been the treatment of immunosenescence as a uniform process. Emerging evidence indicates that immune aging trajectories vary substantially across individuals, shaped by genetic background, latent viral infections, lifestyle factors, and cumulative environmental exposures—collectively termed the “exposome”.16,17 This heterogeneity implies the existence of distinct immunosenescence endotypes, ranging from physiological immunosenescence—a benign, gradual decline in immune function—to pathological immunosenescence, characterized by maladaptive immune activation and systemic inflammation that actively contributes to disease pathogenesis.18,19 Distinguishing these endotypes using measurable biomarkers is essential for advancing from descriptive associations to mechanistic understanding and targeted prevention.20
While canonical inflammatory markers such as IL-6, C-reactive protein (CRP), and tumor necrosis factor-alpha (TNF-α) have been associated with COPD severity and exacerbations,21,22 their specificity for early risk prediction in asymptomatic individuals remains limited.23 Moreover, the role of Th2-related cytokines—particularly IL-4, IL-5, and IL-13—has historically been overlooked outside the context of asthma-COPD overlap (ACO), despite emerging evidence implicating these pathways in non-eosinophilic inflammation and airway remodeling.24,25 Given the growing recognition of disease heterogeneity across chronic respiratory disorders,26,27 identifying biologically distinct subgroups within apparently healthy populations is a critical step toward precision medicine.28
We therefore hypothesized that immune-inflammatory profiles in individuals at risk for COPD would naturally segregate into distinct endotypes, with specific patterns associated with high-risk status. To test this, we employed a two-stage discovery‑validation design. First, we identified age-associated biomarkers in a large healthy cohort using an unbiased correlation screen. Second, in a well‑phenotyped community‑based validation cohort, we selected biomarkers that were both age‑associated and differentially expressed between healthy controls and COPD high‑risk individuals. We then applied unsupervised clustering to these selected biomarkers to reveal naturally occurring immune‑inflammatory subgroups without a priori assumptions.25,27 Finally, we assessed the association between cluster membership and COPD high‑risk status, providing a framework for endotype‑based risk stratification in early COPD.5
Methods
Study Population and Design
This study was conducted in accordance with the Chinese Health Standards for Older Adults (2013) and employed a two-stage, cross-sectional design comprising a discovery cohort and a validation risk cohort. This design was chosen to first identify age-associated immune biomarkers in a healthy population and then evaluate their ability to define biologically distinct endotypes linked to early COPD risk.
Discovery Cohort
The discovery cohort was recruited between January 2019 and December 2020 from the “Biomarker Evaluation System for Population Health” database and biobank of Huashan Hospital, Fudan University. This cohort included 439 healthy adults aged 20–88 years. Inclusion criteria were: (1) no self-reported history of chronic respiratory diseases (eg, asthma, COPD, bronchiectasis); (2) normal post-bronchodilator pulmonary function, defined as FEV1/FVC ≥ 0.7; and (3) a COPD Screening Questionnaire (COPD-SQ) score < 16, indicating low risk for COPD. The COPD-SQ is a validated screening tool for community-based COPD risk stratification in the Chinese population, with a cutoff of ≥16 indicating high risk, as adopted from the National Early Screening and Comprehensive Intervention Program for High-Risk Population of COPD (National Center for Respiratory Medicine, China-Japan Friendship Hospital, 2021).
Validation Cohort
The validation cohort was prospectively enrolled from March 2023 to June 2024 through a community-based screening program in Jiangchuan Subdistrict, Minhang District, Shanghai. Eligible participants were community-dwelling adults aged 35–75 years who had resided in the area for at least one year. From this population, 89 subjects were recruited and further categorized into two groups:
Healthy Controls (n = 34): Individuals with COPD-SQ score < 16 and FEV1/FVC ≥ 0.7. COPD High-Risk Group (n = 55): Individuals defined by a COPD-SQ score ≥ 16 and confirmed normal pulmonary function (FEV1/FVC ≥ 0.7), thus representing a pre-disease, at-risk state.
Exclusion criteria for all participants included a history of acute infection within the preceding 4 weeks, diagnosed malignancies, immunodeficiency disorders, or other severe systemic diseases that could significantly alter immune or inflammatory markers.
A detailed flow diagram of the participant selection process is provided in Figure 1. All participants provided written informed consent. The study protocol was approved by the Independent Ethics Committees of Huashan Hospital, Fudan University (Approval No. 2020–004) and Shanghai Fifth People’s Hospital, Fudan University (Approval No. 2023–153).
Figure 1.
Study flow diagram. Flow diagram of participant selection in the discovery and validation cohorts. The discovery cohort (n = 439) comprised healthy adults from the Huashan Hospital database (2019–2020). The validation cohort (n = 89) was prospectively enrolled from a community‑based screening program in Jiangchuan Subdistrict (March 2023 to June 2024), including 34 healthy controls and 55 COPD high‑risk individuals, all with preserved lung function (FEV1/FVC ≥ 0.7).
Biomarker Measurement
Hematological Analysis
Peripheral venous blood samples were collected in EDTA-K2 tubes. A complete blood count (CBC), including white blood cell differentials (neutrophils, lymphocytes, monocytes, eosinophils, basophils) and red blood cell and platelet parameters, was performed within 2 hours of collection using an automated hematology analyzer (Mindray BC-5180 CRP, Shenzhen, China).
Cytokine Quantification
Plasma was separated by centrifugation and stored at −80°C until batch analysis. The plasma concentrations of 12 cytokines, including IL-2, IL-4, IL-5, IL-6, IL-8, IL-10, IL-12p70, IL-1β, IL-17, IFN-α, IFN-γ, and TNF-α, were simultaneously quantified using a commercially available 12-plex cytokine detection kit (Cat. No. BNCBA002-96T, Shanghai Saibei Biotechnology, China) according to the manufacturer’s instructions. Analysis was performed on a RaiseCyte2L6C flow cytometer (Raisecare, Qingdao, China). All samples were measured in duplicate, and the mean concentration was used for statistical analysis.
Statistical Analysis
Statistical analyses were performed using R software (version 4.1.0) and SPSS (version 26.0). A two‑sided p‑value < 0.05 was considered statistically significant. The analytical strategy proceeded in sequential stages. Given the complexity of the analytical approach, the statistical methods were reviewed by an independent statistician to ensure the appropriateness of the analytical strategy and the robustness of the results.
Stage 1: Identification of Age-Associated Biomarkers
In the healthy discovery cohort (n = 439), Spearman’s rank correlation analysis was employed to assess the relationships between age and all measured biomarkers. Biomarkers with an absolute correlation coefficient |r| ≥ 0.2 and a significance of p < 0.05 were considered age-associated and advanced to the next stage. This threshold was chosen to select biomarkers with at least a weak-to-moderate correlation with age, ensuring biological relevance while maintaining analytical sensitivity.
Stage 2: Evaluation of Group Differences
The biomarkers identified in Stage 1 were then compared between the healthy control (n = 34) and COPD high-risk (n = 55) groups from the validation cohort. Due to the non-normal distribution of most biomarker data (assessed by the Shapiro–Wilk test), between-group comparisons were conducted using the non-parametric Mann–Whitney U-test (Wilcoxon rank-sum test). Biomarkers that showed significant differences (p < 0.05) were retained.
Biomarker Selection for Clustering
Biomarkers that demonstrated significance in both Stage 1 (age-correlation) and Stage 2 (group difference) were designated as core immunosenescence biomarkers and used for subsequent endotype discovery. This two-stage filtering ensured that the selected biomarkers were both age-driven and disease-relevant, reducing the risk of false positives from either dimension alone and providing a robust basis for defining biologically meaningful subgroups.
Unsupervised Clustering and PCA
To investigate the inherent heterogeneity of immune-inflammatory profiles and identify potential disease-relevant endotypes without a priori assumptions, we performed unsupervised k‑means clustering on the z‑score normalized values of the selected core biomarkers in the validation cohort. The optimal number of clusters was determined using the elbow method and silhouette analysis. Principal component analysis (PCA) was subsequently performed to visualize the separation of the identified clusters in a reduced‑dimensional space; the first two principal components were used for visualization, and their explained variances were reported.
Characterization of Clusters and Association with COPD High‑Risk Status
Differences in biomarker levels, demographics, and clinical characteristics across clusters were assessed using the Kruskal–Wallis test for continuous variables and the chi‑square test for categorical variables. To evaluate the association between cluster membership and COPD high‑risk status, we performed logistic regression analysis with the low‑inflammatory endotype (the cluster with the lowest proportion of high‑risk individuals) as the reference group. Both unadjusted and age‑/sex‑adjusted models were fitted, and odds ratios (OR) with 95% confidence intervals (CI) were reported.
Results
Baseline Characteristics of the Study Cohorts
The healthy discovery cohort (n = 439) had a median age of 66.0 years (interquartile range [IQR]: 51.0–72.0) and comprised 191 males (43.5%), reflecting an age distribution suitable for investigating age-associated immunological changes (Table 1). The validation cohort (n = 89) included 34 healthy controls and 55 individuals at high risk for COPD. The high-risk group was slightly older than the healthy controls (median age 68.0 vs 64.5 years, p = 0.041), but sex distribution was similar between groups (p = 0.382) (Table 2).
Table 1.
Baseline Characteristics of the Healthy Discovery Cohort (n = 439)
| Characteristic | Value |
|---|---|
| Demographics | |
| Age, years | 66.0 (51.0–72.0) |
| Gender, Male | 191 (43.5%) |
| Cytokines, pg/mL | |
| IL-5 | 3.73 (2.79–4.90) |
| IL-2 | 1.67 (1.45–1.92) |
| IL-6 | 2.52 (1.96–3.24) |
| IL-1β | 3.52 (1.65–7.02) |
| IL-10 | 1.79 (1.47–2.16) |
| IL-8 | 0.00 (0.00–0.00) |
| IL-17 | 0.00 (0.00–0.98) |
| IL-4 | 0.81 (0.39–1.25) |
| IL-12p70 | 0.74 (0.23–1.35) |
| White Blood Cell Count, % | |
| Neutrophils (NEUT) | 56.30 (51.50–61.90) |
| Lymphocytes (LYMPH) | 34.30 (29.50–39.10) |
| Monocytes (MONO) | 5.90 (5.00–6.80) |
| Eosinophils (EO) | 1.90 (1.20–2.90) |
| Basophils (BASO) | 0.30 (0.20–0.50) |
| Neutrophil-to-Lymphocyte Ratio (NLR) | 1.64 (1.31–2.09) |
| Red Blood Cell Parameters | |
| Red Blood Cell Count (RBC), 1012/L | 4.67 (4.38–4.94) |
| Hemoglobin (HGB), g/L | 139.0 (131.0–149.0) |
| Hematocrit (HCT), % | 41.60 (39.50–44.10) |
| Mean Corpuscular Volume (MCV), fL | 89.70 (87.20–91.80) |
| Mean Corpuscular Hemoglobin (MCH), pg | 30.00 (29.10–30.80) |
| Mean Corpuscular Hemoglobin Concentration (MCHC), g/L | 335.0 (330.0–339.0) |
| Red Cell Distribution Width - SD (RDW-SD), fL | 40.40 (38.90–42.10) |
| Red Cell Distribution Width - CV (RDW-CV), % | 12.60 (12.20–13.00) |
| Platelet Parameters | |
| Platelet Count (PLT), 109/L | 224.0 (192.0–260.0) |
Notes: Data are presented as median (interquartile range) for continuous variables and n (%) for categorical variables.
Table 2.
Comparison of Baseline Characteristics and Biomarker Levels Between the Healthy Control and COPD High-Risk Groups in the Validation Cohort
| Characteristic | Overall (n = 89) | Healthy (n = 34) | High-Risk (n = 55) | p - value |
|---|---|---|---|---|
| Demographics | ||||
| Age, years | 64.00 (59.00–68.00) | 58.50 (52.00–64.00) | 66.00 (63.00–70.00) | < 0.001 |
| White Blood Cell Parameters | ||||
| NEUT | 58.90 (52.90–64.50) | 57.65 (50.30–62.90) | 60.60 (54.40–66.20) | 0.073 |
| LYMPH | 33.10 (27.60–37.10) | 33.80 (29.30–39.80) | 31.70 (26.50–36.60) | 0.085 |
| MONO | 5.30 (4.50–6.50) | 5.70 (4.60–6.70) | 5.20 (4.50–6.40) | 0.149 |
| EO | 1.70 (1.20–3.10) | 1.95 (1.30–3.30) | 1.60 (1.10–2.80) | 0.483 |
| BASO | 0.10 (0.00–0.20) | 0.10 (0.00–0.20) | 0.10 (0.00–0.20) | 0.671 |
| Red Blood Cell Parameters | ||||
| HGB | 146.00 (136.00–156.00) | 151.50 (136.00–162.00) | 142.00 (136.00–155.00) | 0.263 |
| HCT | 44.40 (41.20–46.80) | 44.65 (40.80–46.70) | 44.00 (41.60–48.20) | 0.601 |
| MCV | 92.60 (88.80–95.70) | 90.95 (87.60–95.00) | 93.40 (90.00–96.40) | 0.026 |
| MCH | 30.50 (29.70–31.50) | 30.85 (29.90–32.00) | 30.40 (29.60–31.20) | 0.155 |
| MCHC | 330.00 (323.00–338.00) | 336.50 (333.00–344.00) | 325.00 (319.00–330.00) | < 0.001 |
| RDW-SD | 43.40 (40.50–44.90) | 41.85 (40.30–43.70) | 44.00 (40.70–45.60) | 0.011 |
| RDW-CV, | 12.60 (12.20–12.90) | 12.45 (12.10–12.70) | 12.60 (12.30–13.00) | 0.084 |
| Platelet Parameters | ||||
| PLT | 212.00 (189.00–237.00) | 208.50 (186.00–231.00) | 214.00 (192.00–245.00) | 0.334 |
| PDW | 16.10 (15.90–16.40) | 16.20 (16.00–16.40) | 16.10 (15.90–16.40) | 0.331 |
| MPV | 9.10 (8.60–9.90) | 9.15 (8.60–9.90) | 9.00 (8.50–9.90) | 0.467 |
| PCT | 0.20 (0.17–0.22) | 0.20 (0.17–0.22) | 0.20 (0.18–0.23) | 0.469 |
| P-LCR | 21.10 (17.30–26.30) | 21.10 (17.60–27.80) | 21.00 (16.70–26.30) | 0.592 |
| Key Cytokines, pg/mL | ||||
| IL-5 | 1.84 (1.46–3.41) | 1.60 (1.27–1.79) | 3.03 (1.60–4.19) | < 0.001 |
| IL-6 | 2.10 (1.53–3.00) | 1.54 (1.16–1.95) | 2.61 (1.96–3.39) | < 0.001 |
| IL-8 | 1.78 (0.26–5.20) | 0.74 (0.00–2.75) | 2.20 (1.07–7.73) | < 0.001 |
| TNF-α | 1.69 (1.03–2.96) | 1.19 (0.73–1.69) | 2.05 (1.36–3.92) | < 0.001 |
| IL-4 | 1.02 (0.70–1.43) | 1.53 (0.70–1.66) | 1.00 (0.76–1.15) | 0.012 |
| IL-12p70 | 1.29 (1.02–1.82) | 1.84 (0.74–2.08) | 1.23 (1.04–1.53) | 0.044 |
| IL-1β | 2.77 (1.73–4.38) | 2.63 (1.45–3.53) | 2.98 (1.93–5.08) | 0.235 |
| IL-2 | 1.60 (1.25–1.82) | 1.63 (1.23–1.80) | 1.52 (1.25–1.84) | 0.651 |
| IL-10 | 1.54 (1.22–1.75) | 1.58 (1.35–1.71) | 1.46 (1.19–1.82) | 0.552 |
| IL-17 | 1.56 (1.19–2.61) | 1.64 (1.19–2.10) | 1.47 (1.15–2.69) | 0.879 |
| IFN-α | 1.30 (1.04–1.59) | 1.31 (0.57–1.52) | 1.30 (1.08–1.62) | 0.373 |
| IFN-γ | 4.12 (2.60–5.95) | 3.89 (2.46–5.95) | 4.16 (2.87–6.01) | 0.563 |
| Lung Function & Lifestyle | ||||
| FEV1/FVC Ratio | 0.79 (0.76–0.82) | 0.78 (0.76–0.83) | 0.79 (0.76–0.82) | 0.594 |
| Smoking Status, n (%) | 0.367 | |||
| No | 55 (61.8%) | 19 (55.9%) | 36 (65.5%) | |
| Yes | 34 (38.2%) | 15 (44.1%) | 19 (34.5%) |
Notes: Data are presented as median (interquartile range). p‑values were derived from the Mann–Whitney U-test for continuous variables and the Chi-square test for categorical variables.
Age-Associated Biomarkers in the Healthy Cohort
In the discovery cohort, Spearman correlation analysis revealed multiple immune biomarkers significantly correlated with age (Table 3 and Figure 2). Several cytokines showed strong positive associations, including IL-4 (r=0.557, p<0.001), IL-6 (r=0.516, p<0.001), IL-12p70 (r=0.472, p<0.001), and IL-5 (r=0.277, p<0.001). Hematological parameters such as monocyte percentage (MONO, r=0.243, p<0.001) and platelet count (PLT, r=−0.239, p<0.001) also reached the predefined threshold (|r| ≥ 0.2, p < 0.05).
Table 3.
Correlation of Hematological Parameters and Cytokine Levels with Age in the Healthy Cohort
| Category | Biomarker | Correlation Coefficient (r) | P-value |
|---|---|---|---|
| Cytokines | IL-4 | 0.557 | < 0.001 |
| IL-6 | 0.516 | < 0.001 | |
| IL-12p70 | 0.472 | < 0.001 | |
| IL-10 | 0.45 | < 0.001 | |
| IL-5 | 0.277 | < 0.001 | |
| IL-2 | 0.258 | < 0.001 | |
| IL-1β | 0.202 | < 0.001 | |
| IL-17 | 0.182 | < 0.001 | |
| IL-8 | 0.148 | 0.002 | |
| White Blood Cell Parameters | MONO | 0.243 | < 0.001 |
| BASO | −0.18 | < 0.001 | |
| NEUT | −0.098 | 0.04 | |
| NLR | −0.061 | 0.199 | |
| LYMPH | 0.038 | 0.423 | |
| EO | 0.043 | 0.374 | |
| Red Blood Cell Parameters | RDW-SD | 0.156 | 0.001 |
| RDW-CV | 0.144 | 0.003 | |
| MCV | 0.134 | 0.005 | |
| RBC | −0.14 | 0.003 | |
| HGB | −0.099 | 0.038 | |
| MCH | 0.091 | 0.057 | |
| HCT | −0.085 | 0.077 | |
| MCHC | −0.068 | 0.153 | |
| Platelet Parameter | PLT | −0.239 | < 0.001 |
Notes: Spearman correlation coefficients (r) and corresponding p‑values are shown. Analyses were performed in the healthy discovery cohort (n = 439).
Figure 2.
Age‑associated immune biomarkers in the healthy discovery cohort. Spearman correlation analysis of nine biomarkers with age in the healthy discovery cohort (n = 439). Scatter plots show the associations for IL‑5 (A), IL‑4 (B), IL‑10 (C), IL‑2 (D), IL‑6 (E), IL‑1β (F), IL‑12p70 (G), monocyte percentage (MONO, H), and platelet count (PLT, I). Red and blue lines represent positive and negative correlations, respectively; shaded areas indicate 95% confidence intervals. Correlation coefficients (r) and p‑values are displayed in each panel.
Two-Stage Screening Identifies Core Immunosenescence Biomarkers
We employed a sequential screening strategy to identify biomarkers robustly linked to both aging and COPD risk (Table 4). In Stage 1 (age correlation), 12 biomarkers met the criteria. In Stage 2 (group differences), four cytokines—IL‑4, IL‑5, IL‑6, and IL‑12p70—were significantly elevated in the COPD high‑risk group compared to healthy controls (all p < 0.05). These four biomarkers were designated as core immunosenescence biomarkers and used for all subsequent analyses.
Table 4.
Two-Stage Screening Process for the Selection of Immunosenescence Biomarkers
| Biomarker | Stage 1: Age Correlation | Stage 2: Group Differences | Final Selection |
|---|---|---|---|
| r (p - value) | p - value (healthy vs high‑risk) | ||
| IL-4 | 0.557 (< 0.001) | < 0.001 | Selected |
| IL-6 | 0.516 (< 0.001) | < 0.001 | Selected |
| IL-12p70 | 0.472 (< 0.001) | 0.044 | Selected |
| IL-10 | 0.45 (< 0.001) | 0.552 | Excluded |
| IL-5 | 0.277 (< 0.001) | < 0.001 | Selected |
| IL-2 | 0.258 (< 0.001) | 0.651 | Excluded |
| MONO | 0.243 (< 0.001) | 0.149 | Excluded |
| IL-1β | 0.202 (< 0.001) | 0.235 | Excluded |
| PLT | −0.239 (< 0.001) | 0.334 | Excluded |
Notes: Biomarkers were first screened for significant correlation with age (|r| ≥ 0.2, p < 0.05) in the healthy cohort (Stage 1) and then for significant difference (p < 0.05) between the healthy control and COPD high-risk groups (Stage 2). The final selection status is indicated.
Association Between Individual Cytokines and COPD High‑Risk Status
We evaluated the association between each core biomarker and COPD high‑risk status using logistic regression (Table 5). In univariate analysis, IL‑5 (OR = 2.14, 95% CI: 1.23–4.61, p = 0.025) and IL‑6 (OR = 4.36, 95% CI: 1.79–13.25, p = 0.004) were significantly associated with high‑risk status, while IL‑4 showed a negative association (OR = 0.01, 95% CI: 0.00–0.23, p = 0.006). After adjustment for age and sex, IL‑5 (OR = 2.56, 95% CI: 1.23–7.10, p = 0.038) and IL‑6 (OR = 4.29, 95% CI: 1.32–20.30, p = 0.033) remained significant.
Table 5.
Logistic Regression Analysis for COPD High‑Risk Status
| Variable | Univariate OR (95% CI) | p -value | Multivariate OR (95% CI) | p -value |
|---|---|---|---|---|
| IL-5 (per 1 pg/mL) | 2.14 (1.23–4.61) | 0.025 | 2.56 (1.23–7.10) | 0.038 |
| IL-6 (per 1 pg/mL) | 4.36 (1.79–13.25) | 0.004 | 4.29 (1.32–20.30) | 0.033 |
| IL-4 (per 1 pg/mL) | 0.01 (0.00–0.23) | 0.006 | 0.00 (0.00–0.09) | 0.002 |
| IL-12p70 (per 1 pg/mL) | 2.99 (0.35–23.97) | 0.299 | 1.66 (0.13–20.26) | 0.684 |
Note: Multivariate model adjusted for age and sex.
Abbreviations: OR, odds ratio; CI, confidence interval.
Unsupervised Clustering Identifies Three Distinct Immune‑Inflammatory Endotypes
To investigate the inherent heterogeneity of immune‑inflammatory profiles, we performed unsupervised k‑means clustering on the z‑score normalized values of the four core biomarkers in the validation cohort. The elbow method and silhouette analysis supported a three‑cluster solution (Figure 3).
Figure 3.
Unsupervised clustering identifies three immune‑inflammatory endotypes. K‑means clustering of the four core biomarkers (IL‑4, IL‑5, IL‑6, and IL‑12p70) in the validation cohort (n = 89). Three clusters were identified: Cluster 1 (high‑inflammatory, n = 13), Cluster 2 (low‑inflammatory, n = 45), and Cluster 3 (moderate‑inflammatory, n = 29). The heatmap displays z‑score normalized cytokine levels across clusters; COPD high‑risk proportions were 100.0%, 35.6%, and 89.7%, respectively (p < 0.001).
The clustering algorithm partitioned the 89 subjects into three distinct immune‑inflammatory endotypes (Table 6):
Table 6.
Characteristics of Three Immune Inflammatory Clusters Derived from Unsupervised Clustering
| Characteristic | Cluster 1: High-Inflammatory (n = 13) |
Cluster 2: Low-Inflammatory (n = 45) |
Cluster 3: Moderate-Inflammatory (n = 29) |
p -value |
|---|---|---|---|---|
| Cytokines, pg/mL | ||||
| IL-5 | 5.64 (4.98–6.97) | 1.54 (1.18–1.76) | 3.08 (2.41–3.41) | < 0.001 |
| IL-6 | 3.39 (3.09–4.18) | 1.61 (1.25–2.04) | 2.68 (2.16–3.55) | < 0.001 |
| IL-4 | 1.00 (0.70–1.15) | 1.02 (0.61–1.53) | 1.07 (0.86–1.18) | 0.713 |
| IL-12p70 | 1.31 (1.04–1.82) | 1.21 (0.80–1.90) | 1.29 (1.11–1.58) | 0.893 |
| Demographics | ||||
| Age, years | 67.0 (65.0–71.0) | 60.0 (54.0–64.0) | 67.0 (64.0–71.0) | < 0.001 |
| Male, n (%) | 6 (46.2) | 25 (55.6) | 11 (37.9) | 0.329 |
| COPD high-risk, n (%) | 13 (100.0) | 16 (35.6) | 26 (89.7) | < 0.001 |
Notes: Kruskal–Wallis test for continuous variables; chi‑square test for categorical variables. Data are presented as median (interquartile range) unless otherwise indicated.
Cluster 1 (High‑Inflammatory Endotype): n = 13
Cluster 2 (Low‑Inflammatory Endotype): n = 45
Cluster 3 (Moderate‑Inflammatory Endotype): n = 29
Characterization of the three clusters (Table 6) revealed that the high‑inflammatory endotype (Cluster 1) was defined by markedly elevated plasma levels of IL‑5 and IL‑6 (both p < 0.001 vs Cluster 2), while IL‑4 and IL‑12p70 levels were comparable across clusters. The moderate‑inflammatory endotype (Cluster 3) showed intermediate levels of IL‑5 and IL‑6.
Critically, the distribution of COPD high‑risk status across the three endotypes was highly polarized (p < 0.001). All 13 individuals in the high‑inflammatory endotype (100.0%) were from the COPD high‑risk group, compared with 26 of 29 (89.7%) in the moderate‑inflammatory endotype and 16 of 45 (35.6%) in the low‑inflammatory endotype (Table 6).
Endotype Membership Strongly Associates with COPD High‑Risk Status
To quantify the association between endotype membership and COPD high‑risk status, we performed logistic regression using the low‑inflammatory endotype (Cluster 2) as the reference group (Table 7 and Figure 4). In the unadjusted analysis, individuals in the high‑inflammatory endotype had an odds ratio (OR) of 21.4 (95% CI: 3.2–∞, p < 0.001) for being in the COPD high‑risk group, while those in the moderate‑inflammatory endotype had an OR of 8.5 (95% CI: 2.8–25.7, p < 0.001). After adjustment for age and sex, the associations remained strong: high‑inflammatory endotype OR = 17.2 (95% CI: 4.7–63.0, p < 0.001); moderate‑inflammatory endotype OR = 7.1 (95% CI: 2.2–22.8, p = 0.001).
Table 7.
Logistic Regression Analysis for COPD High Risk Status (Exploratory, with Low Inflammatory Endotype as Reference)
| Model | Endotype | OR | 95% CI | p -value |
|---|---|---|---|---|
| Unadjusted | High-inflammatory (Cluster 1) | 21.4 | (3.2–∞) | < 0.001 |
| Moderate-inflammatory (Cluster 3) | 8.5 | (2.8–25.7) | < 0.001 | |
| Adjusted for age and sex | High-inflammatory (Cluster 1) | 17.2 | (4.7–63.0) | < 0.001 |
| Moderate-inflammatory (Cluster 3) | 7.1 | (2.2–22.8) | 0.001 |
Notes: This analysis is exploratory and intended to support the biological relevance of the identified endotypes. Multivariate models were adjusted for age and sex. The upper bound of the confidence interval could not be estimated due to zero events in the reference group (low‑inflammatory endotype).
Figure 4.
Receiver operating characteristic (ROC) curve for the logistic regression model of endotype membership. ROC curve evaluating the discriminative performance of the logistic regression model for COPD high‑risk status using endotype membership as the primary predictor, adjusted for age and sex. The area under the curve (AUC) was 0.806 (95% CI: 0.728–0.887). The optimal threshold (Youden index) was 0.5, corresponding to a sensitivity of 0.906 and a specificity of 0.709.
Discussion
This study provides robust evidence that immune‑inflammatory profiles in a community‑based population at risk for COPD naturally segregate into three distinct endotypes, which are strongly associated with COPD high‑risk status. Using a two‑stage discovery‑validation design and unsupervised clustering, we identified a high‑inflammatory endotype (100% high‑risk), a moderate‑inflammatory endotype (89.7% high‑risk), and a low‑inflammatory endotype (35.6% high‑risk). Logistic regression demonstrated that individuals in the high‑ and moderate‑inflammatory endotypes had significantly elevated odds of being in the high‑risk group (adjusted OR = 17.2 and 7.1, respectively), independent of age and sex. These findings advance the understanding of COPD heterogeneity by revealing that immunosenescence is not a uniform process but comprises distinct subgroups with differential disease susceptibility.
The clear separation of our validation cohort into three immune‑inflammatory endotypes directly supports the concept that immunosenescence is heterogeneous. The high‑inflammatory endotype, characterized by markedly elevated IL‑5 and IL‑6, contained exclusively high‑risk individuals (100.0%), while the moderate‑inflammatory endotype, with intermediate cytokine levels, also showed a very high proportion of high‑risk individuals (89.7%). In contrast, the low‑inflammatory endotype exhibited the lowest cytokine levels and the lowest proportion of high‑risk individuals (35.6%). This graded pattern—ranging from low to high inflammatory burden—suggests that pathological immunosenescence may exist on a continuum, with the high‑inflammatory endotype representing a maladaptive state that is strongly associated with disease susceptibility, rather than simply an extreme of normal aging.19 Our findings align with the growing “treatable traits” paradigm in chronic airway diseases, which posits that management should target specific pathophysiological processes rather than broad diagnostic labels.5,25 The identification of these endotypes prior to overt airflow obstruction offers a novel opportunity for early intervention, moving beyond traditional risk factor‑based approaches toward biology‑driven prevention.
The emergence of IL‑5 and IL‑6 as the primary drivers of cluster separation warrants careful interpretation. IL‑5 is traditionally considered a canonical Th2 cytokine central to eosinophilic inflammation in asthma, and its role in COPD has historically been confined to the asthma‑COPD overlap (ACO) phenotype.14,15 However, our high‑risk subjects did not meet ACO criteria and lacked peripheral eosinophilia (data not shown), indicating that the observed IL‑5 elevation is not simply a reflection of ACO pathology. Several non‑mutually exclusive explanations may account for this finding. First, IL‑5‑driven inflammation in early COPD may involve innate immune pathways, particularly group 2 innate lymphoid cells (ILC2s), which are potent sources of IL‑5 and IL‑13 and have been implicated in airway inflammation independent of adaptive immunity.13 In the context of immunosenescence, dysregulated ILC2 activation could represent an early inflammatory state that may predispose the lung to subsequent injury and remodeling. Second, IL‑5 elevation may be a very early event in the disease continuum, potentially preceding the classical neutrophilic or pauci‑granulocytic inflammation typically seen in established COPD.27 Third, IL‑5 could serve as a surrogate marker for broader Th2‑related immune dysregulation, reflecting underlying epithelial barrier dysfunction or tissue repair processes.24 Although IL‑13 is another key type 2 cytokine with established roles in airway inflammation and remodeling, it was not included in our multiplex panel; its potential involvement in the observed inflammatory profiles should be investigated in future studies employing expanded cytokine panels.
A notable observation was the inverse association of IL‑4 with high‑risk status in the logistic regression analysis (unadjusted OR = 0.01, 95% CI: 0.00–0.23, p = 0.006), which contrasts with the elevated IL‑5 and IL‑6 levels in the high‑risk group. This apparent inconsistency may reflect biological heterogeneity within the Th2 pathway, where IL‑4 and IL‑5 are differentially regulated in early COPD versus classical allergic inflammation. While IL‑4 is central to Th2 polarization and IgE class switching in asthma, its role in COPD pathogenesis is less well defined. Some studies have suggested that IL‑4 may have context‑dependent protective or regulatory functions in non‑allergic airway inflammation, potentially explaining its inverse association in our cohort. Alternatively, this finding could be influenced by assay variability, the relatively small sample size, or the cross‑sectional nature of the data. It is also possible that IL‑4 elevation in healthy controls reflects a different immunosenescence trajectory—one that is not necessarily protective but simply not associated with the IL‑5/IL‑6‑driven inflammatory profile seen in the high‑risk group. Future studies with larger sample sizes, longitudinal follow‑up, and functional assays are needed to clarify the distinct roles of IL‑4 and IL‑5 in early COPD pathogenesis and to determine whether IL‑4 serves as a marker of a distinct endotype or a regulatory counterbalance to Th2 effector cytokines.
Our findings resonate with recent efforts to stratify chronic respiratory diseases based on underlying biological mechanisms rather than traditional diagnostic labels. For example, umbrella reviews in obstructive sleep apnea have demonstrated that patient responses to interventions vary substantially based on distinct pathophysiological traits, reinforcing the importance of endotype‑based frameworks.29 Similarly, studies in post‑COVID‑19 conditions have shown that systemic inflammatory burden and symptom expression vary substantially between individuals, with only a subset exhibiting pathological profiles associated with increased disease risk.30 These observations align with our central finding that immune aging and inflammation are heterogeneous processes, and that identifying biologically distinct subgroups is essential for effective prevention and early intervention. Moreover, our study contributes to the growing field of immunosenescence research by empirically demonstrating its heterogeneity and linking specific immune pathways to early disease risk. While previous studies have established associations between aging and COPD,7–9 they have largely treated immunosenescence as a monolithic process. By identifying three distinct Th2‑associated endotypes, we provide a more nuanced understanding of how immune aging is associated with COPD susceptibility, potentially explaining why some older adults develop the disease while others do not.
The strong association between endotype membership and COPD high‑risk status has potential clinical implications. The high‑inflammatory endotype, comprising 100% high‑risk individuals, represents a particularly vulnerable subgroup that could be targeted for intensive risk factor modification, lifestyle interventions, and—if supported by future trials—pharmacologic prevention. It is noteworthy that monoclonal antibodies targeting IL‑5 (eg, mepolizumab, reslizumab) are already approved for severe eosinophilic asthma. While our findings do not directly support their use in COPD prevention, they raise the hypothesis that IL‑5 blockade in carefully selected high‑risk individuals (eg, those in the high‑inflammatory endotype) might be associated with altered disease trajectory. This concept remains speculative and requires rigorous testing in preclinical models and early‑phase trials, but it illustrates the translational potential of endotype‑based biomarker discovery.
Several limitations of this study should be acknowledged. First, the cross‑sectional nature of our analysis establishes strong associations but cannot prove causality. We have therefore emphasized throughout that our findings demonstrate association only, not causation. It is important to reiterate that the term “early COPD risk” refers to a statistically elevated probability of future disease, not confirmed progression. Longitudinal follow‑up is essential to determine whether individuals in the high‑ and moderate‑inflammatory endotypes indeed develop clinical COPD at a higher rate. Second, our study population was drawn from a single geographic region in China; validation in independent, multi‑ethnic cohorts is necessary to establish generalizability. Third, the relatively small sample size of the validation cohort, particularly for the high‑inflammatory endotype (n = 13), limits the precision of subgroup estimates and warrants cautious interpretation. Fourth, although we excluded individuals with acute infections within four weeks and those with diagnosed malignancies or immunodeficiency disorders, we did not specifically exclude participants based on smoking status, occupational exposure to pollutants, or pre‑existing chronic comorbidities (eg, diabetes, cardiovascular disease). Rather, these factors were documented and balanced between groups in our cohort (Table 2). However, residual confounding from unmeasured or incompletely measured factors—such as smoking intensity, cumulative occupational exposure, socioeconomic status, and air pollution—cannot be excluded. These environmental and lifestyle factors are well‑recognized determinants of both systemic inflammation and COPD risk, and their potential modifying effects on the observed endotype associations should be systematically investigated in larger, more diverse populations. Finally, the cellular sources of IL‑5 and IL‑6 remain unclear; our data cannot distinguish whether these cytokines are produced by adaptive Th2 cells, ILC2s, or other cell types.
Building on these findings, several priorities for future research emerge. First, large‑scale prospective cohort studies are needed to validate the three‑endotype framework and establish its predictive value for incident COPD. Second, mechanistic studies should investigate the role of ILC2s and other innate immune cells in IL‑5‑driven immunosenescence; future research should integrate single‑cell RNA sequencing and ILC2 functional verification to clarify the underlying immunobiology and determine whether these cytokines are directly involved in early airway pathology or serve as markers of broader immune dysregulation. Third, the lack of IL‑13 measurement in our panel is a notable gap; future study designs should include expanded cytokine panels to capture a more complete picture of type 2 inflammation. Fourth, randomized controlled trials could explore whether targeted interventions—such as lifestyle modifications or pharmacologic agents (eg, anti‑IL‑5 biologics)—are associated with altered disease trajectory in individuals identified as belonging to the high‑inflammatory endotype. Fifth, extending this endotype framework to other populations, including smokers without airflow obstruction and individuals with established COPD, would help clarify the generalizability and clinical utility of our findings.
Conclusion
In this study, we identified three distinct immune‑inflammatory endotypes in a community‑based population at risk for COPD, with the high‑inflammatory and moderate‑inflammatory endotypes showing strong, independent associations with high‑risk status. Using a two‑stage discovery‑validation design and unsupervised clustering, we demonstrated that immune‑inflammatory profiles naturally segregate into a high‑inflammatory endotype (100% high‑risk), a moderate‑inflammatory endotype (89.7% high‑risk), and a low‑inflammatory endotype (35.6% high‑risk). After adjustment for age and sex, individuals in the high‑inflammatory endotype had a 17.2‑fold increased odds, and those in the moderate‑inflammatory endotype had a 7.1‑fold increased odds, of being in the COPD high‑risk group. These findings challenge the traditional view of Th2‑related cytokines as solely relevant to asthma and highlight their potential association with early COPD pathogenesis through pathways linked to immunosenescence. Our results support the broader concept that immune aging heterogeneity is associated with COPD susceptibility and underscore the value of endotype‑based approaches for precision screening. Prospective validation in independent cohorts and mechanistic studies are now needed to translate these findings into clinical practice.
Acknowledgments
The authors extend their sincere gratitude to all participants in this study. We also thank the staff from the “Biomarker Evaluation System for Population Health” database of Huashan Hospital and the Jiangchuan Community Health Service Center for their invaluable assistance in participant recruitment and data collection.
Funding Statement
This work was supported by the Talent Development Plan funded by Shanghai Fifth People’s Hospital, Fudan University (2024WYRCSG12) and the Minhang District Natural Science Research Project (2024MHZ049) to Biying Wu. Yong Lin is supported by the Medical Specialty Construction Project of Minhang District, Shanghai (2025MWTZB01) and the Key Project of Shanghai Fifth People’s Hospital (2022WYZD03). The funding bodies had no role in the design of the study, data collection, analysis, interpretation, or paper writing.
Abbreviations
ACO, asthma–COPD overlap; AUC, area under the curve; BASO, basophils; CBC, complete blood count; CI, confidence interval; COPD, chronic obstructive pulmonary disease; COPD-SQ, COPD Screening Questionnaire; CRP, C-reactive protein; EO, eosinophils; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; HCT, hematocrit; HGB, hemoglobin; IFN, interferon; IL, interleukin; ILC2, group 2 innate lymphoid cell; IQR, interquartile range; LYMPH, lymphocytes; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MONO, monocytes; NEUT, neutrophils; NLR, neutrophil-to-lymphocyte ratio; OR, odds ratio; PCA, principal component analysis; PLT, platelet count; RBC, red blood cell count; RDW-CV, red cell distribution width – coefficient of variation; RDW-SD, red cell distribution width – standard deviation; ROC, receiver operating characteristic; SD, standard deviation; Th2, T helper type 2; TNF-α, tumor necrosis factor-alpha.
Data Sharing Statement
The datasets generated and analyzed during the current study are not publicly available to protect participant privacy, but are available from the corresponding author upon reasonable request, pending approval from the respective ethics committees.
Ethics Approval and Consent to Participate
This study was conducted in accordance with the Declaration of Helsinki and was approved by the Independent Ethics Committees of Huashan Hospital, Fudan University (Approval No. 2020-004) and Shanghai Fifth People’s Hospital, Fudan University (Approval No. 2023-153). Written informed consent was obtained from all individual participants included in the study.
Author Contributions
Biying Wu and Min Yang contributed equally to this work as co-first authors. Biying Wu: Conceptualization, Methodology, Formal analysis, Investigation, Data Curation, Writing - Original Draft, Visualization. Min Yang: Investigation, Data Curation, Project administration, Writing - Review & Editing. Xiaoying Hu: Investigation, Resources, Data Curation, Writing - Review & Editing. Wencai Ke: Investigation, Resources, Data Curation, Validation, Writing - Review & Editing. Qiudan Chen: Conceptualization, Supervision, Project administration, Writing - Review & Editing. Yong Lin: Conceptualization, Supervision, Funding acquisition, Project administration, Writing - Review & Editing. All authors gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
A preprint version of a separate, unrelated study from our research group (investigating tea consumption, IL-6, and mild cognitive impairment) has been posted on Research Square (DOI: https://doi.org/10.21203/rs.3.rs-7601893/v1). The current study is entirely independent, uses the same cohort data to address a different scientific question on lung aging, and does not share any overlapping research content. The authors have no relevant financial or non-financial interests to disclose.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and analyzed during the current study are not publicly available to protect participant privacy, but are available from the corresponding author upon reasonable request, pending approval from the respective ethics committees.