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Transcriptomic and metabolomic insights into neutrophil activity in COPD complicated by metabolic syndrome

BioMedical Engineering OnLine volume 24, Article number: 43 (2025) Cite this article

Abstract

Objectives

Chronic obstructive pulmonary disease (COPD) frequently coexists with metabolic syndrome (MS), compounding its impact on patients’ health and quality of life. This study aimed to elucidate the immune and metabolic response characteristics in COPD patients with and without MS.

Methods

A total of 11,315 COPD patients admitted to the Department of Respiratory and Critical Care Medicine at the Third People’s Hospital of Chengdu between January 1, 2013, and May 1, 2023, were selected. Multivariate logistic regression was conducted to identify the risk factors for acute exacerbation of chronic obstructive pulmonary disease. Moreover, from this cohort, 30 patients (18 with COPD and 12 with COPD–MS) were recruited for a further study to investigate the underlying mechanisms of COPD and COPD–MS. Blood samples were collected from these participants to perform transcriptomic and metabolomic analyses, aiming to explore the differences in immune responses and metabolic alterations between the two groups.

Results

Our findings indicate a significant enhancement of neutrophil-mediated immune responses in COPD–MS patients. Transcriptomic analysis revealed 327 differentially expressed genes (DEGs) significantly involved in neutrophil-mediated immunity. Key metabolic pathways were disrupted, with 39 differential metabolites identified. Notably, metabolites, such as L-homoarginine and diethanolamine, which were elevated in COPD–MS patients, showed strong correlations with DEGs involved in neutrophil pathways and immune checkpoint regulation. The study also found decreased levels of IL4 and IL5RA in COPD–MS patients, suggesting a shift from Th2 to Th1 inflammatory responses, potentially contributing to glucocorticoid resistance.

Conclusions

COPD patients with metabolic syndrome exhibit a heightened neutrophil-mediated inflammatory response and significant metabolic disturbances, which underscores the need for precise therapeutic strategies targeting both metabolic and inflammatory pathways to improve patient outcomes and manage COPD–MS complexities effectively.

Introduction

Chronic obstructive pulmonary disease (COPD) remains a significant global health concern, primarily due to its high prevalence and substantial impact on morbidity and mortality [1]. Characterized by persistent airflow limitation and a progressive decline in lung function, COPD was responsible for approximately 3.3 million deaths worldwide in 2019, with a reported prevalence of 212.3 million cases globally [2]. In China, COPD affects nearly 100 million individuals, with a prevalence rate of 13.7% among those over 40 years of age, escalating to over 27% in individuals aged 60 and above [3]. One of the most critical challenges in managing COPD is addressing acute exacerbations (AECOPD), which significantly complicate patient outcomes and healthcare utilization. AECOPD is characterized by a sudden worsening of respiratory symptoms, often requiring additional treatment and sometimes hospitalization [4]. Despite advances in diagnostic techniques and treatment modalities, the unpredictable nature and clinical complexity of AECOPD continue to present substantial burdens [5]. The variability in exacerbation severity and presentation often leads to underdiagnosis or misdiagnosis, delaying effective treatment and potentially worsening patient outcomes [6]. This underscores the pressing need for tailored therapeutic approaches based on a deeper understanding of the disease mechanisms and patient-specific factors.

Concurrent with the challenges posed by COPD is the rising prevalence of metabolic syndrome (MS), a cluster of metabolic disorders that include elevated triglycerides and apolipoprotein B (ApoB), reduced high-density lipoproteins (HDL), heightened arterial blood pressure, dysregulated glucose homeostasis, abdominal obesity, and insulin resistance (IR) [7]. Shifts in lifestyle and dietary patterns have further amplified the prevalence and impact of MS, drawing increasing attention from the medical community [8]. The intersection of MS with COPD is particularly concerning, as MS significantly heightens the risk of developing COPD, often exacerbated by factors, such as physical inactivity, smoking, and genetic predispositions [7]. Patients with both COPD and MS frequently exhibit severe symptoms, including reduced exercise tolerance and significant dyspnea [9]. They are also prone to complex metabolic disturbances such as insulin resistance, which can severely impact their quality of life and pose substantial health risks [10]. Given the rising global prevalence of COPD and its considerable health and economic impacts, it is imperative to delve deeper into the interactions between COPD and comorbidities, such as MS. Understanding these interactions is crucial for developing integrated treatment strategies that effectively manage both conditions.

This study seeks to elucidate the immune response characteristics in patients with COPD complicated by MS compared to those with COPD alone. By leveraging bioinformatics analysis of transcriptomics and metabolomics data, our findings highlight significant differences in the neutrophil-mediated immune response and lipid metabolism, providing new insights into the pathophysiology of COPD–MS.

Results

Patients experiencing AECOPD have elevated levels of NEU, HDL, and LDL

To discern factors contributing to acute exacerbation of chronic obstructive pulmonary disease, data were amassed from 11,315 patients admitted to our hospital between January 1, 2013, and May 1, 2023, for risk factor analysis (Fig. 1A). Through logistic regression analysis, we unveiled that age [OR (95% CI) = 1.02 (1.01–1.02)], neutrophils (NEU) [OR (95% CI) = 1.01 (1.01–1.01)], HDL [OR (95% CI) = 1.24 (1.10–1.40)], and LDL [OR (95% CI) = 1.18 (1.11–1.26)] were significant risk factors for acute exacerbation of COPD (P < 0.05) (Fig. 1B). This leads us to postulate that lipid metabolism and neutrophils may play pivotal roles in the inflammatory response observed in COPD patients, thus contributing to disease exacerbation.

Fig. 1
figure 1

Patients experiencing AECOPD have elevated levels of NEU, HDL, LDL. A Flowchart illustrates the stepwise process utilized to identify and categorize the risk factors associated with AECOPD. B Forest plot presents the risk factors for AECOPD

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Clinical and biochemical characteristics in COPD and COPD–MS patients

To delve deeper into exploring the potential connection between the metabolic diseases and COPD, peripheral blood samples were obtained from 30 COPD patients for omics analysis. This included 18 patients in the chronic obstructive pulmonary disease alone group (COPD) and 12 patients in the chronic obstructive pulmonary disease combined with metabolic syndrome group (COPD–MS) (Supplementary Fig. 1). The COPD–MS group has a BMI (kg/m2) of 23.89 ± 2.93 and waistline (cm) of 89.17 ± 9.82, which were significantly higher than that of the patients in the COPD group with BMI of 21.24 ± 3.18 (P < 0.05) and waistline of 77.56 ± 7.19 (P < 0.05). Low-density lipoprotein (LDL) levels, leukocytes, and neutrophil were higher in COPD–MS group than in COPD group, and the differences were all statistically significant (P < 0.05). The differences in gender, AE, FEV1/FVC, FEV1%, TC, CLU, PCT, TP, ALB, GLO, Tbil, ALT, AST, and BNP were not statistically significant (P > 0.05) (Table 1).

Table 1 Patient demographics and clinical data
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Transcriptomic insights into neutrophil immune responses in COPD–MS

In our analysis of differentially expressed genes, we employed a threshold of |log2FC|> 1 and adjusted p value <0.05. This led to the identification of 327 DEGs (Supplementary Table 1), with 173 genes upregulated and 154 genes downregulated (Fig. 2A). To gain deeper insights into the biological pathways modulated by these DEGs, we conducted GO and KEGG pathway enrichment analyses. GO functional enrichment analysis of the upregulated DEGs unveiled their predominant involvement in neutrophil-related pathways, such as neutrophil degranulation, neutrophil activation in immune responses, and neutrophil-mediated immunity. In addition, these genes were also implicated in bacterial defense mechanisms, including antimicrobial humoral response and defense against gram-negative bacteria (Fig. 2B). Moreover, in the KEGG functional enrichment analysis, the top 10 pathways enriched among the upregulated DEGs included Staphylococcus aureus infection, transcriptional misregulation in cancer, glycosaminoglycan biosynthesis, hematopoietic cell lineage, basal cell carcinoma, glycosphingolipid biosynthesis, neutrophil extracellular trap (NETs) formation, intestinal immune network of IgA production, sphingolipid metabolism, and cell adhesion molecules (Fig. 2C). Taken together, these findings underscore the pivotal role of neutrophils in the immune response observed in COPD–MS.

Fig. 2
figure 2

Transcriptomic insights into neutrophil immune responses in COPD–MS. A Volcano plots summarize findings from transcriptomics analysis of COPD–MS and COPD. The red and blue dots represent significantly up-regulated and down-regulated DEGs. B GO terms of the up-regulated differentially expressed genes. C KEGG enrichment in the up-regulated differentially expressed genes. D, E MPO (green), citH3 (red), and DAPI (blue) in peripheral blood neutrophils from COPD–MS patients (n = 9) and COPD patients (n = 10) were analyzed using confocal microscopy. F, G Peripheral blood neutrophils were stained with CD66b (red) and DAPI (blue) and analyzed using confocal microscopy. *p < 0.05, **p < 0.01, ***p < 0.001

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To validate the enhanced formation of NETs in COPD–MS, we performed immunofluorescence (IF) staining for myeloperoxidase (MPO) and citrullinated H3 (citH3) in peripheral neutrophils from both COPD and COPD–MS patients. Our results indicate a substantial release of NETs from the neutrophils of COPD–MS patients, whereas a significantly reduced NETs release was observed in the COPD group (Fig. 2D, E). Neutrophil degranulation is a critical component in the progression of pulmonary disorders [11]. We also performed IF staining for CD66b, a marker of neutrophil degranulation. Our findings demonstrate that patients with COPD–MS exhibit significantly enhanced degranulation activity compared to those with COPD (Fig. 2F, G). These results validate the involvement of neutrophil-related pathways, including NET formation and neutrophil degranulation, in the pathophysiology of COPD–MS.

Heightened neutrophil infiltration in COPD–MS patients

In our quest to decipher the immunological mechanisms of COPD–MS, we employed the CIBERSORT method to conduct immune infiltration analysis, calculating the relative proportion scores of 28 immune cells between the two cohorts (Fig. 3A and Supplementary Table 2). Notably, a significant disparity in the expression levels of immune cell markers emerged, indicative of distinct degrees of immune cell infiltration in COPD patients with and without metabolic syndrome. Of particular interest was the heightened infiltration of neutrophils observed in the COPD–MS group compared to their COPD counterparts (P < 0.05), underscoring an intensified neutrophilic response in the presence of metabolic syndrome. Conversely, the infiltration scores for eosinophils and T follicular helper cells were notably diminished in the COPD–MS group relative to the COPD group.

Fig. 3
figure 3

Heightened neutrophil infiltration in COPD–MS patients. A Bar graph shows the differential infiltration of 28 immune cells in two groups of patients. B Heatmap illustrates the correlation between 28 immune cells. C Heatmap demonstrates the correlation of 28 immune cells with immune checkpoint genes. *p < 0.05, **p < 0.01, ***p < 0.001

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Furthermore, correlation analysis among the 28 immune infiltrating cells revealed a robust association between neutrophils and a plethora of immune cells, exhibiting negative correlations with various cell types, including type 1 T helper cell, effector memory CD4 T cell, CD56 dim natural killer cell, MDSC, central memory CD4 T cell, activated CD8 T cell, effector memory CD8 T cell, eosinophils, mast cell, memory B cell, activated CD4 T cell, type 2 T helper cells, natural killer T cell, CD56bright natural killer cell, T follicular helper cell, central memory CD8 T cell, macrophage, gamma delta T cell, and was positively correlated with activated dendritic cell (Fig. 3B). These findings highlight the complex interplay of neutrophil-driven inflammation within the COPD–MS immune landscape, suggesting that neutrophils may exert both pro-inflammatory and immunoregulatory effects through their diverse interactions with other immune cells.

To delve deeper into the immunoregulatory effects on neutrophil-orchestrated inflammation, immune checkpoint genes were introduced into our investigation. Evaluation of the correlation between immune checkpoint genes and immune infiltrating cells revealed intriguing associations. Specifically, genes such as TGFA and CD274 exhibited positive correlations with neutrophil abundance, while inhibitory immune checkpoint-related genes such as PDCD1 and LAG3 displayed strong positive correlations with most immune cells (P < 0.05), but negative correlations with neutrophils (Fig. 3C). Taken together, these findings underscore the pivotal role of immune cell infiltration, particularly the presence of neutrophils, as a driving force behind the inflammatory processes observed in COPD–MS.

Metabolomic divergence between COPD and COPD–MS patients

Our metabolomics data underwent analysis utilizing the OPLS–DA method. The resulting clustering diagram showcased a significant separation in the distributions of samples between the two groups (Fig. 4A), affirming the substantial alteration in the metabolomics profile of the COPD–MS cohort and validating the efficacy of the OPLS–DA model. Our analysis identified a total of 39 differential metabolites (P < 0.05, VIP > 1, |log2FC|> = 1), with 33 metabolites exhibiting increased expression levels and 6 metabolites showing decreased levels in the COPD–MS group (Supplementary Table 3). Further annotation through cross-referencing with the KEGG database enabled the identification of 16 differential metabolites. Noteworthy among these were Stachydrine, Retinol, Proscillaridin a, Piperonal, Pantethine, Myriocin, Bisoprolol, L-pipecolic acid, L-homoarginine, Lactose, Gentianose, Diethanolamine, Argininosuccinic acid, and Betonicine, which displayed significantly elevated expression in the COPD–MS group compared to the COPD group (Fig. 4B). KEGG signaling pathway enrichment analysis of the differential metabolites unveiled that downregulated metabolites were enriched in pathways, such as bile secretion and primary bile acid synthesis, while upregulated metabolites demonstrated enrichment in metabolic pathways including arginine biosynthesis, alanine, aspartate, and glutamate metabolism, FOXO signaling pathway, Huntington’s disease, and histidine metabolism (Fig. 4C and Supplementary Table 4). Furthermore, correlation analysis of the differential metabolites utilizing the Spearman algorithm revealed robust associations among these metabolites (Fig. 4D). These findings not only delineated the metabolic distinctions between COPD and COPD–MS patients but also highlighted the complex metabolic pathways involved.

Fig. 4
figure 4

Metabolomic divergence between COPD and COPD–MS patients. A Results of OPLS–DA score of COPD–MS vs COPD group. B Bar graph displays the expression of differential metabolites in two groups of patients. C KEGG enrichment in the differentially expressed metabolites. Red represents up-regulated metabolite-enriched pathways, while blue represents down-regulated metabolite-enriched pathways. D Heatmap illustrates correlation analysis between differential metabolites. *p < 0.05, **p < 0.01, ***p < 0.001

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L-homoarginine and diethanolamine as key metabolites in COPD–MS

Correlation analysis was conducted between the differential metabolites (DEMs) and the differential genes (DEGs). The results revealed significant positive correlations between L-homoarginine and Diethanolamine with most of the DEGs involved in the top 5 GO pathways (Fig. 2B and Supplementary Table 5). Particularly noteworthy was the association of Diethanolamine with MMP9, S100A12, CD177, HP, ANXA3 and MCEMP1 and L-homoarginine with S100A12, GPR84, HP, RETN, SOX7 and CHI3L1 (Fig. 5A).

Fig. 5
figure 5

L-homoarginine and diethanolamine as key metabolites in COPD–MS. A Correlation analysis of differential genes with differential metabolites. B Bar graph exhibits the expression of key COPD genes in both groups of patients. C Heatmap depicts the correlation of key COPD genes with differential metabolites. D Bar graph shows the expression of immune checkpoint genes in both groups of patients. E Heatmap demonstrates the correlations of immune checkpoint genes with differential metabolites. *p < 0.05, **p < 0.01, ***p < 0.001

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To ascertain the importance of DEMs and DEGs in COPD, we first intersected the list of DEGs identified in this study with COPD-related genes catalogued in the GENECARD database. This intersectional analysis led to the identification of 25 key genes (Fig. 5B) that not only exhibited differential expression in this study but also have been implicated in COPD according to existing literature within the GENECARD database. Among these genes, those related to type II inflammation, such as IL4 and IL5RA, showed reduced expression in the COPD–MS group compared to the COPD group, while the expression of MMP8, CHI3L1, and CLEC5A was significantly elevated in the COPD–MS group (P < 0.05) (Fig. 5B). Subsequent correlation analysis between the 25 key genes and our DEMs revealed significant negative correlations between L-homoarginine, and diethanolamine with IL4 and IL5RA, and positive correlations with MMP8, HP, PTX3, CHI3L1, and CLEC5A (P < 0.05) (Fig. 5C).

Furthermore, given the significance of immune checkpoint molecules in immune infiltration analysis, we examined the expression levels of these molecules in the two groups. Remarkably, the COPD–MS group exhibited notably reduced expression levels of PDCD1 and LAG3 compared to the COPD group (Fig. 5D).

Given the involvement of immune checkpoint molecules in our immune infiltration analysis (Fig. 3C), we assessed their expression in the two groups. Strikingly, the expression levels of PDCD1 and LAG3 were markedly diminished in the COPD–MS group when compared to the COPD group (Fig. 5D). Moreover, we conducted an analysis to explore the relationships between immune checkpoints and DEMs. This investigation unveiled a robust correlation between diethanolamine and ITGB8 (Fig. 5E). Overall, the pronounced correlations between L-homoarginine and diethanolamine with key inflammatory genes highlight their significant roles in interconnecting the genetic landscape of COPD–MS.

Discussion

Neutrophils play a pivotal role in the inflammatory response observed in COPD [12]. This study identified HDL, LDL, and NEU as independent risk factors for acute exacerbations of COPD, indicating that lipid metabolism and neutrophils significantly contribute to disease progression. Studies have demonstrated that LDL cholesterol can induce inflammation, a critical factor in the progression of atherosclerotic diseases. This inflammation is often mediated by the activation of inflammasomes, which are implicated in various inflammatory diseases through their capacity to initiate immunological activities [13, 14]. Conversely, HDL is a pivotal plasma lipoprotein essential for improving vascular endothelial function via its role in reverse cholesterol transport. It exhibits a spectrum of biological activities, including anti-inflammatory, antioxidant, and anti-apoptotic effects, which generally contribute to its classification as ‘beneficial’ cholesterol for cardiovascular protection [15]. Our results showed that HDL cholesterol, with an odds ratio (OR) of 1.24 (95% CI 1.10–1.40), indicates a significant risk factor for exacerbation dynamics of COPD, contradicting the typical protective role attributed to HDL in cardiovascular diseases. Recent studies, however, have revealed a U-shaped relationship between HDL levels and the risk of infectious diseases, suggesting that both excessively high and low concentrations of HDL are associated with an elevated risk of these conditions [16]. Further research, particularly within Chinese populations, has shown a similar U-shaped association between HDL levels and all-cause mortality, cardiovascular mortality, and cancer mortality rates, highlighting complex interactions between HDL levels and health outcomes [17]. Elevated HDL may positively influence metabolic processes by modulating adipose tissue metabolism, enhancing adiponectin expression, and promoting fatty acid oxidation [18, 19]. In addition, in obese individuals, adipose tissue has been found to secrete adipokines that perpetuate a state of chronic, low-grade inflammation [20]. This inflammation stems from an imbalance between pro-inflammatory and anti-inflammatory factors, coupled with the infiltration of macrophages into hypertrophic adipose tissue, which further complicates the inflammatory milieu [21].

Transcriptomic analysis in the present study revealed 327 differentially expressed genes, with significant enrichment in neutrophil-related pathways, such as neutrophil degranulation and neutrophil-mediated immunity. KEGG pathway analysis further underscored the involvement of these DEGs in NETs formation, emphasizing the heightened neutrophilic immune response in COPD–MS. Neutrophils, known for their role in bacterial defense and immune regulation, release proteases and NETs, which can exacerbate airway injury in COPD [22]. NETs, first discovered by Brinkman et al. in 2004 [23], are web-like structures composed of chromatin wrapped in histones and enriched with granule proteins, such as myeloperoxidase (MPO), neutrophil elastase (NE), cathepsin G, and lactoferrin [24]. NETs release DNA, histones, and antimicrobial proteins, contributing to chronic inflammation and tissue damage [23, 25]. In metabolic syndrome, NETs may exacerbate insulin resistance and cardiovascular disease risk by promoting low-grade inflammation and oxidative stress [26]. Experiments have shown the presence of NETs in the airways of patients with both stable and acutely exacerbated COPD [27], and NETs are closely associated with persistent airway inflammation in COPD [28]. Due to the close relationship between NETs and pulmonary diseases, measuring NETs levels is currently considered a potential biomarker [29]. Our findings are consistent with previous studies, which have shown that neutrophils are key contributors to the inflammatory processes in COPD through mechanisms, such as NETs formation and degranulation [30, 31]. Although significant research has been conducted on NETs in Asian populations, studies in non-Asian populations, including European, American, and African groups, remain relatively scarce. Evidence from studies in European and American populations indicates that NET levels are elevated in individuals with obesity and type 2 diabetes and are strongly linked to atherosclerosis [26, 32, 33]. Furthermore, differences in environmental exposures and genetic backgrounds may influence NET formation and function, potentially contributing to variations in disease presentation among different racial groups [34]. Moreover, the increased neutrophil infiltration in COPD–MS, as evidenced by CIBERSORT immune infiltration analysis, indicates a more robust inflammatory response compared to COPD alone. Lipid metabolism disturbances are prominent in COPD–MS patients. These alterations may contribute to systemic oxidative stress and inflammation, further exacerbating the neutrophilic inflammatory response in COPD–MS. Our findings align with previous studies that have shown a positive correlation between oxidative stress, lipid peroxidation, and inflammation in metabolic syndrome [35]. The interplay between lipid metabolism and neutrophil activation underscores the complex pathophysiology of COPD–MS, where metabolic and inflammatory pathways converge to drive disease progression.

This study also explored the role of immune checkpoint molecules in modulating the immune response in COPD–MS. We observed decreased expression of immune checkpoint proteins such as PDCD1 and LAG3 in COPD–MS patients compared to those with COPD alone. These molecules play crucial roles in maintaining immune homeostasis and preventing excessive immune activation [36]. The reduced expression of these checkpoints in COPD–MS suggests a dysregulated immune response, potentially contributing to the heightened neutrophil-mediated inflammation observed in these patients. The correlation analysis between immune checkpoint genes and immune cells revealed significant associations. Notably, CD274 (PD-L1) and TGFA were positively correlated with neutrophil abundance, while inhibitory checkpoint genes such as PDCD1 and LAG3 exhibited negative correlations with neutrophils. This suggests that targeting immune checkpoint pathways could modulate neutrophil activity and inflammation in COPD–MS, offering potential therapeutic avenues for managing the disease.

Our findings also indicated a significant decrease in IL4 and IL5RA levels in COPD–MS patients compared to those with COPD alone. IL-4 and IL-5 are crucial cytokines involved in Th2-mediated immune responses. Decreased levels of these cytokines suggest a shift away from Th2-mediated inflammation towards a more Th1-dominated response, which is characterized by increased neutrophil activity. This shift could contribute to the observed glucocorticoid resistance in COPD–MS patients, as Th1-mediated inflammation is typically less responsive to glucocorticoid treatment [37].

Metabolomic analysis identified 39 differential metabolites between COPD and COPD–MS patients, with significant alterations in metabolic pathways, such as arginine biosynthesis, and alanine, aspartate and glutamate metabolism. L-homoarginine and diethanolamine emerged as key metabolites with strong correlations to DEGs involved in neutrophil pathways and immune checkpoint regulation. These findings highlight the potential of these metabolites as predictive biomarkers for immune responses in COPD–MS. L-homoarginine, a non-essential amino acid, has been implicated in various disease conditions, including cardiovascular diseases [38] and metabolic syndrome [39]. Its role in reprogramming macrophage polarization and cytokine responses suggests it may influence the inflammatory characteristics of COPD–MS [40, 41]. Diethanolamine (DEA), an organic compound widely used in industrial applications (such as surfactants and emulsifiers), has a chemical structure similar to lipid metabolism-related molecules, such as choline and ethanolamine, suggesting its potential to interfere with lipid metabolic pathways. It has been shown to promote inflammation by increasing levels of inflammatory cytokines [42]. Studies have shown that high doses of DEA can lead to hepatic lipid metabolism disorders in experimental animals, resulting in fatty degeneration and lipid deposition [43, 44]. Its potential involvement in lipid dysregulation and cellular stress responses suggests that abnormal DEA levels could serve as an indicator of metabolic imbalances or disease progression. The detection and quantification of these metabolites could provide valuable insights into disease mechanisms and serve as potential diagnostic markers for metabolic disorders. Further studies are needed to validate their clinical relevance and explore their utility in disease monitoring and therapeutic strategies.

The heightened neutrophilic inflammatory response and lipid metabolism disturbances in COPD–MS patients suggest that integrated therapeutic strategies targeting both metabolic and inflammatory pathways are essential for effective disease management. Immune checkpoint inhibitors have shown promise in improving outcomes for patients with lung cancer complicated by COPD [45], and their potential application in COPD–MS warrants exploration. Modulating immune checkpoints to regulate neutrophil activity and inflammation could provide a novel approach to managing COPD–MS.

Current research on COPD–MS is not fully comprehensive, largely due to the relative scarcity of transcriptomic and metabolomic studies on COPD–MS. Combining these two approaches can offer a holistic view of the molecular aspects of the disease process. This study, by applying these technologies, emphasizes the importance of neutrophil infiltration in patients with COPD–MS, filling a critical gap in existing research and providing a new perspective on the molecular mechanisms of COPD–MS.

While the average measures for BMI and waist of patients in this study remain within the ‘normal’ range and below the thresholds typically associated with metabolic syndrome, their significance in the context of COPD should not be underestimated. The diagnosis of metabolic syndrome, based on the criteria by the Diabetes Branch of the Chinese Medical Association, involves multiple parameters, not solely BMI. This also might underscore the heterogeneity within the COPD–MS group and reflects the multifaceted nature of metabolic syndrome, where a range of metabolic disturbances may occur even in patients with ‘normal’ BMI values. Our findings suggest that even normal-range BMI and waist measurements can have significant implications in the pathophysiology of COPD, particularly when combined with other markers of metabolic syndrome. Therefore, although the values remain below the typical thresholds for MS, the statistically significant differences between our study groups in BMI and waist circumference suggest that these metrics may still reveal underlying metabolic disturbances that could predispose individuals to worse outcomes in COPD. Future studies should focus on including larger and more diverse cohorts to comprehensively investigate the implications of these metabolic factors and to validate our findings. In addition, further research is needed to elucidate the underlying mechanisms at play. This study also has other limitations. In vitro and in vivo studies are necessary to further investigate the roles of identified DEGs and metabolites in COPD–MS pathophysiology and their potential as therapeutic targets. In addition, our study subjects exhibited differences in gender and age distribution, which may affect the generalizability of our results. Since gender and age are known factors that can influence a variety of physiological and pathological processes, these differences could have impacted the observed changes in genes and metabolites. Therefore, future research should strive to recruit a more representative sample to enhance the generalizability and reliability of the findings.

Conclusion

In summary, this study provides valuable insights into the immune response and metabolic alterations in COPD–MS, highlighting the crucial role of neutrophils and lipid metabolism in disease progression. The identification of potential biomarkers such as L-homoarginine and diethanolamine offers new avenues for predicting and monitoring the immune response in COPD–MS patients. Integrated therapeutic strategies addressing both metabolic and inflammatory components are essential for improving patient outcomes and managing the complexities of COPD–MS.

Methods

Study design and participant recruitment

The COPD big data platform of our department was established by Nanpeng Artificial Intelligence Technology Research Institute Co. A total of 25,638 patients with COPD who were hospitalized in Chengdu Third People’s Hospital from January 1, 2013 to May 1, 2023 were selected. For inclusion criteria, we targeted patients with AECOPD in accordance with the 2023 GOLD (Global Initiative for Chronic Obstructive Lung Disease) Guidelines [46], based on their symptoms, clinical signs, chest radiography or computed tomography scans, and pulmonary function tests. A total of 25,638 participants were included in the study. To ensure the homogeneity of the study, exclusion criteria were established, encompassing conditions such as asthma, bronchiectasis, active pulmonary tuberculosis, interstitial lung disease, stable COPD, and cases with insufficient clinical data. Ultimately, we included 11,315 patients with AECOPD for data analysis. Our screening process involved both automated data analysis and manual review by medical experts, ensuring each patient’s inclusion was grounded in objective and standardized criteria (Fig. 1).

For the metabolomics and transcriptomics study cohort, patient recruitment adhered to the principles of the Declaration of Helsinki and was approved by the Ethics Committee of the Third People’s Hospital of Chengdu. This cohort consisted of a subset of patients selected from the 11,315 AECOPD cases. Specifically, 30 patients (18 COPD and 12 COPD–MS) who were hospitalized between January 2021 and December 2021 were included for transcriptomic and metabolomic analyses. Both groups met the diagnostic criteria for AECOPD, confirming that all patients were experiencing acute exacerbations at the time of sample collection. In this part, the diagnostic criteria for COPD were referenced from the Global Initiative for Chronic Obstructive Lung Disease 2020 Report [47]. Diagnosis required the presence of respiratory-related clinical symptoms, such as wheezing or dyspnea, chronic cough, a history of exposure to risk factors, and an FEV1/FVC ratio of less than 70% [1]. Metabolic syndrome (MS) was diagnosed based on criteria recommended by the Diabetes Branch of the Chinese Medical Association (2004 edition) [48]. MS was confirmed if three or more of the following four criteria were met: (1 overweight and/or obesity with a body mass index (BMI) greater than 25 kg/m2, (2 hyperglycemia with fasting blood glucose levels exceeding 6.1 mmol/L (110 mg/dL) or 7.8 mmol/L (140 mg/dL) in individuals diagnosed with or treated for diabetes mellitus, (3 hypertension with systolic/diastolic blood pressure greater than 140/90 mmHg or in individuals diagnosed with or treated for hypertension, and (4 dyslipidemia with fasting triglyceride levels exceeding 1.7 mmol/L (150 mg/dL) or fasting HDL cholesterol levels below 0.9 mmol/L (35 mg/dL) for males or 1.0 mmol/L (39 mg/dL) for females. Exclusion criteria encompassed asthma, bronchiectasis, active pulmonary tuberculosis, and interstitial lung disease. Ultimately, 18 COPD patients and 12 COPD–MS patients were recruited following their provision of written informed consent. Clinical data collected included gender (M/F), age, hospital costs, adverse events, BMI, waist circumference, FEV1/FVC ratio, FEV1%, blood glucose levels, total cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL), high-density lipoprotein (HDL), creatinine (Cr), uric acid (UA), C-reactive protein (CRP), procalcitonin (PCT), erythrocyte sedimentation rate (ESR), white blood cell count (WBC), neutrophil count (Neu), total protein (TP), albumin (ALB), globulin (GLO), total bilirubin (Tbil), alanine aminotransferase (ALT), aspartate aminotransferase (AST), D-dimer, brain natriuretic peptide (BNP), and blood pressure (BP).

Risk factor analysis

Based on the method of sample size estimation for predictive models by Riley et al., [49] the sample size can be estimated using the ‘pmsampsize’ package in R software. A logistic regression model was deployed to discern factors associated with acute exacerbations of COPD. Independent variables (predictors), encompassed age, Body Mass Index (BMI), white blood cell count (WBC), neutrophil percentage (NEU), blood urea nitrogen (UA), blood creatinine (Cr), procalcitonin (PCT), C-reactive protein (CRP), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), and D-dimer. The incidence of acute exacerbations served as the dependent variable (outcome). Following model fitting and a stepwise selection process, odds ratios (OR) and 95% confidence intervals (CI) were calculated for each factor (Fig. 1).

This analysis was conducted using SPSS 19.0 and R 4.3.1 software. Measurement data conforming to non-normal distribution were expressed as M (Q1, Q3), with non-parametric rank-sum tests (Mann–Whitney U test) employed for intergroup comparisons. Count data were presented as percentage ratios, and group comparisons were conducted using the chi-square test or Fisher’s exact probability test. A significance level of p < 0.05 was adopted to denote statistical significance.

Transcriptomics

Peripheral whole blood samples of 2.5 mL were collected from subjects in the early morning under fasting conditions using PAXgene tubes. Immediate, thorough mixing of the RNA protectant with the blood was ensured by inverting the tubes 10–20 times. Total RNA was extracted using the TRIzol® Reagent following the manufacturer’s instructions. RNA purity was assessed using the Nanodrop ND-2000 system (Thermo Scientific, USA) to measure the A260/A280 ratio, ensuring minimal protein contamination. RNA integrity was evaluated using the Agilent Bioanalyzer 4150 system (Agilent Technologies, USA) to determine the RNA Integrity Number (RIN), and only samples with sufficient RNA integrity were used for further processing. To ensure RNA quality and reproducibility, technical replicates of selected samples were assessed for consistency before proceeding with library preparation.

Library preparation was performed using the ABclonal mRNA-seq Lib Prep Kit, followed by sequencing on the Illumina Novaseq 6000/MGISEQ-T7 platform. To ensure data quality, raw reads were processed using Fastp (version 0.23.2) to remove adapter sequences, low-quality reads (those with >60% bases below a Phred score of 25), and reads containing more than 5% undetermined bases (N). High-quality clean reads were then aligned to the Homo_sapiens_Ensembl_104 reference genome (http://asia.ensembl.org/Homo_sapiens/Info/Index) using HISAT2 (http://daehwankimlab.github.io/hisat2/) to obtain mapped reads. FeatureCounts (http://subread.sourceforge.net) was employed to quantify gene expression, and TPM (Transcripts Per Million) values were calculated for normalization.

Differential gene expression analysis between groups was conducted using the R4.1.3 software package DESeq2 [50], with |log2FC| > 1 and P < 0.05 serving as criteria for screening differentially expressed genes. The clusterProfiler R package was employed for conducting GO functional enrichment and KEGG pathway analysis [51, 52]. Significance in enrichment differences for both GO and KEGG pathways was determined using a threshold of P < 0.05.

Metabolomics

The plasma samples were extracted and mixed with a pre-cooled solution of methanol and acetonitrile (volume ratio 2:2:1) by vortex mixing. Subsequently, the mixture underwent low-temperature ultrasonic treatment for 30 min and was left at −20 °C for 10 min. After centrifugation at 4 °C, 14,000g for 20 min, the supernatant was collected for vacuum drying. The dried sample was then reconstituted with 100 μL of acetonitrile aqueous solution and subjected to centrifugation at 4 °C, 14,000g for 15 min, with the supernatant reserved for analysis. Utilizing the Agilent 1290 Infinity LC liquid chromatography system with a HILIC column, the samples were then separated under the following conditions: column temperature 25 °C, mobile phase flow rate 0.5 mL/min, and injection volume 2 μL. The elution gradient comprised water + 25 mM ammonium acetate + 25 mM ammonia water (mobile phase A) and acetonitrile (mobile phase B). Mass spectrometry was performed using the AB TripleTOF 6600 mass spectrometer, with mass spectrum acquisition in both positive and negative ion modes. The ESI source conditions were set as follows: ion Source Gas1 (Gas1) at 60, Ion Source Gas2 (Gas2) at 60, curtain gas (CUR) at 30, source temperature at 600 °C, and IonSpray Voltage Floating (ISVF) at ±5500 V. For MS-only acquisition, the instrument was configured to acquire data over the m/z range of 60–1000 Da, with an accumulation time for the TOF MS scan set at 0.20 s per spectrum. In auto MS/MS acquisition mode, the instrument was set to acquire data over the m/z range of 25–1000 Da, with an accumulation time for the product ion scan set at 0.05 s per spectrum. The product ion scan was performed using information-dependent acquisition (IDA) with high sensitivity mode selected. The parameters for this mode were as follows: collision energy (CE) fixed at 35 V with a spread of ±15 eV, declustering potential (DP) at 60 V for positive mode and −60 V for negative mode, exclusion of isotopes within 4 Da, and monitoring up to 10 candidate ions per cycle.

For data analysis, identified metabolites underwent univariate statistical analysis using R4.1.3, encompassing differential fold change analysis and T tests. Subsequently, OPLS–DA analysis was conducted using SIMCA-P16.1, with criteria for differential metabolite screening set at P < 0.05, VIP > 1, |log2FC|> = 1. Furthermore, MBROLE 2.0 was utilized for differential metabolite KEGG pathway enrichment analysis [53]. Fisher’s exact test was utilized to calculate the significance level of metabolite enrichment in each pathway, with P < 0.05 serving as the criterion for selecting significantly different metabolic pathways.

Isolation of peripheral neutrophils

Neutrophils were isolated from peripheral blood obtained from COPD patients and COPD–MS patients using Human Peripheral Blood Neutrophil Isolation Solution (P9040; Solarbio; Tongzhou, Beijing, China). Neutrophil separation media (NSM) was added to 15 mL conical tubes at room temperature. Peripheral blood was carefully layered onto the NSM to create a distinct NSM–blood interface, followed by centrifugation at 900g for 30 min at room temperature. The neutrophil layer was carefully transferred into fresh 15 mL conical tubes and washed with phosphate-buffered saline (PBS). After centrifugation, the supernatant was discarded, leaving a pellet containing neutrophils and a few red blood cells (RBCs). A lysing solution was then added to the pellet to lyse the RBCs. Following a series of washes and centrifugation steps, the final pellet obtained contained isolated neutrophils.

Immunofluorescence (IF) staining

The primary antibodies used for this study were as follows: anti-CD66b antibody (GTX19779, GeneTex), anti-MPO antibody (22225-1-AP, Proteintech), and anti-Histone H3 (citrulline R2 + R8 + R17) antibody (ab281584, Abcam). For IF, pretreated neutrophils (1 × 105 cells per well) were seeded in 96-well plates, washed with PBS, fixed in 4% paraformaldehyde, and permeabilized with 0.3% Triton X-100 (V900502, Sigma, Saint Louis, Missouri, USA) for 10 min at 37 °C. After permeabilization, the cells were incubated with blocking serum for 60 min at 37 °C. The slides were then incubated overnight at 4 °C with the following antibodies labeled using FlexAble CoraLite® Plus dyes: anti-CitH3 with CoraLite® Plus 647, anti-MPO with CoraLite® Plus 488, and anti-CD66b with CoraLite® Plus 555. The next day, the cells were washed with PBS and stained with DAPI to visualize nuclei for 10 min at 37 °C.

Fluorescence images were captured using a fluorescence microscope. All experiments were performed in triplicate to ensure reproducibility.

Evaluation of immune cell infiltration

Quantification of immune cell infiltration, encompassing 28 distinct cell types, within COPD gene expression profiles was facilitated by the CIBERSORT deconvolution algorithm [54]. Visualization and correlation analysis of these infiltrating immune cells were conducted using the “corrplot” R package. Differences in immune cell infiltration between COPD and COPD–MS cohorts were elucidated through boxplots generated via the “ggplot2” package in R.

Integrative analysis of metabolomics and transcriptomics

Following the above analyses, metabolites exhibiting significant differences were scrutinized based on VIP > 1 and p < 0.05 criteria. Differential gene expression was assessed with |log2FC|> = 1 and p < 0.05 thresholds. To comprehensively grasp the gene and metabolic alterations and evaluate the potential mechanisms underlying COPD–MS, correlation analysis employing the Pearson algorithm was undertaken to elucidate the relationship between the identified metabolites and genes.

Statistical analysis

Statistical analyses were conducted utilizing SPSS 19.0, GraphPad Prism 9.0, and the R program. Data conforming to normal distribution were expressed as mean ± standard deviation, and the independent sample t test was used for inter-group comparison. Data with non-normal distribution were represented by M (Q1, Q3), and comparison between groups was performed by non-parametric rank sum test (Mann–Whitney U test). Quantitative data are expressed as mean ± standard deviation (SD). Student’s t test and one-way ANOVA were employed for statistical comparisons, with differences considered significant at P < 0.05.

Availability of data and materials

No datasets were generated or analysed during the current study.

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Funding

This work was supported by the National Natural Science Foundation of China (82370023, 82370022, 82200079), China Postdoctoral Science Foundation (2023M730799), Natural Science Foundation of Sichuan Province (2022NSFSC1324), Sichuan Medical Association (Q21061), Chengdu High-level Key Clinical Specialty Construction Project (ZX20201202020), Chengdu Science and Technology Bureau (2021-YF09-00102-SN), the Third People’s Hospital of Chengdu Scientific Research Project (2023PI15, 2023PI12, 2023PI01, CSY-YN-03-2024-008, CSY-YN-03-2024-026, and CSY-YN-03-2024-027).

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Authors and Affiliations

  1. Laboratory of Allergy and Precision Medicine, Chengdu Institute of Respiratory Health, The Third People’s Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, College of Medicine, Southwest Jiaotong University, Chengdu, China

    Juan Li, Jiliu Liu, Bomiao Qing, Yi Zhang, Jie Gao, Xiang He, Junyi Wang & Guoping Li

  2. Department of Respiratory Medicine, Chengdu Third People’s Hospital Branch of National Clinical Research Center for Respiratory Disease, Affiliated Hospital of ChongQing Medical University, Chengdu, China

    Juan Li, Jiliu Liu, Bomiao Qing, Yi Zhang, Jie Gao, Xiang He, Junyi Wang & Guoping Li

  3. Clinical Medicine Department, North Sichuan Medical College, Nanchong, China

    Xue Wu, Jiahuan Li, Junyi Wang & Guoping Li

  4. Department of Respiratory and Critical Care Medicine, Hospital of Shimian County, Ya’an, China

    Xue Wu

  5. Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, China

    Yufen Fu

  6. Department of Respiratory and Critical Care Medicine, Longchang People’s Hospital, Neijiang, China

    Yufen Fu

  7. State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Macau SAR, China

    Yao Liu

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Contributions

JL: Data curation, Formal analysis, Writing—original draft. XW: Investigation. YFF: Formal analysis. YL: Data curation, Writing—review & editing. JHL: Data curation. BMQ: Data curation. YZ: Data curation. JG: Writing—review & editing. XH: Writing—review & editing, Supervision. JYW: Data curation, Formal analysis, Writing—original draft, Supervision. GPL: Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing—review & editing. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Xiang He, Junyi Wang or Guoping Li.

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This study was conducted in adherence to the principles outlined in the Declaration of Helsinki and received approval from the Ethics Committee of the Third People’s Hospital of Chengdu. The participants provided their written informed consent to participate in this study.

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Li, J., Wu, X., Fu, Y. et al. Transcriptomic and metabolomic insights into neutrophil activity in COPD complicated by metabolic syndrome. BioMed Eng OnLine 24, 43 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12938-025-01378-5

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