Urine volatile organic compounds in predicting chronic obstructive pulmonary disease risk in a national observational study†

Shuojia Xie‡ ^ab, Junfeng Huang‡^a, Gengjia Chen‡^ab, Huiting Luo‡^a, Yuexi Chen^ab, Xiaowen Zhong^ab, Linhang Yang^ab, Ziwen Zheng^a, Zhaowei Yang^a, Shiyue Li*^a, Linwei Tian*^c and Ruchong Chen*^a
aDepartment of Allergy and Clinical Immunology, Guangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. E-mail: chen_rch@163.com; linweit@hku.hk; lishiyue@188.com
^bNanshan School of Medical, Guangzhou Medical University, Guangzhou, China
^cSchool of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hongkong, China

First published on 24th July 2025

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Environmental significance Chronic obstructive pulmonary disease (COPD) represents a significant global health burden increasingly linked to environmental exposures. This study reveals that specific urinary volatile organic compounds (VOCs) demonstrate strong associations with COPD risk, providing a potential non-invasive biomarker for early detection. Our findings highlight the significant impact of indoor pollution, particularly VOC exposure, on respiratory health, with inflammatory pathways mediating these effects. The high diagnostic accuracy (AUC 0.90) of our combined urinary VOC model represents a promising approach for predicting COPD, enabling earlier intervention and improved health outcomes. This research advances our understanding of how environmental pollutants affect human health, emphasizing the importance of reducing indoor VOC exposure through improved air quality management and prevention strategies.

Background

Air pollution exerts adverse effects on the respiratory system, rendering individuals susceptible to respiratory pathologies such as chronic obstructive pulmonary disease (COPD).¹ Research findings underscore that exposure to air pollution is a paramount risk factor,² significantly associated with escalated morbidity and mortality of COPD.³ While outdoor air pollution has been extensively scrutinized over time, particularly concerning its connection with COPD, markedly less scientific exploration has been undertaken to assess the potential perils of indoor environmental exposures to respiratory health.⁴ A small number of studies have found that exposure to indoor pollution is capable of inducing COPD-like symptoms.⁵ Indoor pollution exposure leads to oxidative burdens in patients with COPD, and increased exposure to indoor air pollution is associated with reduced lung function in COPD populations.⁶ Therefore, clarifying the potential role of indoor pollution in COPD is critical for the identification and early prevention.

Volatile organic compounds (VOCs) represent a substantial constituent of indoor air pollution, from household products, including combustion appliances and environmental tobacco smoke.⁷ A large number of VOCs are metabolized within the human body via respiration, circulation, cutaneous secretions, excretion in urine, and fecal matter.⁸ Numerous studies have found that VOCs can significantly deteriorate air quality and seriously affect human health,⁹ particularly by causing or exacerbating respiratory-related problems.¹⁰ Previous studies suggest that exposure to VOCs may induce the development of airway inflammatory responses,¹¹ which are being explored as potential biomarkers for the diagnosis of airway inflammatory conditions.¹² The detection of VOC has been historically deemed efficacious in diagnosing an array of illnesses,¹³ which also include COPD.¹⁴ As an additional metabolic pathway for VOCs, urine sampling is widely used to assess environmental pollutant exposure because it is non-invasive, easily reproducible, and poses little or no risk to the donor for collection.¹⁵ Indeed, studies have found an association between urine VOCs and reduced lung function and exposure to tobacco smoke environments.^16,17 Similarly, COPD is characterized by a progressive decline in lung function,¹⁸ with primary risk factors being attributed to exposure to tobacco smoke emissions.¹⁹ However, the potential value of urine VOCs as a marker of COPD has not been clearly described.

Therefore, the aim of this study was to examine the relationship between urine VOCs concentrations and COPD in a representative sample of US civilians.

Methods and analysis

Study population

Study data were collected from three cycles of the National Health and Nutrition Examination Survey (NHANES) conducted between 2013 and 2018. NHANES is a cross-sectional survey designed to assess the health and nutritional status of the U.S. population living outside of institutions. Urine VOC concentrations were measured in a random sample of 9031 U.S. civilians who participated in NHANES from 2013 to 2018. Participants without complete questionnaires on blood biochemistry, blood routines, disease status, and indoor pollution exposure were excluded from our study. Fig. A1† illustrated the flow of participants included and excluded. NHANES was approved by the Ethical Review Committee of the U.S. Centers for Disease Control's National Center for Health Statistics.

Urine VOCs determination

Urine samples were collected following standardized procedures to ensure the integrity and representativeness of VOC metabolite measurements. No special fasting or dietary instructions were required for urine sample collection, an approach aligned with the study's aim to assess general population exposure to VOCs. Urine collection was standardized to occur upon participant arrival at the Mobile Examination Center (MEC), ensuring a consistent collection window relative to the MEC visit for all participants. To preserve sample integrity and prevent VOC degradation, urine specimens were immediately frozen. The protocol mandated rigorous storage procedures, requiring samples to be transported and stored chilled or frozen at −20 °C before transfer to −70 °C for long-term storage, with freeze–thaw cycles strictly limited to no more than five. NHANES uses ultra-performance liquid chromatography coupled with electrospray tandem mass spectrometry (UPLC-ESI/MSMS) to measure VOCs metabolites in human urine. UPLC-ESI/MSMS provides a highly effective platform for the sensitive and specific quantitative detection of VOCs and their metabolites. Its key advantages include rapid, high-throughput analysis, exceptional sensitivity, extensive multiplexing for simultaneous analyte detection, a gentle and efficient ionization process, and high accuracy, particularly when utilizing internal standard calibration.^20–22 In our study, urine VOCs data that were below the lower limit of detection were excluded to clarify the true concentration of the sampled population. Therefore, 17 urine VOCs were finally included in the study. Table A1 details† the specific analytes measured, their assigned code names, and their respective parent compounds.

Definition of COPD

Data on COPD and any associated symptoms were collected by trained interviewers using a standardized questionnaire. Based on the questionnaire information collected, the following question was used to define COPD: “Informed by the hospital if you have COPD?”.

Covariates

Potential confounders considered to be associated with COPD, such as age, gender, race, and smoking were controlled in the basic analysis. Smoking was defined as “smoked more than 100 cigarettes in life”. Fertile women were defined as non-pregnant women who had two or more previous normal pregnancies without any miscarriage.²³ For age-stratified analysis, subjects were classified into older (≥65 years) and younger (18–64 years) groups for comparative purposes.²⁴ In our multivariate logistic regression model, given the possible influence of renal function and indoor environmental exposures on the concentrations of VOCs in the urine, blood creatinine, blood urea nitrogen and three indoor environmental exposures were also included as confounding factors.

Statistical analysis

The analysis in this study was weighted using appropriate sample weights provided by NHANES to account for the complex sampling design of NHANES. All continuous variables were expressed as median (25th–75th), and categorical variables were expressed as frequencies. Continuous variables were tested using t-tests (normally distributed data) or non-parametric tests (non-normally distributed data), and categorical variables were tested using chi-square tests. Pearson's correlation test was used to analyze the correlation between VOCs and blood cell count. Multivariate logistic regression was used to determine the relationship between VOCs and COPD. Based on this model, the VOCs were regressed separately by varying the concentration units to initially reflect the relationship between VOCs concentration levels and the risk of COPD occurrence.

Unadjusted and adjusted logistic regression models were used to assess the risk of COPD from the potential urine VOCs in different concentration ranges. Meanwhile, environmental exposure subgroups were set within each concentration range to assess the effect of indoor pollution exposure on COPD within a range of urine VOCs concentrations. In the logistic regression model, linear trends (p for trend) were calculated in order of categorical exposure variables to initially determine the significance of the trend with concentration. A restricted cubic spline model was also used to verify the potential linear relationship between changes in urine VOCs concentrations and COPD risk. In addition, we used mediation analysis to measure the intermediary effect of inflammatory factors in the association between urine VOCs and COPD. Receiver Operating Characteristic (ROC) curves were generated to assess the ability of potential urine VOCs markers to predict the occurrence of COPD. The Youden index was used to determine the optimal threshold for the variable. Multivariate logistic regression analysis was applied and a model based on urine VOCs, age and smoking was built for COPD diagnosis.

Patient and public involvement

Patients and the public were not be involved.

Results

The baseline characteristics of included participants

The baseline characteristics of included participants are summarized in Table A2.† 782 participants with valid questionnaire information represented 31363497 US non-institutional residents, and the median age of subjects was 42.0 [25th–75th quartile: 30–57]. In our study population, 58 (7.4%) reported the presence of COPD. There were statistically significant differences in race, indoor pollution exposure, smoke exposure, and smoking between non-COPD and COPD groups (all p < 0.05). As shown in Table 1, the median age of COPD patients was 62 (25th–75th quartile: 55–66) compared with the non-COPD group (median 41; 25% quartile–75% quartile: 30–56). There was a significant difference in age between two groups. A total of 17 VOCs were included in this study for analysis. Except for N-acetyl-S-(2-carbamoylethyl)-L-cysteine, 2-aminothiazoline-4-carboxylic acid, and N-acetyl-S-(3,4-dihydroxybutyl)-L-cysteine, N-acetyl-S-(2-carbamoyl-2-hydroxyethyl)-L-cysteine and mandelic acid, participants in the COPD subgroup showed higher concentrations of 12 urine VOCs than the non-COPD subgroup, and the differences between them were statistically different (p < 0.05). Also, the COPD subgroup showed statistically significant differences in blood basophils compared to the non-COPD subgroup (p < 0.05). In the indoor pollution exposure and smoke exposure subgroups, the exposed had higher urine VOCs concentrations compared with the unexposed group. There were no statistical differences in blood cell counts, blood biochemistry, or age, except in blood urea nitrogen in the smoke exposure subgroups. In groups of other population characteristics, the study results indicate that mandelic acid levels were significantly higher in fertile women compared to non-fertile women (p = 0.04). Conversely, three VOCs including NAS-(2-c)-LC, NAS-(2-c-2-h)-LC and NAS-(2-h)-LC showed significantly lower concentrations in older individuals compared to younger individuals (all p < 0.05) (Table A3†).

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Intercorrelation between urine VOCs metabolites and blood cell counts

In the entire study population, there was a moderate correlation among urine VOCs (the largest value of the correlation coefficient was 0.93). In contrast, weak correlations (r < 0.3) or no significant (Fig. A2†) were found between urine VOCs and various blood cell types, such as leukocytes, eosinophils, neutrophils, basophils, lymphocytes and monocytes. Similar results were observed in the indoor pollution exposure and smoke exposure subgroups (Fig. A3†).

The association among urine VOCs, indoor pollution exposure, and COPD

After adjusting for indoor pollution exposure and other influencing factors, our analysis revealed that seven specific urine VOCs displayed a correlation with an elevated risk of COPD (NAS-(2-cya)-LC: OR: 1.00; NAS-(2-h)-LC: OR: 1.08; NAS-(3-h)-LC: OR: 1.00; NAS-(3-h-1-m)-LC: OR: 1.00; 2-methylhippuric acid: OR: 1.00; NAS-(4-h-2-b)-LC: OR: 1.01; NAS-(p-2-h)-LC: OR: 1.17, p < 0.05) (Fig. 1). Also, these seven urine VOCs were regressed separately by varying the concentration units based on existing models and observed a trend towards an increased relative risk of COPD (OR>1, p < 0.05) (Table A4†). The identified seven potential urine VOCs were divided into high and low concentration groups, with the results indicating a higher risk of COPD among those in the high VOC concentration group in both unadjusted and adjusted logistic regression models compared to those with low concentrations (Table A5†). Five urine VOCs except for NAS-(2-h)-LC and NAS-(4-h-2-b)-LC were statistically different in the population by linear trend test (p for trend <0.05). Also, the risk of COPD within the higher concentration VOC interval subclade after stratification by indoor pollution exposure and smoke exposure was generally consistent with the results in the population. However, the result showed that the high VOC concentrations on the risk of developing COPD appears to be stronger in the case of indoor pollution exposure and smoke exposure than in the case of non-exposure. Therefore, we further analyzed the influence of indoor environmental exposure on COPD within different VOC concentration ranges (Table A6†). In both unadjusted and adjusted logistic regression models, there was a trend towards a higher risk of COPD in people exposed to indoor pollution or smoke within the same VOC concentration interval than in the non-exposed group.

Dose–response relationship between urine VOCs and COPD

To confirm the authentic dose–response connection between urine VOCs and COPD, a comprehensive dose–response correlation was investigated for seven urine VOCs concerning COPD occurrence (Fig. 2). There was an evident dose–response relationship emerged with respect to these urine VOCs and COPD (p overall <0.05). It's noteworthy that all urine VOCs exhibited a direct linear relationship with the risk of COPD, with no indication of non-linearity (nl-p value > 0.05).

Mediation analysis

To delve into the relationship between urine VOCs, inflammatory cells, and COPD, mediation analysis was conducted to explore the role of inflammatory factors as intermediaries in connecting urine VOCs to COPD. Table 2 provides insights into the direct and indirect effects of urine VOCs on COPD, with inflammatory cells serving as the mediators. In summary, leukocytes, basophils, and lymphocytes emerged as significant mediators in the relationship between urine VOCs and COPD. These factors contributed 7.66%, 7.09%, and 10.50%, respectively, to the mediation effect. No significant mediation effects were observed for other blood cells (p > 0.05).

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ROC curve analysis of urine VOCs in the diagnosis of COPD

The ROC curve was used to assess the predictive capability of potential urine VOCs in diagnosing COPD, measuring sensitivity and specificity (Fig. A4†). These seven urine VOCs demonstrated moderate predictive abilities for COPD, with all AUC values exceeding 0.60. Notably, NAS-(3-h-1-m)-LC and NAS-(4-h-2-b)-LC were the most effective single urine VOC predictors. NAS-(3-h-1-m)-LC achieved an AUC of 0.77 with a best cut-off value of 871, providing specificity and sensitivity rates of 89% and 58%, respectively. Meanwhile, NAS-(4-h-2-b)-LC achieved an AUC of 0.75 with a best cut-off value of 21.7, along with specificity and sensitivity rates of 89% and 54%, respectively (Table A7†). To further investigate the predictive value of these two potential VOCs in combination with age and smoking, the combined models were constructed to predict the occurrence of COPD (Fig. 3). The combination of these markers significantly enhanced the predictive power. After combination, NAS-(3-h-1-m)-LC achieved the highest AUC of 0.90, with specificity and sensitivity rates of 87% and 81%, respectively. Likewise, NAS-(4-h-2-b)-LC reached a maximum AUC of 0.90 post-conjugation, with specificity and sensitivity rates of 87% and 80%, respectively (Table A8†).

Discussion

This study explored the potential role of urine VOCs and indoor pollution exposure on the risk of COPD based on a nationally representative sample of the US population. Our results show that seven urine VOCs with higher concentrations were associated with the risk of COPD in the US population, which exhibited a dose–response relationship. Inflammatory factors may act as a mediating variable between urine VOCs and COPD. Furthermore, the presence of indoor pollution exposure and smoke exposure was associated with an increased risk of COPD. The results of ROC analysis demonstrated that the urine VOCs have good precision for the prediction of COPD patients. To our knowledge, this is the first study to systematically assess the effect of urine VOCs on COPD. Overall, the current study may provide preliminary evidence for the use of urine VOCs in the early detection and prediction of COPD.

Clinically, the exposure to elevated levels of VOCs has been observed to contribute to the development of chronic airway disease.²⁵ In comprehensive studies encompassing all age groups, it has been established that heightened levels of VOCs are significantly correlated with diminished lung function.^26,27 Furthermore, exposure to VOCs has been linked to an increase in inflammatory markers associated with airway inflammation.²⁸ A previous controlled study has indicated that patients with chronic obstructive pulmonary disease (COPD) exhibit a lesser reduction in each successive breath sample of isoprene compared to healthy individuals, suggesting a potential association with impaired pulmonary function in COPD.²⁹ A time-series study revealed that benzene and toluene are risk factors for acute exacerbations of COPD and may also elevate the likelihood of hospitalization for acute exacerbations of COPD (AECOPD).³⁰ Exposure to VOC in building paints and household products was positively associated with an increased risk of COPD emergency hospitalization.³¹ Our research aligns with these findings, particularly in regards to the association between N-acetyl-S-(4-hydroxy-2-butenyl)-L-cysteine, an isoprene metabolite, and the risk of COPD. Additionally, it is worth noting that other VOCs detected in urine samples may potentially play a role in the occurrence of COPD. Additionally, our study investigates potential dose–response relationship between VOCs and COPD. The findings from our research offer substantiation for the detrimental impact of VOCs on COPD, and the dose–response relationship identified may establish the threshold doses necessary for future evaluations of VOC-induced harm in relation to COPD.

Research indicates that VOCs may raise the risk of airway inflammation by causing oxidative stress.³² Specifically, studies have shown that certain VOCs significantly increase the production of reactive oxygen species (ROS) in various cell types and animal models.^33,34 VOC exposure can also lead to increased production of mitochondrial ROS, thereby exacerbating cellular damage.³⁵ Furthermore, a study on childhood asthma found a strong dose–response relationship between VOC metabolites and the oxidative stress biomarker.³⁶ In addition to oxidative stress, VOCs are potent inducers of inflammatory responses. Exposure to VOCs triggers inflammation characterized by an increase in pro-inflammatory cytokines. In airway epithelial cells, VOCs can activate NF-κB and AP-1 pathways, leading to chronic airway inflammation.^37,38 Additionally, VOCs can induce chronic inflammatory diseases by enhancing the expression of inflammatory genes (e.g., IL-8).^39,40 In animal experiments, VOC exposure notably increased indicators of Th2 airway inflammation. Exemplified by aldehydes and benzene compounds, exposure to ether and xylene was found to be significantly associated with elevated levels of inflammatory factors,^41,42 suggesting a potential link to Type 2 helper T cells (Th2) inflammatory cytokines and airway inflammation.⁴³ Regarding metabolism, VOC-based biomimetic models indicate their capacity to induce apoptosis and inflammatory responses, often linked to oxidative stress pathways.⁴⁴ VOCs further disrupt metabolic homeostasis by regulating inflammatory mediators and activating key signaling pathways.⁴⁵ Moreover, VOCs can directly inhibit or induce the regulation of key metabolic enzymes, potentially altering the efficiency of various metabolic pathways within the body.⁴⁶ VOCs may also lead to genetic alterations. Inhalation VOCs may alter miRNA patterns that regulate gene expression, possibly leading to inflammatory disease.⁴⁷ Furthermore, inhaling VOCs related to fuels may trigger airway hyperresponsiveness due to DNA damage-induced apoptosis in alveolar septal cells.⁴⁸ Oxidative stress, increased levels of DNA damage and altered miRNA regulation have long been shown to be associated with the pathogenesis of COPD.^49–51 Meanwhile, Th2 lineage plays an important role in the pathogenesis and emergence of acute exacerbations of COPD associated with biomass-burning smoke exposure.⁵² In summary, these evidences suggested that the effects of VOCs may promote or exacerbate pathophysiological changes in the occurrence or development of COPD. However, our results showed a weak correlation between urine VOCs and blood cell counts. It may be due to the fact that urine VOCs metabolites may reflect metabolic conditions within the body and long-term lifestyle.⁵³ The number of blood cell changes may be more susceptible to the effects of short-term stimuli, such as infections and allergies.⁵⁴ Therefore, there may be some association between urine VOCs and blood cell counts in some instances, the correlation may be less pronounced in relatively healthy populations. Nevertheless, we observed that leukocytes, basophils, and lymphocytes were significantly associated with elevated urine VOCs concentrations. On this basis, we reconfirmed the inflammation-mediated pathway between VOCs and COPD through mediation analysis. Interestingly, we found significant mediating roles for basophils, which may possibly due to the fact that basophils inflammation exacerbates the development of emphysema and thus contributes to COPD.⁵⁵ More studies are needed to evaluate the relationship between inflammatory factors and VOCs in COPD.

Biomass fuel smoke and secondhand smoke are significant sources of indoor air pollution, impacting respiratory health, particularly in conditions like COPD.⁵⁶ Epidemiological surveys have found that indoor biomass smoke exposure increases the risk of COPD and accounts for 50% of deaths in COPD patients in developing countries.⁵⁷ Likewise, exposure to solid fuel smoke was significantly linked to increased COPD mortality and COPD prevalence.⁵⁸ Meanwhile, environmental tobacco smoke was associated with an increased risk of COPD.⁵⁹ These results were identical to what was observed in our study, we reconfirmed the dangerous role of indoor pollution exposure, especially indoor smoke exposure, in the occurrence of COPD. Therefore, improving indoor air quality may contribute to the prevention of respiratory diseases such as COPD. Two randomized controlled studies showed that improved indoor air following the use of air purifiers was significantly associated with reduced symptoms and improved respiratory scale scores in COPD.^60,61 In conclusion, more studies are needed to conclude the effect of indoor pollutants that may affect respiratory disease and to provide more evidence for developing interventions to improve respiratory health.

Urine VOCs are increasingly being used as convenient and noninvasive biomarkers for studying the relationship between health and disease. Researchers have found a positive link between urine VOCs and BMI, potentially linked to diabetes risk.⁶² Additionally, urine VOCs are associated with specific dermatitis.⁶³ In terms of respiratory health, urine VOC metabolites were significantly associated with decreased lung function and increased airway inflammation.^16,27 One study indicated that the main site of lung toxicity of VOCs may be located in the airway, especially the small airway.²⁷ It has been shown that urine VOCs provide useful information about oxidative stress damage.⁶⁴ Further, urine VOCs have been shown to be a potentially useful biomarker for assessing exposure to tobacco smoke in environmental studies⁶⁵ and have also been found to be a good differentiator between e-cigarette and combustible cigarette use.⁶⁶ Small airway lesions and oxidative stress are characteristics of COPD, while tobacco smoke is closely related to the development of COPD. Therefore, urine VOCs may play a role in the development and progression of COPD. Overall, urine VOCs have the potential for application in disease assessment, and more studies are still needed to assess their application value.

Due to the continuous development of analytical techniques for detecting VOCs, the diagnostic and predictive models based on VOCs and different diseases show great effects. At present, exhaled breath VOCs have been tried to be applied to the diagnosis of a variety of tumor and non-tumor diseases, such as lung cancer and diabetes.^67,68 Evidence from multiple studies have significantly advanced our understanding of VOCs in respiratory diseases, particularly focusing on breath VOCs.^69,70 As for COPD, VOC-based models have made notable progress in the application of COPD, especially for exhaled breath VOCs in recent years. Exhaled breath VOC profiles of COPD patients differ from healthy populations.⁷¹ Exhaled breath VOCs were also found to correctly classify and differentiate COPD patients from healthy subjects with an overall accuracy of >70%.¹⁴ At the same time, the exhaled breath VOCs could identify bacterial colonization in COPD patients⁷² and initially identify the clinical phenotype of COPD.⁷³ Previous research has explored metabolomics in the context of environmental pollution, highlights the application of metabolomics in assessing human exposure to environmental pollutants, including associations between air pollution and respiratory health (e.g., COPD).⁷⁴ Although breath analysis provides insights into acute or recent exposures and lung-specific VOC profiles, its implementation can be technically demanding.^75,76 Furthermore, the volatile nature of breath compounds may lead to reflections of transient physiological states.⁷⁷ As another metabolic pathway of VOCs, urine VOCs present advantages for exposure assessment. Their extended physiological half-lives and non-invasive collection allow them to function as integrated screening indicators for various environmental and occupational VOC exposures, providing a systemic reflection of absorption and metabolism over a prolonged duration.^77,78 Meanwhile, urine VOCs have been shown to play a good role in the evaluation of some diseases, especially cancer.⁷⁹ Studies have shown that urine VOCs show great performance in cancer screening.⁸⁰ On this basis, our study examined the role of various urine VOCs in COPD and mediated inflammation using multivariate and mediation analyses. To our knowledge, our study was the first to use urine VOCs for the prediction of COPD occurrence, demonstrating relatively good power in a combined prediction model. In conclusion, urine VOCs may be a potential biomarker for the prediction of COPD and more studies are needed in the future to fully evaluate the value of urine VOCs in the prediction of COPD.

Our study has some limitations. First, NHANES only included non-institutionalized civilians; potentially leading to an underrepresentation of certain segments of the US population. Second, the NHANES survey is cross-sectional and can only suggest associations rather than causality. Further prospective studies are needed to demonstrate the causality and potential mechanisms of these associations. Third, the diagnosis of COPD in our study was based on self-reported information. The absence of a physician-led diagnostic protocol in this method may introduce potential bias. Finally, the study population focused on the Americas, which may limit the generalizability of the results. In future studies, it may be necessary to extend our results to other populations to overcome this difficulty.

Conclusions

Our study suggests that seven urine VOCs were associated with an increased risk of COPD. Urine VOCs can mediate inflammatory reactions and then promote the occurrence of COPD. Indoor pollution exposure and smoke exposure may increase the risk of COPD. The model based on urine VOCs demonstrated reasonably good prediction of COPD, which may be beneficial for screening patients with COPD at the early stage. More studies are needed to elucidate the mechanism of the association between urine VOCs and COPD and the potential for clinical application.

List of abbreviations

COPD	Chronic obstructive pulmonary disease
VOCs	Volatile organic compounds
US	United States
NHANES	National health and nutrition examination survey
UPLC-ESI/MSMS	Ultra-performance liquid chromatography coupled with electrospray tandem mass spectrometry
MEC	Mobile examination centre
CAPI	Computer assisted personal interview
ROC	Receiver operating characteristic
nl-p value	Non-linear p-value
AUC	Area under the curve
AECOPD	Acute exacerbations of COPD
Th2	Type 2 helper T cells
MiRNAMicroRNADNA	MicroRNADNADeoxyribonucleic acid
Ecigarette	Electronic cigarette

Ethics approval and consent to participate

In accordance with the Declaration of Helsinki, the data for this study were sourced from the National Health and Nutrition Examination Survey (NHANES) database, which is jointly managed by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS) in the United States.

Consent for publication

Participants had provided explicit consent for the use of their data for scientific research purposes at the time of their involvement in the NHANES survey.

Data availability

NHANES data are publicly available through the Center for Disease Control (https://wwwn.cdc.gov/nchs/nhanes/Default.aspx). The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Author contributions

SL, LT and RC conceived this study and supervised all aspects of its implementation. SX, JH, GC and HL collaborated in the inception of the study and carried out the analysis of the data. YC, XZ, LY, ZZ and ZY collected the data and collaborated in the analysis. All the authors contributed to the interpretation of the results and the proof reading of the manuscript. The corresponding author attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. Transparency: Ruchong Chen affirms that the manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Conflicts of interest

All authors declare that they have no conflict of interest. All authors have completed the ICMJE uniform disclosure form at https://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (grant numbers: 81870079); Guangdong Science and Technology Project (grant numbers: 2021A050520012); Incubation Program of National Science Foundation for Distinguished Young Scholars of Guangzhou Medical university (grant numbers: GMU2020-207) and Guangdong Basic and Appied Basic Research Foundation (grant numbers: 2022B1515120055). The authors express their sincerest gratitude to the study participants for their invaluable contributions to the advancement of medicine. We also extend our heartfelt appreciation to the anonymous reviewers, whose diligent efforts significantly enhanced the quality of this paper.

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Footnotes

† Electronic supplementary information (ESI) available. See DOI: https://doi.org/10.1039/d5em00181a

‡ These authors contributed equally as co-first authors.

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