Biomarkers of the relationship of particulate matter exposure with the progression of chronic respiratory diseases

Article information

Korean J Intern Med. 2024;39(1):25-33

Publication date (electronic) : 2024 January 1

doi : https://doi.org/10.3904/kjim.2023.393

Junghyun Kim ¹^,^*, Soo Jie Chung ¹^,^*, Woo Jin Kim ²^,³

¹Division of Pulmonary, Allergy and Critical Care Medicine, Department of Internal Medicine, Hallym University Dongtan Sacred Heart Hospital, Hwaseong, Korea

²Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, Chuncheon, Korea

³Department of Internal Medicine, School of Medicine, Kangwon National University, Chuncheon, Korea

Correspondence to: Woo Jin Kim, M.D., Ph.D., Department of Internal Medicine and Environmental Health Center, Kangwon National University Hospital, 156 Baengnyeong-ro, Chuncheon, 24289, Korea, Tel: +82-33-258-9303, Fax: +82-33-255-6567, E-mail: pulmo2@kangwon.ac.kr

These authors contributed equally to this manuscript.

Received 2023 September 19; Revised 2023 October 30; Accepted 2023 November 17.

Abstract

A high level of particulate matter (PM) in air is correlated with the onset and development of chronic respiratory diseases. We conducted a systematic literature review, searching the MEDLINE, EMBASE, and Cochrane databases for studies of biomarkers of the effect of PM exposure on chronic respiratory diseases and the progression thereof. Thirty-eight articles on biomarkers of the progression of chronic respiratory diseases after exposure to PM were identified, four of which were eligible for review. Serum, sputum, urine, and exhaled breath condensate biomarkers of the effect of PM exposure on chronic obstructive pulmonary disease (COPD) and asthma had a variety of underlying mechanisms. We summarized the functions of biomarkers linked to COPD and asthma and their biological plausibility. We identified few biomarkers of PM exposure-related progression of chronic respiratory diseases. The included studies were restricted to those on biomarkers of the relationship of PM exposure with the progression of chronic respiratory diseases. The predictive power of biomarkers of the effect of PM exposure on chronic respiratory diseases varies according to the functions of the biomarkers.

Keywords: Asthma; Biomarkers; Lung diseases; Pulmonary disease, chronic obstructive; Particulate matter

INTRODUCTION

What is a biomarker?

According to the United States National Institutes of Health, a biomarker is a “characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacologic responses to a therapeutic intervention” [1]. Thus, biomarkers are measurable indicators of molecular, histologic, radiographic, or physiologic characteristics [2].

Particulate matter (PM)

Air pollution is a major public health concern, and PM is a major component of polluted air [3]. PM comprises a combination of solid and liquid particles [4]. The typical chemical components of PM include sulfate, nitrate, and carbon in both elemental and organic forms, as well as organic substances, biological materials, and heavy metals [5]. An increase in PM exposure has been linked to the mortality and morbidity associated with respiratory diseases [3,6–8].

BIOMARKERS OF PM EXPOSURE: CHRONIC RESPIRATORY DISEASES

Chronic obstructive pulmonary disease (COPD)

According to the Global Burden of Disease Study [9], exposure to fine PM (PM_2.5, aerodynamic diameter ≤ 2.5 μm), which is linked to COPD, has increased, particularly in the elderly population [10]. Exposure to PM_2.5 decreases lung function and aggravates respiratory diseases [11], leading to hospitalization, morbidity, and mortality [12–14]. Biomarkers are categorized as blood, urine, sputum, and fractional exhaled nitric oxide (FeNO) (Table 1).

Table 1

Biomarkers of PM and COPD

Exposure to PM alters the levels of blood biomarkers in patients with COPD. Ubiquitin and beclin 1 levels change with increasing PM exposure [15]. The blood levels of some interleukins (ILs) increased, whereas the levels of others decreased, with increasing PM exposure [16,17]. An increased blood C-reactive protein (CRP) level is a marker of inflammation [18–20]. Also, biomarkers related to epigenetic regulation, such as reduced DNA methylation, have been identified [21].

Exposure to PM modulates the levels of urine biomarkers. The levels in urine of the metabolites 8-hydroxy-2′-deoxyguanosine (8-OHdG) and malondialdehyde (MDA) increased with increasing PM exposure [22,23]. Additionally, the levels of uric acid, glyceric acid 1,3-biphosphate (GABP), and dopamine-4-sulfate were elevated with increasing PM exposure [24].

Regarding sputum biomarkers, the black carbon (BC) content of airway macrophages (AMs), which target external environmental substances such as PM in the respiratory tract, increases as the PM level increases, suggesting potential as a biomarker of elevated PM exposure [25]. In addition, the level of FeNO, a marker of airway inflammation, increases after PM exposure in COPD [21,26].

Asthma

The air exhaled by patients with asthma contains biomarkers of PM exposure. The levels of biomarkers of asthma can be measured in exhaled breath condensate (EBC), blood, urine, and saliva (Table 2).

Table 2

Biomarkers of PM and asthma

Whether exposure to PM is associated with oxidative stress was evaluated in an epidemiological study. The authors assessed exposure to PM by quantifying nitrite plus nitrate (NOx) in the EBC of 133 subjects with asthma or COPD [27]. The pH of EBC has promise as a biomarker of airway inflammation. In a cross-sectional study, asthma was significantly associated with a low EBC pH independent of indoor air quality [28].

Blood biomarkers have been suggested to be associated with PM exposure in patients with asthma. One study evaluated the effect of asthma status on the association between polluted air exposure and systemic effects by measuring blood levels of cytokines. Exposure to NO₂ and PM was associated with higher levels of the proinflammatory cytokines IL-6 and TNF-α [29]. The levels of proinflammatory (IgE, IL-4, and IL-13) biomarkers were elevated following exposure to metals in PM_2.5 in outdoor air around the homes of patients with asthma [30]. Indeed, air pollution, including PM, reduced the serum levels of TNF-α, IL-6, IL-8, and IL-10 in patients with asthma for 2 months [31].

Urinary leukotriene E4 is a biomarker of exposure to < 2.5 mm ambient polluted air (AMB-PM_2.5) or secondhand smoke (SHS-PM_2.5) [32]. In children with asthma, urinary 6-sulfatoxymelatonin (aMT6s), a surrogate marker of the circulating melatonin level, is associated with an increased level of MDA, a biomarker of systemic oxidative stress. Increased daily personal exposure to ozone (O₃) and PM_2.5 was associated with an increased level of aMT6s in patients with asthma [33].

Sputum markers of lung inflammation have been reported. Chemometric and regression results showed that the sputum CD63 level explained 72% of the variance in asthma incidence among patients exposed to PM_2.5, and the models had high predictive accuracies. CD63 is associated with the activation and degranulation of eosinophils and neutrophils [34].

BIOMARKERS OF THE EFFECT OF PM EXPOSURE ON THE PROGRESSION OF CHRONIC RESPIRATORY DISEASES

Most biomarkers of the effect of PM exposure on chronic respiratory diseases have been evaluated in a small number of epidemiological cross-sectional studies. This literature review focuses on the mechanisms underlying the progression of chronic respiratory diseases linked to PM, with the aim of identifying biomarkers of the effect of PM exposure in patients with chronic respiratory diseases.

Methods

Eligibility criteria

Disease progression is defined in terms of incidence, progression, or the clinical deterioration of patients with chronic respiratory diseases (e.g., COPD, asthma, or idiopathic pulmonary fibrosis [IPF]). However, none of the studies focused on IPF; therefore, we reviewed studies on COPD and asthma. We performed a literature search based on the following inclusion criteria: population: patients with chronic respiratory diseases (COPD and asthma); intervention and comparator: PM (PM_2.5, PM₁₀) exposure; outcomes: biomarkers of disease progression according to PM exposure; published after 2013; and full-text articles in English. The exclusion criteria were as follows: studies that did not target patients; studies that did not involve exposure to PM; studies that did not report the outcomes of interest; and duplicate studies.

Information sources and search strategy

We searched the Ovid MEDLINE, Ovid EMBASE, and Cochrane Central Register of Controlled Trials electronic databases on 20 January 2023, for articles describing studies of biomarkers of the effect of PM exposure on chronic respiratory diseases published after 1 January 2013. The search was performed using the following combinations of keywords: (“particulate matter” OR “PM₁₀” OR “PM_2.5”) AND (“chronic obstructive pulmonary disease” OR “COPD”) AND (“biomarker” OR “Biomarkers”) AND (“Incidence” OR “Disease progression” OR “Clinical deterioration”). Chronic respiratory disease was considered to encompass diseases such as asthma and IPF. Synonyms for PM, biomarkers, chronic respiratory disease, and disease progression were included among Medical Subject Headings (MeSH) terms, EMBASE subject headings, and text words. The search was limited to studies published in the English language. The search strategy is shown in Supplementary File 1.

Study selection and statistical analysis

Two expert pulmonologists (JK and SJC) screened the titles and abstracts of articles according to the inclusion criteria. Each author independently assessed the eligibility of the studies; conflicts were resolved by discussion. Full texts were assessed by two authors (JK and SJC) to reach a final decision on article inclusion or exclusion. Disagreement was resolved by discussion with a third author (WJK). No other statistical analysis was performed.

Risk of bias assessment

A validated tool was used to evaluate risk of bias based on study design. The Risk of Bias Assessment Tool for Non-randomized Studies (RoBANS) 2.0 was used for nonrandomized studies [35]. This tool comprises eight domains: possibility of target group comparisons, target group selection, confounders, exposure measurement, blinding of assessors, outcome assessment, incomplete outcome data, and selective outcomes. Each domain was classified as low, high, or unclear risk of bias. Quality assessments were conducted by two authors (JK and SJC), and disagreements were resolved by discussion with a third author (WJK).

Results

A total of 58 studies were identified, and 20 duplicate studies were removed before screening. Of the remaining 38 studies, 30 were excluded after screening the titles and abstracts. Subsequently, the full texts of eight studies were reviewed for eligibility. Ultimately, four studies were included (Fig. 1). Two articles contained related contents by same authors. The excluded studies and reasons for their exclusion are provided in Supplementary File 2.

Figure 1

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.

The characteristics of the included studies are listed in Table 3. Three studies [36–38] had a high risk of bias in the possibility of target group comparisons domain, and another [38] in the confounders domain. One study [39] had a high risk of bias in the exposure measurement domain, and another [38] in the outcome domain (Fig. 2).

Table 3

Characteristics of the included studies on the relationships of biomarkers of PM exposure with the progression of chronic respiratory diseases

Figure 2

Risk of bias of the included studies according to the judgement of the authors.

Few studies have evaluated the relationships of biomarkers of PM exposure with the progression of chronic respiratory diseases. This review focuses on biomarkers of the effect of PM on chronic respiratory diseases (COPD and asthma) and their biological plausibility.

BC in AMs

AMs, which are important immune cells for tissue repair in COPD, are exposed to airborne pollutants, including PM. AM-BC, measured as the mean BC cross-sectional area (mm²) per macrophage, is a biomarker of exposure to airborne PM and tobacco smoke. Tejwani et al. [40] investigated the relationships of the AM-BC level and PM_2.5 exposure with sputum, spirometry, and respiratory outcomes. They used spatiotemoral modeling according to subjects’ address for estimating outdoor pollution [41,42] and also used mesurement modeling from previous study for estimating indoor polution [43]. The results showed that the AM-BC level has potential as a biomarker of COPD exacerbation and PM exposure [40]. An increase in the indoor PM_2.5 concentration was associated with an increase in AM-BC area (β = 9.96%, 95% confidence interval [CI] 0.47–20.4%) and percentage (β = 13.1%, 95% CI 3.49–23.6%). An increase in AM-BC area was not associated with an increase in overall COPD exacerbation frequency over 1 year but was associated with an increased risk of severe exacerbations (odds ratio [OR] = 1.52, 95% CI 1.01–2.28). Similarly, an increase in AM-BC percentage was not correlated with an increase in the overall exacerbation frequency but was associated with an increased risk of severe exacerbations (OR = 2.19, 95% CI 1.0–44.63).

CRP and prothrombin time (PT)

Abnormal inflammatory responses to environmental factors, including PM, have been linked to COPD. CRP, a marker of the systemic inflammatory response, has been proposed as a biomarker of COPD. Tang et al. measured the blood CRP level and PT in 317 patients with acute COPD exacerbation (AECOPD) 1 day before admission; the CRP level was increased during COPD exacerbation and PM elevation [38]. Of the subjects, 13.25% were smokers, and their occupational histories were not evaluated. Patients with AECOPD exposed to > 25 mg/L PM_2.5 had a significantly shorter PT and a significantly higher CRP level compared to those exposed to ≤ 25 mg/L PM_2.5.

Ultrafine particles (UFPs)

UFPs are small, toxic materials that reflect the severity of inflammation in patients with COPD. Einat et al. evaluated the UFP levels in the EBC and blood of patients with COPD and 40 healthy controls. A low UFP level in EBC and a high level in serum were indicative of high PM exposure [36]. In addition, a low UFP level in EBC was an independent predictor of frequent exacerbations (OR = 3.6, 95% CI 1.06–7.97; p = 0.04). Therefore, a low UFP level in EBC has potential as a biomarker of COPD exacerbation and high PM exposure [37].

Salivary IL-6 level

Exposure to air pollution causes inflammation in the oral cavity, which may exacerbate airway inflammation and asthma symptoms. He et al. [39] reported that asthmatic children had an elevated salivary IL-6 level, a biomarker of an increased PM_2.5 level, and exacerbation of asthma symptoms. They used a specific sensor which checked the average of weekly exposure for each individual and also used the analysis technology suggested in previous research [44]. Additionally, the asthma control test (ACT) score decreased as the salivary IL-6 level increased, indicating worsening of asthma symptoms.

Epidemiologic perspectives

Environmental factors, including PM exposure, have a marked effect on the development and course of chronic respiratory diseases, including asthma and COPD [7,8]. Therefore, monitoring and reducing PM exposure is important for controlling these diseases [1]. Biomarkers are important considerations when formulating regulations to restrict emissions [45]. Therefore, the identification of biomarkers of PM exposure would improve the management of chronic respiratory diseases.

In this study, we described biomarkers of the effect of PM exposure on asthma and COPD and reviewed studies on biomarkers of PM exposure during exacerbations of those diseases. Only one study evaluated biomarkers associated with PM exposure in patients with COPD [46].

This review had several limitations. Most of the included studies evaluated a single disease-related biomarker in populations distinguished based on age, race, country, and respiratory disease status compared to healthy controls or the effect of PM exposure on the levels of biomarkers. This introduces a risk of selection bias when attempting to generalize the results (Fig. 2). Additionally, although standardized assessment methods facilitate the assessment of PM exposure, uncertainty remains because of rapid technological advancements. Other potential covariates—such as components of outdoor/indoor air pollutants, smoking history, and occupational history—which are risk factors for disease, should also be evaluated. These covariates were not accounted for in some studies, so further research is needed.

CONCLUSIONS

Few studies have explored the relationships of PM biomarkers with the progression of chronic respiratory diseases. The predictive power of biomarkers of the effect of PM exposure on chronic respiratory diseases varies according to the functions of the biomarkers. Because of the limitations of the included studies, there are few data on biomarkers of the effect of PM exposure on chronic respiratory diseases. Further prospective studies are required to establish regulations to reduce airborne PM and develop public health strategies for the prevention and management of respiratory diseases caused by environmental factors.

Supplementary Information

Acknowledgments

This research was supported by the Korea Environment Industry and Technology Institute through the Core Technology Development Project for Environmental Disease Prevention and Management, funded by the Korea Ministry of Environment (number 2022003310009).

Notes

CRedit authorship contributions

Junghyun Kim: conceptualization, methodology, data curation, writing - original draft, writing - review & editing; Soo Jie chung: methodology, writing - original draft; Woo Jin Kim: conceptualization, methodology, writing - review & editing, funding acquisition

Conflicts of interest

The authors disclose no conflicts.

Funding

This study was supported by the Korea Environment Industry and Technology Institute through the Core Technology Development Project for Environmental Disease Prevention and Management, funded by the Korea Ministry of the Environment (number 2022003310009).

References

1. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001;69:89–95.

2. Califf RM. Biomarker definitions and their applications. Exp Biol Med (Maywood) 2018;243:213–221.

3. Kelly FJ, Fussell JC. Air pollution and public health: emerging hazards and improved understanding of risk. Environ Geochem Health 2015;37:631–649.

4. World Health Organization. Regional Office for Europe. Health effects of particulate matter: policy implications for countries in eastern Europe, Caucasus and central Asia Copenhagen: World Health Organization; c2013. [cited 2023 Sep 1]. Available from: https://iris.who.int/handle/10665/344854 .

5. Kim KH, Kabir E, Kabir S. A review on the human health impact of airborne particulate matter. Environ Int 2015;74:136–143.

6. Peters A, Wichmann HE, Tuch T, Heinrich J, Heyder J. Respiratory effects are associated with the number of ultrafine particles. Am J Respir Crit Care Med 1997;155:1376–1383.

7. Kim D, Chen Z, Zhou LF, Huang SX. Air pollutants and early origins of respiratory diseases. Chronic Dis Transl Med 2018;4:75–94.

8. Zhao J, Li M, Wang Z, et al. Role of PM_2.5 in the development and progression of COPD and its mechanisms. Respir Res 2019;20:120.

9. Yang X, Zhang T, Zhang Y, Chen H, Sang S. Global burden of COPD attributable to ambient PM_2.5 in 204 countries and territories, 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. Sci Total Environ 2021;796:148819.

10. Han C, Oh J, Lim YH, Kim S, Hong YC. Long-term exposure to fine particulate matter and development of chronic obstructive pulmonary disease in the elderly. Environ Int 2020;143:105895.

11. Bloemsma LD, Hoek G, Smit LAM. Panel studies of air pollution in patients with COPD: systematic review and meta-analysis. Environ Res 2016;151:458–468.

12. DeVries R, Kriebel D, Sama S. Outdoor air pollution and COPD-related emergency department visits, hospital admissions, and mortality: a meta-analysis. COPD 2017;14:113–121.

13. Dominici F, Peng RD, Bell ML, et al. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 2006;295:1127–1134.

14. Gan WQ, FitzGerald JM, Carlsten C, Sadatsafavi M, Brauer M. Associations of ambient air pollution with chronic obstructive pulmonary disease hospitalization and mortality. Am J Respir Crit Care Med 2013;187:721–727.

15. Lee KY, Chiang LL, Ho SC, et al. Associations of autophagy with lung diffusion capacity and oxygen saturation in severe COPD: effects of particulate air pollution. Int J Chron Obstruct Pulmon Dis 2016;11:1569–1578.

16. Audi C, Baïz N, Maesano CN, et al. Serum cytokine levels related to exposure to volatile organic compounds and PM_2.5 in dwellings and workplaces in French farmers - a mechanism to explain nonsmoking COPD. Int J Chron Obstruct Pulmon Dis 2017;12:1363–1374.

17. Gao N, Xu W, Ji J, et al. Lung function and systemic inflammation associated with short-term air pollution exposure in chronic obstructive pulmonary disease patients in Beijing, China. Environ Health 2020;19:12.

18. Garshick E, Grady ST, Hart JE, et al. Indoor black carbon and biomarkers of systemic inflammation and endothelial activation in COPD patients. Environ Res 2018;165:358–364.

19. Huang S, Garshick E, Vieira CLZ, et al. Short-term exposures to particulate matter gamma radiation activities and biomarkers of systemic inflammation and endothelial activation in COPD patients. Environ Res 2020;180:108841.

20. Busenkell E, Collins CM, Moy ML, et al. Modification of associations between indoor particulate matter and systemic inflammation in individuals with COPD. Environ Res 2022;209:112802.

21. Chen R, Qiao L, Li H, et al. Fine particulate matter constituents, nitric oxide synthase DNA methylation and exhaled nitric oxide. Environ Sci Technol 2015;49:11859–11865.

22. Grady ST, Koutrakis P, Hart JE, et al. Indoor black carbon of outdoor origin and oxidative stress biomarkers in patients with chronic obstructive pulmonary disease. Environ Int 2018;115:188–195.

23. Huang S, Koutrakis P, Grady ST, et al. Effects of particulate matter gamma radiation on oxidative stress biomarkers in COPD patients. J Expo Sci Environ Epidemiol 2021;31:727–735.

24. Huang Q, Hu D, Wang X, et al. The modification of indoor PM_2.5 exposure to chronic obstructive pulmonary disease in Chinese elderly people: a meet-in-metabolite analysis. Environ Int 2018;121(Pt 2):1243–1252.

25. Belli AJ, Bose S, Aggarwal N, et al. Indoor particulate matter exposure is associated with increased black carbon content in airway macrophages of former smokers with COPD. Environ Res 2016;150:398–402.

26. Abramson MJ, Wigmann C, Altug H, Schikowski T. Ambient air pollution is associated with airway inflammation in older women: a nested cross-sectional analysis. BMJ Open Respir Res 2020;7:e000549.

27. Manney S, Meddings CM, Harrison RM, et al. Association between exhaled breath condensate nitrate + nitrite levels with ambient coarse particle exposure in subjects with airways disease. Occup Environ Med 2012;69:663–669.

28. Rama TA, Paciência I, Cavaleiro Rufo J, et al. Exhaled breath condensate pH determinants in school-aged children: a population-based study. Pediatr Allergy Immunol 2021;32:1474–1481.

29. Klümper C, Krämer U, Lehmann I, et al, ; GINIplus and LISAplus study groups. Air pollution and cytokine responsiveness in asthmatic and non-asthmatic children. Environ Res 2015;138:381–390.

30. Zahedi A, Hassanvand MS, Jaafarzadeh N, Ghadiri A, Shamsipour M, Dehcheshmeh MG. Effect of ambient air PM_2.5-bound heavy metals on blood metal(loid)s and children’s asthma and allergy pro-inflammatory (IgE, IL-4 and IL-13) biomarkers. J Trace Elem Med Biol 2021;68:126826.

31. Gao J, Xu X, Ying Z, et al. Post-effect of air quality improvement on biomarkers for systemic inflammation and microparticles in asthma patients after the 2008 Beijing olympic games: a pilot study. Inflammation 2017;40:1214–1224.

32. Strand M, Rabinovitch N. Health effects of concurrent ambient and tobacco smoke-derived particle exposures at low concentrations in children with asthma. J Expo Sci Environ Epidemiol 2020;30:785–794.

33. He L, Norris C, Cui X, et al. Role of endogenous melatonin in pathophysiologic and oxidative stress responses to personal air pollutant exposures in asthmatic children. Sci Total Environ 2021;773:145709.

34. Mohd Isa KN, Hashim Z, Jalaludin J, Lung Than LT, Hashim JH. The effects of indoor pollutants exposure on allergy and lung inflammation: an activation state of neutrophils and eosinophils in sputum. Int J Environ Res Public Health 2020;17:5413.

35. Kim S, Seo H, Lee Y, Park J. Study design algorithm for medical literature of intervention (DAMI) and risk of bias for non-randomized studies (robans) ver 2.0 by HIRA Seoul: Health Insurance Review & Assessment Service; 2013. p. 65–66.

36. Fireman Klein E, Adir Y, Krencel A, et al. Ultrafine particles in airways: a novel marker of COPD exacerbation risk and inflammatory status. Int J Chron Obstruct Pulmon Dis 2019;14:557–564.

37. Fireman Klein E, Adir Y, Fireman E, Kessel A. Cigarette-related cadmium and environmental pollution exposure are reflected in airway ultrafine particle content. ERJ Open Res 2020;6:00361–2019.

38. Tang L, Shi S, Wang B, et al. Effect of urban air pollution on CRP and coagulation: a study on inpatients with acute exacerbation of chronic obstructive pulmonary disease. BMC Pulm Med 2021;21:296.

39. He L, Norris C, Cui X, et al. Oral cavity response to air pollutant exposure and association with pulmonary inflammation and symptoms in asthmatic children. Environ Res 2022;206:112275.

40. Tejwani V, Woo H, Liu C, et al. Black carbon content in airway macrophages is associated with increased severe exacerbations and worse COPD morbidity in SPIROMICS. Respir Res 2022;23:310.

41. Sampson PD, Szpiro AA, Sheppard L, Lindström J, Kaufman JD. Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data. Atmos Environ 2011;45:6593–6606.

42. Szpiro AA, Sampson PD, Sheppard L, Lumley T, Adar SD, Kaufman J. Predicting intra-urban variation in air pollution concentrations with complex spatio-temporal dependencies. Environmetrics 2009;21:606–631.

43. Zusman M, Gassett AJ, Kirwa K, et al. Modeling residential indoor concentrations of PM2.5, NO2, NOx, and secondhand smoke in the Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) Air study. Indoor Air 2021;31:702–716.

44. He L, Li Z, Teng Y, et al. Associations of personal exposure to air pollutants with airway mechanics in children with asthma. Environ Int 2020;138:105647.

45. Lam PK, Gray JS. The use of biomarkers in environmental monitoring programmes. Mar Pollut Bull 2003;46:182–186.

46. Kim J, Kim NY, Kim WJ. Biomarkers of particulate matter exposure in patients with chronic obstructive pulmonary disease: a systematic review. J Thorac Dis 2023;15:3453–3465.

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Type of biomarker	Biomarker	Response to increased PM
Blood	Ubiquitin, Beclin 1 [15]	↑ Ubiquitin, ↑ Beclin 1
	Various ILs [16,17]	↓ IL4, IL6, IL8, IL13, ↑ IL2, IL12, IL17A
	CRP [18–20]	↑ CRP
	NOS2A methylation [21]	↓ NOS2A methylation
Urine	Human urinary metabolome (8-OHdG, MDA) [22,23]	↑ 8-OHdG, ↑ MDA
	Uric acid, GABP, dopamine 4-sulfate [24]	↓ Uric acid, ↓ GABP, ↓ Dopamine 4-sulfate
Sputum	Airway macrophages black carbon content [25]	↑ Black carbon content of airway macrophage
FeNO	FeNO [21,26]	↑ FeNO

Type of biomarker	Biomarker	Response to increased PM
Exhaled breath condensate	EBC NO [27]	↑ EBC NO
	EBC pH [28]	↓ pH
Blood	IL-6,8,10, MCP-1, TNF-alpha [29]	↑ IL-6, IL-8, IL-10, MCP-1 TNF-a
	IgE, IL-4,13 [30]	↑ IgE, IL-4 IL-13
	Circulating various serum cytokines (TNFα, IL-6, IL-8, IL-10) [31]	↓ TNFα, IL-6, IL-8, and IL-10 (after intervention)
Urine	Leukotriene E4 [32]	↑ Leukotrien E4
	aMT6s, MDA [33]	↑ aMT6s, MDA
Saliva	CD63 [34]	↑ CD63

Author, year	Study design	Country	Diseases	Subjects (n)	Pollutant	PM measurement method	Biomarker	Sample	Effect estimate	Smoking history	Occupational history
Tejwani et al., 2022 [40]	Retrospective cohort	USA	COPD	Patients (324)	PM_2.5, NO_2, ozone (outdoor), nicotine (indoor)	Using Spatiotemporal modeling according to subjects’ address (outdoor) [41,42] Using measurement modeling from previous studies (indoor) [43]	AM-BC content	Sputum	↑AM-BC content	Current smoker (44.4%)	Not mentioned
Tang et al., 2021 [38]	Prospective cohort	China	COPD	Patients (317)	PM_2.5, PM₁₀	Hourly concentrations of PM2.5 and PM10 were measured at the Chinese environmental monitoring station and then averaged daily	CRP, PT	Blood	↑CRP, ↓PT	Current smoker (13.25%)	Not mentioned
Fireman Klein et al., 2019 [36], 2020 [37]	Prospective cohort	Israel	COPD	Patients (58) vs. controls (40)	PM_2.5, PM₁₀	The short-term exposure (3 days) and the long-term exposure of 3 months were obtained by estimating according to location information using the Israeli Ministry of Environment’s online database	UFP	EBC, blood	↓UFP (urine)	COPD (96%), and controls (57.5%) had smoking history.	Not mentioned
He et al., 2022 [39]	Prospective cohort	China	Asthma	Patients (43)	PM_2.5, ozone	Using a specific sensor which checked the average of weekly exposure for each individual and also used the analysis technology suggested in previous research [44]	IL-6	Salivary	↑IL-6	Not mentioned	Not mentioned