Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2020 May 13.
Published in final edited form as: J Toxicol Environ Health A. 2019 May 13;82(6):411–421. doi: 10.1080/15287394.2019.1614500

Household Coal Combustion, Indoor Air Pollutants, and Circulating Immunologic/Inflammatory Markers in Rural China

Jason YY Wong 1, Bryan A Bassig 1, Wei Hu 1, Wei Jie Seow 1, Meredith S Shiels 1, Bu-Tian Ji 1, George S Downward 2, Yunchao Huang 3, Kaiyun Yang 3, Jihua Li 4, Jun He 4, Ying Chen 3, Allan Hildesheim 1, Roel Vermeulen 2,*, Qing Lan 1,*, Nathaniel Rothman 1,*
PMCID: PMC6594692  NIHMSID: NIHMS1528964  PMID: 31084278

Abstract

The study aim was to investigate whether household bituminous (“smoky”) coal use and personal exposure to combustion emissions were associated with immunologic/inflammatory marker levels. A cross-sectional study of healthy never-smoking women from rural Xuanwei and Fuyuan, China was conducted, which included 80 smoky coal and 14 anthracite (“smokeless”) coal users. Personal exposure to fine particulate matter (PM2.5) and benzo[a]pyrene (BaP) was assessed using portable devices, while 67 circulating plasma immunologic/inflammatory markers were measured using multiplex bead-based assays. Multivariable linear regression models were employed to estimate associations between smoky coal versus smokeless coal use, indoor air pollutants, and immunologic/inflammatory markers. Six markers were altered among smoky coal users compared to smokeless coal, including significantly decreased interferon-inducible T-cell alpha chemoattractant (CXCL11/I-TAC), and increased serum amyloid P component (SAP). CXCL11/I-TAC was previously found to be reduced in workers exposed to high levels of diesel engine exhaust, which exhibits similar constituents as coal combustion emissions. Further, there was evidence that elevated PM2.5 and BaP exposure was associated with significantly diminished levels of the serum amyloid A (SAA); however, the false discovery rates (FDRs) were >0.2 after accounting for multiple comparisons. Inflammatory processes may thus mediate the carcinogenic effects attributed to smoky coal emissions.

Keywords: Smoky Coal, Combustion, Indoor Air Pollution, Immunologic Inflammatory Markers

Introduction

Combustion of bituminous (“smoky”) coal for heating and cooking has led to hazardous levels of indoor air pollutants in households in Xuanwei and Fuyuan, rural counties of southwestern China that have the highest rates of lung cancer among never-smoking women in the country (Shen et al. 2009; Lan et al. 2008; Downward et al. 2014b; Mumford et al. 1987). Lifetime smoky coal use was previously found to be associated with a nearly 100-fold increased risk of lung cancer mortality compared to lifetime anthracite (“smokeless”) coal use in Xuanwei and Fuyuan (Barone-Adesi et al. 2012). The suspected genotoxic components of smoky coal including fine particulate matter (PM2.5) and polycyclic aromatic hydrocarbons (PAHs) were reported to promote local and systemic inflammation, lipid peroxidation of cellular membranes, and oxidative damage to the genome (Li et al. 2016; Wei et al. 2009; Sorensen et al. 2003; Hajat et al. 2015; Alshaarawy et al. 2013; Liu et al. 2008; Farmer et al. 2003). Although significant research has been directed towards characterizing the etiology underlying this relationship (Seow et al. 2014; Hu et al. 2014; Hosgood et al. 2008; Keohavong et al. 2005; Lan and He 2004; Lan et al. 1993; 2001; 2002), the biological mechanisms by which these household air pollutants exert their carcinogenic effects remain unclear.

Chronic inflammation is one of many established predisposing factors of carcinogenesis (Malkinson et al. 2000; O’Byrne and Dalgleish 2001; Shiels et al. 2013; 2015; Lee and Lawrence 2018). Certain circulating levels of immunologic/inflammatory markers are reflective of inflammation and were noted to be altered in response to local and systemic tissue damage, in addition to disease states (Brenner et al. 2014; Raulf et al 2016; Kim et al 2018; Lee and Lawrence 2018). In addition, altered levels of some circulating immunologic/inflammatory markers were found to be associated with future enhanced risk of lung cancer (Shiels et al. 2013; 2015; 2017). Further, an occupational study of men exposed to diesel engine exhaust emissions, which possesses common components similar to coal emissions, demonstrated altered levels of 9 immunologic/inflammatory markers that were previously linked to increased lung cancer risk in an investigation of never-smoking women (Bassig et al 2017; Shiels et al. 2017).

Previously in China, coal combustion emissions were shown to be related to impaired immune responses (Zhang and Smith 2007; Jin et al. 2002). Further, components of coal combustion emissions including PM2.5 were demonstrated to be associated with altered immune responses (Granum and Lovik 2002; Zhao et al. 2013; Williams et al. 2011), while carcinogenic PAHs were demonstrated to suppress the immune system in animal studies (Davila et al. 1995; Burchiel and Luster 2001). Circulating levels of immunologic/inflammatory biomarkers are reflective of immune response, both directly and indirectly. However, whether certain immunologic/inflammatory markers are influenced by smoky coal combustion emissions and components of indoor air pollution was not comprehensively investigated in humans. As an initial step towards understanding the impact of smoky coal combustion emissions on immune/inflammatory responses, a cross-sectional molecular epidemiologic study of disease-free, never-smoking women from Xuanwei and Fuyuan, China was conducted. The aim of this study was to investigate smoky versus smokeless coal use, as well as personal exposure to PM2.5 and benzo[a]pyrene (BaP; a surrogate for other PAH species), in relation to circulating levels of 67 immunologic/inflammatory markers, which are proxies for various immunologic/inflammatory activities.

Materials and Methods

Study Population

The Xuanwei Exposure Assessment Study was previously described in detail (Hu et al. 2014). Briefly, this cross-sectional study of healthy never-smoking women from Xuanwei and Fuyuan was conducted to characterize indoor exposure to key components of coal combustion products to provide data for a companion case-control study (Wong et al. 2018). Disease-free female heads-of-household were recruited from 30 villages across Xuanwei and portions of neighboring Fuyuan. Up to 5 households were selected in each village based upon having a stove that used solid fuel; the residence was more than 10 years old; the same cooking or heating equipment was utilized for the past 5 years; and presence of a never-smoking healthy woman aged 20−80 years who was primarily responsible for cooking. During the screening and enrollment phase, the women were asked to report their personal medical history on questionnaires for chronic bronchitis, asthma, tuberculosis, emphysema, pneumonia, benign or malignant tumors, and other conditions. Women who reported any occurrence of these medical conditions were excluded. Data were collected during two time-periods. In the first visit, 148 participants were recruited from August 2008 to February 2009. In the second visit, 53 of the initial participants from 16 villages were re-sampled and 15 additional women recruited from March to June 2009.

Two trained interviewers collected information on household, demographic, and anthropometric characteristics. Activities including cooking, heating, and fuel use during the sampling periods were assessed employing activity questionnaires. Information on fuel types utilized for cooking and heating were also collected. Self-reported fuel type was confirmed via geochemical analysis of collected fuel samples (Downward et al. 2014a). The first assessment of 80 smoky and 14 smokeless coal users was analyzed. Two sequential personal 24-hr air measurements were collected. Whole blood was obtained on the second day after the air measurements.

Personal Exposure Assessment: PM2.5 and PAHs

Personal PM2.5 measurements across 24-hr were collected on pre-weighed 37 mm Teflon filters using portable devices with an aerodynamic cutoff of 2.5 μm (Model BGI, GK2.05SH) at a flow rate of 3.5 L/min (±20%) as previously described by Hu et al (2014). The pump was packed in a hip bag and the portable device attached near the breathing zone of each participant. The sampling bag was placed next to the women’s bed while sleeping. All exposed filters were individually placed in Petri slides, sealed in plastic bags, and stored at −80°C before post-weighing. Particulate mass was determined by pre- and post-weighing of the filters in duplicate. PM2.5 concentrations (μg/m3) were calculated by dividing weights by total volume of air drawn through filters. Approximately 10% of households were randomly selected to have duplicate PM2.5 measurements to assess reproducibility. The coefficient of variation (CV) for the PM2.5 measurements was 13%.

Further, particle-bound PAHs including fluoranthene (FLT), pyrene (PYR), benzo[a]anthracene (BaA), chrysene (CHR), benzo[b]fluoranthene (BbF), benzo[k]flouranthene (BkF), benzo[a]pyrene (BaP, a surrogate for other highly correlated PAHs), dibenz(ah)anthracene (DBA), benzo[ghi]perylene (BPE), and indeno(1,2,3-cd)pyrene (IPY) were assessed in addition to gas-phase PAHs including fluorine (FLU), naphthalene (NAP), acenaphthylene (ANY), and phenanthrene (PHE). Particle-bound PAHs (ng/m3) were collected with 37 mm Teflon filters in portable devices, while personal gas phase PAHs were measured with XAD-2 sorbent tubes at a median air flow rate of 63 mL/min, as previously described (Downward et al. 2014b). PAH species were identified using gas chromatograph connected to a mass spectrometer (Shimadzu QP2010 plus) (Downward et al. 2014b). The CVs for the PAH readings from 13 duplicate samples were 25.6% (FLT), 28.3% (PYR), 20.8% (BaA), 7.6% (CHR), 22.1% (BbF), 21.3% (BkF), 25.4% (BaP), 32.4% (IPY), 41.5% (DBA), 25% (BPE), 17.6% (NAP), 30.4% (ANY), 29.3% (FLU), and 46.9% (PHE).

Outcome: Immunologic/inflammatory Marker Panel

Circulating levels of 67 immunologic/inflammatory markers (pg/ml) were measured in plasma from whole blood samples. Markers were measured employing multiplex Luminex bead-based assays (Millipore Inc., Billerica, MA), which were tested for sensitivity and reproducibility previously by Chaturvedi et al (2011). The intraclass correlations (ICC), CVs, and percentage of samples below the lower limit of quantification (LLOQ) are presented in Supplementary Table 1. Concentrations were calculated using either a 4- or 5-parameter standard curve. The assay consisted of 6 panels of immunologic/inflammatory markers including cytokines, chemokines, soluble receptors, and acute phase markers. Samples were determined in singlets except for the high sensitivity cytokine panel, in which plasma samples were measured in duplicate and averaged to calculate the concentration of each marker. Blinded duplicates within each batch and a pooled plasma sample across batches were included to assess assay reproducibility across batches. Markers with high proportions below the LLOQ including IL-33 (82%), thymic stromal lymphopoietin (TSLP) (97%), and interferon type 1 (IFN1) (45%) were excluded from the analyses. Measurements below the LLOQ were assigned a value of ½ the LLOQ. CCL20/MIP3a was measured on two panels and had similar CVs and ICC, thus only data from the high sensitivity panel were analyzed.

Statistical Analysis

The distribution and normality of continuous variables were assessed using histograms and Shapiro-Wilks tests, respectively. PM2.5, BaP, and immunologic/inflammatory marker levels were natural log-transformed to approximate normal distributions. Correlations between PM2.5 and PAH species were reflected by Spearman’s rank correlation coefficient.

Several independent sets of analyses were conducted using separate multivariable linear regression models for each of the 63 immunologic/inflammatory markers as outcomes. First, the associations were assessed for smoky coal versus smokeless coal adjusting for age. Second, the associations were estimated with personal PM2.5 exposure adjusting for age, body mass index (BMI), and fuel type. Third, the associations with BaP were assessed adjusting for age, BMI, and fuel type. Fourth, associations were determined with PM2.5 and BaP restricted to smoky coal users while adjusting for age and BMI.

False discovery rates (FDR) were employed to account for inflated family-wise error rate from multiple testing. Results with p-values <0.05 and FDR <0.2 were considered statistically significant. All analyses were conducted using SAS v9.3 (SAS Institute Inc., Cary, NC, USA).

Results

Study Population Characteristics

A cross-sectional analysis of 80 smoky and 14 smokeless coal users was conducted. The mean ages were 55 (15.4 SD) and 58 (12.2 SD) at their initial visit, respectively. In addition, the average BMI of the smoky coal and smokeless coal users was 22 (3.4 SD) and 22 (3.3 SD), respectively. Among smoky coal users, 59% of women were from Xuanwei, while 93% of smokeless coal users originated from Fuyuan.

Correlation between air pollutants

Personal PM2.5 was moderately correlated with BaP and other PAHs (Supplementary Table 2). BaP was significantly correlated with most PAH-species including BPE, BaA, BbF, BkF, CHR, DBA, FLT, IPY, and PYR; while moderately correlated with ANY, FLU, NAP, and PHE. Taking these observations into consideration, BaP was deemed as an appropriate surrogate for other PAHs.

Associations between smoky coal versus smokeless coal use and immunologic/inflammatory marker levels

Six markers were markedly altered in smoky coal users compared to smokeless coal users (Table 1). Most notably, interferon-inducible T-cell alpha chemoattractant (CXCL11/I-TAC) was significantly decreased, while acute phase response plasma protein serum amyloid P component (SAP) was significantly elevated. Further, there was evidence that epithelial-derived neutrophil-activating peptide 78 (ENA-78), granulocyte chemotactic protein 2 (CXCL6/GCP2), tumor necrosis factor alpha (TNF-α), and thymus and activation regulated chemokine (CCL17/TARC) levels were markedly reduced among smoky coal users after accounting for multiple comparisons. The other markers with non-significant associations are shown in Supplementary Table 3.

Table 1:

Notable Comparisons of Circulating Immunologic/inflammatory Marker Concentrations Among Coal Users in Xuanwei, China

Immunologic/Inflammatory Marker Smoky Coal, Median, n=80 25th Pctl 75th Pctl Smokeless Coal, Median, n=14 25th Pctl 75th Pctl Crude Median Difference, Smoky vs. Smokeless Coal, pg/mL β-estimate, Smoky vs. Smokeless Coal 95% CI Low 95% CI Upper p-value FDR
CXCL11/I-TAC 40.4 30.8 73.8 67.6 51.6 137.3 −27.2 −0.51 −0.87 −0.15 0.01 0.20
SAP 3.8E+07 3.0E+07 4.7E+07 3.0E+07 2.8E+07 3.5E+07 7.6E+06 0.25 0.07 0.43 0.01 0.20
CXCL5/ENA78 704.5 443.7 985.4 1172.3 800.1 1352.1 −467.9 −0.38 −0.68 −0.08 0.01 0.27
CXCL6/GCP2 38.9 24.6 61.4 50.5 42.9 110.4 −11.5 −0.41 −0.76 −0.06 0.02 0.33
TNF-α 16.4 13.2 19.9 19.8 16.4 23.0 −3.4 −0.18 −0.35 −0.01 0.04 0.40
CCL17/TARC 21.9 14.7 40.8 36.6 25.5 59.8 −14.7 −0.44 −0.85 −0.02 0.04 0.40
Open in a new tab
*

Immunologic/inflammatory markers with p<0.05 were considered noteworthy. Linear regression models were adjusted for age. β-estimates reflect the average difference in the marker levels (ln pg/ml) between smoky and smokeless coal users. Immunologic/inflammatory markers were natural log transformed. Abbreviations: false discovery rate (FDR), confidence intervals (CI)

Associations between personal exposure to PM2.5 and BaP and immunologic/inflammatory marker levels

Overall and among smoky coal users, there was evidence that increased PM2.5 and BaP was associated with decreased levels of acute phase response protein serum amyloid A (SAA) (Table 2). Further, there was evidence that elevated PM2.5 exposure was associated with lowered soluble interleukin-4 receptor (sIL-4R) and monocyte chemoattractant protein (CCL13/MCP-4) levels (Table 2). However, the FDRs for the relationships were >0.2 after accounting for multiple comparisons. The other non-significant markers are presented in Supplementary Table 4.

Table 2:

Notable Associations between PM2.5, BaP, and Immunologic/Inflammatory Marker Concentrations Among Coal Users in Xuanwei, China

I) PM2.5, Among All Coal Users, n=94
 II) PM2.5, Among Smoky Coal Users, n=80
Immunologic /Inflammatory Marker β-estimate 95% CI Low 95% CI Upper p-value FDR Immunologic /Inflammatory Marker β-estimate 95% CI Low 95% CI Upper p-value FDR
SAA −0.62 −1.20 −0.04 0.04 0.72 SAA −0.72 −1.41 −0.04 0.04 0.80
sIL-4R −0.13 −0.24 −0.02 0.02 0.72 sIL-4R −0.12 −0.25 0.00 0.05 0.80
CCL13/MCP-4 −0.20 −0.37 −0.03 0.02 0.72 MDC 0.14 0.00 0.28 0.05 0.80
sVEGFR3 −0.20 −0.42 0.01 0.06 0.72 sVEGFR3 −0.25 −0.51 0.00 0.05 0.80
III) BaP, Among All Coal Users, n=94
 IV) BaP, Among Smoky Coal Users, n=80
Immunologic /Inflammatory Marker β-estimate 95% CI Low 95% CI Upper p-value FDR Immunologic /Inflammatory Marker β-estimate 95% CI Low 95% CI Upper p-value FDR
SAA −0.44 −0.75 −0.13 0.01 0.38 SAA −0.47 −0.82 −0.13 0.01 0.53
CCL19/MIP-3b 0.11 0.01 0.22 0.03 0.62 sIL-4R −0.06 −0.13 0.00 0.05 0.82
MDC 0.08 0.01 0.15 0.02 0.62 MDC 0.07 0.00 0.14 0.06 0.82
CXCL12a-b/SDF-1a-b 0.06 0.00 0.12 0.05 0.75 CCL19/MIP-3b 0.10 −0.01 0.22 0.07 0.82
Open in a new tab
*

Immunologic/inflammatory markers with p<0.05 among smoky and smokeless coal users was considered notable. I and III) Linear regression models were adjusted for age, BMI, and coal type. II and IV) Linear regression models were adjusted for age and BMI. β-estimates reflect the incremental change in the marker levels (ln pg/ml) per unit increase in PM2.5 (ln μg/m3) or BaP (ln ng/m3) exposure level. Abbreviations: false discovery rate (FDR), confidence intervals (CI)

Discussion

Household combustion of carcinogenic smoky coal was significantly associated with altered levels of two immunologic/inflammatory markers, namely CXCL11/I-TAC and SAP, compared to smokeless coal. Further, there was evidence that increased PM2.5 and BaP exposure was associated with reduced SAA levels. In addition, there was some indication that elevated PM2.5 may be related to lower CCL13/MCP-4 and sIL-4R levels. However, the relationships with PM2.5 and BaP were not significant after accounting for multiple comparisons. Taken together, combustion emissions from smoky coal may induce immunologic/inflammatory responses of greater magnitude compared to those derived from smokeless coal.

CXCL11/I-TAC was the most notably altered marker when comparing smoky to smokeless coal and was also reduced in relation to increasing exposure to diesel engine exhaust, an established carcinogen (IARC 2014; Silverman 2018), in agreement with a previous molecular epidemiologic study (Bassig et al. 2017). This overlap suggests that combustion emission components common to both smoky coal and diesel engine exhaust may influence immunologic/inflammatory parameters. CXCL11/I-TAC is a small cytokine of the CXC chemokine family that is induced by interferons and highly expressed in white blood cells (Cole et al. 1998). CXCL11/I-TAC are key molecules involved in white blood cell trafficking, migration, recruitment, and activation during immune/inflammatory response. More specifically, CXCL11/I-TAC displays potent chemoattractant activity for interleukin (IL)-2–activated T cells, but not for unstimulated T cells, neutrophils, or monocytes (Cole et al. 1998). Further, CXCL11/I-TAC counteract the responses mediated by many other inflammatory chemokines that act not only through the CCR3 receptor, but also CCR5 (Petkovic et al. 2004).

SAP was found to be increased among smoky coal users compared to smokeless coal users. SAP is an acute phase glycoprotein of the highly conserved pentraxin family that shares substantial structural similarity with C-reactive protein (CRP) (Pepys et al. 1980). Similar to CRP, SAP exhibits calcium-dependent binding to pathogens and several different molecules including bacterial lipopolysaccharides (LPS; endotoxins) during immune response (Srinivasan et al. 1994; de Haas et al. 1998). However, the biological role of SAP in cancer risk and human health at large remains poorly characterized (Yuste et al. 2007).

The results for CXCL11/I-TAC and SAP suggest that smoky coal emissions may alter recruitment of certain immune cells during innate and cell-mediated inflammatory response to affected tissues, impair immunosurveillance of pre-cancerous cells, and promote certain pro-inflammatory responses. However, the role of these markers in carcinogenesis and consequences of decreased circulating levels are currently unclear. Previously, Shiels et al (2017) did not detect significant associations between CXCL11/I-TAC, SAP, and incidence of lung cancer risk in a cohort study of Chinese women. However, these observations may be attributed to issues related to statistical power, as well as measuring immunologic/inflammatory markers at etiologically relevant time-windows for lung carcinogenesis (White et al. 1998).

Increased PM2.5 and BaP concentrations were associated with reduced serum amyloid A (SAA) levels; however, the findings were not significant after accounting for multiple comparisons. Similar to SAP, SAA is an acute phase protein produced by the liver that is regulated by various inflammatory cytokines during both acute and chronic inflammation (Liu 2012). In addition, evidence indicates that CCL13/MCP-4 was diminished in relation to increased personal exposure to PM2.5 but was not significant after accounting for multiple comparisons. CCL13/MCP-4 is a chemoattractant for macrophages, leukocytes, immature dendritic cells, and T-cells (Mendez-Enriquez and Garcia-Zepeda 2013; Garcia-Zepeda et al. 1996). The lack of significant associations for these markers might be attributed to limited statistical power. Taken together with the findings for smoky versus smokeless coal, the lack of significant results for PM2.5 and PAHs does not imply that these substances are not important carcinogenic components of smoky coal. Rather, other constituents of smoky coal may contribute to the overall carcinogenicity.

Previous studies found correlations between air pollutants and altered immunologic/inflammatory marker levels (Carosino et al 2015; Holz et al 2018). With respect to PM, Pope et al (2016) noted in Provo, Utah found that PM2.5 exposure was associated with decreased circulating levels of pro-angiogenic growth factors such as epidermal growth factor (EGF), increased levels of anti-angiogenic (i.e. TNF-α) and proinflammatory cytokines including CCL2/MCP2, MIP1α/β, Interleukins-6 (IL6), and IL1β), and markers of endothelial adhesion such as sICAM1 and sVCAM1. Further, Hajat et al (2015) reported that long-term ambient PM2.5 exposure was found to be associated with elevated IL6 concentrations in the Multi-ethnic Study of Atherosclerosis (MESA). In addition, a study in Beijing, China demonstrated that exposure to PM2.5 constituents from ambient air pollution was associated with altered immunologic/inflammatory markers including sVCAM1 (Wu et al. 2016). Differences in altered markers among these investigations may be attributed to variations in study design, statistical power, and composition of PM2.5. Although few studies examined PAHs, elevated urinary biomarkers of exposures were found to be correlated with increased CRP, brain-derived neurotrophic factor, and activated leukocyte cell adhesion molecule levels (Alshaarawy et al. 2013; Yang et al. 2016a).

This study had notable strengths. First, the study of non-smoking women mitigated confounding by active cigarette smoking and gender. Second, the participants were exposed to very high levels of indoor air pollution, which enhances the chances of observing a biological effect. Third, the study employed personal air monitoring, which is more accurate and precise compared with area monitors, reducing exposure misclassification. Lastly, the blood samples were collected after air monitoring, which establishes temporality between exposure and outcome.

This study had several limitations. First, the sample size was small; therefore, one could not discount the possibility of chance findings. Second, the cross-sectional nature of the analyses could not capture trajectories in immunologic/inflammatory marker levels. However, reverse causation was unlikely given that marker levels could not feasibly affect coal use, as this was primarily determined by residential distance to coal sources. Third, there was limited covariate data; therefore, both unmeasured (e.g., secondhand smoke exposure levels) and residual (e.g., outdoor air pollution exposure levels) confounding are possibilities.

In summary, evidence indicated that household combustion of carcinogenic smoky coal was associated with altered levels of multiple immunologic/inflammatory markers compared to smokeless coal. Coal combustion emissions may decrease recruitment of certain immune cells to inflamed tissues, impair immunosurveillance of pre-cancerous cells, and/or promote certain pro-inflammatory responses. The altered markers have yet to be firmly linked to other environmental air pollutants or increased lung cancer risk in human studies. As such, the previously established relations between smoky coal exposure and elevated lung cancer risk in Xuanwei may be potentially mediated through other factors unrelated to these or other immunologic/inflammatory markers. Findings from this investigation may shed light into potential biological mechanisms by which coal combustion and indoor air pollutants influence lung cancer development. However, given the biological complexity of immune response and limitations of the study, caution is recommended when interpreting the findings. Each marker exerts multiple roles in different states of inflammation and may operate as activators or repressors of specific targets. A comprehensive network analysis of longitudinal, repeated marker measurements and continuous exposure monitoring would be required to better understand immune/inflammatory response to coal combustion emissions.

Supplementary Material

1

Acknowledgements

We extend our deepest appreciation to Chin-San Liu, Wen-Ling Cheng, and the late Robert S. Chapman for their support. This study was supported by intramural funding from the National Cancer Institute.

Footnotes

Declaration of Interest Statement

We declare no conflicts of interest.

References

  1. Alshaarawy O, Zhu M, Ducatman A, Conway B, and Andrew ME. 2013. Polycyclic aromatic hydrocarbon biomarkers and serum markers of inflammation. A positive association that is more evident in men. Environ Res 126:98–104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Barone-Adesi F, Chapman RS, Silverman DT, He X, Hu W, Vermeulen R, Ning B, Fraumeni JF Jr., Rothman N, and Lan Q. 2012. Risk of lung cancer associated with domestic use of coal in Xuanwei, China: Retrospective cohort study. Br Med J 345: e5414. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bassig BA, Dai Y, Vermeulen R, Ren D, Hu W, Duan H, Niu Y, Xu J, Shiels MS, Kemp TJ, Pinto LA, Fu W, Meliefste K, Zhou B, Yang J, Ye M, Jia X, Meng T, Wong JYY, Bin P, Hosgood HD 3rd, Hildesheim A, Silverman DT, Rothman N, Zheng Y, and Lan Q. 2017. Occupational exposure to diesel engine exhaust and alterations in immune/inflammatory markers: A cross-sectional molecular epidemiology study in China. Carcinogenesis 38 :1104–1111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Brenner DR, Scherer D, Muir K, Schildkraut J, Boffetta P, Spitz MR, Le Marchand L, Chan AT, Goode EL, Ulrich CM, and Hung RJ. 2014. A review of the application of inflammatory biomarkers in epidemiologic cancer research. Cancer Epidemiol Biomarkers Prev 23 :1729–1751. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Burchiel SW, and Luster MI. 2001. Signaling by environmental polycyclic aromatic hydrocarbons in human lymphocytes. Clin Immunol 98 :2–10. [DOI] [PubMed] [Google Scholar]
  6. Carosino CM, Bein KJ, Plummer LE, Castañeda AR, Zhao Y, Wexler AS, Pinkerton KE. 2015. Allergic airway inflammation is differentially exacerbated by daytime and nighttime ultrafine and submicron fine ambient particles: Heme oxygenase-1 as an indicator of PM-mediated allergic inflammation. J Toxicol Environ Health A. 78 : 254–266. [DOI] [PubMed] [Google Scholar]
  7. Chaturvedi AK, Kemp TJ, Pfeiffer RM, Biancotto A, Williams M, Munuo S, Purdue MP, Hsing AW, Pinto L, McCoy JP, and Hildesheim A. 2011. Evaluation of multiplexed cytokine and inflammation marker measurements: A methodologic study. Cancer Epidemiol Biomarkers Prev 20 :1902–1911. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cole KE, Strick CA, Paradis TJ, Ogborne KT, Loetscher M, Gladue RP, Lin W, Boyd JG, Moser B, Wood DE, Sahagan BG, and Neote K. 1998. Interferon-inducible T cell alpha chemoattractant (I-TAC): A novel non-ELR CXC chemokine with potent activity on activated T cells through selective high affinity binding to CXCR3. J Exp Med 187 : 2009–2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Davila DR, Davis DP, Campbell K, Cambier JC, Zigmond LA, and Burchiel SW. 1995. Role of alterations in Ca(2+)-associated signaling pathways in the immunotoxicity of polycyclic aromatic hydrocarbons. J Toxicol Environ Health 45 :101–126. [DOI] [PubMed] [Google Scholar]
  10. de Haas CJ, van der Tol ME, Van Kessel KP, Verhoef J, and Van Strijp JA. 1998. A synthetic lipopolysaccharide-binding peptide based on amino acids 27–39 of serum amyloid P component inhibits lipopolysaccharide-induced responses in human blood. J Immunol 161 :3607–3615. [PubMed] [Google Scholar]
  11. Downward GS, Hu W, Large D, Veld H, Xu J, Reiss B, Wu G, Wei F, Chapman RS, Rothman N, Qing L, and Vermeulen R. 2014a. Heterogeneity in coal composition and implications for lung cancer risk in Xuanwei and Fuyuan counties, China. Environ Int 68: 94–104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Downward GS, Hu W, Rothman N, Reiss B, Wu G, Wei F, Chapman RS, Portengen L, Qing L, and Vermeulen R. 2014b. Polycyclic aromatic hydrocarbon exposure in household air pollution from solid fuel combustion among the female population of Xuanwei and Fuyuan counties, China. Environ Sci Technol 48 : 14632–14641. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Farmer PB, Singh R, Kaur B, Sram RJ, Binkova B, Kalina I, Popov TA, Garte S, Taioli E, Gabelova A, and Cebulska-Wasilewska A. 2003. Molecular epidemiology studies of carcinogenic environmental pollutants. Effects of polycyclic aromatic hydrocarbons (PAHs) in environmental pollution on exogenous and oxidative DNA damage. Mutat Res 544 : 397–402. [DOI] [PubMed] [Google Scholar]
  14. Garcia-Zepeda EA, Combadiere C, Rothenberg ME, Sarafi MN, Lavigne F, Hamid Q, Murphy PM, and Luster AD. 1996. Human monocyte chemoattractant protein (MCP)-4 is a novel CC chemokine with activities on monocytes, eosinophils, and basophils induced in allergic and nonallergic inflammation that signals through the CC chemokine receptors (CCR)-2 and −3. J Immunol 157 : 5613–5626. [PubMed] [Google Scholar]
  15. Granum B, and Lovik M. 2002. The effect of particles on allergic immune responses. Toxicol Sci 65 : 7–17. [DOI] [PubMed] [Google Scholar]
  16. Hajat A, Allison M, Diez-Roux AV, Jenny NS, Jorgensen NW, Szpiro AA, Vedal S, and Kaufman JD. 2015. Long-term exposure to air pollution and markers of inflammation, coagulation, and endothelial activation: A repeat-measures analysis in the Multi-Ethnic Study of Atherosclerosis (MESA). Epidemiology 26 : 310–320. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Holz O, Heusser K, Müller M, Windt H, Schwarz K, Schindler C, Tank J, Hohlfeld JM, Jordan J. 2018. Airway and systemic inflammatory responses to ultrafine carbon black particles and ozone in older healthy subjects. J Toxicol Environ Health A 81 : 576–588. [DOI] [PubMed] [Google Scholar]
  18. Hosgood HD 3rd, Chapman R, Shen M, Blair A, Chen E, Zheng T, Lee KM, He X, and Lan Q. 2008. Portable stove use is associated with lower lung cancer mortality risk in lifetime smoky coal users. Br J Cancer 99 : 1934–1939. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Hu W, Downward GS, Reiss B, Xu J, Bassig BA, Hosgood HD 3rd, Zhang L, Seow WJ, Wu G, Chapman RS, Tian L, Wei F, Vermeulen R, and Lan Q. 2014. Personal and indoor PM2.5 exposure from burning solid fuels in vented and unvented stoves in a rural region of China with a high incidence of lung cancer. Environ Sci Technol 48 : 8456–8464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. IARC Working Group on the Evaluation of Carcinogenic Risks. 2014. Diesel and Gasoline Engine Exhausts and Some Nitroarenes. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. IARC Monogr Eval Carcinogen Risks Human 105:9–699. [PMC free article] [PubMed] [Google Scholar]
  21. Jin Y, Cheng Y, Wang H, Zhao C. 2002. [Effect of coal-burning air pollution on children immune function].Wei Sheng Yan Jiu 31 :379–381. [PubMed] [Google Scholar]
  22. Keohavong P, Lan Q, Gao WM, Zheng KC, Mady HH, Melhem MF, and Mumford JL. 2005. Detection of p53 and K-ras mutations in sputum of individuals exposed to smoky coal emissions in Xuan Wei County, China. Carcinogenesis 26 : 303–308. [DOI] [PubMed] [Google Scholar]
  23. Kim MK, Kim KB, Yoon K, Kacew S, Kim HS, Lee BM. 2018. IL-1α and IL-1β as alternative biomarkers for risk assessment and the prediction of skin sensitization potency. J Toxicol Environ Health A 81 : 830–843. [DOI] [PubMed] [Google Scholar]
  24. Lan Q, Chapman RS, Schreinemachers DM, Tian L, and He X. 2002. Household stove improvement and risk of lung cancer in Xuanwei, China. J Natl Cancer Inst 94 : 826–835. [DOI] [PubMed] [Google Scholar]
  25. Lan Q, Chen W, Chen H, and He XW. 1993. Risk factors for lung cancer in non-smokers in Xuanwei County of China. Biomed Environ Sci 6 : 112–118. [PubMed] [Google Scholar]
  26. Lan Q, Feng Z, Tian D, He X, Rothman N, Tian L, Lu X, Terry MB, and Mumford JL, 2001. p53 gene expression in relation to indoor exposure to unvented coal smoke in Xuan Wei, China. J Occup Environ Med 43 : 226–230. [DOI] [PubMed] [Google Scholar]
  27. Lan Q, and He X. 2004. Molecular epidemiological studies on the relationship between indoor coal burning and lung cancer in Xuan Wei, China. Toxicology 198 : 301–305. [DOI] [PubMed] [Google Scholar]
  28. Lan Q, He X, Shen M, Tian L, Liu LZ, Lai H, Chen W, Berndt SI, Hosgood HD, Lee KM, Zheng T, Blair A, and Chapman RS. 2008. Variation in lung cancer risk by smoky coal subtype in Xuanwei, China. Int J Cancer 123 : 2164–2169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lee F, Lawrence DA. 2018. From Infections to Anthropogenic Inflicted Pathologies: Involvement of Immune Balance. J Toxicol Environ Health B 21: 24–46. [DOI] [PubMed] [Google Scholar]
  30. Li W, Wilker EH, Dorans KS, Rice MB, Schwartz J, Coull BA, Koutrakis P, Gold DR, Keaney JF Jr., Lin H, Vasan RS, Benjamin EJ, and Mittleman MA. 2017. Short-term exposure to air pollution and biomarkers of oxidative stress: The Framingham Heart Study. J Am Heart Assoc 5 :5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Liu C 2012. Serum amyloid a protein in clinical cancer diagnosis. Pathol Oncol Res 18 : 117–21. [DOI] [PubMed] [Google Scholar]
  32. Liu G, Niu Z, Van Niekerk D, Xue J, and Zheng L. 2008. Polycyclic aromatic hydrocarbons (PAHs) from coal combustion: Emissions, analysis, and toxicology. Rev Environ Contam Toxicol 192:1–28. [DOI] [PubMed] [Google Scholar]
  33. Malkinson AM, Bauer A, Meyer A, Dwyer-Nield L, Koski K, Keith R, Geraci M, and Miller Y. 2000. Experimental evidence from an animal model of adenocarcinoma that chronic inflammation enhances lung cancer risk. Chest 117 (5 Suppl 1): 228S. [DOI] [PubMed] [Google Scholar]
  34. Mendez-Enriquez E, and Garcia-Zepeda EA. 2013. The multiple faces of CCL13 in immunity and inflammation. Inflammopharmacology 21 : 397–406. [DOI] [PubMed] [Google Scholar]
  35. Mumford JL, He XZ, Chapman RS, Cao SR, Harris DB, Li XM, Xian YL, Jiang WZ, Xu CW, Chuang JC, and et al. 1987. Lung cancer and indoor air pollution in Xuan Wei, China. Science 235 : 217–220. [DOI] [PubMed] [Google Scholar]
  36. O’Byrne KJ, and Dalgleish AG. 2001. Chronic immune activation and inflammation as the cause of malignancy. Br J Cancer 85 : 473–483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pepys MB, Becker GJ, Dyck RF, McCraw A, Hilgard P, Merton RE, and Thomas DP. 1980. Studies of human serum amyloid P-component (SAP) in relation to coagulation. Clin Chim Acta 105 : 83–91. [DOI] [PubMed] [Google Scholar]
  38. Petkovic V, Moghini C, Paoletti S, Uguccioni M, and Gerber B. 2004. I-TAC/CXCL11 is a natural antagonist for CCR5. J Leukoc Biol 76 : 701–708. [DOI] [PubMed] [Google Scholar]
  39. Pope CA, Bhatnagar A, McCracken J, Abplanalp WT, Conklin DJ, and O’Toole TE. 2016. Exposure to fine particulate air pollution is associated with endothelial injury and systemic inflammation. Circ Res. 119: 1204–1214. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Raulf M, Weiss T, Lotz A, Lehnert M, Hoffmeyer F, Liebers V, Van Gelder R, Udo Käfferlein H, Hartwig A, Pesch B, Brüning T. 2016. Analysis of inflammatory markers and metals in nasal lavage fluid of welders. J Toxicol Environ Health A 79 :1144–1157. [DOI] [PubMed] [Google Scholar]
  41. Seow WJ, Hu W, Vermeulen R, Hosgood Iii HD, Downward GS, Chapman RS, He X, Bassig BA, Kim C, Wen C, Rothman N, and Lan Q. 2014. Household air pollution and lung cancer in China: A review of studies in Xuanwei. Chin J Cancer 33 : 471–475. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Shen M, Chapman RS, Vermeulen R, Tian L, Zheng T, Chen BE, Engels EA, He X, Blair A, and Lan Q. 2009. Coal use, stove improvement, and adult pneumonia mortality in Xuanwei, China: A retrospective cohort study. Environ Health Perspect 117 : 261–266. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Shiels MS, Katki HA, Hildesheim A, Pfeiffer RM, Engels EA, Williams M, Kemp TJ, Caporaso NE, Pinto LA, and Chaturvedi AK. 2015. Circulating inflammation markers, risk of lung cancer, and utility for risk stratification. J Natl Cancer Inst 107 :10. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Shiels MS, Pfeiffer RM, Hildesheim A, Engels EA, Kemp TJ, Park JH, Katki HA, Koshiol J, Shelton G, Caporaso NE, Pinto LA, and Chaturvedi AK. 2013. Circulating inflammation markers and prospective risk for lung cancer. J Natl Cancer Inst 105 : 1871–1880. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Shiels MS, Shu XO, Chaturvedi AK, Gao YT, Xiang YB, Cai Q, Hu W, Shelton G, Ji BT, Pinto LA, Kemp TJ, Rothman N, Zheng W, Hildesheim A, and Lan Q. 2017. A prospective study of immune and inflammation markers and risk of lung cancer among female never smokers in Shanghai. Carcinogenesis 38 : 1004–1010. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Silverman DT 2018. Diesel exhaust and lung cancer-aftermath of becoming an IARC Group 1 carcinogen. Am J Epidemiol 187 : 1149–1152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Sorensen M, Daneshvar B, Hansen M, Dragsted LO, Hertel O, Knudsen L, and Loft S. 2003. Personal PM2.5 exposure and markers of oxidative stress in blood. Environ Health Perspect 111 : 161–166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Srinivasan N, White HE, Emsley J, Wood SP, Pepys MB, and Blundell TL. 1994. Comparative analyses of pentraxins: Implications for protomer assembly and ligand binding. Structure 2 :1017–1027. [DOI] [PubMed] [Google Scholar]
  49. Wei Y, Han IK, Shao M, Hu M, Zhang OJ, and Tang X. 2009. PM2.5 constituents and oxidative DNA damage in humans. Environ Sci Technol 43 : 4757–4762. [DOI] [PubMed] [Google Scholar]
  50. White E, Hunt JR, and Casso D. 1998. Exposure measurement in cohort studies: The challenges of prospective data collection. Epidemiol Rev 20 : 43–56. [DOI] [PubMed] [Google Scholar]
  51. Williams L, Ulrich CM, Larson T, Wener MH, Wood B, Chen-Levy Z, Campbell PT, Potter J, McTiernan A, and De Roos AJ. 2011. Fine particulate matter (PM2.5) air pollution and immune status among women in the Seattle area. Arch Environ Occup Health 66 :155–165. [DOI] [PubMed] [Google Scholar]
  52. Wong JYY, Downward GS, Hu W, Portengen L, Seow WJ, Silverman DT, Bassig BA, Zhang J, Xu J, Ji BT, Li J, He J, Yang K, Tian L, Shen M, Huang Y, Vermeulen R, Rothman N, and Lan Q. 2019. Lung cancer risk by geologic coal deposits: A case-control study of female never-smokers from Xuanwei and Fuyuan, China. Int J Cancer 144 : 2918–2927. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wu S, Yang D, Pan L, Shan J, Li H, Wei H, Wang B, Huang J, Baccarelli AA, Shima M, Deng F, and Guo X. 2016. Chemical constituents and sources of ambient particulate air pollution and biomarkers of endothelial function in a panel of healthy adults in Beijing, China. Sci Total Environ 560–561: 141–149. [DOI] [PubMed] [Google Scholar]
  54. Yang B, Deng Q, Zhang W, Feng Y, Dai X, Feng W, He X, Huang S, Zhang X, Li X, Lin D, He M, Guo H, Sun H, Yuan J, Lu J, Hu FB, Zhang X, and Wu T. 2016a. Exposure to polycyclic aromatic hydrocarbons, plasma cytokines, and heart rate variability. Sci Rep 6: 19272. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Yuste J, Botto M, Bottoms SE, and Brown JS. 2007. Serum amyloid P aids complement-mediated immunity to Streptococcus pneumoniae. PLoS Pathol 3 : 1208–1219. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Zhang JJ, and Smith KR. 2007. Household air pollution from coal and biomass fuels in China: Measurements, health impacts, and interventions. Environ Health Perspect 115 : 848–855. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Zhao J, Gao Z, Tian Z, Xie Y, Xin F, Jiang R, Kan H, and Song W. 2013. The biological effects of individual-level PM(2.5) exposure on systemic immunity and inflammatory response in traffic policemen. Occup Environ Med 70 : 426–431. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1528964-supplement-1.pdf (615.6KB, pdf)

RESOURCES