Association between air pollutants and blood cell counts in pediatric patients with asthma: A retrospective observational study

doi:10.21203/rs.3.rs-4684513/v1

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Association between air pollutants and blood cell counts in pediatric patients with asthma: A retrospective observational study

https://doi.org/10.21203/rs.3.rs-4684513/v1

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Background

Asthma is a common respiratory disease in children, and air pollution is a risk factor for pediatric asthma. However, how air pollution affects blood cells in pediatric patients with asthma remains unclear.

Methods

This retrospective observational study, performed in 2007–2018 at a medical center, enrolled non-trauma patients aged < 17 years who visited the emergency department and had asthma. Medical records and blood cell counts, including absolute neutrophil count (ANC), eosinophil count, and platelet count were extracted. The concentrations of PM_2.5, PM₁₀, sulfur dioxide (SO₂), nitrogen dioxide (NO₂), and ozone (O₃) were measured from 11 air-monitoring stations in Kaohsiung City.

Results

One-unit increases in PM_2.5 (regression coefficient = 0.098; S.E. =5.937; p < 0.001), PM₁₀ (regression coefficient = 0.129; S.E. =3.541; p < 0.001), NO₂ (regression coefficient = 0.110; S.E. =15.857; p < 0.001), SO₂ (regression coefficient = 0.049; S.E. =30.339; p = 0.007), and O₃ (regression coefficient = 0.048; S.E. =8.831; p = 0.022) on lag 0–6 (7 d average) correlated positively with ANC. One-unit increases in PM_2.5 (regression coefficient = 0.055; S.E. =0.357; p = 0.016), PM₁₀ (regression coefficient = 0.078; S.E. =0.213; p = 0.001), and SO₂ (regression coefficient = 0.041; S.E. =1.811; p = 0.024) on lag 0–6 correlated positively with eosinophil count. Additionally, one-unit increases in PM_2.5 (regression coefficient = 0.068; S.E. =0.101; p = 0.003) and PM₁₀ (regression coefficient = 0.086; S.E. =0.06; p < 0.001) on lag 0–6 correlated positively with platelet count. In a two-pollutant model, the impacts of PM_2.5 and PM_C on ANC and platelet count remained statistically significant after adjusting for other air pollutants. Additionally, PM_C correlated significantly with eosinophil count after adjusting for PM_2.5, NO₂, SO₂, and O₃. Quartile increases in PM_2.5 and PM_C levels correlated positively with ANC, eosinophil count, and platelet count, with differences of approximately 13.8% and 16.2%, 18.5% and 17.3%, and 4.4% and 4.6% between the upper and lower quartiles, respectively.

Conclusions

PM_2.5, PM_C, and NO₂ were independently and positively associated with ANC, PM_C was positively associated with eosinophil count, and PM_2.5 and PM_C were positively associated with platelet count in pediatric patients with asthma. Our results highlight the impact of air pollution on immunity and coagulation in pediatric patients with asthma.

Numerous studies have indicated that short-term variations in air pollution can have adverse health effects such as cause respiratory and cardiovascular diseases and increase daily mortality (Cheng et al. 2020; Tian et al. 2018; Baneras et al. 2018). Fine particulate matter, defined as particulate matter with an aerodynamic diameter of less than 2.5 micrometers (PM_2.5), is currently considered to pose a greater health risk than larger particles (Chang et al. 2022; Kang et al. 2016). These health effects may vary seasonally and regionally. Multiple city-level studies have shown that the health effects associated with PM differ regionally (Kim et al. 2018; Liu et al. 2017). One possible reason for this is that the composition of PM_2.5 and the concentrations of other air pollutants, such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃), change regionally and seasonally. For example, the concentrations of certain heavy metals in PM_2.5 from anthropogenic sources, such as cadmium, lead, antimony, and selenium, are higher in Ningbo, China, in winter (Wu et al. 2019). In industrial areas, the concentrations of NO₂ and SO₂ are high, while the concentration of O₃ is related to photochemical reactions. The health risks associated with PM_2.5 vary owing to differences in their composition and interactions with other air pollutants (Tsai et al. 2021).

Respiratory diseases are common causes of mortality in children, resulting in 1.3 million deaths in children under five years of age between 2010 and 2011 (Walker et al. 2013). The components of air pollution, such as particulate matter (PM₁₀), PM_2.5, NO₂, and O₃, are associated with respiratory inflammation (Xing et al. 2016). Epidemiological studies have also found that PM_2.5 increases the risk of respiratory diseases, including pneumonia, asthma, and bronchitis, especially in children (Huang, Ivey, et al. 2019; Bono et al. 2016).

Recent studies on humans also found that short-term exposure to air pollution interfered with inflammatory responses in healthy volunteers, and this impact varied according to individuals’ characteristics. Hassanvand et al. (2017) found that older volunteers (> 65 years old) showed significantly higher levels of high-sensitivity C-reactive protein (hs-CRP) after PM_2.5 exposure, whereas no significant increase was observed in younger healthy volunteers (Hassanvand et al. 2017). Lee et al. (2018) also observed a significant increase in inflammation and coagulation-related cytokines after PM_2.5 exposure, particularly in individuals with a history of chronic obstructive pulmonary disease (COPD) (Lee et al. 2018). Additionally, the effects of different air pollutants on increases in inflammatory biomarkers vary; for example, carbon monoxide (CO) is associated with increases in leukocytes and neutrophils, whereas sulfur dioxide (SO₂) is associated with a decrease in leukocytes and neutrophils (Lee, et al. 2018). Although research has found a correlation between air pollution exposure and inflammation markers as well as white blood cell (WBC) indices in blood, the impact on pediatric patients, especially those with asthma, remains unclear. Among blood cell tests, absolute neutrophil count (ANC) is an indicator of systemic inflammation, eosinophils are associated with allergic reactions and asthma attacks, and platelet count is an indicator of coagulation and thrombosis (Wechsler et al. 2021; Guthrie et al. 2013; Yeung, Li, and Holinstat 2018). Therefore, this study aimed to evaluate the effects of short-term exposure to PM2.5, and other air pollutants, on blood cell counts, especially ANC and eosinophil and platelet count, in pediatric patients with asthma.

2.1 Study population

This retrospective observational study was conducted between January 1, 2007, and December 31, 2018, at an urban tertiary medical center with an average of 73,000 emergency department (ED) visits annually. The medical records of non-trauma patients under 17 years of age who visited the ED with a primary diagnosis of asthma (ICD-9: 493 or ICD-10: J45) were extracted from the ED administrative database. Data on age, sex, and underlying co-morbidities, including hypertension, diabetes, malignancy, heart disease, respiratory disease, liver disease, and neurological disease, were collected from the patients' medical records. Blood tests, including WBC count, ANC, and platelet count, were also recorded. This study was approved by the Institutional Review Board of the Chang Gung Medical Foundation (IRB number 202101652B0) in accordance with the guidelines of the Declaration of Helsinki. Owing to the retrospective nature of this study, informed consent was not required.

2.2 Pollutant and meteorological data

Air pollutant data and information regarding meteorological conditions were obtained from 11 air quality monitoring stations in Kaohsiung City established in 1994 by the Taiwanese Environmental Protection Administration. During the study period, the hourly concentrations of five "criteria" pollutants, namely PM₁₀ and PM_2.5 (measured using beta-ray absorption), NO₂ and SO₂ (measured using ultraviolet fluorescence), and O₃ (measured using ultraviolet photometry), were measured. Weather conditions, including temperature and relative humidity, were also monitored. The daily average concentrations of air pollutants and weather conditions were then calculated. Patients' addresses were collected from their medical records, and the 24-h average levels of these pollutants were computed based on data from the nearest monitoring station.

2.3 Statistical analysis

Continuous variables were expressed as medians with interquartile ranges (25th and 75th percentiles) or mean ± standard error, depending on whether they followed a normal distribution. Multiple linear regression analyses were performed on complex samples to examine the relationship between air pollutants and blood cell count, adjusting for baseline covariates and weather conditions. The day of an asthma emergency visit was represented as lag0, the day before as lag1, and so on. ANOVA was used to determine whether there were statistically significant trends in blood cell indices across different levels of air pollution exposure. Statistical significance was set at p < 0.05.. All analyses were conducted using Statistical Product and Service Solutions (version 22.0; IBM, Armonk, NY, United States).

3.1 Characteristics of included patients

During the 12-year study period in Kaohsiung, 5,907 pediatric asthma cases were recorded. Of these, 1,058 were excluded because they were either not residents of Kaohsiung City or had incomplete address information. Additionally, 132 patients were excluded owing to missing blood test results. Consequently, 4,717 patients were included in this study. Table 1 presents the patients’ demographic characteristics. Among them, 3,059 (64.9%) were male, and the mean age was 5.4 ± 3.3 years. A total of 3,183 (67.5%) patients were admitted to the hospital, 106 (2.2%) were admitted to the intensive care unit, and 2,080 (44.1%) visited the ED during the warm season (April–September).

Table 1

Demographic characteristics of patients.
All	Number = 4717	%
Demographic characteristics of patients
Age (mean ± SD)	(5.4 ± 3.3)
Male sex	3059	64.9
Hypertension	64	1.4
Neurologic disease	22	0.5
Liver disease	152	3.2
Malignancy	9	0.2
Warm season	2080	44.1
Triage I&II	2949	62.5
Admission	3183	67.5
ICU	106	2.2

3.2 Conditions of meteorology and air pollution during study period

Table 2 presents the air pollutant and weather conditions of Kaohsiung during the study period. The mean levels of PM_2.5, PM₁₀, NO₂, SO₂, and O₃ were 36.5 µg/m3, 68.3 µg/m3, 18.0 parts per billion (ppb), 5.6 ppb, and 29.4 ppb, respectively.

Table 2

Summary statistics for meteorology, and air pollution in Kaohsiung, 2007–2018.
	Minimum	Percentiles			Maximum	Mean	Warm season (Mean ± SD)	Cold season (Mean ± SD)
	Minimum	25%	50%	75%	Maximum	Mean	Warm season (Mean ± SD)	Cold season (Mean ± SD)	p	IQR
PM_2.5 (µg/m³)	1.00	19.00	34.00	50.00	162.00	36.45	24.11 ± 15.28	48.64 ± 19.09	< 0.001	31
PM₁₀(µg/m³)	6.00	40.00	64.00	91.00	724.00	68.33	47.3 ± 23.13	89.19 ± 31.99	< 0.001	51
NO₂ (ppb)	0.31	11.29	16.78	23.81	69.41	17.98	12.93 ± 6.09	23.01 ± 8.14	< 0.001	12.52
SO₂(ppb)	0.10	3.20	4.80	7.00	52.60	5.63	5.04 ± 3.27	6.22 ± 3.81	< 0.001	3.8
O₃(ppb)	2.10	19.30	28.00	37.70	90.70	29.35	27.77 ± 13.5	30.92 ± 12.13	< 0.001	18.4
Temperature(^。C)	6.49	22.52	26.47	28.92	34.95	25.46	28.42 ± 2.04	22.49 ± 3.67	< 0.001	6.4
Humidity (%)	30.79	69.98	74.34	778.70	98.95	74.37	75.99 ± 7.19	72.74 ± 7.38	< 0.001	708.72

3.3 Association between air pollutant exposure on blood cell counts for pediatric patients with asthma

The adjusted regression coefficients (S.E.) for changes in ANC, eosinophil count, lymphocyte count, and platelet count in response to a one-unit increase in air pollutants are summarized in Table 3. A one-unit increase in PM_2.5 (regression coefficient = 0.098; S.E. = 5.937; p < 0.001), PM₁₀ (regression coefficient = 0.129; S.E. = 3.541; p < 0.001), NO₂ (regression coefficient = 0.110; S.E. = 15.857; p < 0.001), SO₂ (regression coefficient = 0.049; S.E. = 30.339; p = 0.007), and O₃ (regression coefficient = 0.048; S.E. = 8.831; p = 0.008) on lag 0–6 correlated positively with ANC. Similarly, a one-unit increase in PM_2.5 (regression coefficient = 0.055; S.E. = 0.357; p = 0.016), PM₁₀ (regression coefficient = 0.078; S.E. = 0.213; p = 0.001), and SO₂ (regression coefficient = 0.041; S.E. = 1.811; p = 0.024) on lag 0–6 correlated positively with eosinophil counts. Additionally, a one-unit increase in PM_2.5 (regression coefficient = 0.068; S.E. = 0.101; p = 0.003) and PM₁₀ (regression coefficient = 0.086; S.E. = 0.06; p < 0.001) on lag 0–6 correlated positively with platelet count.

Table 3

The associations between common air pollutants and blood cells after adjusting gender, age, temperature and humidity.
	Absolute Neutrophil Count (1000/µL)			Eosinophil Counts (1000/µL)			Lymphocyte Counts (1000/µL)
	Regression coefficient	SE	p	Regression coefficient	SE	p	Regression coefficient	SE	p	Regression coefficient	SE	p
PM_2.5 (µg/m³)
lag 0	0.047	4.848	0.027	0.039	0.291	0.069	-0.007	1.547	0.714	0.039	0.082	0.065
lag 1	0.082	4.784	< 0.001	0.039	0.289	0.06	-0.012	1.532	0.552	0.065	0.081	0.002
lag 2	0.094	4.737	< 0.001	0.056	0.286	0.008	-0.026	1.519	0.188	0.06	0.081	0.004
lag 3	0.069	4.778	0.001	0.05	0.288	0.018	-0.004	1.53	0.846	0.069	0.081	0.001
lag 0–2	0.087	5.326	< 0.001	0.051	0.321	< 0.001	-0.017	1.702	0.421	0.063	0.091	0.003
lag 0–6	0.098	5.937	< 0.001	0.055	0.357	0.016	-0.02	1.895	0.346	0.068	0.101	0.003
PM₁₀(µg/m³)
lag 0	0.067	2.759	0.001	0.039	0.166	0.064	-0.015	0.88	0.46	0.044	0.047	0.035
lag 1	0.099	2.771	< 0.001	0.05	0.167	0.017	-0.027	0.886	0.178	0.067	0.047	0.001
lag 2	0.105	2.865	< 0.001	0.053	0.173	0.011	-0.049	0.92	0.016	0.056	0.049	0.008
lag 3	0.09	2.822	< 0.001	0.049	0.169	0.021	-0.034	0.902	0.098	0.075	0.048	< 0.001
lag 0–2	0.107	3.183	< 0.001	0.056	0.192	0.011	-0.036	1.018	0.082	0.067	0.054	0.002
lag 0–6	0.129	3.541	< 0.001	0.078	0.213	0.001	-0.035	1.132	0.115	0.086	0.06	< 0.001
NO₂(ppb)
lag 0	0.051	13.24	0.026	0.02	0.797	0.391	-0.021	4.229	0.34	0.01	0.225	0.682
lag 1	0.057	12.828	0.011	0.025	0.772	0.273	-0.021	4.085	0.32	0.031	0.217	0.167
lag 2	0.11	12.478	< 0.001	0.048	0.753	0.031	-0.042	3.99	0.049	0.05	0.212	0.026
lag 3	0.101	12.613	< 0.001	0.039	0.757	0.082	-0.026	4.027	0.222	0.048	0.214	0.031
lag 0–2	0.092	14.523	< 0.001	0.038	0.875	0.113	-0.034	4.629	0.131	0.039	0.247	0.097
lag 0–6	0.110	15.857	< 0.001	0.047	0.956	0.06	-0.04	5.054	0.095	0.046	0.27	0.061
SO₂ (ppb)
lag 0	0.010	24.5	0.578	0.011	1.478	0.556	-0.007	9.89	0.696	0.014	0.416	0.431
lag 1	0.031	24.485	0.083	0.029	1.517	0.103	-0.004	8.089	0.807	0.037	0.431	0.04
lag 2	0.031	23.388	0.084	0.047	1.395	0.009	-0.016	7.438	0.369	0.002	0.396	0.933
lag 3	0.049	24.430	0.007	0.047	1.457	0.01	0.002	7.783	0.912	0.017	0.416	0.337
lag 0–2	0.03	27.785	0.103	0.034	1.659	0.062	-0.011	8.844	0.521	0.02	0.471	0.27
lag 0–6	0.049	30.339	0.007	0.041	1.811	0.024	-0.012	9.649	0.485	0.024	0.515	0.185
O₃(ppb)
lag 0	0.021	7.102	0.276	0.031	0.424	0.107	0.001	2.258	0.98	0.026	0.121	0.165
lag 1	0.028	6.979	0.132	0.031	0.417	0.095	-0.01	2.219	0.565	0.014	0.118	0.444
lag 2	0.035	6.76	0.053	0.021	0.405	0.248	-0.003	2.155	0.877	0.036	0.115	0.043
lag 3	0.021	6.067	0.234	0.023	0.396	0.187	-0.022	2.106	0.203	0.023	0.112	0.186
lag 0–2	0.034	7.93	0.065	0.033	0.473	0.081	-0.006	2.522	0.738	0.029	0.135	0.12
lag 0–6	0.048	8.831	0.008	0.022	0.528	0.229	-0.022	2.811	0.212	0.027	0.15	0.131

A two-pollutant model was used to determine which individual contaminants influenced blood cell counts independently of other pollutant effects. In accordance with the results obtained from the single-pollutant models, multi-pollutant models were fitted using different pollutant combinations (with up to two pollutants per model) to assess the stability of the effects of air pollutants. The results are summarized in Table 4. In the two-pollutant model, the results of PM_2.5 adjusted for PM₁₀ are not presented because the variance inflation factor value exceeded 5 in the collinearity diagnostics. Instead, coarse particulate matter (PM_C, defined as particulate matter with an aerodynamic diameter of 2.5 µm to 10 µm) was used for adjustment in the two-pollutant model to evaluate whether PM_2.5 or PM_C independently affected the various blood cell counts. A µg/m³ increase in PM_2.5 was significantly related to the ANC after adjusting for PM_C (regression coefficient = 0.067; S.E. =6.326) and SO₂ (regression coefficient = 0.091; S.E. =6.954, p = 0.001). A µg/m³ increase in PM_C was significantly related to the ANC after adjusting for PM_C (regression coefficient = 0.067; S.E. =6.326) and SO₂ (regression coefficient = 0.091; S.E. =6.954, p = 0.001). After adjusting for PM_2.5 (regression coefficient = 0.076; S.E. =20.407, p = 0.017), PM_C (regression coefficient = 0.069; S.E. =17.983, p = 0.014), SO₂ (regression coefficient = 0.117; S.E. =20.833, p < 0.001), and O₃ (regression coefficient = 0.108; S.E. =15.904, p < 0.001), NO₂ was significantly related to the ANC. In addition, PM_C was significantly associated with eosinophil count after adjusting for PM_2.5 (regression coefficient = 0.056; S.E. =0.368, p = 0.01), NO₂ (regression coefficient = 0.061; S.E. =0.391, p = 0.007), SO₂ (regression coefficient = 0.055; S.E. =0.355, p = 0.008), and O₃ (regression coefficient = 0.065; S.E. =0.357, p = 0.002). PM_2.5 correlated positively with platelet count after adjusting for PM_C, NO₂, SO₂, and O₃. PM_C also correlated positively with platelet count after adjusting for PM_2.5, NO₂, SO₂, and O₃. The association between NO₂, SO₂, O₃, and platelet count was not statistically significant after adjusting for PM_2.5 or PM_C.

Table 4

Two pollutants model: the associations of combined effects between common air pollutants (lag 0–6) and absolute neutrophil count.
		Absolute Neutrophil Count
	Adjust PM2.5		Adjust PMc	Adjust NO2	Adjust SO2	Adjust O3
PM _2.5 (β, SE)			0.067 (6.326)***	0.054 (7.616)	0.091 (6.954)**	0.088 (6.620)***
PM_C (β, SE)	0.081 (6.126)**			0.075 (6.499)**	0.093 (5.941)***	0.095 (5.952)***
NO₂ (β, SE)	0.076 (20.407)*		0.069 (17.983)*		0.117 (20.833)***	0.108 (15.904)***
SO₂ (β, SE)	0.014 (35.423)		0.03 (31.361)	-0.006 (39.697)		0.048 (30.369)**
O₃ (β, SE)	0.017 (9.819)		0.028 (9.112)	0.043 (8.820)*	0.047 (8.831)**
		Eosinophil Counts
	Adjust PM2.5		Adjust PMc	Adjust NO2	Adjust SO2	Adjust O3
PM _2.5 (β, SE)			0.032 (0.382)	0.046 (0.458)	0.031 (0.416)	0.052 (0.397)*
PM_C (β, SE)	0.056 (0.368) *			0.061 (0.391)**	0.055 (0.355)**	0.065 (0.357)**
NO₂ (β, SE)	0.016 (1.229)		0.012 (1.088)		0.01 (1.250)	0.047 (0.957)
SO₂ (β, SE)	0.029 (2.118)		0.029 (1.881)	0.037 (2.377)		0.041 (1.810)*
O₃ (β, SE)	0.004 (0.589)		0.007 (0.547)	0.02 (0.529)	0.019 (0.526)
		Platlet Counts
	Adjust PM2.5		Adjust PMc	Adjust NO2	Adjust SO2	Adjust O3
PM 2.5 (β, SE)			0.048 (0.108)*	0.069 (0.130)*	0.073 (0.118)*	0.064 (0.113)*
PMc (β, SE)	0.049 (0.104)*			0.060 (0.111)**	0.061 (0.101)**	0.06 (0.102)**
NO2 (β, SE)	-0.002 (0.348)		0.01 (0.307)		0.043 (0.354)	0.044 (0.271)
SO2 (β, SE)	-0.007 (0.602)		0.009 (0.534)	0.004 (0.675)		0.024 (0.516)
O3 (β, SE)	0.005 (0.167)		0.015 (0.155)	0.025 (0.150)	0.026 (0.150)
* p < 0.05, p < 0.01, *p < 0.001

3.4 Correlations between the quartiles of air pollutants and blood cell counts for pediatric patients with asthma

The linear trend analysis and correlations between the quartiles of each air pollutant and blood cell counts are shown in Fig. 1. With increasing quartiles of PM_2.5 (p-value for trend < 0.001), PMC (p-value for trend < 0.001), and NO2 (p-value for trend < 0.001) levels, the mean ANC increased significantly, while the mean differences in ANC between the highest and lowest quartiles of PM_2.5, PM_C and NO₂ were 13.8%, 16.2%, and 13.0%, respectively (Fig. 1a). With increasing quartiles of PM_2.5 (p-value for trend = 0.002), PM_C (p-value for trend = 0.002), and NO₂ (p-value for trend = 0.005) levels, the mean eosinophil count significantly increased. Compared to the lowest quartile, the mean values in the highest quartile for eosinophil count were 18.5%, 17.3%, and 12.3% higher, respectively (Fig. 1b). Furthermore, as the quartiles of PM_2.5 (p-value for trend < 0.001), PM_C (p-value for trend = 0.001), and NO₂ (p-value for trend = 0.003) increased, the platelet count increased significantly. Compared to the lowest quartile, the mean values in the highest quartile for platelet count were 4.4%, 4.6%, and 4.1% higher, respectively (Fig. 1c).

This study found a statistically significant association between air pollutant exposure and blood cell counts in pediatric patients with asthma. After adjusting for confounding factors and other air pollutants, the levels of PM_2.5, PM_C, and NO₂ correlated positively with ANC, PM_C level correlated positively with eosinophil levels, and PM_2.5 and PM_C levels correlated positively with platelet levels. In addition, quartile increases in the levels of PM_2.5, PM_C, and NO₂ correlated positively with ANC, with differences of approximately 13.8%, 16.2%, and 13.0% between the upper and lower quartiles, respectively. Quartile increases in the levels of PM_2.5, PM_C, and NO₂ correlated positively with eosinophil count by approximately 18.5%, 17.3, and 12.3%, respectively. Furthermore, quartile increases in the levels of PM_2.5, PM_C, and NO₂ correlated positively with platelet count, with differences of approximately 4.4%, 4.6%, and 4.1% between the upper and lower quartiles, respectively.

Asthma is a common disease in pediatric EDs. According to the International Study of Asthma and Allergies in Childhood, approximately 14% of children worldwide are likely to have experienced asthma symptoms in the past year (Mallol et al. 2013). Air pollution may be a risk factor contributing to the incidence of pediatric asthma (Madaniyazi and Xerxes 2021). Numerous epidemiological studies have shown a positive correlation between air pollution exposure and the risk of ED visits for pediatric asthma (Ho et al. 2021; Tiotiu et al. 2020). However, the specific air pollutants that increase the risk of asthma remain unclear. For example, Bouazza et al. collected data from 20 EDs, comprising 47,107 pediatric asthma cases and various air pollutant measurements, including PM_2.5, PM₁₀, NO₂, and O₃ measurements. The results showed that only PM_2.5 had a positive correlation with the risk of asthma-related emergency visits, while the impact of other air pollutants was not statistically significant (Bouazza et al. 2018). Another study collected data from 2,507 pediatric patients who visited a hospital for asthma. The results revealed a positive correlation between PM_2.5, PM₁₀, and NO₂ levels and the risk of hospital visits for asthma. However, the impact of O₃ on hospital visits for asthma was not statistically significant (Ding et al. 2017). A review paper collected 20 studies on the correlation between air pollution and asthma exacerbation and conducted a meta-analysis. PM_2.5 and NO₂ had stronger associations with asthma exacerbation in children, whereas the effects of PM₁₀ and O₃ did not reach statistical significance (Orellano et al. 2017). The present study found that PM_2.5, PM_C, and NO₂ correlated positively with ANC and eosinophil count. ANC is typically associated with the body's inflammatory response, whereas eosinophils are usually related to allergic reactions and allergic asthma (Guthrie et al. 2013; Wechsler et al. 2021). The results suggest that exposure to air pollution may disrupt the regulation of immune responses in children, potentially increasing the risk of asthma.

Several toxicological studies have indicated that both short- and long-term exposure to air pollution can lead to pulmonary inflammation (Lin et al. 2018; Cakmak et al. 2017). Molecular evidence suggested that reactive oxygen species-dependent pathways play a crucial role in the health effects of air pollution, leading to increased oxidative stress in the lungs (Pardo et al. 2018). These inflammatory and oxidative stress-related cytokines, such as endotoxins and histamine, can enter other organs through the systemic circulation. Meanwhile, ultrafine particles (particles with a diameter less than 0.1 µm) and some lipophilic components of PM_2.5, such as polycyclic aromatic hydrocarbons (PAHs), can directly cross vascular barriers and enter the systemic circulation, further increasing oxidative stress and systemic inflammatory responses in extra-pulmonary organs such as the kidneys, liver, and heart (Yuan et al. 2022; Pardo et al. 2018; Olivo et al. 2022). The inflammatory response in the lungs, along with inflammation and oxidative stress-related cytokines that enter the systemic circulation, may further influence leukocyte differentiation. Studies on humans have also found that exposure to PM_2.5, PM₁₀, and PM_C leads to increased levels of inflammation-related cytokines, such as interleukin (IL)-6, in the blood, hs-CRP, and WBC counts (Hassanvand et al. 2017). Our results also showed a positive correlation between air pollutant exposure and its impact on the white cell count distribution. The pulmonary and systemic inflammatory responses induced by these air pollutants may further impair lung function, thereby exacerbating asthma attacks. Studies on humans have also shown that exposure to PM_2.5 is associated with decreased lung function indicators, including the on-second forced expiratory volume (FEV1), forced vital capacity (FVC), and ratio between these two (FEV1/FVC) (Isiugo et al. 2019). The disruption of inflammatory responses in children caused by air pollution, along with its impact on lung function, may further exacerbate asthma severity. Epidemiological studies have shown a positive correlation between PM_2.5 and NO₂ exposure and the risk of asthma-related ED visits and hospitalizations (Haikerwal et al. 2016; Hwang et al. 2017; Tiotiu et al. 2020). Additionally, research has indicated that exposure to PM_2.5 is positively associated with the length of stay for children hospitalized with asthma (Baek et al. 2020). These results indicate the hazardous effects of air pollution on asthma exacerbations and adverse outcomes.

The present study found a positive association between PM_2.5, PM_C, and NO₂ and blood cell and platelet counts in pediatric patients with asthma. Recent studies have shown that exposure to air pollution may disrupt the expression of inflammatory cells, coagulation-related cells, and cytokines in the human body. However, the extent of these effects may vary according to individuals’ characteristics and the types of air pollutants. Hassanvand et al. (2017) found that, in older volunteers (> 65 years), hs-CRP levels increased significantly following PM_2.5 exposure, whereas younger healthy volunteers did not exhibit a notable increase (Hassanvand et al. 2017). Another study observed significant increases in inflammation and coagulation-related cytokines following PM_2.5 exposure, particularly in individuals with a history of COPD. The effects of different air pollutants on the elevation of inflammatory biomarkers varied. For instance, CO was associated with increased WBC and neutrophil count, whereas SO₂ was associated with decreased counts (Lee et al. 2018). Furthermore, Rich et al. (2012) collected blood biomarkers from 125 healthy volunteers and analyzed their changes before, during, and after the Beijing Olympics. During the Olympics, owing to government regulations on air pollution, PM_2.5 concentrations dropped from an average of 100.9 µg/m³ to 69.4 µg/m³, SO₂ levels decreased from 35 ppb to 24 ppb, and NO₂ levels declined from 35 ppb to 33 ppb. Concurrently, soluble P-selectin (sCD62P) decreased by 34.0%, and von Willebrand factor (vWF) decreased by 13.1%. Moreover, the percentage of volunteers with CRP concentrations above the detectable limit (0.3 mg/L) decreased from 55% before the Olympics to 46% during the Olympics (Rich et al. 2012). In Taiwan, Hung et al. collected data from volunteers residing near air quality monitoring stations and analyzed the association between air pollution and blood inflammatory markers. The results showed that CO correlated positively with total WBC, neutrophils, monocytes, and lymphocytes, while SO₂ correlated negatively with WBC, neutrophils, and monocytes. (Hung et al. 2021). There are several possible reasons for the differences beyond individual characteristics. First, the health effects of different PM_2.5 compositions appear to vary. Animal studies have found that both the water extract and insoluble particles of PM_2.5 caused liver damage in mice. However, the water extract primarily induced liver hyperplasia, while the particles mainly caused inflammation and apoptosis (Yuan et al. 2021). Epidemiological studies have also revealed that higher levels of PAHs in PM_2.5 are associated with an increased risk of asthma ED visits (Hsu et al. 2020). A study on humans collected data from 44 volunteers aged > 65 years living in school dormitories and 40 young healthy volunteers. They were exposed to PM from various sources, including industrial emissions, road dust, and vehicle exhaust. The study found significant increases in WBC, vWF, and tumor necrosis factor-soluble receptor-II (sTNFRII) in the blood samples of elderly participants, whereas no significant changes were observed in young healthy participants. Additionally, PM from industrial emissions and road dust had a more pronounced effect on WBC, vWF, and sTNFRII, whereas PM from vehicle exhaust did not have a statistically significant impact on these markers (Altuwayjiri et al. 2021). Another possible reason is that certain components of PM_2.5 may have synergistic effects with other air pollutants and climatic conditions. Xiao et al. (2016) found that the combined effects of O₃ and the nitrate and sulfate components in PM_2.5 could be associated with an increased risk of pediatric pneumonia (Xiao et al. 2016). In another study, exposure to PM_2.5 was associated with morning hypertension, and this effect was exacerbated by lower temperatures.(Imaizumi, Eguchi, and Kario 2015).

Our study found that exposure to PM_2.5 and PM_C was positively associated with platelet count in pediatric patients with asthma. Platelets play important roles in coagulation and thrombosis. Animal studies have shown that co-exposure to silica nanoparticles and lead acetate led decreased anticoagulant function indicators such as thrombin time, prothrombin time, activated partial thromboplastin time, and tissue-type plasminogen activator, as well as an increase in coagulation-related factors such as fibrinogen (Feng et al. 2018). Similarly, in a meta-population study including 3,275 participants, long-term increases in PM_2.5 were matched with significantly increased CRP levels and platelet counts (Viehmann et al. 2015). Xu et al. (2019) collected data from 73 healthy adults and conducted a three-year follow-up study. They found that increases in PM_2.5 were significantly associated with decreases in tissue inhibitors of matrix metalloproteinases (TIMP-1 and TIMP-2), which led to increased thrombogenicity. This was also related to elevated levels of biomarkers of platelet activity, such as the soluble CD40 ligand and soluble P-selectin (Xu et al. 2019). However, numerous prevalence studies have found that exposure to PM_2.5 was associated with an increased risk of cardiovascular and cerebrovascular diseases (Huang et al. 2023; Huang, Liang, et al. 2019). These findings suggest that exposure to air pollution is linked to the activation of coagulation functions and an increased number and activation of platelets, which may subsequently increase the risk of cardiovascular events.

In the present study, we found a statistically significant association between air pollution and ANC and eosinophil and platelet count in pediatric patients with asthma. After adjusting for confounding factors and other air pollutants, the levels of PM_2.5, PM_C, and NO₂ were found to correlate positively with ANC levels, the PM_C level was positively associated with eosinophil levels, and the PM_2.5 and PM_C levels were positively associated with platelet levels. In addition, quartile increases in the levels of PM_2.5, PM_C, and NO₂ correlated positively with ANC, with differences of approximately 13.8%, 16.2%, and 13.0% between the upper and lower quartiles, respectively. Additionally, quartile increases in the levels of PM_2.5, PM_C, and NO₂ correlated positively with eosinophil and platelet counts. Our results highlight the impact of air pollution on immunity and coagulation in pediatric patients with asthma.

Limitations

This study had some limitations. First, the data were collected from Kaohsiung, an industrial city in a tropical region. The results may differ from those of cities with different climatic conditions or levels of air pollution. Second, asthma diagnoses were based on clinical judgment, which can vary among physicians, potentially leading to case underreporting and affecting the results. Third, the study did not record the time spent on indoor and outdoor activities or the use of personal protective equipment, which may have resulted in discrepancies between estimated and actual air pollution exposure.

Acknowledgments

We appreciate the statistical support provided by the Biostatistics Center of Kaohsiung Chang Gung Memorial Hospital.

Funding

This study was supported in part by research grants from Kaohsiung Chang Gung Memorial Hospital (grant number CMRPG8N0711). The sponsor played no role in the study design, data collection, analysis, interpretation, writing of the report, or the decision to submit the article for publication.

Availability of data and materials

Air pollutant data and meteorological data used and analyzed were acquired from https://airtw.moenv.gov.tw/, which is an open public access. The datasets of patients used and analyzed during the current study were acquired from Kaohsiung Chang Gung Memorial Hospital. The datasets were available from the corresponding author on reasonable request.

Ethics approval and consent to participate

This study was approved by the Ethics Committee of Chang Gung Memorial Hospital (IRB NO: 202101652B0) and was conducted according to the tenets of the 1964 Declaration of Helsinki and its later amendments. The need for informed consent was waived owing to the retrospective nature of the study

Competing Interests

The authors declare that they have no competing interests.

Author details

Kuo-Chen Huang^a：[email protected]

Hsiu-Yung Pan^a：[email protected]

Ting-Min Hsieh^b：[email protected]

Fu-Jen Cheng^a^{, c*}：[email protected]

^a Department of Emergency Medicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City, Taiwan.

^bDivision of Trauma, Department of Surgery, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung City, Taiwan.

^cInstitute for Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung City, Taiwan.

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Association between air pollutants and blood cell counts in pediatric patients with asthma: A retrospective observational study

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Abstract

Background

Methods

Results

Conclusions

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Background

Methods

2.1 Study population

2.2 Pollutant and meteorological data

2.3 Statistical analysis

Results

3.1 Characteristics of included patients

3.2 Conditions of meteorology and air pollution during study period

3.3 Association between air pollutant exposure on blood cell counts for pediatric patients with asthma

Discussion

Conclusions

Limitations

Declarations

References

Additional Declarations

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Version 1