Association between blood aluminum and beta-2 receptor gene methylation with childhood asthma control

Abstract

Previous studies have shown that environmental exposure to heavy metals has been related to epigenetic changes, such as DNA methylation in receptors involved in pathogenesis of asthma. One of these receptors is beta-2 adrenergic receptor (ADRB2). We conducted this study to examine the association between blood aluminum concentration, blood ADRB2 5′ untranslated region (5′-UTR) methylation level, and childhood asthma control level. Our results showed a significant positive association between high blood aluminum concentration (odds ratio, 16, 95% confidence interval (CI) [3.57 to 71.76], p < 0.001) and high blood ADRB2 5′-UTR methylation level (odds ratio, 4.75, 95% CI [1.39 to 16.2], p = 0.013), and risk of uncontrolled asthma. Multivariable logistic regression revealed that higher blood aluminum concentration was independently associated with increased risk of uncontrolled bronchial asthma (odds ratio, 9.10, 95% CI [2.38 to 34.85], p = 0.0013], after controlling for age, sex, and blood ADRB2 5′-UTR methylation level. In addition, blood ADRB2 5′-UTR methylation level significantly correlated with whole blood aluminum concentration in asthmatic children (r = 0.480, p < 0.001). We concluded that increasing blood aluminum concentration is an important independent correlate of risk for uncontrolled bronchial asthma as well as increased blood aluminum concentration caused ADRB2 5′-UTR hyper-methylation with increasing risk of uncontrolled bronchial asthma.

Keywords

aluminum asthma control bronchial asthma DNA methylation

Introduction

Bronchial asthma is a respiratory condition that results from a complex interplay between genetic and environmental factors, which is known to be mediated by epigenetics.^1,2 Asthma is among the top 20 childhood chronic illnesses for global ranking of disability-adjusted life years. Worldwide, approximately 334 million people suffer from asthma. Mortality rates from asthma in children range from 0.0 to 0.7 per 100,000. Albeit, these figures may be an underestimation.^3,4

Aluminum is the most abundant metal in the earth’s crust, is widely distributed in the environment, and is usually introduced into the human body through ingestion and inhalation. Aluminum is not considered an essential element, and it is recognized as a toxic agent in high concentrations. Most of the body load of aluminum is stored in the bones.⁵ Sources of aluminum exposure include environmental and non-environmental ones: acid rains, drinking water, food, utensils, vaccines, parenteral nutrition solutions, and pharmaceutical products. It is also present in ambient and occupational airborne particulates.^6

–9

Previous Egyptian studies have linked aluminum exposure to neurological disorders in children, such as epilepsy and autism spectrum disorder.^10,11 Moreover, occupational exposure to aluminum among Egyptian workers was associated with metabolic disarrangement, for example, parathyroid and musculoskeletal disorders as well as oxidative and DNA damage.^12,13

Some studies revealed the role of certain environmental heavy metals as proposed risk factors for childhood asthma development and the underlying mechanisms. However, there are conflicting results regarding the exact incriminated heavy metals.^14,15 Also, studies concerning the epigenetic impact of environmental metal exposure are modest.^15,16

Epigenetics can be defined as heritable alterations in gene expression without changes in DNA sequence. The four common epigenetic mechanisms, namely DNA methylation, histone modification, microRNAs, and nucleosome remodeling, regulate gene activity and play a role in a number of complex human diseases, severity, and prevalence. Epigenetic mechanisms regulate the expression of genes involved in the inflammatory processes related to bronchial asthma.^17
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DNA methylation is the most comprehensively studied epigenetic mechanism in the pathogenesis of asthma.²⁰ DNA methylation is a biochemical process in which a methyl group is added to the 5′ position of the pyrimidine ring of cytosine nucleotides lying next to a guanine, which is referred as CpG sites.²¹ The gene encoding the beta-2 adrenergic receptor (ADRB2) has been widely studied as a candidate gene for asthma susceptibility, asthma phenotypes, and response to therapy.²²

The ADRB2 is a G-protein-coupled receptor found in respiratory epithelium, smooth muscle in airways, and lymphocytes, and it is the main target of beta-agonist bronchodilators. ADRB2 has been expansively studied as a good candidate gene for asthma predisposition, asthma phenotypes, and prognosis.^22,23 In complex diseases such as asthma, DNA methylation offers a potential mechanism for environmental modification of genetic responses, including those at ADRB2 gene loci. Genetic alterations in ADRB2 gene have been incriminated in the level of asthma control, but some reported data are contradictory.²⁴

Generally, association studies between heavy metal exposure and risk of childhood asthma are inconclusive. Guo et al. in their case–control study pointed to the association between the plasma aluminum and the development of childhood asthma as well as aluminum role in the pathogenesis of asthma, in terms of induction of oxidative stress, inflammation, and Th1/Th2 lymphocyte imbalance.¹⁴ However, the underlying epigenetic mechanism of aluminum exposure in the pathogenesis of the bronchial asthma as well as its relation to asthma severity has not been studied yet.

In the current study, we hypothesized that increased blood aluminum concentration, along with increased blood ADRB2 5′ untranslated region (5′-UTR) methylation, is associated with increased level of uncontrolled bronchial asthma in children. This study was designed to examine the association between blood aluminum concentration, blood ADRB2 5′-UTR methylation level, and childhood asthma control level.

Subjects and methods

Subjects and study setting

The present study is a case–control one. Patients were randomly selected from Outpatient Chest Clinics and Inpatient Pediatrics Ward, Zagazig University Hospitals in Egypt, between December 2018 and May 2019. The study protocol was approved by the Research Ethics Committee of the Faculty of Medicine, Zagazig University. Informed written consent and/or assent were obtained from all study participants and/or their caregivers.

Seventy patients (35 boys and 35 girls) were recruited, and their ages ranged from 5 years to 18 years. Asthmatic children were further classified into three subgroups on the basis of asthma control level according to Global Initiative for Asthma (GINA) guidelines in 2015.²⁵

Childhood asthma was diagnosed according to global strategy for asthma management and prevention (GINA) guidelines in 2015, summarized by Reddel et al.²⁵ Exclusion criteria for the study included patients younger than 5 years as spirometry evaluation cannot be assessed; patients with severe comorbidities such as cardiopulmonary malformations, cancer, autoimmune diseases, and sepsis; and patients on systemic steroids or beta blockers.

In addition, 70 apparently healthy children who were matched to cases by age, sex, exposure to tobacco smoke, and the same geographical regions were randomly selected and served as the control group. The healthy children showed negative skin prick tests, normal total immunoglobulin E levels, and normal pulmonary function tests. None of the control children had a history of chronic diseases, asthma or other allergic diseases, or respiratory tract diseases. They were undergoing routine checkups or presurgical examinations for elective minor surgeries.

Study procedures

All eligible participants underwent a detailed history-taking. A full clinical examination was performed for both asthmatic and control children.

Blood samples collection

Blood samples were collected from each study participant. About 5–10 mL of venous blood samples were taken from each participant under complete aseptic condition. Blood samples were collected in sterile ethylenediaminetetraacetic acid-containing tubes for DNA extraction and whole blood aluminum estimation.

DNA for methylation analysis

Genomic DNA was isolated and purified from blood samples using the QIAamp DSP DNA Blood Mini Kit purchased from Qiagen GmbH (Hilden, Germany). DNA was extracted according to the method of Looi et al. in which the standard DNA was isolated from 200 µL of whole blood in standard 45-min protocol, including RNase treatment. Expected yield was 5–15 µg DNA.²⁶

Extracted DNA was then bisulfite-treated using the EZ DNA Methylation Kit (Zymo Research, Irvine, California, USA) according to the manufacturer’s protocol, which converts unmethylated cytosines into uracil and leaves methylated cytosines unchanged.

5′-UTR CpG island identification and methylation analysis A CpG Island in the 5′-UTR region of ADRB2 were identified using the CpG Island Searcher web tool (www.cpgislands.com). Methylation-specific polymerase chain reaction (PCR) primers for this region were designed using the MethPrimer program (www.urogene.org/methprimer), with one pair designed to amplify methylated DNA and one pair designed to amplify unmethylated DNA.

The two methylated primer sequences were F:5′-GTATATAACGGGTAGAACGTATTGC-3′ and R:5′-GTCCTACACACTCAACTTATCGA-3′, and the two unmethylated primer sequences were F:5′-TGTATATAATGGGTAGAATGTATTGTG- 3′ and R:5′-TCATCCTACACACTCAACTTATCAA- 3′.

Quantitative methylation-specific PCR was performed using the primers described above and the SYBR Fast Universal qPCR Kit (Kapa Biosystems, Woburn, Massachusetts, USA), according to the protocols of the manufacturer, to distinguish methylated from unmethylated DNA sequences.

Measurement of whole blood aluminum

Estimations of whole blood aluminum concentrations were done by atomic absorption spectrophotometry, (Model 210 VGP, Buck Scientific, Norwalk, CT, USA).

Digestion of the blood sample was done by using acid: 1 mL blood in 3:1:1 (v/v/v) nitric acid (HNO3): perchloric acid (HClO4): sulfuric acid (H2SO4). That is, mixing three volumes concentration (conc.) of HNO3 with 1 volume conc. of HClO4 and with 1 volume conc. of H2SO4. Hot plate temperature was increased to 250°C and continued until the volume of blood remained is 1 mL (2–3 h). Atomic absorption and the specimen were repeated for assurance by inductively coupled plasma mass spectrometry. The blank used was an acid blank, and 10 μg/mL multielement solutions are used.²⁷

Spirometry

Pulmonary function tests were conducted for all children using a fully computerized Spirometer (Jaeger MasterScreen™ IOS, version 5.2 manufactured by VIASYS Healthcare GmbH, Hoechberg, Germany). Pulmonary function tests were assessed using forced expiratory volume in one second (FEV1), forced vital capacity (FVC), and the FEV1/FVC ratio, measured and expressed as a percentage of predicted values with a ratio higher than 0.9 being normal.

Sample size

G*power 3.1 was used to compute the achieved power.²⁸ Our study had 99% power. A post hoc power calculation was made on the basis of the correlation between ADRB2 5′-UTR methylation level and whole blood aluminum concentration (Pearson correlation coefficient r = 0.480), at sample size = 140 (70 in each arm) and type I error threshold (α ≤ 0.05).

Statistical analysis

Categorical variables were presented by the count (percentage). Continuous variables were presented as median (interquartile range (IQR)), defined as the 25th to 75th percentile, and data were not normally distributed. Normality was checked by Kolmogorov–Smirnov test. The χ ² test is used to discover whether there is a relationship between two categorical variables or Fisher’s exact test for RXC table is an alternative to the χ ² test when the expected cell count is less than five. Mann–Whitney U test is used to determine whether a difference exists between the medians of two independent groups on a continuous dependent variable violated from assumption of normality. Binary logistic regression analysis is a multifactorial regression model used with a binary outcome. Variables that have a p value of <0.25 in univariate analysis were included in multivariable analysis. Logistic regression analysis results are presented in terms of odds ratios with 95% confidence intervals (CIs). Asthma control status is dichotomized into controlled versus uncontrolled asthma. Pearson’s correlation is used to estimate the strength of linear relationship between two continuous variables; at least one of the two variables must follow a normal distribution. The differences were considered significant at p < 0.05. All statistical comparisons were two tailed. All statistical analyses were performed using Statistical Package of Social Science version 24.0 (IBM; Armonk, New York, USA).

Results

Baseline characteristics of the participants

A total of 70 (35 boys, 35 girls) asthmatic children and 70 (40 boys, 30 girls) healthy control children were enrolled in this study.

The median age of the asthmatic children was 8 years (IQR 6.8–10 years) and that of the control children was 9 years (IQR 6–10 years). We found no statistically significant differences between the asthmatic children and the control children in terms of age, gender distribution, exposure to passive smoking, and residency (p > 0.05), as presented in Table 1.

Table 1.

Baseline characteristics of healthy controls and asthmatic children.

Baseline characteristics	Healthy controls (n = 70)	Asthmatic children (n = 70)	p Value
Age (years), median (IQR)	9 (6.8–10)	8 (6–10)	0.71^a
Sex, n (%)
Boys	40 (57.1)	35 (50)	0.40^b
Girls	30 (42.9)	35 (50)	0.40^b
Passive smoking exposure, n (%)
Yes	36 (51.4)	29 (41.4)	0.24^b
No	34 (48.6)	41 (58.6)	0.24^b
Residency, n (%)
Rural	43 (61.4)	49 (70)	0.29^b
Urban	27 (38.6)	21 (30)	0.29^b

IQR: interquartile range.

^a Mann–Whitney U test.

^bχ² test.

Whole blood aluminum concentration and blood ADRB2 5′-UTR methylation level of the participants

Compared to the control children, asthmatic children had a highly statistically significantly higher whole blood aluminum concentration (median (IQR) 33.7 (23.7–45.6) vs. 15.7 (12.7–17.4) μg/L, p < 0.001) and blood ADRB2 5′-UTR methylation level (median (IQR) 2.4 (1.8–2.8) vs. 0.67 (0.58–0.78), p < 0.001), as presented in Table 2.

Table 2.

Laboratory findings of healthy controls and asthmatic children.^a

Laboratory findings	Healthy controls (n = 70)	Asthmatic children (n = 70)	p Value^b
Blood aluminum concentration (μg/L)	15.7 (12.7–17.4)	33.7 (23.7–45.6)	<0.001
ADRB2 5′-UTR methylation level	0.67 (0.58–0.78)	2.4 (1.8–2.8)	<0.001

ADRB2: beta-2 adrenergic receptor; 5′-UTR: 5′ untranslated region.

^a Data are median (interquartile range). Boldface values indicate a highly statistically significant difference at the p < 0.001 level.

^b Mann–Whitney U test.

Spirometry findings of the participants

Compared to the control children, asthmatic children had statistically significantly lower spirometry findings (FEV1%, FVC%, and FEV1/FVC %) (median (IQR) 94 (90.8–95) vs. 86 (75.8–90), 98 (95–99) vs. 94.5 (88–97), 96 (95–98) vs. 90 (85–95)%, (p < 0.001 for each)], as presented in Table 3.

Table 3.

Spirometry findings of healthy controls and asthmatic children.^a

Spirometry findings	Healthy controls (n = 70)	Asthmatic children (n = 70)	p Value
FEV1%	94 (90.8–95)	86 (75.8–90)	<0.001 ^b
FVC%	98 (95–99)	94.5 (88–97)	<0.001 ^b
FEV1/ FVC%	96 (95–98)	90 (85–95)	<0.001 ^b

FEV1: forced expiratory volume in one second; FVC: forced vital capacity.

^a Data are median (interquartile range). Boldface values indicate a highly statistically significant difference at the p < 0.001 level.

^b Mann–Whitney U test.

Association between blood aluminum concentration and ADRB2 5′-UTR methylation level stratified by asthma control level

We found a significant association between blood aluminum concentration (p < 0.001), ADRB2 5′-UTR methylation level (p = 0.029), and asthma control level.

Percentage of asthmatic children with high blood aluminum concentration was predominant in partly controlled (46%) and uncontrolled subgroups (42%). Similarly, percentage of asthmatic children with high ADRB2 5′-UTR methylation level was predominant in partly controlled (39%) and uncontrolled subgroups (35%), as presented in Table 4.

Table 4.

Blood aluminum concentration and ADRB2 5′-UTR methylation level stratified by asthma control level.^a

	Controlled (n = 29)	Partly controlled (n = 27)	Uncontrolled (n = 14)	p Value
Blood aluminum, n (%)
Low (<26.69 μg/L)	16 (70)	6 (26)	1 (4)	<0.001 ^b
Intermediate (26.69–41.85 μg/L)	10 (44)	10 (44)	3 (13)
High (≥41.86 μg/L)	3 (13)	11 (46)	10 (42)
ADRB2 5′-UTR methylation level, n (%)
Low (<2.10)	14 (64)	5 (23)	3 (14)	0.029 ^b
Intermediate (2.10–2.69)	8 (36)	12 (55)	2 (9)
High (≥2.70)	7 (27)	10 (39)	9 (35)

ADRB2: beta-2 adrenergic receptor; 5′-UTR: 5′ untranslated region.

^a Boldface values indicate a highly statistically significant difference at the p < 0.05 level.

^b Fisher’s exact test.

Association between various predictors and asthma control level

Univariate logistic regression analysis revealed that decreasing age, increasing blood aluminum concentrations, and increasing ADRB2 5′-UTR methylation levels were common risk factors and significantly associated with an increased risk for uncontrolled bronchial asthma. We found a significant positive association between high blood aluminum concentration (odds ratio 16, 95% CI [3.57 to 71.76], p < 0.001) and high blood ADRB2 5′-UTR methylation level (odds ratio 4.75, 95% CI [1.39 to 16.2], p = 0.013), as presented in Table 5.

Table 5.

Association between various predictors and asthma control level: univariate logistic regression.^a

Variables	Univariate logistic regression analysis
Variables	β coefficient	Standard error	Unadjusted odds ratio	95% CI	p Value
Age (years)	−0.187	0.092	0.830	0.639 to 0.994	0.042
Sex
Girls^b			1.00
Boys	0.837	0.497	2.31	0.872 to 6.12	0.092
Passive smoking exposure
No^b			1.00
Yes	0.247	0.495	1.28	0.485 to 3.38	0.62
Residency
Rural^b			1.00
Urban	−0.084	0.528	0.92	0.327 to 2.60	0.87
Blood aluminum concentration
Low^b (<26.69 μg/L)			1.00
Intermediate (26.69–41.85 μg/L)	1.089	0.618	2.97	0.884 to 9.98	0.078
High (≥41.86 μg/L)	2.773	0.766	16	3.57 to 71.76	<0.001
ADRB2 5′-UTR methylation level
Low^b (<2.10)			1.00
Intermediate (2.10–2.69)	1.119	0.627	3.06	0.897 to 10.46	0.074
High (≥2.70)	1.558	0.626	4.75	1.39 to 16.20	0.013

ADRB2: beta-2 adrenergic receptor; 5′-UTR: 5′ untranslated region; CI: confidence interval.

^a Boldface values indicate a highly statistically significant difference at the p < 0.05 level.

^b Subjects in this category served as the reference group.

Stepwise multivariable logistic regression revealed that higher blood aluminum concentration was independently associated with increased risk of uncontrolled bronchial asthma (odds ratio 9.10, 95% CI [2.38 to 34.85], p = 0.0013), after controlling for age, sex, and blood ADRB2 5′-UTR methylation level, as presented in Table 6.

Table 6.

Association between various predictors and asthma control level: stepwise multivariable logistic regression analysis.^a

Blood aluminum concentration	Stepwise multivariable logistic regression analysis
Blood aluminum concentration	β coefficient	Standard error	Adjusted odds ratio^b	95% CI	p Value
Low^c (<26.69 μg/L)			1.00
High (≥41.86 μg/L)	2.208	0.685	9.10	2.38 to 34.85	0.0013

ADRB2: beta-2 adrenergic receptor; 5′-UTR: 5′ untranslated region; CI: confidence interval.

^a Boldface values indicate a highly statistically significant difference at the p < 0.05 level.

^b Adjusted for age, sex and ADRB2 5′-UTR methylation level.

^c Subjects in this category served as the reference group.

Figure 1 shows a significantly positive correlation between blood ADRB2 5′-UTR methylation level and whole blood aluminum concentration (r = 0.480, p < 0.001).

Figure 1.

Correlation between ADRB2 5′-UTR methylation level and whole blood aluminum concentration of the studied asthmatic children. ADRB2: beta-2 adrenergic receptor; 5′-UTR: 5′ untranslated region; n: number; r: Pearson correlation coefficient.

Joint effects between high blood aluminum concentration and high ADRB2 5′-UTR methylation level on asthma control level

Multivariable logistic regression analysis was conducted to assess the existence of joint effects between high blood aluminum concentration and high ADRB2 5′-UTR methylation level on asthma severity control. We found a positive significant association between uncontrolled asthma and high blood aluminum concentration (odds ratio 11.61, 95% CI [2.14 to 63.037], p = 0.004) but no significant association between uncontrolled asthma and high level of blood ADRB2 5′-UTR methylation (odds ratio 0.693, 95% CI [0.157 to 3.057], p < 0.001), after controlling for high blood aluminum concentration. These observations indicated that increasing blood aluminum concentration is a significant risk factor for uncontrolled asthma compared to increasing levels of ADRB2 5′-UTR methylation, as presented in Table 7.

Table 7.

Joint effects between high blood aluminum concentration and high ADRB2 5′-UTR methylation level in asthma control level: multivariate logistic regression analysis.^a

Variables	β coefficient	Standard error	Odds ratio (95% CI)	p Value
Blood aluminum concentration (μg/L)
Low^b (<26.69 μg/L)			1.00
High (≥41.86 μg/L)	2.452	0.863	11.61(2.14 to 63.037)	0.004
ADRB2 5′-UTR methylation level
Low^b (<2.10)			1.00
High (≥2.70)	−0.367	0.757	0.693(0.157 to 3.057)	0.63

ADRB2: beta-2 adrenergic receptor; 5′-UTR: 5′ untranslated region; CI: confidence interval.

^a Boldface value indicates a highly statistically significant difference at the p < 0.05 level.

^b Subjects in this category served as the reference group.

Discussion

Our results showed that asthma control is mainly affected by elevated blood aluminum concentrations compared to elevated levels of ADRB2 5′-UTR methylation. Furthermore, we found a significant positive association between blood ADRB2 5′-UTR methylation with the whole blood aluminum in uncontrolled childhood asthma. The observation of a concentration-dependent response between blood aluminum concentrations and risk for uncontrolled asthma suggests its potential usefulness in providing important proof on susceptibility of severe uncontrolled asthma.

In support, Lukiw stated that aluminum is hypothesized to condense adenine + thymine-rich chromatin domains. This phenomenon is known as “aluminum biocompaction,” which in turn can cause silencing of the expression of specific kind of genetic information.²⁹

According to Yang et al., prenatal and early life exposure to environmental factors, from diet to exposure to pollutants, has been linked to epigenetic changes, mainly DNA methylation.³⁰

Under normal condition, airway epithelium is the initial barrier against inhaled injurious agents. In bronchial asthma, the airway epithelium shows extensive structural and functional abnormalities. Therefore, asthmatic patients are highly susceptible to environmental toxins.³¹

In addition, our findings suggest that increased blood aluminum may be responsible for DNA methylation-associated asthma exacerbations. Probably, the effect of ADRB2 5′-UTR methylation on asthma control is modified by blood aluminum concentration.

Anderson informed that ADRB2 is the main target of short- and long-acting beta agonists, the two principal classes of medications used for the treatment and control of asthmatic symptoms. Hyper-methylation of ADRB2 in airway smooth muscle may cause downregulation of ADRB2 gene expression, which may reduce its capacity to act as a mediator of airway relaxation and the body’s response to both endogenous and synthetic β₂-agonists. Previous molecular and genetic research found that both ADRB2 expression and at least one ADRB2 genetic variant can alter bronchoreactivity to β₂-agonists.^32,33

Moreover, our findings revealed that asthmatic patients had statistically significantly higher blood aluminum concentrations, ADRB2 5′-UTR methylation levels, and abnormal spirometry findings than healthy children.

Similar to our results, Guo et al. in their study on 27 patients with allergic asthma observed that asthmatic patients had significantly higher plasma aluminum load and respiratory function was lower than the healthy controls.¹⁴

Increased systemic aluminum load may be an allergic trigger that stimulates inflammatory mediators and augments antibody production. It is therefore possible that chronic exposure to aluminum can induce several biological alternations incriminated in asthma.¹⁴

Variations in DNA methylation forms, both at specific loci and overall in the genome, have been linked with many different health outcomes.³⁴

Our results coincided with the results of Fu et al. who concluded that DNA hyper-methylation was positively related to severity of asthma and level of control.³⁵

Limitations of the study

First, pregnancy is the critical period of susceptibility, since intrauterine life is the herald for the health status of adulthood, and many risks for developing diseases in late life could be prevented.³⁶ Immature neonates and children are susceptible for aluminum adverse health effects even at low doses.³⁷ Albeit, prenatal history for aluminum exposure in our study was not available.

Second, we should address other non-environmental sources for aluminum exposure, but we had no facilities to implement.

Third, our study is considered preliminary. Future large-scale and interventional studies of large samples of children in the same as well as other geographical location are crucial to confirm the findings of the present study.

Fourth, risk assessments for aluminum exposure must take into consideration individual predisposing factors (such as socioeconomic status, parent occupations, and renal function).

Conclusion

We explored a significant positive association between blood aluminum concentration, blood ADRB2 5′-UTR methylation, and childhood asthma control level. We also found a joint effect between blood aluminum and blood ADRB2 5′-UTR methylation, suggesting the probability that increased blood aluminum concentration caused ADRB2 5′-UTR hyper-methylation with increasing risk of uncontrolled bronchial asthma. The severity of childhood asthma is related to the blood load of aluminum and ADRB2 5′-UTR methylation.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

OE Nafea

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