Genetic Polymorphisms of CYP2C9/CYP2C19 in Chronic Obstructive Pulmonary Disease

Abstract

Chronic obstructive pulmonary disease (COPD) is a high incidence in the elderly and significantly affects the quality of life. CYP2C9 and CYP2C19 play an important role in tobacco-related diseases and inflammatory reactions. Thus, we aim to investigate the association between CYP2C9/CYP2C19 polymorphisms and the risk of COPD. In this study, a total of 821 subjects were recruited which include 313 COPD cases and 508 healthy controls. Seven SNPs of CYP2C9/CYP2C19 were selected for genotyping. The odds ratios (ORs) and 95% confidence interval (95% CI) were calculated using logistic regression analysis to evaluate the association between COPD risk and CYP2C9/CYP2C19 polymorphisms. Our study showed that A allele of rs9332220 in CYP2C9 was associated with reducing COPD risk (OR = 0.64, 95% CI = 0.43–0.94, p = 0.021). And rs111853758 G allele carrier could significantly decrease 0.35-fold COPD risk compared with T allele carrier (OR = 0.65, 95% CI = 0.45–0.96, p = 0.027). Furthermore, sex-based stratification analysis showed that rs9332220 and rs111853758 polymorphisms were associated with the risk of COPD in males. This is the first study to investigate the association between CYP2C9 and CYP2C19 genetic polymorphisms and COPD risk, which may give a new perspective on the prevention and diagnosis of COPD.

Keywords:

• Chronic obstructive pulmonary disease
• CYP2C9
• CYP2C19
• case–control study

Introduction

Chronic obstructive pulmonary disease (COPD) has a high incidence rate in older people and significantly affects the quality of life. COPD is characterized by airflow limitation. That is, an incompletely reversible chronic persistent inflammatory process [1]. Smoking is the major environmental risk factor [2]. About 10%–15% of smokers developed COPD [3]. COPD is more common in people who have the family history of COPD [4]. The COPD also showed a higher risk in identical twins than fetal twins [5], it means that genetic factors play key role in the occurrence of COPD [6–8]. Therefore, to identify the genetic factor about smoking and inflammatory in COPD is beneficial to early prevention and accurate diagnosis of COPD.

The cytochrome P450 family 2 subfamily C (CYP2C) is a key player in the generation of epoxyeicosatrienoic acids (EETs), CYP2C9 and CYP2C19 enzymes are the major CYP2C involved in EETs production [9,10]. The CYP2C19 metabolizes arachidonic acid (AA) to produce EETs. The EETs anti-inflammatory actions possess an established protective effect on the human cardiovascular system [11]. The CYP2C9 enzymes have been detected not only in liver, but also in the lungs, pancreas, stomach, and kidneys [12,13]. CYP2C9 can metabolize polycyclic aromatic hydrocarbons in tobacco smoke and thereby generate disease-causing metabolites. Genetic variation in genes coding for CYP2C9 enzymes has shown changes in enzyme activity that affects metabolite levels, resulting in a potential risk of disease, suggesting that CYP2C9 plays a key role in tobacco-related diseases [14].

In consideration of the fact that CYP2C9 and CYP2C19 have an important role in tobacco-related diseases and inflammatory reactions, therefore in this study, we carried out the association analysis between the CYP2C9/CYP2C19 polymorphisms and the risk of COPD. A total of four single nucleotide polymorphisms (SNPs) (rs10509679, rs1934967, rs1234968, and rs9332220) in CYP2C9 and three SNPs (rs111853758, rs4494250, and rs75665761) in CYP2C19 were detected in this study. This study may provide the new marker for the diagnosis of COPD.

Materials and methods

Study population

In the current study, a total of 821 unrelated Chinese participants were recruited from Hainan Affiliated Hospital of Hainan Medical University, which included 313 COPD cases and 508 healthy controls. All patients were newly diagnosed with clinical symptoms such as shortness of breath, chronic cough, and sputum, and a post-bronchodilator FEV₁/FVC < 70% confirms the presence of persistent airflow limitation and identifies the presence of COPD in patients according to the criteria of Executive Summary of the Global Strategy for the Diagnosis, Management, and Prevention of COPD (GOLD) 2017 Report [15]. The healthy controls were recruited from the physical examination center of Hainan Affiliated Hospital of Hainan Medical University and all of them exhibited normal pulmonary function (FEV₁/FVC ratio > 70% and FEV₁ > 80% predicted). All the participants must meet the following criteria: (1) Without a history of pulmonary fibrosis, bronchiectasis, lung tumor, tuberculosis, heart disease, and other respiratory diseases. (2) Inability to perform a chest X-ray examination and pulmonary function tests for any reason. (3) Without genetic disorders or general medical diseases.

Each participant was informed the purpose of the study and signed the informed consent. The characteristic information of cases and controls such as age, sex, body mass index (BMI), smoking status, FEV₁ (predicted), FVC (predicted), respiratory rate, and FEV₁/FVC ratio obtained by medical records are summarized in Table 1. This study was approved by the ethics committee of Hainan Affiliated Hospital of Hainan Medical University. All experiments were carried out depending on the protocol of Helsinki’s Declaration.

Table 1. The basic information of cases and controls.

Variables	Case (n = 313)	Control (n = 508)	p
Age ^a	60.05 ± 6.48	71.80 ± 10.09	<0.001*
Sex ^b			0.003*
Male	238 (76.0%)	337 (66.3%)
Female	75 (24.0%)	171 (33.7%)
Smoking status ^b			0.082
Yes	149 (47.60%)	216 (42.5%)
No	164 (52.4%)	292 (57.5%)
BMI, kg/m^2 (mean ± SD) ^a	24.67 ± 4.62	24.35 ± 4.58	0.587
Lung function (mean ± SD) ^a
Respiratory rate, breaths/min	22.85 ± 3.65	15.78 ± 5.23	<0.001*
FEV1, predicted (%)	47.99 ± 8.45	93.19 ± 10.56	<0.001*
FVC, predicted (%)	68.94 ± 9.78	97.39 ± 8.69	<0.001*
FEV1/FVC	0.56 ± 0.05	0.79 ± 0.04	<0.001*

BMI: body mass index; FEV₁: forced expiratory volume in 1 s; FVC: forced vital capacity.

^a Student’s t-test is used.

^b Pearson’s χ ² test is used.

* p < 0.05 indicates statistical significance.

Selection and genotyping of SNPs

Based on the database of the 1000 genomes project (www.internationalgenome.org/), the SNP data of CYP2C9 and CYP2C19 were downloaded. First, we chose the SNPs based on r ² (the measure value of linkage disequilibrium (LD)) < 0.8, MAF (minor allele frequency) > 0.05 in the global population and the linkage disequilibrium of SNPs been detected by Haploview software. Second, the primers of the selected SNPs were designed by Agena online software. Four SNPs (rs10509679, rs1934967, rs1234968, and rs9332220) in CYP2C9 and three SNPs (rs111853758, rs4494250, and rs75665761) in CYP2C19 were in linkage disequilibrium and well-designed primer SNPs were chosen.

Genomic DNA of all participants was extracted from peripheral blood using GoldMag-Mini whole blood genomic DNA purification kit (GoldMag Co. Ltd. Xi’an City, China). The Agena online software was used to design the primers (https://agenacx.com/online-tools/), and the primers of this study are listed in Supplementary Table S1. The genotyping of each SNP was detected by the Agena MassARRAY platform (Agena Bioscience, SanDiego, CA, USA). The genotyping data was organized and analyzed by the Agena Bioscience TYPER software 4.

Statistical analysis

Differences of demographic or clinical characteristics between cases and controls were compared by a t-test or χ ² tests. Hardy–Weinberg equilibrium (HWE) of each SNP was evaluated by the exact test. The p value less than 0.05 was defined as statistically significant. The association between CYP2C9/CYP2C19 genetic variants and COPD risk was detected by calculating ORs and 95% CI using logistic regression analysis with PLINK software under allele, dominant, codominant/genotype, recessive, and additive models adjusted for age and sex. Statistical analyses were conducted using the SPSS 20.0 software. All statistical tests were two-tailed and p < 0.05 indicated statistically significant.

Results

Briefly, 313 COPD cases and 508 healthy controls were enrolled in this study. The characteristics of COPD cases and healthy controls are presented in Table 1. The average age was 60.05 ± 6.48 years in COPD cases and 71.80 ± 10.09 years in the healthy controls. There were significant differences in age and sex between case and control group (p < 0.001, p = 0.003, respectively). In order to reduce the influence of age and sex on the results, we adjusted the age and sex in the later analysis. We also collected the smoking status, BMI index, and FEV₁/FVC information of cases and controls.

The basic information of SNPs from CYP2C9 and CYP2C19 are presented in Table 2. Rs9332220 “A” allele was associated with a reduced COPD risk (OR = 0.64, 95% CI = 0.43–0.94, p = 0.021). And rs11853758 “G” allele carrier with significant decreased risk of 0.35-fold of COPD compared with T allele carrier (OR = 0.65, 95% CI = 0.45–0.96, p = 0.027).

Table 2. Basic information of candidate SNPs in this study.

SNP	Chr	Gene	Position	Allele A/B	p ^a -HWE	OR (95% CI)	p ^b
rs10509679	10	CYP2C9	9,49,48,469	A/G	1.000	0.89 (0.72–1.12)	0.321
rs1934967	10	CYP2C9	9,49,81,669	T/C	1.000	1.16 (0.90–1.50)	0.250
rs1934968	10	CYP2C9	9,49,82,060	A/G	1.000	0.98 (0.80–1.22)	0.884
rs9332220	10	CYP2C9	9,49,84,186	A/G	0.433	0.64 (0.43–0.94)	0.021*
rs111853758	10	CYP2C19	9,47,93,932	G/T	0.138	0.65 (0.45–0.96)	0.027*
rs4494250	10	CYP2C19	9,48,04,000	A/G	0.467	1.05 (0.81–1.35)	0.714
rs75665761	10	CYP2C19	9,48,06,855	A/G	0.709	1.31 (0.89–1.93)	0.176

SNP: single nucleotide polymorphisms; Chr: chromosome; HWE: Hardy–Weinberg equilibrium; OR: odds ratio; 95% CI: 95% confidential interval.

^a p values were calculated by exact test. p < 0.05 are excluded.

^b p values were calculated by two-sided χ ².

*p < 0.05 indicates statistical significance.

The rs9332220 and rs111853758 were associated with COPD risk under the allele model, but the association between rs9332220 and rs111853758 and COPD risk under various genetic models were not significant. After adjusted for age and sex, we found that the AA genotype of rs1934968 was not associated with COPD under allele model but significantly associated with COPD under recessive model (OR = 0.54, 95% CI = 0.30–0.96, p = 0.037, Table 3).

Table 3. Genotype frequencies of the SNPs and their associations with risk of COPD.

SNP	Gene	Model	Genotype	Case	Control	OR (95% CI)	p
rs111853758	CYP2C19	Genotype	TT	257	414	1
			TG	40	85	0.86 (0.52–1.42)	0.564
			GG	0	8	/	/
		Dominant	TT	257	414	1
			TG-GG	40	93	0.77 (0.47–1.26)	0.295
		Recessive	TT-TG	297	499	1
			GG	0	8	/	/
		Additive	–	–	–	0.71 (0.45–1.12)	0.145
rs4494250	CYP2C19	Genotype	GG	201	320	1
			AG	100	158	1.01 (0.69–1.48)	0.964
			AA	12	15	1.66 (0.62–4.42)	0.313
		Dominant	GG	201	320	1
			AG + AA	112	173	1.06 (0.73–1.54)	0.765
		Recessive	GG + AG	301	478	1
			AA	12	15	1.65 (0.62–4.38)	0.312
		Additive	–	–	–	1.10 (0.80–1.52)	0.558
rs75665761	CYP2C19	Genotype	GG	264	448	1
			AG	49	58	1.59 (0.97–2.63)	0.068
			AA	0	2	/	/
		Dominant	GG	264	448	1
			AG + AA	49	60	1.51 (0.92–2.48)	0.103
		Recessive	GG + AG	313	506	1
			AA	0	2	/	/
		Additive	–	–	–	1.40 (0.87–2.26)	0.170
rs10509679	CYP2C9	Genotype	GG	161	245	1
			AG	129	213	1.15 (0.77–1.71)	0.500
			AA	23	46	0.59 (0.31–1.09)	0.092
		Dominant	GG	161	245	1
			AG + AA	152	259	1.01 (0.69–1.47)	0.980
		Recessive	GG + AG	289	458	1
			AA	23	46	/	/
		Additive	–	–	–	1.40 (0.87–2.26)	0.170
rs1934967	CYP2C9	Genotype	CC	201	346	1
			CT	100	145	1.29 (0.87–1.90)	0.202
			TT	11	15	1.66 (0.60–4.60)	0.328
		Dominant	CC	201	346	1
			TC-TT	111	160	1.32 (0.91–1.92)	0.148
		Recessive	CC-TC	301	491	1
			TT	11	15	1.54 (0.56–4.23)	0.402
		Additive	–	–	–	1.29 (0.93–1.79)	0.127
rs9332220	CYP2C9	Genotype	GG	274	418	1
			AG	39	84	0.79 (0.48–1.30)	0.357
			AA	0	6	/	/
		Dominant	GG	274	418	1
			AG + AA	39	90	0.73 (0.45–1.20)	0.214
		Recessive	GG + AG	313	502	1
			AA	0	6	/	/
		Additive	–	–	–	0.70 (0.44–1.11)	0.128
rs1934968	CYP2C9	Genotype	GG	96	186	1
			AG	152	240	1.15 (0.77–1.71)	0.500
			AA	33	77	0.59 (0.31–1.09)	0.092
		Dominant	GG	96	186	1
			AG + AA	185	317	1.01 (0.69–1.47)	0.980
		Recessive	GG + AG	248	426	1
			AA	33	77	0.54 (0.30–0.96)	0.037*
		Additive	–	–	–	0.86 (0.65–1.14)	0.295

OR: odds ratio; 95% CI: 95% confidential interval.

p‐values were calculated by unconditional logistic regression analysis with adjustment for age and sex.

* p < 0.05 indicates statistical significance.

As shown in Table 4, a sex-based stratification analysis was performed. Rs9332220 of CYP2C9 was associated with an decreased risk of COPD in the males (A vs. G, OR = 0.59, 95% CI = 0.37–0.92, p = 0.020), carrying G allele at SNP rs111853758 in the CYP2C19 gene was also associated with decreased risk of COPD under allele model (OR = 0.62, 95% CI = 0.40–0.96, p = 0.032), but other SNPs were not associated with COPD risk in females.

Table 4. The association between SNPs and COPD under gender stratification analysis.

SNP	Model	Allele/Genotype	Male				Female
			Case	Control	OR (95% CI)	p	Case	Control	OR (95% CI)	p
CYPC219
rs111853758	Allele	T	418	602	1		136	311	1
		G	30	70	0.62 (0.40–0.96)	0.032*	10	31	0.74 (0.35–1.55)	0.419
	Genotype	TT	194	270	1		63	144	1
		TG	30	62	0.78 (0.42–1.43)	0.420	10	23	1.10 (0.46–2.63)	0.830
		GG	0	4	/	/	0	4	/	/
	Dominant	TT	194	270	1		63	144	1
		TG-GG	30	66	0.72 (0.39–1.32)	0.290	10	27	0.90 (0.38–2.09)	0.799
	Recessive	TT-TG	224	332	1		73	167	1
		GG	0	4	/	/	0	4	/	/
	Additive	–	–	–	0.69 (0.39–1.22)	0.200	–	–	0.78 (0.37–1.64)	0.507
rs4494250	Allele	G	387	534	1		115	264	1
		A	89	118	1.04 (0.77-1.41)	0.797	35	70	1.15 (0.72–1.82)	0.558
	Genotype	GG	159	221	1		42	99	1
		AG	69	92	0.94 (0.57–1.55)	0.806	31	66	1.11 (0.60–2.04)	0.478
		AA	10	13	1.54 (0.49–4.84)	0.456	2	2	2.75 (0.31–24.44)	0.364
	Dominant	GG	159	221	1		42	99	1
		AG + AA	79	105	1.00 (0.62–1.60)	0.990	33	68	1.15 (0.63–2.11)	0.647
	Recessive	GG + AG	228	313	1		73	165	1
		AA	10	13	1.57 (0.51–4.88)	0.434	2	2	2.64 (0.30–23.10)	0.380
	Additive	–	–	–	1.05 (0.71–1.58)	0.796	–	–	1.20 (0.69–2.11)	0.517
rs75665761	Allele	G	437	633	1		140	321	1
		A	39	41	1.38 (0.87–2.17)	0.166	10	21	1.09 (0.50–2.38)	0.825
	Genotype	GG	199	298	1		65	150	1
		AG	39	37	1.98 (0.92–3.63)	0.063	10	21	/	/
		AA	0	2	/	/	0	0	/	/
	Dominant	GG	199	298	1		65	150	1
		AG + AA	39	39	1.76 (0.94–3.29)	0.075	10	21	1.14 (0.48–2.68)	0.772
	Recessive	GG + AG	238	335	1		75	171	1
		AA	0	2	/	/	0	0	/	/
	Additive	–	–	–	1.55 (0.86–2.79)	0.149	–	–	1.14 (0.48–2.68)	0.772
CYP2C9
rs10509679	Allele	G	340	465	1		111	238	1
		A	136	205	0.91 (0.70–1.17)	0.460	39	100	0.84 (0.54–1.29)	0.418
	Genotype	GG	121	163	1		40	82	1
		AG	98	139	0.98 (0.62–1.55)	0.918	31	74	1.07 (0.58–1.99)	0.826
		AA	19	33	1.00 (0.44–2.26)	0.995	4	13	0.81 (0.23–2.85)	0.741
	Dominant	GG	121	163	1		40	82	1
		AG + AA	117	172	0.98 (0.63–1.52)	0.927	35	87	1.03 (0.57–1.88)	0.918
	Recessive	GG + AG	219	302	1		71	156	1
		AA	19	33	1.01 (0.46–2.22)	0.983	4	13	0.78 (0.23–2.67)	0.697
	Additive	–	–	–	0.99 (0.70–1.40)	0.951	–	–	0.98 (0.61–1.59)	0.939
rs1934967	Allele	C	389	561	1		113	276	1
		T	85	111	1.10 (0.81–1.51)	0.531	37	64	1.41 (0.89–2.24)	0.141
	Genotype	CC	161	238	1		40	108	1
		CT	67	85	1.18 (0.72–1.95)	0.516	33	60	1.49 (0.80–2.74)	0.206
		TT	9	13	1.47 (0.44–4.91)	0.532	2	2	3.16 (0.36–27.86)	0.301
	Dominant	CC	161	238	1		40	108	1
		TC-TT	76	98	1.21 (0.75–1.96)	0.436	35	62	1.54 (0.84–2.81)	0.163
	Recessive	CC-TC	228	323	1		73	168	1
		TT	9	13	1.40 (0.42–4.65)	0.580	2	2	2.70 (0.31–23.46)	0.369
	Additive	–	–	–	1.19 (0.79–1.79)	0.396	–	–	1.54 (0.88–2.70)	0.128
rs9332220	Allele	G	447	607	1		140	313	1
		A	29	67	0.59 (0.37–0.92)	0.020*	10	29	0.77 (0.37–1.63)	0.493
	Genotype	GG	209	273	1		65	145	1
		AG	29	61	0.71 (0.39–1.31)	0.275	10	23	1.03 (0.43–2.44)	0.947
		AA	0	3	/	/	0	3	/	/
	Dominant	GG	209	273	1		65	145	1
		AG + AA	29	64	0.67 (0.37–1.23)	0.194	10	26	0.90 (0.39–2.11)	0.816
	Recessive	GG + AG	238	334	1		75	168	1
		AA	0	3	/	/	0	3	/	/
	Additive	–	–	–	0.65 (0.36–1.15)	0.139	–	–	0.82 (0.38–1.77)	0.611
rs1934968	Allele	G	258	408	1		86	204	1
		A	172	260	1.05 (0.82–1.34)	0.721	46	134	0.81 (0.54–1.24)	0.336
	Genotype	GG	73	124	1		23	62	1
		AG	112	160	1.06 (0.64–1.74)	0.831	40	80	1.33 (0.69–2.56)	0.402
		AA	30	50	0.71 (0.34–1.51)	0.378	3	27	0.26 (0.06–1.03)	0.055
	Dominant	GG	73	124	1		23	62	1
		AG + AA	142	210	0.97 (0.60–1.57)	0.914	43	107	1.05 (0.55–1.99)	0.890
	Recessive	GG + AG	185	284	1		63	142	1
		AA	30	50	0.69 (0.35–1.38)	0.297	3	27	0.22 (0.06–10.82)	0.325
	Additive	–	–	–	0.90 (0.63–1.27)	0.542	–	–	0.77 (0.48–1.23)	0.274

OR: odds ratio; 95% CI: 95% confidential interval.

p values were calculated by logistic regression adjusted by age.

* p < 0.05 indicates statistical significance.

Discussion

In the current study, we found that the rs9332220 A allele of CYP2C9 and rs111853758 G allele of CYP2C19 were associated with a decreased risk of COPD based on 821 participants from Hainan population. Moreover, rs9332220 and rs111853758 may influence the COPD risk of males in sex stratification analysis.

COPD is a complex disease caused by the interaction of environmental and genetic factors. Several environmental risk factors for COPD have been identified [16,17], while the genetic risk factors were less well understood. The cytochrome P450 proteins catalyze many reactions involved in drug metabolism and synthesis of cholesterol, steroids, and other lipids [18,19]. In the genetic polymorphisms of cytochrome P450 in COPD, Cheng et al. found that CYP1A1 is an independent risk factor for very severe COPD [20]. Yang et al. also reported that CYP1A1 polymorphisms were associated with COPD risk [21], and Wang et al.’s meta-analysis proved that CYP1A1 polymorphism might play a role in COPD risk among Asian population [22]. Seo et al. suggested that CYP3A4 may play a role in airway injury of smokers [23]. All CYP enzymes share common structures and similar functions in the same way [11]. Thus, the association between the polymorphism of CYP2C9 and CYP2C19 and COPD risk was assessed in this study.

CYP2C9 and CYP2C19 gene were located within a cluster of cytochrome P450 genes on chromosome 10q24. Previous studies reported the polymorphisms of CYP2C9 and CYP2C19 and the risk of coronary heart disease (CHD) in the Russian population [24]. In one study, bladder cancer decreased risk was associated with CYP2C9 variant loci carriers [25]. Combined with this study, CYP2C9 was not only associated with tobacco-related cancers but also associated with tobacco-related COPD. Kamata et al. noted an up-regulation of CYP2C19 mRNA expressions in alveolar epithelial type II (ATII) cells isolated from patients with COPD compared with smokers without COPD [26]. Kamata et al.’s study may have important implications for the research of COPD pathogenesis. Our findings firstly revealed that rs111853758 of CYP2C19 was related to COPD risk.

The CYP epoxygenases catalyze the epoxidation of the intracellular AA that gives rise to corresponding EETs [27]. EETs have anti-inflammatory, antiapoptotic, and antioxidative activities, and autophagy is believed to be involved in the pathogenesis of COPD [28]. EETs may protect the heart and liver by regulating autophagy [29]. The SNPs rs9332220 and rs111853758 polymorphisms in CYP2C9 and CYP2C19 may regulate the epoxygenases activity and influence EETs production. It may be the protective property of COPD by inhibiting autophagy. Besides, we observed that rs9332220 and rs111853758 were associated with a decreased susceptibility to COPD in males and no significant association was found in females. The reason the association is significant in males and not females may be due to sample size.

There are some limitations in our study. The sample size is relatively small. In future, we will need to expand the sample size to verify our results. It’s necessary to study how CYP2C9 and CYP2C19 polymorphism influence the underlying pathogenesis of COPD for further study. Despite the above limitations, our present work provides the available evidence of CYP2C9 and CYP2C19 gene with COPD for the future study.

Conclusion

In conclusion, our present work indicated that rs9332220 in CYP2C9 and rs111853758 in CYP2C19 were associated with reduced risk of COPD.

Availability data and material

All the data regarding the findings are available within the manuscript.

Anyone interested in the information should contact the corresponding author.

Acknowledgment

We would like to thank all the patients and individuals in this study for their participation.

Conflict of interest statement

The authors declare that they have no competing interests.

Additional information

Funding

This study was supported by the Hainan Provincial Natural Science Foundation of China (No. 818QN315) and the National Natural Science Foundation of China (No. 81660013 and No. 81860015).