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Original article
Open Access
Available online 16 September 2022
Genetic polymorphisms in MIR1208 and MIR5708 are associated with susceptibility to COPD in the Chinese population
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Y. Zhoua,1, F. Baib,1, X. Lic, G. Zhoud, X. Tiane, G. Lic, Y. Zhangf, X. Zhouf, D. Xug,
Corresponding author
brick3048011@163.com

Corresponding authors.
, Y. Dingf,
Corresponding author
ypding@263.net

Corresponding authors.
a Center of Appointment Clinic Service, Hainan General Hospital, Hainan affiliated Hospital of Hainan Medical University, Hainan, China
b Department of Science and Education Department, Hainan General Hospital, Hainan affiliated Hospital of Hainan Medical University, Hainan, China
c Department of General Practice, People's Hospital of Wanning, Hainan, China
d Department of Nursing, People's Hospital of Wanning, Hainan, China
e Department of Medical, People's Hospital of Wanning, Hainan, China
f Department of General Practice, Hainan General Hospital, Hainan affiliated Hospital of Hainan Medical University, Hainan, China
g Department of Emergency, Hainan General Hospital, Hainan affiliated Hospital of Hainan Medical University, Hainan, China
Received 06 November 2020. Accepted 24 July 2021
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Abstract
Background

Chronic obstructive pulmonary disease (COPD) is a complex disease characterized by limited airflow and is influenced by genetic and environmental factors. The purpose of this study was to investigate the effects of gene polymorphisms in MIR5708 and MIR1208 on COPD risk.

Methods

Four single nucleotide polymorphisms (SNPs) in MIR5708 (rs6473227 and rs16907751) and MIR1208 (rs2608029 and rs13280095) were selected and genotyped among 315 COPD patients and 314 healthy controls using the Agena MassARRAY platform. SPSS 18.0 was used for statistical analysis and data processing. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the association between genetic variants of MIR1208 and MIR5708 and COPD risk.

Results

The results suggested that rs16907751 variants in MIR5708 contributed to an increased susceptibility to COPD in the allelic (P = 0.001), co-dominant (homozygous) (P = 0.001), dominant (P = 0.017), recessive (P = 0.002), and additive (P = 0.002) models. The effects of MIR5708 and MIR1208 gene polymorphisms on the risk of COPD were age-, sex-, smoking status-, and BMI-related. Furthermore, the C-A and G-A haplotypes of rs2608029 and rs13280095 in MIR1208 were identified as risk factors for COPD in the population over 70 years (P = 0.029) and in women (P = 0.049), respectively. Finally, significant associations between rs16907751genotypes with pulse rate and forced expiratory volume in 1 s were found among COPD patients.

Conclusion

Genetic polymorphisms in MIR5708 and MIR1208 are associated with increased risk of COPD in China.

Keywords:
MIR5708
MIR1208
Chronic obstructive pulmonary disease
Single nucleotide polymorphism
Abbreviations:
COPD
miRNAs
SNPs
FEV1
FVC
MAF
OR
CI
HWE
BMI
RR
PR
Full Text
Introduction

Chronic obstructive pulmonary disease (COPD) is a multifactorial chronic respiratory disease characterized by irreversible pulmonary airflow obstruction.1,2 Many studies have shown that the development of COPD is influenced by environmental factors and a complex set of genetic traits.2–4 Recently, genome-wide association and exome sequencing studies have been used to identify the hereditary factors of COPD among different populations, but the effect of genetic variants on COPD is still unclear.5

Micro RNAs (miRNAs) are a class of endogenous non-coding RNAs, 22–25 nucleotides in length that can bind to the 3′-UTR region of mRNA transcripts, thereby inhibiting their translation and expression.6,7 Variants in miRNAs are thought to be involved in post-transcriptional regulation and influence disease susceptibility.7,8 Zhou et al and Wang et al found that the miR-146a rs2910164 single nucleotide polymorphism (SNP) is associated with improved lung function in smokers with COPD.7,8 Related studies in the Korean population have suggested that gene polymorphisms in miR-196a2, miR-146a, and miR-499 could be associated with asthma phenotypes.6 Fawzy et al also found a significant association between the miR-196a2 rs11614913 polymorphism and the bronchodilator response in Egyptians with COPD.9 Therefore, it is important to explore the effect of miRNA variants on susceptibility to COPD.

MIR5708 and MIR1208 are miRNAs located on chromosome 8. MIR5708 has been identified as a risk locus for rheumatoid arthritis through gene-based association testing.10MIR1208 is thought to be involved in the regulation of tumor-related pathways by binding to the upstream gene circMTUS1.11 Relevant studies have also recognized MIR1208 as a tumor suppressor gene that can increase sensitivity of renal cancer cells to cisplatin.12 Moreover, genetic polymorphisms of MIR1208 rs2648841 have been found to be related to chemotherapy toxicity in children with acute lymphoblastic leukemia.13 However, to the best of our knowledge, the influence of MIR5708 and MIR1208 polymorphisms on the risk of COPD has not been studied.

Therefore, we performed this case-control study to investigate the relationship between MIR5708 (rs6473227 and rs16907751) and MIR1208 (rs2608029 and rs13280095) polymorphisms and susceptibility of Chinese to COPD, and to explain the roles of genetic factors in the pathogenesis of COPD through statistical analysis.

MethodsStudy population

This study recruited 314 COPD patients (76% men) and 315 healthy controls (75% men). Patients with appropriate symptoms including dyspnea, cough, sputum production, wheezing, and chest tightness were considered as suspected cases of COPD, which were further confirmed by a post-bronchodilator fixed ratio of forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC) < 0.70.14 Meanwhile, patients with other respiratory diseases, autoimmune diseases, cardiovascular diseases, and others were excluded. Healthy controls were recruited from the physical examination center of Hainan General Hospital, matched by age and sex with FEV1/FVC > 0.70, and no disease or family history. Demographic and clinical characteristics of all subjects were surveyed, and informed consent was obtained regarding the purpose and procedure of this study. This study was approved by the Hainan General Hospital and followed the Declaration of Helsinki guidelines.

SNP selection and genotyping

The selection of SNPs was based on haplotype or genotype data. Two SNPs in MIR1208 (rs2608029 and rs13280095) and two in MIR5708 (rs6473227 and rs16907751) were selected from the database of the 1000 Genomes Project (https://www.internationalgenome.org/) with the criteria of minor allele frequency (MAF) > 0.05, and r2 > 0.80. The online design of the corresponding primers was performed in AgenaCx Tools (https://agenacx.com/online-tools/), and the primer sequences are listed in Supplementary Table 1. Genomic DNA of all participants was extracted and purified from cryopreserved serum using a GoldMag-Beads Kit (GoldMag, Xi'an, Shaanxi, China), and spectrophotometry (Beckman, Fullerton, CA, USA) was used to determine DNA concentration. SNP genotyping and data collection were conducted using the Agena MessARRAY platform and Agena Bioscience TYPER software (Agena Bioscience, San Diego, CA, USA), respectively.

Statistical analysis

The statistical data of this case-control study were processed using SPSS software (version 22.0, SPSS Inc., Chicago, IL, USA), Microsoft Excel software, and PLINK software (version 1.07) (http://www.cog-genomics.org/plink2/). Independent-sample t-test and chi-squared test were used to assess differences in clinical indexes between COPD patients and healthy controls, where appropriate. The association between each SNP and the risk of COPD was evaluated by logistic regression analysis under different genetic models, and was represented by odds ratios (ORs) and 95 % confidence intervals (CIs) after adjusting for age and sex. Hardy-Weinberg equilibrium (HWE) for each SNP in the control group was determined using Fisher's exact test. Statistical significance was set at p < 0.05.

ResultsClinical indexes of participants and genotypic characteristics of selected SNPs

Demographics and clinical indexes of all 629 participants comprising 315 COPD patients (mean age 71.93 ± 10.11 years) and 314 healthy controls (mean age 71.23 ± 6.83 years old) are displayed in Table 1. Statistically, cases and controls were matched by age (P = 0.306) and sex (P = 0.908).

Table 1.

Clinical characteristics of cases and controls.

Variables  Case (n = 315)  Control (n = 314)  P 
Age (Mean ± SD)  71.93 ± 10.11  71.23 ± 6.83  0.306a 
≤ 70  127 (40%)  137 (44%)   
> 70  188 (60%)  177 (56%)   
Gender      0.908b 
Male  239 (76%)  237 (75%)   
Female  76 (24%)  77 (25%)   
BMI (kg/m2     
≤ 24  251 (80%)  67 (21%)   
> 24  29 (9%)  78 (25%)   
Tobacco smoking status       
Yes  147 (47%)  52 (17%)   
No  166 (53%)  118 (38%)   
Comorbidity       
Yes  93 (30%)     
No  174 (55%)     

SD, standard deviation; BMI, body mass index.

a

P was calculated by t test.

b

P was calculated by Pearson's chi-squared test.

Table 2 lists the detailed information of the four SNPs in MIR2708 and MIR1208; MAFs > 0.05 and SNPs in the control group conformed to HWE (P > 0.05). Moreover, a significant association between the rs16907751 polymorphism and COPD risk was found in the allelic model (P = 0.001).

Table 2.

Information regarding SNPs in MIR5708 and MIR1208.

GeneSNPChromosomePositionAllele A/BMAFHWE-POR(95% CI)P
Case  Control 
MIR5708  rs6473227  81285892  A/C  0.448  0.462  0.651  0.94(0.76–1.18)  0.614 
MIR5708  rs16907751  81375457  T/C  0.238  0.164  0.411  1.59(1.20–2.11)  0.001 
MIR1208  rs2608029  129170126  C/G  0.167  0.145  0.648  1.18(0.87–1.60)  0.298 
MIR1208  rs13280095  129179090  C/A  0.116  0.112  1.04(0.73–1.48)  0.821 

SNP, single nucleotide polymorphism; MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium; OR, odds ratio; 95% CI, 95% confidence interval.

Bold P < 0.05 indicates statistical significance.

Association of MIR1208 and MIR5708 polymorphisms and COPD susceptibility

We carried out an association analysis on genetic variants and COPD susceptibility and the relevant data are presented in Table 3. The results suggested that the TT genotype at rs16907751 in MIR5708 was a risk factor for COPD in the co-dominant (OR = 4.62, 95% CI = 1.84–11.62, P = 0.001), dominant (OR = 1.49, 95% CI = 1.07–2.08, P = 0.017), recessive (OR = 4.27, 95% CI = 1.71–10.66, P = 0.002), and additive (OR = 1.57, 95% CI = 1.19–2.07, P = 0.002) models.

Table 3.

Association of MIR5708 and MIR1208 polymorphisms with COPD risk.

SNP  Model  Allele/Genotype  Case  Control  OR(95% CI)  P 
rs6473227Co-dominant (HOM)  AA vs CC  65(20.6%)  69(22.0%)  0.88(0.57–1.38)  0.584 
Co-dominant (HET)  AC vs CC  152(48.3%)  152(48.4%)  0.94(0.65–1.35)  0.724 
Dominant  AA-AC vs CC  217(68.9%)  221(70.4%)  0.92(0.65–1.29)  0.632 
Recessive  AA vs AC-CC  65(20.6%)  69(22.0%)  0.92(0.63–1.35)  0.669 
Additive        0.94(0.75–1.17)  0.579 
rs16907751Co-dominant (HOM)  TT vs CC  23(7.3%)  6(1.9%)  4.62(1.84–11.62)  0.001 
Co-dominant (HET)  TC vs CC  101(32.1%)  91(29.0%)  1.29(0.91–1.82)  0.149 
Dominant  TT-TC vs CC  124(39.4%)  97(30.9%)  1.49(1.07–2.08)  0.017 
Recessive  TT vs TC-CC  23(7.3%)  6(1.9%)  4.27(1.71–10.66)  0.002 
Additive        1.57(1.19–2.07)  0.002 
rs2608029Co-dominant (HOM)  CC vs GG  5(1.6%)  5(1.6%)  1.13(0.32–3.97)  0.852 
Co-dominant (HET)  CG vs GG  95(30.1%)  81(25.8%)  1.26(0.89–1.79)  0.199 
Dominant  CC-CG vs GG  100(31.7%)  86(27.4%)  1.25(0.89–1.77)  0.202 
Recessive  CC vs CG-GG  5(1.6%)  5(1.6%)  1.05(0.30–3.68)  0.940 
Additive        1.21(0.88–1.67)  0.233 
rs13280095Co-dominant (HOM)  CC vs AA  3(1.0%)  4(1.3%)  0.82(0.18–3.71)  0.792 
Co-dominant (HET)  CA vs AA  67(21.3%)  62(19.7%)  1.11(0.75–1.64)  0.608 
Dominant  CC-CA vs AA  70(22.2%)  66(21.0%)  1.09(0.74–1.60)  0.656 
Recessive  CC vs CA-AA  3(1.0%)  4(1.3%)  0.80(0.18–3.61)  0.768 
Additive        1.06(0.75–1.52)  0.731 

SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval; HOM, homozygous; HET, heterozygous

P value was calculated by logistic regression analysis with adjustment for age and sex.

Bold P < 0.05 indicates statistical significance.

To further explore the association between SNPs and COPD risk, we conducted a stratified analysis according to age, sex, BMI, and smoking status. According to the data listed in Table 4, rs6473227 in MIR5708 was associated with an increased risk of COPD in the population with a body mass index (BMI, kg/m2) less than or equal to 24 in the allelic (OR = 1.66, 95% CI = 1.13–2.44, P = 0.010), homozygous co-dominant (OR = 2.97, 95% CI = 1.34–6.62, P = 0.008), recessive (OR = 2.15, 95% CI = 1.06–4.35, P = 0.033), and additive (OR = 1.72, 95% CI = 1.16–2.55, P = 0.007) models. Moreover, rs16907751 in MIR5708 was found to be significantly associated with an increased risk of COPD in males, non-smokers, patients older than 70 years, and individuals with a BMI ≤ 24. In addition, females heterozygous at rs2608029 (OR = 2.24, 95% CI = 1.09–4.64, P = 0.029) were more susceptible to COPD risk in both dominant (OR = 2.19, 95% CI = 1.07–4.47, P = 0.031) and additive (OR = 1.99, 95% CI = 1.02–3.91, P = 0.045) models.

Table 4.

Stratification analysis of the associations between MIR1208 and MIR5708 polymorphisms and COPD risk.

SNPModelAge >70MaleFemaleNon-SmokingBMI ≤24
OR (95% CI)  P  OR (95% CI)  P  OR (95% CI)  P  OR (95% CI)  P  OR (95% CI)  P 
rs6473227  Allele  0.91(0.68–1.22)  0.546  0.96(0.74–1.24)  0.753  0.90(0.57–1.41)  0.637  0.84(0.60–1.17)  0.300  1.66(1.13–2.44)  0.010 
  Co-dominant (HOM)  0.75(0.40–1.39)  0.353  0.93(0.55–1.55)  0.771  0.79(0.33–1.91)  0.596  0.71(0.36–1.37)  0.306  2.97(1.34–6.62)  0.008 
  Co-dominant (HET)  1.08(0.66–1.79)  0.756  0.89(0.59–1.36)  0.604  1.09(0.52–2.26)  0.818  0.84(0.48–1.45)  0.527  1.68(0.89–3.18)  0.107 
  Dominant  0.97(0.61–1.56)  0.905  0.90(0.61–1.34)  0.618  0.98(0.50–1.94)  0.958  0.79(0.47–1.33)  0.378  1.16(0.66–2.07)  0.603 
  Recessive  0.71(0.42–1.20)  0.203  0.99(0.64–1.55)  0.976  0.75(0.35–1.62)  0.462  0.79(0.44–1.40)  0.413  2.15(1.06–4.35)  0.033 
  Additive  0.88(0.65–1.20)  0.413  0.96(0.74–1.24)  0.734  0.90(0.58–1.40)  0.651  0.84(0.60–1.17)  0.302  1.72(1.16–2.55)  0.007 
rs16907751  Allele  2.00(1.36–2.95)  <0.001  1.63(1.18–2.26)  0.003  1.47(0.84–2.59)  0.179  1.62(1.03–2.56)  0.036  1.67(1.00–2.78)  0.047 
  Co-dominant (HOM)  16.14(2.03–128.4)  0.009  4.64(1.68–12.78)  0.003  4.63(0.50–42.97)  0.178  –  –  2.90(0.64–13.16)  0.168 
  Co-dominant (HET)  1.45(0.90–2.34)  0.127  1.27(0.85–1.90)  0.238  1.30(0.66–2.57)  0.448  1.10(0.64–1.88)  0.723  1.48(0.79–2.78)  0.225 
  Dominant  1.75(1.10–2.77)  0.018  1.51(1.03–2.21)  0.036  1.43(0.74–2.78)  0.286  1.36(0.81–2.29)  0.249  2.03(1.12–3.67)  0.020 
  Recessive  14.47(1.83–114.70)  0.011  4.31(1.58–11.79)  0.004  4.22(0.46–38.70)  0.203  –  –  2.59(0.57–11.64)  0.216 
  Additive  1.89(0.65–1.20)  0.002  1.58(1.15–2.18)  0.005  1.51(0.84–2.70)  0.171  1.57(0.99–2.47)  0.054  1.57(0.95–2.59)  0.079 
rs2608029  Allele  1.05(0.68–1.61)  0.837  1.02(0.72–1.45)  0.924  1.86(0.99–3.50)  0.053  0.94(0.61–1.47)  0.800  1.11(0.66–1.87)  0.688 
  Co-dominant (HOM)  1.42(0.08–23.83)  0.810  1.08(0.26–4.42)  0.916  1.30(0.08–21.55)  0.855  0.32(0.06–1.70)  0.180  0.44(0.09–2.11)  0.304 
  Co-dominant (HET)  1.03(0.63–1.68)  0.918  1.05(0.70–1.57)  0.827  2.24(1.09–4.64)  0.029  1.22(0.71–2.09)  0.467  1.79(0.92–3.46)  0.085 
  Dominant  1.03(0.63–1.69)  0.894  1.05(0.70–1.56)  0.817  2.19(1.07–4.47)  0.031  1.10(0.66–1.85)  0.717  1.27(0.72–2.24)  0.402 
  Recessive  1.41(0.08–23.61)  0.813  1.06(0.26–4.34)  0.931  1.01(0.06–16.59)  0.994  0.30(0.06–1.58)  0.155  0.37(0.08–1.75)  0.209 
  Additive  1.04(0.65–1.67)  0.867  1.04(0.73–1.50)  0.815  1.99(1.02–3.91)  0.045  0.97(0.61–1.53)  0.896  1.28(0.74–2.22)  0.384 
rs13280095  Allele  0.74(0.45–1.23)  0.247  0.89(0.60–1.33)  0.576  1.67(0.82–3.40)  0.156  0.77(0.46–1.28)  0.308  1.08(0.58–2.00)  0.816 
  Co-dominant (HOM)  –  –  0.72(0.12–4.39)  0.718  1.15(0.07–18.97)  0.923  0.32(0.06–2.03)  0.247  0.38(0.05–2.85)  0.343 
  Co-dominant (HET)  0.69(0.39–1.22)  0.203  0.93(0.59–1.46)  0.760  1.91(0.86–4.26)  0.114  0.91(0.50–1.67)  0.769  1.68(0.78–3.61)  0.182 
  Dominant  0.68(0.39–1.19)  0.178  0.92(0.59–1.43)  0.715  1.85(0.85–4.05)  0.122  0.84(0.47–1.49)  0.546  1.56(0.83–2.91)  0.164 
  Recessive  –  –  0.73(0.12–4.45)  0.732  1.00(0.06–16.36)  0.998  0.37(0.07–2.06)  0.255  0.33(0.04–2.51)  0.287 
  Additive  0.67(0.39–1.17)  0.159  0.92(0.61–1.38)  0.675  1.68(0.82–3.47)  0.158  0.80(0.48–1.32)  0.375  1.24(0.66–2.35)  0.508 

SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval; HOM, homozygous; HET, heterozygous

“-” indicates no results.

P value was calculated by logistic regression analysis with adjustment for age and sex.

Bold P < 0.05 indicates statistical significance.

We continued with a haplotype analysis and found a block (rs2608029 and rs13280095) in MIR1208 (Table 5). The C-A haplotype of rs2608029 and rs13280095 was found to be related to an increased risk of COPD (OR = 2.59, 95% CI = 1.10–6.06, P = 0.029) in the population over 70 years, whereas Grs2608029Ars13280095 was found to be a risk factor for COPD in women.

Table 5.

Haplotype analysis of the association between MIR5708 and MIR1208 polymorphisms and COPD risk.

Gene  SNP  Subgroup  Haplotype  Fre-case  Fre-control  OR(95% CI)  P 
MIR1208rs2608029|rs13280095Overall  CC  0.116  0.111  1.08(0.76–1.54)  0.681 
  CA  0.051  0.034  1.55(0.88–2.73)  0.128 
  GA  0.167  0.146  1.21(0.88–1.66)  0.242 
MIR1208rs2608029|rs13280095Age (>70)  CC  0.080  0.105  0.67(0.38–1.16)  0.155 
  CA  0.053  0.023  2.59(1.10–6.06)  0.029 
  GA  0.133  0.128  1.04(0.65–1.67)  0.867 
MIR1208rs2608029|rs13280095Female  CC  0.145  0.086  1.82(0.87–3.80)  0.110 
  CA  0.053  0.026  2.13(0.61–7.41)  0.237 
  GA  0.197  0.118  1.96(1.00–3.86)  0.049 

SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval; BMI, body mass index

P value was calculated by logistic regression analysis with adjustment for age and sex.

Bold P < 0.05 indicates statistical significance

Association analysis of clinical indexes of COPD and gene polymorphisms

Then, the effects of genetic variants in MIR5708 and MIR1208 on COPD were investigated in terms of clinical indices, including respiratory rate (RR), pulse rate (PR), FVC, FEV1, and FEV1/FVC (Table 6). The results suggested that rs16907751 in MIR5708 was significantly associated with PR (P = 0.009) and FEV1 (P = 0.049) in the genotypic model. Furthermore, no association was found between rs6473227, rs2608029, and rs13280095 and the clinical indices of COPD.

Table 6.

Association analysis on the clinical indexes of COPD and gene polymorphisms.

SNPRR (breaths/min)  PR (beats/min)  FVC (L)  FEV1 (L)  FEV1/FVC (%) 
rs6473227AA  22.00 ± 2.44  84.44 ± 9.36  3.30 ± 1.78  1.21 ± 0.55  31.65 ± 30.57 
CA  22.42 ± 2.43  87.28 ± 12.34  1.95 ± 0.74  1.24 ± 0.55  40.08 ± 29.36 
CC  22.33 ± 2.55  86.06 ± 11.96  1.99 ± 0.60  1.19 ± 0.49  46.26 ± 27.17 
P  0.543  0.286  0.164  0.907  0.078 
rs16907751TT  23.35 ± 3.05  93.90 ± 12.96  1.95 ± 0.80  0.99 ± 0.35  36.96 ± 27.96 
TC  22.10 ± 2.42  85.93 ± 13.06  1.90 ± 0.50  1.11 ± 0.40  41.70 ± 27.62 
CC  22.28 ± 2.41  85.54 ± 10.44  2.49 ± 4.70  1.31 ± 0.68  39.79 ± 30.68 
P  0.123  0.009  0.639  0.049  0.866 
rs2608029CC  22.60 ± 3.05  83.60 ± 5.18  2.35 ± 0.78  1.53 ± 0.83  49.21 ± 33.36 
GC  22.43 ± 2.45  88.06 ± 10.13  1.86 ± 0.71  1.19 ± 0.58  44.47 ± 30.47 
GG  22.24 ± 2.47  85.62 ± 12.38  2.45 ± 4.54  1.22 ± 0.60  37.24 ± 28.53 
P  0.812  0.239  0.640  0.558  0.276 
rs13280095AA  22.26 ± 2.53  85.98 ± 12.18  2.39 ± 4.28  1.22 ± 0.60  38.27 ± 28.97 
CA  22.36 ± 2.18  87.64 ± 9.82  1.85 ± 0.69  1.19 ± 0.58  42.96 ± 30.68 
CC  24.67 ± 1.53  86.67 ± 4.16  2.45 ± 0.92  1.70 ± 0.92  65.46 ± 9.31 
P  0.239  0.629  0.740  0.367  0.215 

RR, respiratory rate; PR, pulse rate; FVC, forced vital capacity; FEV1, forced expiratory volume in 1s.

Bold P < 0.05 indicates statistical significance.

Discussion

According to the latest Global Health Observatory data, COPD was ranked as the third leading cause of death worldwide, accounting for approximately 5% of all deaths globally in 2015 (3.17 million deaths). The prevalence and burden of COPD are projected to increase over the coming decades.15 It has been clearly demonstrated that the interaction between genotype and environment plays an important role in COPD phenotypes.16,17 Genetic background plays a critical role in the development of COPD, and related studies have proposed significant differences in genetic susceptibility to COPD among different races and ethnicities.18 Therefore, the effect of genetic polymorphisms of candidate genes on the risk of COPD among Chinese was examined.

As a result, statistical analysis of the genotyping results of 315 COPD patients and 314 healthy controls among Chinese patients identified MIR5708 rs16907751 as a risk factor of COPD, and significant associations between rs16907751 and clinical indexes including PR and FEV1 in COPD patients were found. The examined mutation loci have been rarely reported; only rs6473227 showed a close relationship with atopic dermatitis in a related case-control analysis.19 Our results suggest that rs6473227 may contribute to an increased risk of COPD in individuals with a BMI less than 24. A related study pointed out that neuroimmune interactions are implicated in chronic inflammations such as atopic dermatitis, COPD, and asthma20; therefore, we hypothesized that MIR5708 rs6473227 may increase susceptibility to inflammation-related disorders. In addition, we found that MIR5708 rs16907751 may increase the risk of COPD in patients with normal BMI. There have been studies supporting our findings and reported that low BMI is a risk factor for accelerated decline in lung function compared to normal BMI,21 thereby suggesting a protective role of high BMI in COPD patients.

Tobacco smoking is widely recognized as the most important risk factor for COPD,22 but it is not the only risk factor for COPD. Qian et al. concluded that both non-smokers and smokers are likely to suffer from COPD, which may be the result of differential miRNA expression between.23 At present, there has been no report on the association between miR5708 and miR1208 and the risk of disease COPD. However, our study further provided evidence that allele T of MIR5708 rs16907751 may increase susceptibility of nonsmokers to COPD. There have been reports confirming that up to 30% of patients with COPD do not have a smoking history.18 Therefore, multiple genes and exposure to environmental and occupational factors are thought to jointly affect COPD development.

Furthermore, we also found that the effects of MIR5708 and MIR1208 on the risk of COPD were age- and sex-dependent. Related epidemiological studies have shown that the prevalence of COPD increases with age,24 and Mercado et al. also showed that aging of the lung resulted in loss of lung elasticity and reduced ability to respond to environmental stress and damage.25 Our findings also indicated MIR5708 rs16907751 as a risk factor for COPD in patients over 70 years of age. Based on the stratified analysis, our results suggested that MIR5708 rs16907751 and MIR1208 rs2608029 are associated with increased COPD risk in men and women, respectively, and further confirmed gender differences in COPD susceptibility conferred by these two SNPs. Aryal et al. stated that the differences in sex were derived mainly by smoking status, hormone levels, and behavioral differences.26 Current data indicate that in China, the prevalence of COPD in men is higher than that in women, but the difference varies with exposure to risk factors and socioeconomic development in different geographic regions.27,28 However, in our current study, we did not focus on the effect of environment, occupational differences, and regional constraints. Moreover, the small sample size and incomplete sample information in some cases are also limitations of our study. Subsequent functional experiments are needed to verify the effects of these SNPs on COPD.

Conclusion

In summary, this study is the first to indicate that genetic variants in MIR5708 and MIR1208 are associated with an increased risk of COPD in the Chinese population, and the effects of these SNPs are related to age, sex, smoking status, and BMI.

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Yu Zhou and Fenghua Bai contributed equally to this work.

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