Obstructive sleep apnea (OSA) is one of the most prevalent sleep disorders, and the major clinical manifestations are sleep apnea and frequent snoring, daytime sleepiness and trance. OSA is significantly and independently associated with increased cardiovascular and cerebrovascular morbidity and mortality. Twin studies, familial studies, chromosomal mapping, and racial studies have provided evidence for the possible link between OSA and genetic factors. However, the pathogenesis of OSA is not fully understood, and is affected by genetic and environmental factors.
Recent attention has been directly attached to tandem of P domains in a weak inwardly rectifying K+ channel (TWIK)-related acid-sensitive K+ channel-1 (TASK-1, also called KCNK3), which is one of the members of the two-pore-domain potassium channel family widely expressed in the body.TASK-1 is highly expressed in respiratory-related brainstem neurons.[5,6] It has been evidenced that the TASK-1 channel is sensitive to hypoxia and changes in extra-cellular pH, even in the physiological range,[7,8] which are also the hallmarks of OSA. Furthermore, a large genome-wide association meta-analysis revealed that the KCNK3 gene has been linked to obesity, which is an independent risk factor for OSA. In addition, TASK-1 is involved in the regulation of lipid metabolism, and this can be used to assess the number of thermogenic adipocytes within human adipose tissues.
Based on the above evidence, it would be reasonable to consider that the TASK-1 gene is relevant to metabolic risk factors (obesity and lipid metabolism) and some of the characteristics (intermittent hypoxia, hypercapnia, and acidity) of OSA. At present, the evidence of the association of the TASK-1 gene with OSA has mainly been derived from animal experiments. In the present study, two candidate single nucleotide polymorphisms (SNPs) in the TASK-1 gene were selected, and the genotype distributions were determined among 171 individuals without OSA and 164 patients with severe OSA. The mechanism of OSA was explored by assessing the association between the human TASK-1 gene and OSA.
The present study was conducted in accordance with the Declaration of Helsinki, and was approved by the Local Ethics Committee of People's Hospital of Xinjiang (No. 2016016). An informed written consent was obtained from each patient before enrollment into the study.
Similar to a previous study conducted in our center, the inclusion criteria were as follows: consecutive patients, who were ≥18 years old, clinically suspicious of OSA, and were referred for sleep monitoring for the first time when characterized with one of the following: (1) snoring at night, (2) witnessed apnea and arousal during sleep and daytime sleepiness, (3) morning headache, (4) resistant hypertension, and (5) unexplainable cyanosis. These patients were recruited from the Center for Hypertension of People's Hospital of Xinjiang Uygur Autonomous Region, China from April to December 2016. All subjects underwent polysomnography (PSG) monitoring. The clinical data and venous blood samples of all subjects were collected.
The exclusion criteria for the present study were as follows: (1) central sleep apnea; (2) asthma, acute, and chronic obstructive pulmonary disease, interstitial lung disease, pulmonary tuberculosis, and other respiratory diseases; (3) malignant tumors, acute infections, and autoimmune diseases; (4) acute cardiovascular and cerebrovascular diseases; (5) regular intake of steroid, bronchodilator, antihistamines, and anti-inflammatory drugs; (6) thyroid dysfunction; (7) long-term exposure to industrial dust and toxic gas, and a history of thoracic surgery; (8) severe heart and renal dysfunction; (9) secondary hypertension, such as renal and renovascular hypertension, pheochromocytoma, aldosterone adenoma, Cushing syndrome, and other common secondary hypertensions; (10) psychosis and severe sleep disorders; (11) failure in the nocturnal attended PSG. Subjects who fulfilled the above criteria were excluded, based on their medical history, physical examination, chest X-ray, and PSG monitoring. Finally, a total of 335 subjects (164 severe OSA subjects and 171 non-OSA subjects) were enrolled in the study.
All participants underwent overnight attended PSG (Compumedics E series, Australia), according to a previous study.
The OSA diagnosis was made according to the “Guidelines for the diagnosis and treatment of OSA hypopnea syndrome” in 2009. OSA severity was evaluated based on the apnea-hypopnea index (AHI), and the severity of OSA for the present study was considered, as follows: non-OSA (AHI <5 events/h); and severe OSA (AHI ≥30 events/h).
General data and anthropological indicators
The general information included the following: age, gender, nationality, and occupation; previous and present history (hypertension family history, systolic blood pressure, diastolic blood pressure, and vascular risk stratification); personal history, including whether the subject snores, smoking status (smokers were defined as those who smoked one or more cigarettes a day for longer than one year), and alcohol intake; anthropological indicators, which mainly included height, weight (body mass index [BMI] = weight/height2, kg/m2), neck circumference, and abdominal circumference.
Two equal samples of fasting peripheral venous blood (empty stomach for at least 10 h or more, cubital vein blood sample) were collected in the morning after the PSG. The serum was collected from one sample after centrifuging for 60 min (3000 r/min, lasted for 10 min, at 4°C) and stored at –20°C for later use. The biochemical evaluation (blood routine test, urine routine test, erythrocyte sedimentation rate, lipids, glycosylated hemoglobin, fasting blood glucose, renal function, electrolyte, etc) was measured at the central laboratories of the hospital using standard techniques.
Genetic DNA extraction
Three milliliters of venous blood from another sample was collected in ethylene diamine tetraacetic acid (EDTA)-containing tubes for all subjects. The genomic DNA was extracted from whole blood by using a commercially available DNA isolation kit (Beijing, China), according to manufacturer's instructions. Then, the purity of the DNA was measured using a spectrophotometer (NanoDrop, California, USA), and the absorbance ratios that ranged from 1.8 to 2.0 at a length of A260/A280 were determined from the samples. Afterward, the extracted DNA was preserved at −80°C (Plymouth, Minnesota, USA).
SNP selection and genotyping
SNPs were selected based on their functional relevance and bioinformatics. The selection criteria were as follows: (1) SNPs in the TASK-1 gene were selected using the HaploView software, an open-source project hosted by SourceForge (https://www.broadinstitute.org/haploview/downloads), in which 11 SNPs were collected. Tag SNPs were identified in accordance with the following criteria: a linkage disequilibrium threshold of r2 > 0.8 and a minor allele frequency of ≥0.05. (2) SNP mutations led to amino acid changes according to the single nucleotide polymorphism database (dbSNP) (https://www.ncbi.nlm.nih.gov/snp/). (3) The National Center for Biotechnology Information keyword search was used for research terms TASK-1 or KCNK3, in combination with the following: gene or polymorphism or variants or alleles. Finally, two common SNPs of the TASK-1 gene were included (rs1275988 and rs2586886). The genotyping was determined using the kompetitive allele specific polymerase chain reaction genotyping system (LGC Science, Shanghai, China). Then, 10% of the samples were randomly selected for type validation and quality control for the accuracy of typing.
The statistical analysis was performed using the SPSS 22.0 statistical software (SPSS Inc., Chicago, IL, USA). Quantitative variables are expressed as mean ± standard deviation (for normally distributed data) or median (Q1, Q3) (for non-normally distributed data). The associations between quantitative variables were examined using t-test when the data distribution was normal, using Mann-Whitney U test for data that are not normally distributed. Deviations from the Hardy-Weinberg equilibrium of the genetic polymorphisms were evaluated using the chi-square test. The comparison of genotypic distribution and allele frequencies between groups was assessed using the R × C contingency table chi-square test. SNPAlyze V8.1.1 (Japan) program was used to compare the frequency distribution of haplotypes between the case and control groups. The odds ratio (OR) of the different genotypes and 95% confidence intervals (CIs) were calculated to represent the relative risk by logistic regression analysis. P < 0.05 was considered statistically significant.
The anthropometrical and clinical characteristics of subjects without OSA and severe OSA patients are presented in Table 1. Severe OSA was more prevalent in male subjects (86.0% vs. 52.6%, P < 0.001). Furthermore, severe OSA subjects comprised of more smokers (53.7% vs. 26.9%, P < 0.001) and drinkers (51.2% vs. 31.6%, P < 0.001), when compared to non-OSA subjects. Moreover, severe OSA subjects had a significantly higher BMI (29.40 vs. 26.16 kg/m2, P < 0.001), and larger abdominal (110.00 vs. 100.00 cm, P < 0.001) and neck (44.00 vs. 40.00 cm, P < 0.001) circumference. Triglyceride (1.96 vs. 1.45 mmol/L, P < 0.001) and total cholesterol (4.62 vs. 4.27 mmol/L, P = 0.011) were significantly higher in the severe OSA group, when compared to the non-OSA group, while high density lipoprotein (0.90 vs. 1.00 mmol/L, P < 0.001) exhibited an opposite trend. There were no significant differences in terms of age, serum potassium, 24-h urine potassium, urine pH value, and systolic and diastolic blood pressure. Apnea index, hypopnea index, AHI, and lowest oxygen saturation were significantly higher in subjects with severe OSA, when compared to non-OSA subjects.
The genotype distributions for rs1275988 in the total population did not consist of the predicted Hardy-Weinberg equilibrium values, but this was in agreement for both severe OSA and non-OSA patients. Furthermore, rs2586886 was in agreement with the predicted Hardy-Weinberg equilibrium values (data not shown). As presented in Table 2, the distribution of the two selected SNP (rs1275988 and rs2586886) genotypes, alleles, recessive model, and dominant model had no significant differences between the severe and non-OSA group.
Since the KCNK3 gene locus is linked to obesity, which is an independent predictor for OSA, subjects were sub-divided into obese (BMI ≥28 kg/m2) and non-obese (BMI <28 kg/m2) groups [Table 3]. For the obese population, in the rs1275988 loci, the genotype distribution (GG 57.5% vs. 41.0%, GA 35.8% vs. 35.9%, AA 6.6% vs. 23.1%, P = 0.014), allele frequency (G 75.5% vs. 59.0%, A 24.5% vs. 41.0%, P = 0.006), and implicit model (GG + GA 93.4% vs. 76.9%, AA 6.6% vs. 23.1%, P = 0.005) exhibited significant differences between the severe OSA and non-OSA group, while the dominant model exhibited no significant differences between the severe and non-OSA groups. Furthermore, the rs2586886 loci genotype distributions (GG 56.1% vs. 41.0%, GA 36.7% vs. 35.9%, AA 7.1% vs. 23.1%, P = 0.026), allele frequencies (G 74.5% vs. 59.0%, A 25.5% vs. 41.0%, P = 0.011), and implicit model (GG + GA 92.9% vs. 76.9%, AA 7.1% vs. 23.1%, P = 0.009) were significantly different between the severe and non-OSA group, while the dominant model exhibited no significant differences between the two groups. Moreover, significantly fewer patients with OSA possessed the AA genotype, while significantly more patients possessed the GG genotype, when compared to controls. For non-obese subjects, the SNP (rs1275988 and rs2586886) genotype distributions, allele frequencies, implicit model, and dominant model exhibited no significant differences between the severe and non-OSA group.
In order to further confirm the link between these two selected SNPs (rs1275988 and rs2586886) and OSA in obese patients, simple logistic regression modeling was employed, and the genotype together with the two SNPs were used as the input. As shown in Table 4, it can be identified that the GG genotype is a risk factor for obese OSA in both SNPs (OR = 4.902, 95% CI 1.582–15.186 and OR = 4.420, 95% CI 1.422–13.734, respectively). The G allele in the rs1275988 loci was a risk factor of OSA (GG>GA>AA), as well as for the rs2586886 loci. In multivariate logistic regression analysis, which was used to correct the confounding factors of rs1275988, rs2586886, sex, smoking, drinking, total cholesterol, and BMI in total. The interaction between rs1275988 locus of TASK-1 gene and BMI entered the equation. In addition, gender and total cholesterol were risk factors for severe OSA, in Table 5. The combination of GA and GG genotypes of rs1275988 with BMI ≥28 kg/m2 increased the risk of severe OSA (OR = 4.180, 95% CI 1.988–8.788, P < 0.001; OR = 8.916, 95% CI 4.506–17.645, P < 0.001, respectively).
There is growing evidence that genetic factors and their interaction with environmental exposures influence the development of OSA. In the present study, it was hypothesized that variability in the gene regulating TASK-1 channel might be involved in OSA. Hence, two SNPs (rs1275988 and rs2586886) were genotyped in severe and non-OSA subjects, and the association between the polymorphism of the TASK-1 gene and OSA was assessed.
The importance of the TASK background potassium channels in central and peripheral respiratory chemoreception has been widely expressed in a variety of putative respiratory neurons in the brainstem,[6,15,16] and has been demonstrated in previous studies.[17,18] The TASK-1 channel is sensitive to hypoxia and changes in extra-cellular pH, even in the physiological range, and plays an important role in respiratory regulation. Intermittent hypoxia combined with hypercapnia in animal models of central sleep apnea is positively correlated with the expression of TASK-1 and TASK-3 in the brainstem. In addition, respiratory chemoreception in the central nervous system maintains physiologically appropriate pH and PCO2via the control of breathing, and studies on animal models have revealed that TASK channel knock-out mice are profoundly pH-sensitive and capable of driving respiratory output in response to local pH changes in vivo. Indeed, it has been evidenced in TASK-1 knock-out mice models that TASK-1 channels significantly contribute to the increase in carotid body chemoafferent discharge in response to a decrease in arterial PO2 or an increase in P (CO2)/[H+], and this was also found to be functionally expressed in isolated carotid body cells.
In fact, OSA induces markedly negative intra-thoracic pressure, and also provokes hypoxia, hypercapnia, and acidity, which is similar to the TASK-1 gene, in terms of physiological characteristics. In the total population, the distribution of genotypes, alleles, recessive model, and dominant model of the two selected SNPs exhibited no significant differences between the severe and non-OSA group. However, it was found that the distribution of GG genotypes and G allele frequencies in the two SNP locus cases was higher, when compared to the control group. In the rs1275988 loci, G allele frequencies in controls and cases were 72.7% and 77.3%, respectively, while the prevalence of the GG homozygote were 55.6% and 60.7%, respectively. In the rs2586886 loci, G allele frequencies in controls and cases were 71.6% and 75.8%, respectively, while the prevalence of the GG homozygote were 54.9% and 57.6%, respectively. In the present study, there was no significant difference between TASK-1 gene polymorphism and OSA in the severe OSA and non-OSA groups. Considering that the TASK-1 gene polymorphism is a polygenic disease with OSA, the TASK-1 gene polymorphism only had a slight effect on OSA, while gender, age, and obesity were independent and powerful risk factors of OSA.
The present study revealed that the GG genotype is highly prevalent in obese OSA patients, and that obesity may be a strong interaction factor. In addition, a further simple logistic regression analysis demonstrated that the GG genotype is an independent risk factor for OSA in both SNPs. The KCNK3 gene locus has been linked to obesity,[10,22] can serve as molecular markers secondary to UCP1 for assessing the number of thermogenic adipocytes within human adipose tissues,[12,23] and is involved in lipid metabolism. Therefore, it is reasonable to consider that the results of the present study are consistent with the hypothesis of the investigators, in which the genetic polymorphisms of TASK-1 are involved in OSA, and are characterized as a risk factor of the rs1275988 and rs2586886 GG genotype. The simple logistic regression analysis was able to further prove that the GG genotype was a risk factor for OSA. Furthermore, the incidence of OSA significantly increased as general and abdominal obesity increased. Thus, obesity is an independent risk factor for OSA.[16,24] In the present baseline data, BMI was significantly higher in the severe OSA group, when compared to the non-OSA group [Table 1]. However, differences were not observed in the results between two groups when obesity was not considered [Table 2]. For the obese population, the distribution of SNP (rs1275988 and rs2586886) genotype distributions, allele frequencies and implicit model had significant differences between the severe and non-OSA groups [Table 3].
The limitations of the present study included the following: First, since the TASK-1 gene is a minor gene, extreme (severe OSA subjects) cases were chosen to amplify the effect of the genes. Hence, this needs to be further validated in mild and moderate OSA populations. Second, due to the lack of artery blood gas analysis during sleep, the degree of hypoxemia and hypercapnia could not be assessed. The pH of arterial blood is influenced by many factors. Although this may be corrected during the day, this cannot reflect the degree of nocturnal hypoxia and hypercapnia. Third, in the present study, a candidate gene strategy was used to investigate the association between the disease and gene polymorphism, in which other SNPs of the TASK-1 gene polymorphism associated with severe OSA may have been omitted.
In conclusion, both the GG genotype of rs1275988 and GG genotype of rs2586886 in the TASK-1 gene may play as potential risk factors in obese patients with OSA. The mechanism responsible for the correlation between rs1275988 and rs2596996 with OSA remains unknown. Thus, the exact mechanism and contribution of the TASK-1 GG genotype in OSA need further studies.
This study was supported by a grant of Natural Science Joint Fund of Xinjiang Autonomous Region (No. 2016D01C127).
Conflicts of interest
1. Lévy P, Kohler M, McNicholas WT, Barbé F, McEvoy RD, Somers VK, et al. Obstructive sleep apnoea syndrome. Nat Rev Dis Primers
2015; 1:15015doi: 10.1038/nrdp.2015.15.
2. Wang X, Ouyang Y, Wang Z, Zhao G, Liu L, Bi Y, et al. Obstructive sleep apnea
and risk of cardiovascular disease and all-cause mortality: a meta-analysis of prospective cohort studies. Int J Cardiol
2013; 169:207–214. doi: 10.1016/j.ijcard.2013.08.088.
3. Casale M, Pappacena M, Rinaldi V, Bressi F, Baptista P, Salvinelli F, et al. Obstructive sleep apnea
syndrome: from phenotype to genetic basis. Curr Genomics
2009; 10:119–126. doi: 10.2174/138920209787846998.
4. Cohen A, Ben-Abu Y, Zilberberg N. Gating the pore of potassium leak channels. Eur Biophys J
2009; 39:61–73. doi: 10.1007/s00249-009-0457-6.
5. Trapp S, Aller MI, Wisden W, Gourine AV. A role for TASK-1 (KCNK3) channels in the chemosensory control of breathing. J Neurosci
2008; 28:8844–8850. doi: 10.1523/JNEUROSCI.1810-08.2008.
6. Washburn CP, Bayliss DA, Guyenet PG. Cardiorespiratory neurons of the rat ventrolateral medulla contain TASK-1 and TASK-3 channel mRNA. Respir Physiol Neurobiol
2003; 138:19–35. doi: 10.1016/S1569-9048(03)00185-X.
7. Lesage F, Lazdunski M. Molecular and functional properties of two-pore-domain potassium channels
. Am J Physiol Renal Physiol
2000; 279:F793–F801. doi: 10.1152/ajprenal.2000.279.5.F793.
8. Lotshaw DP. Biophysical, pharmacological, and functional characteristics of cloned and native mammalian two-pore domain K+ channels. Cell Biochem Biophys
2007; 47:209–256. doi: 10.1007/s12013-007-0007-8.
9. Kikuchi R, Tsuji T, Watanabe O, Yamaguchi K, Furukawa K, Nakamura H, et al. Hypercapnia accelerates adipogenesis: a novel role of high CO2 in exacerbating obesity. Am J Respir Cell Mol Biol
2017; 57:570–580. doi: 10.1165/rcmb.2016-0278OC.
10. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index
yield new insights for obesity biology. Nature
2015; 518:197–206. doi: 10.1038/nature14177.
11. Basoglu OK, Tasbakan MS. Comparison of clinical characteristics in patients with obesity hypoventilation syndrome and obese obstructive sleep apnea
syndrome: a case-control study. Clin Respir J
2014; 8:167–174. doi: 10.1111/crj.12054.
12. Shinoda K, Luijten IH, Hasegawa Y, Hong H, Sonne SB, Kim M, et al. Genetic and functional characterization of clonally derived adult human brown adipocytes. Nat Med
2015; 21:389–394. doi: 10.1038/nm.3819.
13. Shao L, Li N, Yao X, Heizati M, Abdireim A, Wang Y, et al. Relationship between surfactant proteins B and C and obstructive sleep apnea
: is serum SP-B concentration a potential biomarker of obstructive sleep apnea
? Sleep Breath
2016; 20:25–31. doi: 10.1007/s11325-015-1179-x.
14. Epstein LJ, Kristo D, Strollo PJ Jr, Friedman N, Malhotra A, Patil SP, et al. Clinical guideline for the evaluation, management and long-term care of obstructive sleep apnea
in adults. J Clin Sleep Med
15. Leist M, Rinné S, Datunashvili M, Aissaoui A, Pape HC, Decher N, et al. Acetylcholine-dependent upregulation of TASK-1 channels in thalamic interneurons by a smooth muscle-like signalling pathway. J Physiol
2017; 595:5875–5893. doi: 10.1113/JP274527.
16. Karschin C, Wischmeyer E, Preisig-Müller R, Rajan S, Derst C, Grzeschik KH, et al. Expression pattern in brain of TASK-1, TASK-3, and a tandem pore domain K(+) channel subunit, TASK-5, associated with the central auditory nervous system. Mol Cell Neurosci
2001; 18:632–648. doi: 10.1006/mcne.2001.1045.
17. Kim D. Endocytosis: another pathway in receptor-Gq-TASK signalling. J Physiol
2017; 595:6811–6812. doi: 10.1113/JP275279.
18. Buehler PK, Bleiler D, Tegtmeier I, et al. Abnormal respiration under hyperoxia in TASK-1/3 potassium channel double knockout mice. Respir Physiol Neurobiol
2017; 244:17–25. doi: 10.1016/j.resp.2017.06.009.
19. Zhang C, Wang J, Wang GF, Wang Y, Su L. Relationship between TWIK-related acid-sensitive K+ channel-1 and TWIK-related acid-sensitive K+ channel-3 expression and the occurrence of central sleep apnea: experiment with rats. Nat Med J China
2007; 87:2929–2931. doi: 10.3760/j.issn:0376-2491.2007.41.011.
20. Mulkey DK, Talley EM, Stornetta RL, Siegel AR, West GH, Chen X, et al. TASK channels determine pH sensitivity in select respiratory neurons but do not contribute to central respiratory chemosensitivity. J Neurosci
2007; 27:14049–14058. doi: 10.1523/JNEUROSCI.4254-07.2007.
21. Kim D, Cavanaugh EJ, Kim I, Heitzmann D, Both C, Georgieff M, et al. Heteromeric TASK-1/TASK-3 is the major oxygen-sensitive background K+ channel in rat carotid body glomus cells. J Physiol
2009; 587:2963–2975. doi: 10.1113/jphysiol.2009.171181.
22. Chen Y, Zeng X, Huang X, Heitzmann D, Both C, Georgieff M. Crosstalk between KCNK3-mediated ion current and adrenergic signaling regulates adipose thermogenesis and obesity. Cell
2017; 171:836–848. doi: 10.1016/j.cell.2017.09.015.
23. Pisani DF, Beranger GE, Corinus A, Giroud M, Ghandour RA, Altirriba J, et al. The K+ channel TASK1 modulates (-adrenergic response in brown adipose tissue through the mineralocorticoid receptor pathway. FASEB J
2016; 30:909–922. doi: 10.1096/fj.15-277475.
24. Lee YG, Lee YJ, Jeong DU. Differential effects of obesity on obstructive sleep apnea
syndrome according to age. Psychiatry Investig
2017; 14:656–661. doi: 10.4306/pi. 2017.14.5.656.
Keywords:© 2019 Chinese Medical Association
Potassium channels; Obstructive sleep apnea; Single nucleotide polymorphism; Body mass index