Exp Brain Res DOI 10.1007/s00221-014-3987-9
Research Article
Association of μ‑opioid receptor gene (OPRM1) haplotypes with postoperative nausea and vomiting Shigekazu Sugino · Tomo Hayase · Misako Higuchi · Katsuhiko Saito · Hiroyuki Moriya · Yukihiro Kumeta · Nahoko Kurosawa · Akiyoshi Namiki · Piotr K. Janicki
Received: 30 January 2014 / Accepted: 6 May 2014 © Springer-Verlag Berlin Heidelberg 2014
Abstract Genetic variants, such as single-nucleotide polymorphisms (SNPs), of the μ-opioid receptor gene (OPRM1) might be associated with individual differences in opioid sensitivity, as well as with the incidence and severity of postoperative nausea and vomiting (PONV). The goal of the present study was to determine, in a cohort of Japanese surgical patients, genotypes and haplotypes of several SNPs in the OPRM1 gene, and their association with PONV during the early (first 24 h) postoperative period. We examined the incidence and severity of PONV, during the first 24 h after surgery, in 85 Japanese patients receiving intravenous patientcontrolled analgesia fentanyl analgesia for postoperative pain control. Eight tag SNPs of the OPRM1 gene (rs1799971, A/G; rs510769, G/A; rs4870266, G/A; rs3798683, G/A; rs1323042, A/C; rs609623, C/T; rs9397685, A/G; and rs644261, C/G) were selected based on their minor allele frequency (>10 %) and linkage disequilibrium strength (10 % in the investigated patients, including GGGAACAC (33 %), AGGGACAC (19 %), GGGAACGC (12 %), and AGAGACAC (10 %). The severity of PONV in carriers of the GGGAACGC haplotype was significantly lower than in the carriers of the other haplotypes (P A), rs3798683 (G>A), rs1323042 (A>C), rs609623 (C>T), rs9397685 (A>G), and rs644261 (C>G). Figure 1a shows the linkage disequilibrium plot calculated by Haploview. Genomic DNA was extracted from 2 mL of blood using the Gentra Puregene Blood Kit (Gentra Systems Inc.,
Deviation of the genotype frequencies from Hardy–Weinberg equilibrium was tested using the chi-square test. Data for fentanyl consumption and VAS scores were analyzed among the genotypes of each SNP using the Kruskal–Wallis test with a Steel–Dwass test for multiple comparisons. Data for fentanyl consumption and VAS scores were analyzed between alleles of each SNP using the Mann–Whitney test. Associations of the incidence of PONV with genotype of each SNP were analyzed using the chi-square test. Associations of the incidence of PONV with alleles of each SNP were analyzed using Fisher’s exact test. Association of the severity of PONV with genotype was analyzed using the Kruskal–Wallis test with a Steel–Dwass test. Association of the severity of PONV with alleles was analyzed using the Mann–Whitney test. Relationships among the eight SNPs were identified by linkage disequilibrium analysis using Haploview software. The r2 values were pairwise calculated using a genotype dataset of the eight SNPs. Haplotypes were constructed from the genotype dataset using PHASE software (ver. 2.1.1) as we previously described (Stephens et al. 2001; Stephens and Scheet 2005; Saito et al. 2006). Data for incidence of PONV among haplotype structure were analyzed using the chi-square test. Data for fentanyl consumption, VAS scores, and severity of PONV among haplotype structures were analyzed using the Kruskal–Wallis test with a Steel–Dwass test for multiple comparisons. Differences were considered significant at P 0.05). The VAS scores at rest and while moving, at 24 h after surgery, were 0 (10) and 30 (40) [median (interquartile range)], respectively. These scores did not significantly differ among genotypes or between alleles in all eight SNPs (All P > 0.05). Table 2 shows the incidence of PONV for genotypes or alleles of each SNP during the first 24 h after surgery. Although the incidence of PONV did not correlate with genotypes and alleles of the A118G-SNP, we demonstrated that the incidence of PONV was related to genotype and alleles of OPRM1 SNP rs9397685. Being heterozygous for SNP rs9397685 was significantly associated with a decrease of the severity of PONV (P = 0.02). In addition, the minor G allele of this SNP was significantly associated with reduced severity of PONV (P = 0.004).
Table 2 Associations of each genotyped SNP with incidence of PONV rs#
Position
Region
Variant
MAF
HWP
Genotypes
P
Alleles
P
Wild-type
Hetero
Homo
Major
Minor
(case/con)
(case/con)
(case/con)
(case/con)
(case/con)
rs1799971 rs510769 rs4870266 rs3798683 rs1323042 rs609623 rs9397685
154402490 154403712 154410691 154460107 154463537 154466651 154474926
Exon Intron Intron Intron Intron Intron Intron
A>G G>A G>A G>A A>C C>T A>G
0.49 0.12 0.12 0.49 0.19 0.10 0.13
0.22 1.00 0.46 0.29 0.64 1.00 0.45
7/20 20/44 20/46 8/18 15/41 16/50 22/41
10/20 3/14 3/10 10/21 6/15 7/8 1/16
6/19 0/1 0/3 5/20 2/3 0/1 0/2
0.71 0.44 0.47 0.56 0.40 0.18 0.04
24/60 43/102 43/102 26/57 36/97 39/108 45/98
22/58 3/16 3/16 20/61 10/21 7/10 1/20
1.00 0.28 0.28 0.39 0.66 0.25 0.009
rs644261
154483943
Intron
C>G
0.08
0.59
17/52
6/7
0/0
0.11
40/111
6/7
0.19
Incidences are presented as number MAF: minor allele frequency, HWP: P value in Hardy–Weinberg Equilibrium, Hetero: heterozygous mutant, Homo: homozygous mutant, Major: major allele, Minor: minor allele, case: patients with PONV, con: patients without PONV, P: P values in statistics
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Exp Brain Res Table 3 Haplotype allele frequency Set
Frequency
H1 H2 H3 H4
GGGAACAC AGGGACAC GGGAACGC AGAGACAC
0.33 0.19 0.12 0.10
Others
AAGGCCAC AGGGCTAG AAGGCTAC AGGGCCAC AGGAACGC AAGGCTAG GGGGACAC AGGAACAC GAGAACAC AGAAACAC GGGGCCAC
0.06 0.06 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01
GGAGACAC
0.01
3
Severity of PONV
Haplotype
2
1
0
H1
H2
H3
H4
Others
Fig. 2 Associations of OPRM1 haplotypes with severity of PONV at 24 h after surgery. Post hoc test shows less severity in patients with the H3 haplotype than in those having H1, H2, and other groups (P = 0.013). Box represents median and 25th–75th percentiles. Error bars represent 10th or 90th percentiles. *P 0.05). The severity of PONV at 24 h after surgery displayed statistically significant differences between haplotype groups (P = 0.013; Fig. 2). Although the incidence of PONV during the first 24 h period after surgery did not statistically differ among haplotypes (P = 0.13), the observed incidence of PONV in patients with the H3 haplotype was lower less than in the other groups. The additional univariate analysis suggested the additional involvements of female gender, history of motion sickness, and the presence of the rs9397685 variant, in the incidence of PONV (Table 4). Multivariate analysis (i.e., logistic regression analysis) identified female gender and genotypes without H3 as independent predictors of PONV (Table 5). The odds ratios of PONV were 4.95-fold and 10.5-fold higher, respectively, among females and patients with genotypes without H3 than in males and patients with the H3 genotype.
Variable
Odds ratio
95 % confidence interval
P value
Age Weight Female gender Non-smoker History of motion sickness Use of nitrous oxide Duration of anesthesia rs1799971 A>G rs9397685 A>G H3 haplotype carrier
1.01 1.02 4.29 1.00 4.09
0.97–1.05 0.97–1.07 1.30–14.1 0.16–6.09 0.97–17.3
0.59 0.43 0.02 1.00 0.06
1.80 1.00 1.17 0.10 0.12
0.63–4.88 0.99–1.00 0.41–3.31 0.01–0.83 0.02–0.98
0.26 0.96 0.76 0.03 0.04
H4 haplotype carrier
0.74
0.18–2.96
0.66
H3 haplotype carrier: the patient has GGGAACGC haplotype allele, H4 haplotype carrier: the patient has AGAGACAC haplotype allele
Table 5 Risk factors for incidence of PONV in multivariate analysis Variable
Odds ratio
95 % Confidence interval
P value
Female gender History of motion sickness
4.95 4.76
1.18–20.8 0.92–24.8
0.03 0.06
No H3 haplotype carrier
10.5
1.24–116
0.04
No H3 haplotype carrier: the patient has not GGGAACGC haplotype allele
Discussion
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Individual analysis of OPRM1 gene SNPs in investigated patients The postoperative analgesia achieved using IV-PCA was equivalent and adequate in each genotype group of investigated patients, with the exception of the group having the SNP A118G, which produced a nominally significant increase in the requirement for fentanyl during the first 24 h period after surgery. These findings are consistent with previously reported results (Chou et al. 2006; Sia et al. 2008). On the other hand, this SNP was not associated with differences in frequency or severity of PONV during the first 24 h period after surgery. Our results are consistent with two previously published studies of female patients after gynecological surgery (Chou et al. 2006; Zhang et al. 2011). The present results cannot be, however, directly compared with the previous findings because of differences in gender distribution. We found in our study that female gender was very closely associated with the incidence of PONV. Furthermore, the results of another study, which examined women who had undergone cesarean section and received intravenous morphine for analgesia (Sia et al. 2008), indicate greater severity of nausea in patients of the genotype AA for SNP A118G, when compared with those of AG and GG genotypes. Moreover, Walter and Lötsch (2009) concluded from a meta-analysis that the association of the A118G-SNP with opioid-induced nausea and vomiting is controversial because only a small dataset was analyzed in the included studies. In the current study, as shown Table 2, another SNP in OPRM1, rs9397685, was associated with differences in the occurrence and severity of PONV. This SNP is located in an intron of the OPRM1 gene. It is widely assumed that only functional SNPs in protein-coding sequences (i.e., non-synonymous SNPs) have a critical role in altering the functions of protein. However, meta-analyses of multiple genomewide association studies have shown that 88 % of all disease-associated SNPs have been found within intronic or intergenic regions (Hindorff et al. 2009; Singleton et al. 2010). Surprisingly, recent studies have revealed that certain biochemical functions could be assigned to 80 % of the genome, particularly in introns or intergenic regions (ENCODE Project Consortium 2012). Many intronic and intergenic SNPs may be located in cis-regulatory elements, and almost nothing is known about the effects of genetic polymorphisms in cis-regulatory elements or their ability to respond to signal transduction (MacKenzie et al. 2013). Our results might be consistent with such observations, but little is known about how this particular genetic variation in a non-coding sequence influences the function of proteins or cell signal transduction. One possible explanation is that the SNPs, within cis-regulatory elements (e.g., promoter, enhancer, and silencer) of the gene, alter interactions
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Exp Brain Res
between the elements and transcription factors. Our experimental conditions cannot, however, determine whether the intronic location of the rs9397685-SNP includes cis-regulatory elements or not. Therefore, the functional significance of this SNP remains unclear. Although the statistical P value of the comparisons between the rs9397685-SNP and the incidence of PONV was G
Exp Brain Res
variant was actually associated with PONV, and the variants of two other SNPs (i.e., rs609623 C>T and rs644261 C>G) also trended toward an increase in the incidence of PONV (P ranged from 0.1 to 0.2). These SNPs in noncoding sequences may be in strong linkage disequilibrium with other SNPs within protein-coding regions and those unknown SNPs actually may be associated with PONV. In addition, our results indicate that these two SNPs were in high linkage disequilibrium each other (r2 = 0.74, Fig. 1b) and were in weak linkage disequilibrium with SNP A118G (rs1799972) (r2 = 0.11 and 0.08, Fig. 1b). Although these SNPs in intronic parts of the gene may potentially influence SNP A118G function in the protein-coding sequence, our study could not fully explain the effects of the other seven intronic SNPs except for SNP A118G. Since haplotype-based analysis is generally considered more powerful than single-marker analysis (Levran et al. 2011), we demonstrated that the specific GGGAACGC haplotype (H3) attenuated the severity of PONV in patients (Fig. 2). However, the underlying molecular mechanism remains unclear. Genome studies in larger populations will be needed for testing the association of unknown genetic variants with PONV. Animal studies may also be needed for testing expression of emetic-related genes and their interactions with various transcriptional factors in the whole brain or vomiting centers. Clinical implications Our results indicate that specific sets of SNP genotypes in the OPRM1 gene might be involved in the inter-individual differences in the incidence and severity of PONV. The incidence of PONV might be predicted partially using preoperative genotyping of several SNPs in the OPRM1 gene. As shown in Table 5, the present study found that significantly greater odds ratios were associated with female gender and patients lacking the GGGAACGC haplotype. These findings might serve as a novel predictor of PONV. The present study could not identify the underlying mechanism through which female patients more frequently vomit after emergence from general anesthesia. The variability of opioid-induced nausea and vomiting in postoperative patients might involve not only genetic variation but also environmental factors, such as female-specific epigenetic modifications resulting from hormones such as estrogen (McCarthy et al. 2009). Although such epigenetic modifications would reduce gene expression in specific brain regions, its involvement in PONV remains controversial. Limitations The results of the present, preliminary study were obtained in a very limited cohort and will require validation in a larger
population. In addition, genetic variation is subject to race and ethnic diversity (Tan et al. 2009; Kasai and Ikeda 2011). The HapMap data show that the MAF of the A118G SNP differs among races and/or ethnicities. Although the MAFs of the A118G SNP in the Japanese and Chinese population are 0.47 and 0.36, respectively, those in Caucasian and African (Yoruba) populations are 0.16 and almost 0, respectively. Therefore, the present findings might not be applicable to other populations. We expect that the haplotype set of SNPs in the OPRM1 gene of other races will be similar to H3 observed in the present study; however, this remains to be validated. Another limitation is that other genes might also be involved in regulation of PONV sensitivity The results of several previous studies indicate that other genes also can be associated with pain and PONV in humans, including COMT, CYP2D6, MC1R, ABCB1, and KCNJ6 (Stamer et al. 2003; Mogil et al. 2003; Rakvåg et al. 2005; Park et al. 2007; Lötsch et al. 2010). In addition, genes such as DRD2, HTR3A, HTR3B, and CHRM3 recently have been associated with PONV (Nakagawa et al. 2008; Rueffert et al. 2009; Laugsand et al. 2011; Janicki et al. 2011). Summary We found a novel intronic SNP of the OPRM1 gene (rs9397685) that was associated with differences in the incidence and severity of PONV in the Japanese surgical population. The GGGAACGC haplotype constructed from eight SNPs, including A118G and the novel intronic SNP, in the OPRM1 gene, was associated with decreased severity of PONV. These novel findings might contribute to better understanding of PONV in surgical patients. Acknowledgments This study was supported by a Grant-in-Aid for Young Scientists (B) (No. 20791085, 2008–2010, to Shigekazu Sugino) from the Ministry of Education, Culture, Sports Science and Technology, Tokyo, Japan. Presented in part at the American Society of Anesthesiologists 2010 Annual Meeting, San Diego, California, October 16–20, 2010. The author (S.S.) thanks the staff of the Department of Anesthesiology, Sapporo Medical University School of Medicine, for providing valuable comments regarding the preparation of this manuscript.
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Research Article
Association of μ‑opioid receptor gene (OPRM1) haplotypes with postoperative nausea and vomiting Shigekazu Sugino · Tomo Hayase · Misako Higuchi · Katsuhiko Saito · Hiroyuki Moriya · Yukihiro Kumeta · Nahoko Kurosawa · Akiyoshi Namiki · Piotr K. Janicki
Received: 30 January 2014 / Accepted: 6 May 2014 © Springer-Verlag Berlin Heidelberg 2014
Abstract Genetic variants, such as single-nucleotide polymorphisms (SNPs), of the μ-opioid receptor gene (OPRM1) might be associated with individual differences in opioid sensitivity, as well as with the incidence and severity of postoperative nausea and vomiting (PONV). The goal of the present study was to determine, in a cohort of Japanese surgical patients, genotypes and haplotypes of several SNPs in the OPRM1 gene, and their association with PONV during the early (first 24 h) postoperative period. We examined the incidence and severity of PONV, during the first 24 h after surgery, in 85 Japanese patients receiving intravenous patientcontrolled analgesia fentanyl analgesia for postoperative pain control. Eight tag SNPs of the OPRM1 gene (rs1799971, A/G; rs510769, G/A; rs4870266, G/A; rs3798683, G/A; rs1323042, A/C; rs609623, C/T; rs9397685, A/G; and rs644261, C/G) were selected based on their minor allele frequency (>10 %) and linkage disequilibrium strength (10 % in the investigated patients, including GGGAACAC (33 %), AGGGACAC (19 %), GGGAACGC (12 %), and AGAGACAC (10 %). The severity of PONV in carriers of the GGGAACGC haplotype was significantly lower than in the carriers of the other haplotypes (P A), rs3798683 (G>A), rs1323042 (A>C), rs609623 (C>T), rs9397685 (A>G), and rs644261 (C>G). Figure 1a shows the linkage disequilibrium plot calculated by Haploview. Genomic DNA was extracted from 2 mL of blood using the Gentra Puregene Blood Kit (Gentra Systems Inc.,
Deviation of the genotype frequencies from Hardy–Weinberg equilibrium was tested using the chi-square test. Data for fentanyl consumption and VAS scores were analyzed among the genotypes of each SNP using the Kruskal–Wallis test with a Steel–Dwass test for multiple comparisons. Data for fentanyl consumption and VAS scores were analyzed between alleles of each SNP using the Mann–Whitney test. Associations of the incidence of PONV with genotype of each SNP were analyzed using the chi-square test. Associations of the incidence of PONV with alleles of each SNP were analyzed using Fisher’s exact test. Association of the severity of PONV with genotype was analyzed using the Kruskal–Wallis test with a Steel–Dwass test. Association of the severity of PONV with alleles was analyzed using the Mann–Whitney test. Relationships among the eight SNPs were identified by linkage disequilibrium analysis using Haploview software. The r2 values were pairwise calculated using a genotype dataset of the eight SNPs. Haplotypes were constructed from the genotype dataset using PHASE software (ver. 2.1.1) as we previously described (Stephens et al. 2001; Stephens and Scheet 2005; Saito et al. 2006). Data for incidence of PONV among haplotype structure were analyzed using the chi-square test. Data for fentanyl consumption, VAS scores, and severity of PONV among haplotype structures were analyzed using the Kruskal–Wallis test with a Steel–Dwass test for multiple comparisons. Differences were considered significant at P 0.05). The VAS scores at rest and while moving, at 24 h after surgery, were 0 (10) and 30 (40) [median (interquartile range)], respectively. These scores did not significantly differ among genotypes or between alleles in all eight SNPs (All P > 0.05). Table 2 shows the incidence of PONV for genotypes or alleles of each SNP during the first 24 h after surgery. Although the incidence of PONV did not correlate with genotypes and alleles of the A118G-SNP, we demonstrated that the incidence of PONV was related to genotype and alleles of OPRM1 SNP rs9397685. Being heterozygous for SNP rs9397685 was significantly associated with a decrease of the severity of PONV (P = 0.02). In addition, the minor G allele of this SNP was significantly associated with reduced severity of PONV (P = 0.004).
Table 2 Associations of each genotyped SNP with incidence of PONV rs#
Position
Region
Variant
MAF
HWP
Genotypes
P
Alleles
P
Wild-type
Hetero
Homo
Major
Minor
(case/con)
(case/con)
(case/con)
(case/con)
(case/con)
rs1799971 rs510769 rs4870266 rs3798683 rs1323042 rs609623 rs9397685
154402490 154403712 154410691 154460107 154463537 154466651 154474926
Exon Intron Intron Intron Intron Intron Intron
A>G G>A G>A G>A A>C C>T A>G
0.49 0.12 0.12 0.49 0.19 0.10 0.13
0.22 1.00 0.46 0.29 0.64 1.00 0.45
7/20 20/44 20/46 8/18 15/41 16/50 22/41
10/20 3/14 3/10 10/21 6/15 7/8 1/16
6/19 0/1 0/3 5/20 2/3 0/1 0/2
0.71 0.44 0.47 0.56 0.40 0.18 0.04
24/60 43/102 43/102 26/57 36/97 39/108 45/98
22/58 3/16 3/16 20/61 10/21 7/10 1/20
1.00 0.28 0.28 0.39 0.66 0.25 0.009
rs644261
154483943
Intron
C>G
0.08
0.59
17/52
6/7
0/0
0.11
40/111
6/7
0.19
Incidences are presented as number MAF: minor allele frequency, HWP: P value in Hardy–Weinberg Equilibrium, Hetero: heterozygous mutant, Homo: homozygous mutant, Major: major allele, Minor: minor allele, case: patients with PONV, con: patients without PONV, P: P values in statistics
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Exp Brain Res Table 3 Haplotype allele frequency Set
Frequency
H1 H2 H3 H4
GGGAACAC AGGGACAC GGGAACGC AGAGACAC
0.33 0.19 0.12 0.10
Others
AAGGCCAC AGGGCTAG AAGGCTAC AGGGCCAC AGGAACGC AAGGCTAG GGGGACAC AGGAACAC GAGAACAC AGAAACAC GGGGCCAC
0.06 0.06 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01
GGAGACAC
0.01
3
Severity of PONV
Haplotype
2
1
0
H1
H2
H3
H4
Others
Fig. 2 Associations of OPRM1 haplotypes with severity of PONV at 24 h after surgery. Post hoc test shows less severity in patients with the H3 haplotype than in those having H1, H2, and other groups (P = 0.013). Box represents median and 25th–75th percentiles. Error bars represent 10th or 90th percentiles. *P 0.05). The severity of PONV at 24 h after surgery displayed statistically significant differences between haplotype groups (P = 0.013; Fig. 2). Although the incidence of PONV during the first 24 h period after surgery did not statistically differ among haplotypes (P = 0.13), the observed incidence of PONV in patients with the H3 haplotype was lower less than in the other groups. The additional univariate analysis suggested the additional involvements of female gender, history of motion sickness, and the presence of the rs9397685 variant, in the incidence of PONV (Table 4). Multivariate analysis (i.e., logistic regression analysis) identified female gender and genotypes without H3 as independent predictors of PONV (Table 5). The odds ratios of PONV were 4.95-fold and 10.5-fold higher, respectively, among females and patients with genotypes without H3 than in males and patients with the H3 genotype.
Variable
Odds ratio
95 % confidence interval
P value
Age Weight Female gender Non-smoker History of motion sickness Use of nitrous oxide Duration of anesthesia rs1799971 A>G rs9397685 A>G H3 haplotype carrier
1.01 1.02 4.29 1.00 4.09
0.97–1.05 0.97–1.07 1.30–14.1 0.16–6.09 0.97–17.3
0.59 0.43 0.02 1.00 0.06
1.80 1.00 1.17 0.10 0.12
0.63–4.88 0.99–1.00 0.41–3.31 0.01–0.83 0.02–0.98
0.26 0.96 0.76 0.03 0.04
H4 haplotype carrier
0.74
0.18–2.96
0.66
H3 haplotype carrier: the patient has GGGAACGC haplotype allele, H4 haplotype carrier: the patient has AGAGACAC haplotype allele
Table 5 Risk factors for incidence of PONV in multivariate analysis Variable
Odds ratio
95 % Confidence interval
P value
Female gender History of motion sickness
4.95 4.76
1.18–20.8 0.92–24.8
0.03 0.06
No H3 haplotype carrier
10.5
1.24–116
0.04
No H3 haplotype carrier: the patient has not GGGAACGC haplotype allele
Discussion
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Individual analysis of OPRM1 gene SNPs in investigated patients The postoperative analgesia achieved using IV-PCA was equivalent and adequate in each genotype group of investigated patients, with the exception of the group having the SNP A118G, which produced a nominally significant increase in the requirement for fentanyl during the first 24 h period after surgery. These findings are consistent with previously reported results (Chou et al. 2006; Sia et al. 2008). On the other hand, this SNP was not associated with differences in frequency or severity of PONV during the first 24 h period after surgery. Our results are consistent with two previously published studies of female patients after gynecological surgery (Chou et al. 2006; Zhang et al. 2011). The present results cannot be, however, directly compared with the previous findings because of differences in gender distribution. We found in our study that female gender was very closely associated with the incidence of PONV. Furthermore, the results of another study, which examined women who had undergone cesarean section and received intravenous morphine for analgesia (Sia et al. 2008), indicate greater severity of nausea in patients of the genotype AA for SNP A118G, when compared with those of AG and GG genotypes. Moreover, Walter and Lötsch (2009) concluded from a meta-analysis that the association of the A118G-SNP with opioid-induced nausea and vomiting is controversial because only a small dataset was analyzed in the included studies. In the current study, as shown Table 2, another SNP in OPRM1, rs9397685, was associated with differences in the occurrence and severity of PONV. This SNP is located in an intron of the OPRM1 gene. It is widely assumed that only functional SNPs in protein-coding sequences (i.e., non-synonymous SNPs) have a critical role in altering the functions of protein. However, meta-analyses of multiple genomewide association studies have shown that 88 % of all disease-associated SNPs have been found within intronic or intergenic regions (Hindorff et al. 2009; Singleton et al. 2010). Surprisingly, recent studies have revealed that certain biochemical functions could be assigned to 80 % of the genome, particularly in introns or intergenic regions (ENCODE Project Consortium 2012). Many intronic and intergenic SNPs may be located in cis-regulatory elements, and almost nothing is known about the effects of genetic polymorphisms in cis-regulatory elements or their ability to respond to signal transduction (MacKenzie et al. 2013). Our results might be consistent with such observations, but little is known about how this particular genetic variation in a non-coding sequence influences the function of proteins or cell signal transduction. One possible explanation is that the SNPs, within cis-regulatory elements (e.g., promoter, enhancer, and silencer) of the gene, alter interactions
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Exp Brain Res
between the elements and transcription factors. Our experimental conditions cannot, however, determine whether the intronic location of the rs9397685-SNP includes cis-regulatory elements or not. Therefore, the functional significance of this SNP remains unclear. Although the statistical P value of the comparisons between the rs9397685-SNP and the incidence of PONV was G
Exp Brain Res
variant was actually associated with PONV, and the variants of two other SNPs (i.e., rs609623 C>T and rs644261 C>G) also trended toward an increase in the incidence of PONV (P ranged from 0.1 to 0.2). These SNPs in noncoding sequences may be in strong linkage disequilibrium with other SNPs within protein-coding regions and those unknown SNPs actually may be associated with PONV. In addition, our results indicate that these two SNPs were in high linkage disequilibrium each other (r2 = 0.74, Fig. 1b) and were in weak linkage disequilibrium with SNP A118G (rs1799972) (r2 = 0.11 and 0.08, Fig. 1b). Although these SNPs in intronic parts of the gene may potentially influence SNP A118G function in the protein-coding sequence, our study could not fully explain the effects of the other seven intronic SNPs except for SNP A118G. Since haplotype-based analysis is generally considered more powerful than single-marker analysis (Levran et al. 2011), we demonstrated that the specific GGGAACGC haplotype (H3) attenuated the severity of PONV in patients (Fig. 2). However, the underlying molecular mechanism remains unclear. Genome studies in larger populations will be needed for testing the association of unknown genetic variants with PONV. Animal studies may also be needed for testing expression of emetic-related genes and their interactions with various transcriptional factors in the whole brain or vomiting centers. Clinical implications Our results indicate that specific sets of SNP genotypes in the OPRM1 gene might be involved in the inter-individual differences in the incidence and severity of PONV. The incidence of PONV might be predicted partially using preoperative genotyping of several SNPs in the OPRM1 gene. As shown in Table 5, the present study found that significantly greater odds ratios were associated with female gender and patients lacking the GGGAACGC haplotype. These findings might serve as a novel predictor of PONV. The present study could not identify the underlying mechanism through which female patients more frequently vomit after emergence from general anesthesia. The variability of opioid-induced nausea and vomiting in postoperative patients might involve not only genetic variation but also environmental factors, such as female-specific epigenetic modifications resulting from hormones such as estrogen (McCarthy et al. 2009). Although such epigenetic modifications would reduce gene expression in specific brain regions, its involvement in PONV remains controversial. Limitations The results of the present, preliminary study were obtained in a very limited cohort and will require validation in a larger
population. In addition, genetic variation is subject to race and ethnic diversity (Tan et al. 2009; Kasai and Ikeda 2011). The HapMap data show that the MAF of the A118G SNP differs among races and/or ethnicities. Although the MAFs of the A118G SNP in the Japanese and Chinese population are 0.47 and 0.36, respectively, those in Caucasian and African (Yoruba) populations are 0.16 and almost 0, respectively. Therefore, the present findings might not be applicable to other populations. We expect that the haplotype set of SNPs in the OPRM1 gene of other races will be similar to H3 observed in the present study; however, this remains to be validated. Another limitation is that other genes might also be involved in regulation of PONV sensitivity The results of several previous studies indicate that other genes also can be associated with pain and PONV in humans, including COMT, CYP2D6, MC1R, ABCB1, and KCNJ6 (Stamer et al. 2003; Mogil et al. 2003; Rakvåg et al. 2005; Park et al. 2007; Lötsch et al. 2010). In addition, genes such as DRD2, HTR3A, HTR3B, and CHRM3 recently have been associated with PONV (Nakagawa et al. 2008; Rueffert et al. 2009; Laugsand et al. 2011; Janicki et al. 2011). Summary We found a novel intronic SNP of the OPRM1 gene (rs9397685) that was associated with differences in the incidence and severity of PONV in the Japanese surgical population. The GGGAACGC haplotype constructed from eight SNPs, including A118G and the novel intronic SNP, in the OPRM1 gene, was associated with decreased severity of PONV. These novel findings might contribute to better understanding of PONV in surgical patients. Acknowledgments This study was supported by a Grant-in-Aid for Young Scientists (B) (No. 20791085, 2008–2010, to Shigekazu Sugino) from the Ministry of Education, Culture, Sports Science and Technology, Tokyo, Japan. Presented in part at the American Society of Anesthesiologists 2010 Annual Meeting, San Diego, California, October 16–20, 2010. The author (S.S.) thanks the staff of the Department of Anesthesiology, Sapporo Medical University School of Medicine, for providing valuable comments regarding the preparation of this manuscript.
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