Cytochrome P450IA1 polymorphisms along with PM10 exposure contribute to the risk of birth weight reduction

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Reproductive Toxicology 24 (2007) 281–288

Cytochrome P450IA1 polymorphisms along with PM10 exposure contribute to the risk of birth weight reduction Young-Ju Suh a,b , Byung-Mi Kim a,b , Bo-Hyun Park a,b , Hyesook Park a , Young-Ju Kim c , Ho Kim d , Yun-Chul Hong e , Eun-Hee Ha a,∗ a

Department of Preventive Medicine, College of Medicine, Ewha Medical Research Center, Ewha Womans University, 911-1 Mok-6 dong, Yangcheon-ku, Seoul 158-056, Republic of Korea b BK21 Research Division for Medicine, Ewha Womans University, Seoul, Republic of Korea c Department of Obstetrics and Gynecology, College of Medicine, Ewha Womans University, Seoul, Republic of Korea d Department of Epidemiology and Biostatistics, School of Public Health, Seoul National University, Seoul, Republic of Korea e Department of Preventive Medicine, College of Medicine, Seoul National University, Seoul, Republic of Korea Received 4 January 2007; received in revised form 25 June 2007; accepted 2 July 2007 Available online 7 July 2007

Abstract We explored the effects of particulate matter <10 ␮m (PM10 ) exposure along with CYP1A1 polymorphisms of MspI (T6235C) and NcoI (Ile462Val) on reduced birth weight (BW). A prospective cohort study was done with women who delivered from 2001 to 2004 at Ewha Womans University Hospital, Seoul, Korea. We compared the estimated least squares means of BW in the generalized linear model, after adjusting for controlling factors. High PM10 exposure at the 90th percentile level and above during the 1st trimester conferred a significant risk for reduced BW, compared with low PM10 exposure below the 90th percentile level. The effect of high PM10 exposure during the 1st trimester of pregnancy compared with low PM10 exposure was greater for women with MspI TC/CC and NcoI IleVal/ValVal genotypes than for those with MspI TT and NcoI IleIle genotypes. In conclusion, high PM10 exposure during the 1st trimester increased the risk for reduced BW in concert with MspI TC/CC and NcoI IleVal/ValVal genotypes in Korean women. © 2007 Elsevier Inc. All rights reserved. Keywords: PM10 ; CYP1A1; MspI; NcoI; Birth weight; Korean; Cohort

1. Introduction Infant birth weight (BW), gestational age, and growth of fetus may impact on the perinatal health. Low birth weight (LBW) and preterm delivery cause perinatal death, infant sickness, and adult’s diseases [1,2]. Many researchers have studied the adverse pregnancy outcomes of LBW and preterm delivery using the approach of environmental epidemiology. These studies have examined environmental exposure problems such as maternal cigarette smoking [3–5], environmental tobacco smoke (ETS) [6,7], organic solvent [8,9], and alcohol consumption [10]. Many studies have investigated the relationship between birth outcomes and air pollution exposure such as total suspended particulates [11–14], sulfur dioxide (SO2 ) [11–15],

∗

Corresponding author. Tel.: +82 2 2650 5757; fax: +82 2 2653 1086. E-mail address: [email protected] (E.-H. Ha).

0890-6238/$ – see front matter © 2007 Elsevier Inc. All rights reserved. doi:10.1016/j.reprotox.2007.07.001

carbon monoxide (CO) [14–18], ozone (O3 ) [18,19], nitrogen dioxide (NO2 ) [14,17], and particulate matter <10 ␮m (PM10 ) [12,17,19–22]. In our previous studies performed in Seoul, we reported a positive relationship between air pollution exposure during pregnancy and LBW [14,23,24]. Even though environmental pollutants have been proven to give a harmful influence on human health by enhancing inflammatory reaction and increasing blood viscosity [25,26], the causative mechanism of this relationship has not yet been accurately verified. Besides, human health is known to be affected by both genetic and environmental factors. Genetic susceptibility might explain the substantial variability in delivery outcomes of pregnant women exposed to the same environmental risk factors [27,28]. The cytochrome P450IA1 (CYP1A1) is a good candidate susceptibility gene for LBW, along with environmental risk factors. The CYP1A1 gene, which is a well-known phase 1 enzyme, is particularly related with the chemical metabolism following exposure to environmental pollutants with regard to

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infant’s BW [27] and is highly polymorphic in the population [29,30]. Wang et al. [27] reported that CYP1A1 is one of the major genes associated with LBW by conferring susceptibility to environmental pollutants. They emphasized that the geneenvironmental interaction approach is a powerful technique in the etiological study of LBW. In their study [27], they reported an association between infant LBW and maternal CYP1A1–MspI (TC/CC) genotypes, along with maternal smoking, by using a case-control design (odds ratio = 3.2, 95% CI = 1.6–6.4). Sram et al. [31] examined the relationship between LBW and the CYP1A1 polymorphisms of MspI and NcoI variants incorporated with maternal smoking and ETS in a nested case-control design. However, the relationship between LBW and CYP1A1 genotypes, along with exposure to air pollution, has not been examined yet. Therefore, we hypothesized that there is an association between air pollution exposure during pregnancy and the risk of reduced BW, and it is modified by maternal genetic polymorphisms of CYP1A1. In the present study, we performed genotype analysis of the CYP1A1–MspI(T/C) and the CYP1A1–NcoI(Ile/Val) polymorphisms along with air pollution exposure during pregnancy in a prospective cohort study in Seoul, Korea. We aimed to determine the cause of reduced BW in connection with the genetic polymorphism of CYP1A1 and exposure of air pollution such as PM10 in Korean pregnant women. 2. Materials and methods 2.1. Population and data collection This prospective cohort study was conducted in Ewha Womans University Hospital, Seoul, Korea between 1 May 2001 and 31 December 2004 and was approved by the Institutional Review Board (IRB) of Ewha Womans University Hospital. Women taking prenatal examinations in the hospital were recruited and followed up until infant delivery, and each participant signed the consent forms before enrollment. Trained interviewers assisted participants to record general information on epidemiological and clinical data, including demographic factors such as maternal age and education, paternal education, socioeconomic status, parity, previous and current medical history, complications of current gestation, smoking habits, exposure to secondary smoke, and alcohol use during pregnancy. The weight, height and blood samples of each participant were taken according to standard protocols. We determined gestational age in accordance with the onset of the last menstrual period or the first ultrasonographic estimation in case that the last menstrual period was unreliable. BW < 2500 g was considered LBW. We excluded subjects who had gestation less than 25 weeks or more than 42 weeks, gave birth to multiple infants, or had lived outside of Seoul during their pregnancy. Finally, 199 pregnant women were included in the analysis.

2.2. Air pollution data Air pollution data, measured hourly to give 24-h averages at 27 monitoring stations in Seoul, were obtained from the National Institute of Environmental Research (2005). We excluded exposure data when PM10 is 200 ␮g/m3 and over, which might be affected by the Asian Dust from dessert of Mongolia or China. Evaluating air pollution exposures for each participant according to the exposure level at the closest monitoring station from the participants’ home address, gestational age, and the delivery date, we estimated the average exposure levels of PM10 , CO, NO2 and SO2 at each of the three trimesters (defined as 3 calendar months) before delivery.

2.3. Genotyping analysis Genomic DNA was extracted from whole blood using an QIAmp blood kit (Qiagen, Hilden, Germany). Polymerase chain reactions (PCR) were performed in a total volume of 50 ␮l in the presence of 10 mM Tris–HCI, pH 8.3; 50 mM KCI; 0.2 mM of each dNTP; 2.0 mM MgCl2 ; 1.25 units Taq DNA polymerase (Takara Shuzo Co., Shiga, Japan); 20 pmol of each primer; and 100 ng of genomic DNA as a template, as described by Kim et al. [32]. Two separate, point mutation polymorphisms in CYP1A1 were analyzed by PCR-RFLP (restriction fragment length polymorphism). The MspI site at the 264th base from the polyadenylate additional signal in the 3 -flanking region, and the NcoI site, which is an exon 7 mutation coding for valine (Val), rather than isoleucine (Ile), were investigated according to the method described by Hayashi et al. [33] and Shields et al. [34], respectively. For MspI, digestion of the PCR product resulted in 340, 200, and 140 bp fragments. The polymorphism in this coding region of the gene was T6235C. We considered TT (m1m1) as the homozygous wild type, for the absence of restriction sites, TC (m1m2) as the heterozygous type, and CC (m2m2) as the homozygous mutant type, for the presence of restriction sites. Digestion of the PCR product for NcoI led to 163, 32, and 195 bp fragments. The genotype in the coding region was Ile462Val. The homozygous wild type was identified by IleIle (absence of restriction sites), the heterozygous type by IleVal, and the homozygous mutant type by ValVal (presence of restriction sites) for NcoI polymorphisms.

2.4. Statistical analysis For the preliminary analysis, we examined the relationship between averaged infant BW and covariates, such as infant’s sex, maternal age, maternal and paternal education, parity, presence of illness during pregnancy, delivery month and gestational age (squared), using t-test, ANOVA or Pearson correlation coefficient test. We performed analysis of covariance (ANCOVA) for BW in the generalized linear model and estimated the least squares mean (LS mean) of BW for the effect according to the CYP1A1–MspI and –NcoI polymorphisms and degree of air pollution exposures during pregnancy. The LS mean indicates the predicted marginal mean over a balanced population. We also tested differences of the LS means of BW for the effect using pairwise t-test. All statistical analyses were performed using SAS (version 9.1, SAS Institute, Cary, NC).

3. Results The characteristics of study subjects are presented in Table 1. The overall rate of LBW in the subjects was 6.03%. Comparing the averaged BWs in each category, they were different by paternal education (p < 0.05 by t-test), and were significantly correlated with the gestational age (r = 0.77, p < 0.01). However, the average BWs did not differ according to other factors such as infant sex, maternal age and education, parity, presence of medical history and delivery month. The genotype distributions of MspI and NcoI polymorphisms are shown in Table 1. MspI T and C, and NcoI Ile and Val allele frequencies in the population were 0.603 (2N = 240), 0.397 (2N = 158), 0.744 (2N = 296) and 0.256 (2N = 102), respectively. The distribution of the MspI and NcoI polymorphisms fits the Hardy–Weinberg equilibrium (p > 0.1 by Pearson χ2 -test). The LS means of BW were estimated for a given genotype in generalized linear model, after adjusting for the controlling factors such as infant sex, maternal age, maternal/paternal education, parity, disease status during pregnancy, delivery month and gestational age (squared). The LS means of BW were different by MspI polymorphisms (p = 0.015 by ANCOVA). In particular, the LS mean of BW in the TT group was significantly higher than the averaged value in the other groups of TC and CC (p = 0.012

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Table 1 Distribution of study population characteristics

Infant sex Male Female

N (%)

Mean birth weight ± S.D. (g)

98 (49.3) 101 (50.8)

3213 ± 593 3118 ± 608

0.267a

Maternal age (31.56 ± 3.62 years) Maternal education (years) ≤12 >12 Paternal education (years) ≤12 >12 Parity 0 1 ≥2 Presence of illness during pregnancy Yes No Delivery month 1 2 3 4 5 6 7 8 9 10 11 12

0.932b 0.095a 41 (27.2) 110 (72.9)

3089 ± 560 3241 ± 471 0.030a

22 (13.8) 138 (86.3)

2903 ± 665 3243 ± 477

82 (41.2) 96 (48.2) 21 (10.6)

3183 ± 541 3125 ± 679 3277 ± 421

36 (24.3) 112 (75.7)

3059 ± 694 3265 ± 424

0.548c

0.099a

0.848c 40 (20.1) 30 (15.1) 14 (7.0) 13 (6.5) 12 (6.0) 14 (7.0) 16 (8.0) 7 (3.5) 8 (4.0) 14 (7.0) 14 (7.0) 17 (8.5)

3195 3013 3021 3196 3259 3239 3199 3037 3321 3075 3386 3168

± ± ± ± ± ± ± ± ± ± ± ±

377 796 717 389 430 402 882 506 259 1033 426 413

Gestational age (38.35 ± 2.60 weeks) Maternal genotypes MspI TT TC CC NcoI IleIle IleVal ValVal Total (N)

p-Value

<0.0001b

0.015d 76 (38.2) 88 (44.2) 35 (17.6)

3244 ± 591 (3341, 56)e 3122 ± 585 (3105, 52) 3101 ± 658 (3235, 81) 0.404d

110 (55.3) 76 (38.2) 13 (6.5) 199

3141 ± 662 (3238, 46) 3178 ± 527 (3167, 55) 3291 ± 461 (3354, 139) 3165 ± 601

a

p-Value as obtained by t-test. p-Value as tested for Pearson correlation coefficient. c p-Value as obtained by ANOVA test. d p-Value as obtained by ANCOVA adjusting for controlling factors such as infant sex, maternal age, maternal/paternal education, parity, disease status during pregnancy, delivery month and gestational age (squared). e Numbers in the parenthesis stand for the least squares (LS) mean of birth weight and its standard error which are estimated in generalized linear model adjusting for controlling factors. b

by one-sided t-test). However, the LS means of BW were not significantly different by NcoI genotypes at the 0.05 significant level. Table 2 shows the distributions of the three different concentration levels of air pollutants such as PM10 , CO, NO2 and SO2 according to exposure period during pregnancy. Although

the exposure levels of each pollutant did not differ markedly between the 1st and 2nd trimesters, the level in the 3rd trimester was slightly higher than that in the 1st and 2nd trimesters. The concentration levels of four air pollutants were positively correlated with each other (p < 0.01). The average values of correlation coefficient between each pair of four pollutants were

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Table 2 Distribution of air pollutant concentration levels according to pregnancy period of exposure Period of exposure

Mean ± S.D.

Percentile Min

25th

50th

75th

90th

Max

(␮g/m3 )

PM10 1st trimester 2nd trimester 3rd trimester

76.41 ± 28.80 77.84 ± 31.63 95.61 ± 26.15

21.00 31.45 23.45

55.28 48.65 77.10

71.09 72.36 96.35

92.38 108.00 116.68

124.02 125.10 128.74

151.65 139.13 172.75

CO (100 ppb) 1st trimester 2nd trimester 3rd trimester

9.30 ± 4.65 10.38 ± 4.72 13.17 ± 4.73

2.54 3.39 3.73

5.70 6.36 9.62

7.30 8.82 12.75

11.86 13.60 16.54

17.03 18.68 19.23

21.15 21.15 38.75

NO2 (ppb) 1st trimester 2nd trimester 3rd trimester

46.07 ± 22.29 51.76 ± 25.43 66.57 ± 20.89

16.94 16.60 21.38

30.65 29.10 49.44

36.41 41.48 67.74

51.99 80.15 84.05

86.97 88.55 90.86

107.95 100.26 124.50

SO2 (ppb) 1st trimester 2nd trimester 3rd trimester

4.87 ± 3.11 5.46 ± 3.34 7.35 ± 4.23

1.89 1.62 1.44

2.75 2.74 4.29

3.55 4.24 6.31

6.64 7.67 9.29

9.25 9.94 12.73

20.11 16.98 33.38

0.797, 0.857 and 0.714 in the 1st, 2nd and 3rd trimester, respectively. In Fig. 1, we illustrated the estimated LS means of BW for each exposure level of PM10 , CO, NO2 and SO2 according to trimesters, after adjusting for the controlling factors. The overall pattern of BW change by the exposure level was not markedly

different among pollutants. The estimated LS means of BW tended to be low at the high exposure level (≥90th percentile). In particular, the 1st trimester during pregnancy increased the risk for reduced BW in a dose–response way, which was apparent in the PM10 concentrations. The LS means of BW were marginally different by PM10 exposure level during the 1st

Fig. 1. Association between infant birth weight and the exposure levels of each air pollutant such as PM10 , CO, NO2 and SO2 during pregnancy. LS mean stands for the least squares (LS) means of birth weight (BW) estimated in the generalized linear model, adjusting for controlling factors such as infant sex, maternal age, maternal/paternal education, parity, disease status during pregnancy, delivery month and gestational age (squared). p-Value was obtained by ANCOVA adjusting for the controlling factors.

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Table 3 Association of the PM10 exposure levels with birth weight by the MspI and the NcoI genotypes Genotype

PM10 levela

1st trimester N (%)

All

MspI TT

TC/CC

NcoI IleIle

IleVal/ValVal

2nd trimester LS means

(S.E.)b

LS means (S.E.)

158(90.3) 17(9.7)

153(89.5) 18(10.5)

162(90.5) 17(9.5)

<90th ≥90th

60(34.3) 5(2.9)

59(34.5) 6(3.5)

61(34.1) 6(3.4)

<90th ≥90th

98(56.0) 12(6.9)

94(55.0) 12(7.0)

101(56.4) 11(6.2)

<90th ≥90th

87(49.7) 7(4.0)

82(48.0) 11(6.4)

90(50.3) 9(5.0)

3350 (64) 3001 (229) p-Value = 0.147 Adjusted p = 0.186, 0.430, 0.155

3193 (48) 2799 (169) p-Value = 0.033 Adjusted p = 0.073, 0.150, 0.036

3244 (52) 2983 (232) p-Value = 0.289 Adjusted p = 0.344, 0.641, 0.293 71(40.6) 3262 (56) 10(5.7) 2773 (171) p-Value = 0.009 Adjusted p = 0.031, 0.058, 0.010

3253 (39) 3026 (157) p-Value = 0.177 Adjusted p = 0.203, 0.151, 0.151

N (%)

<90th ≥90th

<90th ≥90th

3253 (37) 2841 (145) p-Value = 0.009c Adjusted p = 0.041, 0.092, 0.012d

N (%)

3rd trimester

3335 (66) 3281 (249) p-Value = 0.833 Adjusted p = 0.833, 0.778, 0.806

3200 (52) 2933 (176) p-Value = 0.161 Adjusted p = 0.172, 0.152, 0.158

3243 (55) 3185 (207) p-Value = 0.790 Adjusted p = 0.783, 0.707, 0.733 71(41.5) 3264 (61) 7(4.1) 2862 (208) p-Value = 0.076 Adjusted p = 0.093, 0.063, 0.061

LS means (S.E.)

3226 (38) 3122 (140) p-Value = 0.487 Adjusted p = 0.748, 0.420, 0.466

3327 (65) 3227 (300) p-Value = 0.749 Adjusted p = 0.980, 0.635, 0.687

3165 (49) 3087 (147) p-Value = 0.626 Adjusted p = 0.978, 0.551, 0.614

3239 (53) 2944 (198) p-Value = 0.161 Adjusted p = 0.279, 0.134, 0.150 72(40.2) 3207 (58) 8(4.5) 3262 (180) p-Value = 0.777 Adjusted p = 0.607, 0.843, 0.791

a

The concentration levels of PM10 exposures in the 1st, 2nd and 3rd trimesters of pregnancy, respectively. Least squares (LS) means of birth weight (BW) estimated in generalized linear model (GLM) adjusting for controlling factors such as infant sex, maternal age, maternal/paternal education, parity, disease status during pregnancy, delivery month and gestational age (squared). c p-Value as obtained by ANCOVA adjusting for the controlling factors. d p-Values as obtained by ANCOVA adjusting for the controlling factors and the exposure level of CO, NO and SO , respectively. 2 2 b

trimester (p = 0.064). The LS mean of BW in the group with high PM10 exposure (i.e. ≥90th percentile) was significantly lower than the average of the LS means of BW in the group with lower PM10 levels (i.e. <50th, 50–75th and 75–90th percentile groups) during the 1st trimester (p = 0.010 by t-test). However, the overall LS means of BW were not different by exposure to PM10 during the 2nd or 3rd trimester (p > 0.1). To investigate the effect of PM10 on BW during pregnancy in accordance with the CYP1A1 genotypes, we performed ANCOVA in consideration of the MspI and NcoI polymorphism. Besides, considering another air pollutant of CO, NO2 or SO2 as one of controlling factors in the generalized linear model, we explored the effect of other pollutants co-varied with PM10 levels on BW reduction (Table 3). The LS means of BW were different by the effect of PM10 levels during the 1st trimester (p = 0.009). The effect of PM10 level on BW reduction kept its significance not along with NO2 level (p > 0.05) but along with CO (p = 0.041) or SO2 (p = 0.012) level. When we performed separate ANCOVA analyses according to the MspI genotypes (TC/CC versus TT), the group carrying the TC or CC genotype only conferred a risk of reduced BW when accompanied by high exposure level (≥90th percentile) of PM10 during the 1st trimester (p = 0.033). The risk of PM10 level on reduced BW kept its significance in company with SO2 level in the group carrying the TC or CC genotype (p = 0.036). In the independent ANCOVA analyses in accordance with the NcoI genotypes (IleVal/ValVal

versus IleIle), the LS means of BW were different only by the group carrying the IleVal or ValVal genotype. The group carrying the IleVal or ValVal genotype along with high exposure level (≥90th percentile) of PM10 during the 1st trimester resulted in decreased BW, compared with the same group with a lower exposure level of PM10 during the 1st trimester (p = 0.009). There was a significant difference in the LS means of BW by the combined effect of NcoI genotypes and PM10 levels even when we considered each pollutant of CO (p = 0.031), NO2 (p = 0.058) or SO2 (p = 0.010) together during the 1st trimester. However, we could not detect any significant association of air pollutants along with genotype information with BW reduction in the 2nd and 3rd trimesters during pregnancy (p > 0.05). Next, we explored which combination of the MspI and NcoI variant types along with air pollution exposure significantly conferred a risk for reduced BW. In this analysis, we focused on the effect of PM10 exposure on BW during the 1st trimester without adjusting for other pollutants, which provided more significant results along with each polymorphism of the MspI TC/CC and NcoI IleVal/ValVal, as shown in Table 3. There were significant differences in the LS means of BW by the pooled effects of the MspI and NcoI variant types along with PM10 exposure during the 1st trimester (p = 0.016). In particular, the effect of high PM10 exposure level during the 1st trimester (≥90th percentile) was greater for women with the MspI TC/CC plus NcoI IleVal/ValVal genotypes than for those with the MspI TT

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Fig. 2. Combined effects of the MspI and NcoI variant types on reduced BW along with the PM10 exposure levels during the 1st trimester. Least squares (LS) means of birth weight (BW) were estimated in generalized linear model, adjusting for controlling factors such as infant sex, maternal age, maternal/paternal education, parity, disease status during pregnancy, delivery month and gestational age (squared). *Significant at the 0.05 level by ANCOVA (p = 0.018). **Significant at the 0.01 level by ANCOVA (p = 0.004).

plus NcoI IleIle genotypes, as illustrated in Fig. 2. Carrying out separate analyses according to combination of the MspI and NcoI genotypes along with PM10 exposure level during the 1st trimester, we detected a significant difference in the LS means of BW according to PM10 exposure level during the 1st trimester (i.e. PM10 ≥ 90th percentile versus PM10 < 90th percentile) for subjects carrying the TC/CC plus NcoI IleVal/ValVal genotypes (p = 0.018). In the case of women with the MspI TT plus NcoI IleIle genotypes, however, the LS means of BW were not different by PM10 exposure level during the 1st trimester (p > 0.05). In addition, the risk for decreased BW was significantly higher for women carrying the MspI TC/CC plus NcoI IleVal/ValVal genotypes along with PM10 ≥ 90th percentile than for those carrying the MspI TT plus NcoI IleIle genotypes along with a low level of PM10 < 90th percentile during 1st trimester (p = 0.004). 4. Discussion Birth weight (BW) is determined by multiple genetic and environmental factors. Air pollution exposure is recognized as one of the causes to reduce BW. Several researchers have reported that specific CYP1A1 genotypes confer a risk for reduced BW. In this study, we explored how the CYP1A1 polymorphism can change the risk for reduced BW from exposures of PM10 along with other pollutant of CO, NO2 or SO2 . To the best of our knowledge, this is the first study to investigate a gene–environment interaction between CYP1A1–MspI/NcoI polymorphisms and exposure of air pollution such as PM10 during pregnancy as a risk factor for reduced BW. The CYP1A1 polymorphism is involved in the detoxification metabolism of polycyclic aromatic hydrocarbons (PAHs) [35] which are derived from an automobile car exhaust gas and therefore included in the air pollutants such as particulate matters [36]. Variant types of the CYP1A1–MspI and –NcoI have been associated with increased enzyme activity [27,32,37]. Through this mechanism on enhanced enzyme activity, pregnancy out-

comes may be more affected by environmental toxins such as maternal smoking [27,28] and air pollution. Our finding of an effect by the MspI TC/CC genotype on BW was consistent with the studies of Wang et al. [27] and Chen et al. [28]. Concerning the NcoI Val allele, Sram et al. [31] evaluated that the genotype was associated with decreased BW in connection with harmful environmental exposure such as maternal smoking and ETS. Homozygous variant type frequency of the MspI in our Korean population (17.6%) was quite different from that reported for Caucasians (7–10%) and Japanese (33%) [38]. However, the frequency of the NcoI homozygous variant type in the present population, about 6.5%, was similar to the 6% reported in Caucasians [39]. In this study, focusing on PM10 exposure during pregnancy, we investigated the combined effects of the CYP1A1 polymorphism and PM10 exposure with or without adjusting for CO, NO2 and SO2 which might co-vary with PM10 levels on BW reduction. The present study results confirmed that high PM10 exposure during pregnancy with the MspI C and/or NcoI Val allelic variant conferred a risk for decreased BW. The estimated LS means of BW decreased in the presence of the MspI C allele (i.e. TC or CC genotype) and/or the NcoI Val allele (i.e. IleVal or ValVal genotype) with increasing degree of PM10 exposure during the 1st trimester. In particular, we detected the greatest BW reduction with high PM10 exposure (≥90th percentile) during the 1st trimester. The trends of these results were not noticeably changed when we examined the effects of PM10 exposure along with the CYP1A1 polymorphism on BW adjusting for exposure of other pollutants such as CO, NO2 and SO2 during the 1st trimester. Besides, we performed an independent association analysis of each pollutant of CO, NO2 and SO2 with BW by the CYP1A1 polymorphisms. However, we could not detect significant interactions between the CYP1A1 polymorphism and exposure of CO or SO2 during the 1st trimester (p > 0.05). We only found a significant interaction of NO2 exposure with the NcoI genotype during the 1st trimester. Regarding interaction with CYP1A1 polymorphism, there was a difference between results with PM10 exposure and those with other gaseous pollutant exposures during the 1st trimester, which can be explained by different metabolism or detoxification process between particulate and gaseous matters [40–42]. In agreement with our finding, LBW has been reported to be positively associated with exposure to particulate pollutants in the first trimester during pregnancy [12,17,20,43]. Mohorovic [44] verified the mechanism that the role of inhaled environmental toxin in the initial development of human embryo and in the reduction of neonatal BW would be caused by oxidative stress, misbalanced production of reactive oxygen species and other negative metabolic processes on early embryogenesis. Although our study suffered several limitations in terms of the small sample size and the low concentration of air pollutants in residential areas, this study exhibited the following interesting features and strengths. In particular, high PM10 exposure in the 1st trimester during pregnancy conferred a risk for BW reduction. The study results showed the evidence for gene–environment interactive effects associated with reduced BW.

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