Effects of prenatal exposure to ambient air pollutant PM10 on ultrasound-measured fetal growth

Effects of prenatal exposure to ambient air pollutant PM10 on ultrasound-measured fetal growth Abstract Background Limited epidemiological studies have investigated the relationship between prenatal exposure to ambient particulate matter and risk of abnormal fetal growth, and have reached inconclusive results. No study has been conducted in areas with very high air pollution levels. We investigated the hypothesis that exposure to high levels of particulate matter with aerodynamic diameter no larger than 10 µm (PM10) during pregnancy increases the risk of abnormal fetal growth. Methods A birth cohort study was performed in Lanzhou, China, 2010–12, including 8877 pregnant women with 18 583 ultrasound measurements of four fetal growth parameters during pregnancy, including biparietal diameter (BPD), femur length (FL), head circumference (HC) and abdominal circumference (AC). Mixed-effects modelling was used to examine the associations between PM10 exposure and risk of abnormal fetal growth. Results When average PM10 exposure from conception until the ultrasound examination exceeded 150 µg/m3, there were significant increases in standardized FL (β = 0.095, P = 0.0012) and HC (β = 0.090, P = 0.0078) measures. When average PM10 exposure was treated as continuous variable, we found a significant decrease in standardized BPD (β = −0.018, P = 0.0016) as per 10 µg/m3 increase in PM10. After examining the associations by various exposure windows, positive associations between higher levels of PM10 and fetal overgrowth were consistently seen for HC measures. Conclusions Our study suggested that prenatal exposure to high levels of ambient PM10 increased the risk of abnormal fetal growth. The findings from our study have important public health implications and also call for future studies to explore the underlying mechanisms and post-natal consequences of these findings. Fetal growth, PM10, air pollution, ultrasound measures, pregnancy Key Messages No study has been conducted in areas with very high air pollution levels. Exposure to high levels of ambient PM10 increases the risk of abnormal fetal growth. The risk may vary by different fetal growth measurements and fetal growth patterns. Positive associations between higher levels of PM10 and fetal over- growth were consistently seen for head circumference measures by various exposure windows. Introduction A growing body of evidence indicates that fetal growth restriction (FGR), also known as intrauterine growth restriction or fetal under-growth, is an important predictor of neonatal death.1–4 Over the past decade, numerous studies have indicated that ambient air pollution during pregnancy influences fetal growth.5,6 A majority of these previous epidemiological studies have associated maternal air pollution exposure with fetal growth by using various neonatal anthropometric measurements at birth, such as birthweight and length, size for gestational age, head circumference (HC) and abdominal circumference (AC).7–17 However, the late assessment of growth restriction potentially introduces bias,18 and patterns of fetal growth during pregnancy cannot be assessed in these studies.5 In addition, clinical studies have suggested that birthweight as a proxy for fetal growth may not be the best indicator, and the conditions for growth restriction and small size for gestational age are not synonymous.19,20 Furthermore, others suggest that birthweight poorly reflects fetal under-growth during the first two trimesters, because growth impairment and imbalance that may have occurred during early period may be compensated for in the remaining pregnancy period.21 Assessing fetal growth in utero using ultrasound measures could provide more accurate classification of restricted or excessive growth by reducing the time between exposure and outcome assessment.18,22 A small number of studies have investigated the association between exposure to various air pollutants [i.e. nitrogen monoxide, nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone, carbon monoxide and particulate matter with aerodynamic diameter < 10 µm (PM10)] during pregnancy and fetal growth measured by ultrasound.5,22–29 Only three of these have focused on the effects of exposure to PM10 during pregnancy, and these studies reached inconsistent results.5,22,23 All of these studies investigated different exposure time windows. In addition, all of them were conducted in Europe or the USA, where the concentrations of ambient PM10 were lower than in Asia, with mean levels of PM10 ranging from 17.8 µg/m3 to 40.7 µg/m35,22,23 versus 140.8 µg/m3 in the current study (Table 1; and Table A1, available as Supplementary data at IJE online). Table 1 Distributions of selected characteristics of the study population (N = 8877) Characteristics N Percent (%) Maternal age (years)  < 30 5636 63.5  ≥ 30 3239 36.5 Highest education level  < College 3309 37.3  ≥ College 5568 62.7 Family monthly income (RMB per capita)  < 3000 5111 57.6  ≥ 3000 3766 42.4 Employment during pregnancy  No 4101 46.2  Yes 4776 53.8 Smoking (active and passive) during pregnancy  No 7189 81.0  Yes 1688 19.0 Alcohol consumption during pregnancy  No 8857 99.8  Yes 20 0.2 Birthweight (g)  < 2500 438 4.9  2500–4000 7806 87.9  ≥ 4000 633 7.1 Pre-pregnancy BMIa  ≤ 18.5 1822 20.5  18.5–24.0 6118 68.9  ≥ 24.0 937 10.6 Weight gain during pregnancy (kg)  ≤ 15.0 3247 36.6  15–18.5 2089 23.5  > 18.5 3248 36.6 Parity  Primiparous 6669 75.1  Multiparous 2208 24.9 Caesarean section  No 5599 63.1  Yes 3278 36.9 Preeclampsia  No 8673 97.7  Yes 204 2.3 Maternal diabetes  No 8774 98.8  Yes 103 1.2 Season of conceptionb  Autumn 2431 27.4  Winter 2025 22.8  Spring 1854 20.9  Summer 2567 28.9 Cooking fuel  Gas or electricity 7635 86.0  Biomass or coal 1242 14.0 Newborn gender  Boys 4656 52.5  Girls 4221 47.5 Continuous variable Mean (SD) Range Gestational age (weeks) 38.7 (1.7) 22–44 Averaged daily PM10 during pregnancy (µg/m3) 140.8 (24.8) 66.5–242.6 Averaged daily SO2 during pregnancy (µg/m3) 49.0 (13.1) 14.7–87.1 Averaged daily NO2 during pregnancy (µg/m3) 43.9 (6.0) 26.0–64.6 Averaged daily temperature during pregnancy (°C) 10.3 (3.1) 1.7–20.9 Birth AC (mm) 324.0 (25.2) 200–530 Birth HC (mm) 343.6 (19.0) 250–510 Characteristics N Percent (%) Maternal age (years)  < 30 5636 63.5  ≥ 30 3239 36.5 Highest education level  < College 3309 37.3  ≥ College 5568 62.7 Family monthly income (RMB per capita)  < 3000 5111 57.6  ≥ 3000 3766 42.4 Employment during pregnancy  No 4101 46.2  Yes 4776 53.8 Smoking (active and passive) during pregnancy  No 7189 81.0  Yes 1688 19.0 Alcohol consumption during pregnancy  No 8857 99.8  Yes 20 0.2 Birthweight (g)  < 2500 438 4.9  2500–4000 7806 87.9  ≥ 4000 633 7.1 Pre-pregnancy BMIa  ≤ 18.5 1822 20.5  18.5–24.0 6118 68.9  ≥ 24.0 937 10.6 Weight gain during pregnancy (kg)  ≤ 15.0 3247 36.6  15–18.5 2089 23.5  > 18.5 3248 36.6 Parity  Primiparous 6669 75.1  Multiparous 2208 24.9 Caesarean section  No 5599 63.1  Yes 3278 36.9 Preeclampsia  No 8673 97.7  Yes 204 2.3 Maternal diabetes  No 8774 98.8  Yes 103 1.2 Season of conceptionb  Autumn 2431 27.4  Winter 2025 22.8  Spring 1854 20.9  Summer 2567 28.9 Cooking fuel  Gas or electricity 7635 86.0  Biomass or coal 1242 14.0 Newborn gender  Boys 4656 52.5  Girls 4221 47.5 Continuous variable Mean (SD) Range Gestational age (weeks) 38.7 (1.7) 22–44 Averaged daily PM10 during pregnancy (µg/m3) 140.8 (24.8) 66.5–242.6 Averaged daily SO2 during pregnancy (µg/m3) 49.0 (13.1) 14.7–87.1 Averaged daily NO2 during pregnancy (µg/m3) 43.9 (6.0) 26.0–64.6 Averaged daily temperature during pregnancy (°C) 10.3 (3.1) 1.7–20.9 Birth AC (mm) 324.0 (25.2) 200–530 Birth HC (mm) 343.6 (19.0) 250–510 a BMI, body mass index. b Autumn: September, October and November; winter: December, January and February; spring: March, April and May; summer: June, July, and August. Table 1 Distributions of selected characteristics of the study population (N = 8877) Characteristics N Percent (%) Maternal age (years)  < 30 5636 63.5  ≥ 30 3239 36.5 Highest education level  < College 3309 37.3  ≥ College 5568 62.7 Family monthly income (RMB per capita)  < 3000 5111 57.6  ≥ 3000 3766 42.4 Employment during pregnancy  No 4101 46.2  Yes 4776 53.8 Smoking (active and passive) during pregnancy  No 7189 81.0  Yes 1688 19.0 Alcohol consumption during pregnancy  No 8857 99.8  Yes 20 0.2 Birthweight (g)  < 2500 438 4.9  2500–4000 7806 87.9  ≥ 4000 633 7.1 Pre-pregnancy BMIa  ≤ 18.5 1822 20.5  18.5–24.0 6118 68.9  ≥ 24.0 937 10.6 Weight gain during pregnancy (kg)  ≤ 15.0 3247 36.6  15–18.5 2089 23.5  > 18.5 3248 36.6 Parity  Primiparous 6669 75.1  Multiparous 2208 24.9 Caesarean section  No 5599 63.1  Yes 3278 36.9 Preeclampsia  No 8673 97.7  Yes 204 2.3 Maternal diabetes  No 8774 98.8  Yes 103 1.2 Season of conceptionb  Autumn 2431 27.4  Winter 2025 22.8  Spring 1854 20.9  Summer 2567 28.9 Cooking fuel  Gas or electricity 7635 86.0  Biomass or coal 1242 14.0 Newborn gender  Boys 4656 52.5  Girls 4221 47.5 Continuous variable Mean (SD) Range Gestational age (weeks) 38.7 (1.7) 22–44 Averaged daily PM10 during pregnancy (µg/m3) 140.8 (24.8) 66.5–242.6 Averaged daily SO2 during pregnancy (µg/m3) 49.0 (13.1) 14.7–87.1 Averaged daily NO2 during pregnancy (µg/m3) 43.9 (6.0) 26.0–64.6 Averaged daily temperature during pregnancy (°C) 10.3 (3.1) 1.7–20.9 Birth AC (mm) 324.0 (25.2) 200–530 Birth HC (mm) 343.6 (19.0) 250–510 Characteristics N Percent (%) Maternal age (years)  < 30 5636 63.5  ≥ 30 3239 36.5 Highest education level  < College 3309 37.3  ≥ College 5568 62.7 Family monthly income (RMB per capita)  < 3000 5111 57.6  ≥ 3000 3766 42.4 Employment during pregnancy  No 4101 46.2  Yes 4776 53.8 Smoking (active and passive) during pregnancy  No 7189 81.0  Yes 1688 19.0 Alcohol consumption during pregnancy  No 8857 99.8  Yes 20 0.2 Birthweight (g)  < 2500 438 4.9  2500–4000 7806 87.9  ≥ 4000 633 7.1 Pre-pregnancy BMIa  ≤ 18.5 1822 20.5  18.5–24.0 6118 68.9  ≥ 24.0 937 10.6 Weight gain during pregnancy (kg)  ≤ 15.0 3247 36.6  15–18.5 2089 23.5  > 18.5 3248 36.6 Parity  Primiparous 6669 75.1  Multiparous 2208 24.9 Caesarean section  No 5599 63.1  Yes 3278 36.9 Preeclampsia  No 8673 97.7  Yes 204 2.3 Maternal diabetes  No 8774 98.8  Yes 103 1.2 Season of conceptionb  Autumn 2431 27.4  Winter 2025 22.8  Spring 1854 20.9  Summer 2567 28.9 Cooking fuel  Gas or electricity 7635 86.0  Biomass or coal 1242 14.0 Newborn gender  Boys 4656 52.5  Girls 4221 47.5 Continuous variable Mean (SD) Range Gestational age (weeks) 38.7 (1.7) 22–44 Averaged daily PM10 during pregnancy (µg/m3) 140.8 (24.8) 66.5–242.6 Averaged daily SO2 during pregnancy (µg/m3) 49.0 (13.1) 14.7–87.1 Averaged daily NO2 during pregnancy (µg/m3) 43.9 (6.0) 26.0–64.6 Averaged daily temperature during pregnancy (°C) 10.3 (3.1) 1.7–20.9 Birth AC (mm) 324.0 (25.2) 200–530 Birth HC (mm) 343.6 (19.0) 250–510 a BMI, body mass index. b Autumn: September, October and November; winter: December, January and February; spring: March, April and May; summer: June, July, and August. Abnormal fetal growth includes both under-growth and over-growth. There is accumulating evidence that macrosomia, or being large-for-gestational age, as a result of excessive and unbalanced intrauterine growth places the individual at increased risk for metabolic short-term complications and can influence metabolic disorders in adulthood.30,31 It has been suggested that air pollution causes weight gain and obesity through oxidative stress and inflammatory response.32–34 However, to the best of our knowledge, no study has explored the effect of air pollution on fetal over-growth. In light of the limited studies investigating prenatal exposure to PM10 and risk of fetal under-growth, and the lack of studies investigating the association between air pollution and fetal over-growth, we analysed data from the Lanzhou Birth Cohort Study to investigate the hypothesis that exposure to high levels of PM10 during pregnancy increases the risk of abnormal fetal growth. Methods Study population The study population has been described previously.35–39 In brief, pregnant women who came to the Gansu Provincial Maternity and Child Care Hospital (GPMCCH) in Lanzhou, China, for delivery in 2010–12, and who were 18 years or older with a gestational age of ≥ 20 weeks, were eligible. Individuals with mental illness would have limited ability to answer questions. During the study period, a total of 13 pregnant women had a diagnosis of mental illness (including autism, bipolar disorder, depression, obsessive-compulsive disorder) and were excluded from the beginning of the study recruitment. A total of 10 542 (73.4%) women participated in the study. All study procedures were approved by the Human Investigation Committees at the GPMCCH and Yale University. After obtaining written consent, an in-person interview was conducted at the hospital by trained study interviewers using a standardized and structured questionnaire to collect information on demographics, reproductive and medical history, lifestyle factors, occupation and residential history. Information on birth outcomes and maternal complications were abstracted from the medical records. Gestational age was estimated using the date of the last menstrual period reported at recruitment and confirmed by the first ultrasound examination.39 Fetal growth characteristics The repeated ultrasound measurements of HC, AC, femur length (FL) and biparietal diameter (BPD) during pregnancy were abstracted from the medical records. All readings were recorded in millimetres. Since an ultrasound examination was not routinely performed during each of the three trimesters and not all examination records contained four fetal growth parameters, we restricted our analyses to pregnant women who had at least one ultrasound measurement. Of 8969 subjects who had singleton live births and lived in Lanzhou City,36 we excluded participants: who had no records of ultrasound measures; whose records were considered as unreasonable [values below or above 3 standard deviations (SD) from the normal ranges of fetal growth standards, N = 62, 23, 41 and 33 for HC, AC, FL and BPD, respectively];40 or who had babies with birthweights < 500 g (N = 38) or gestational age ≥ 309 days (N = 1); which yielded a final sample size of 8877. Similar distributions of maternal characteristics were observed between pregnant women with and without ultrasound measurement information (data not shown). Across different times during pregnancy, study participants had up to three ultrasound measurements available for FL and BPD, and up to four ultrasound measurements available for HC and AC, because HC and AC were also measured by ultrasound just before birth. The time of ultrasound examination for each fetal growth parameter was presented as week of gestation and was commonly performed after the first trimester (see Figure A1, available as Supplementary data at IJE online). We used the SGPLOT procedure to generate the smooth nonparamatric curves; the fetal growth patterns of BPD, FL and HC during pregnancy against the ultrasound examination time showed a nonlinear growth pattern (see Figure A2, available as Supplementary data at IJE online), which was consistent with the standard fetal growth curves worldwide.40 To avoid results that cannot be compared for each gestational week, the standardized fetal parameters (Z scores) were used as an outcome variable based on recently updated international standards for all four fetal growth measurements.40 The Z scores were calculated through the formula (actual value-mean)SD, where actual value was the observed ultrasound measures, and mean and SD were the standard values in the same weeks of gestation provided by Papageorghiou et al.40 Exposure assessment As described in previous studies,36–38 data on ambient air pollutants were obtained from the Gansu Provincial Environmental Monitoring Central Station, which collects 24-h average concentration for PM10, sulphur dioxide (SO2) and nitrogen dioxide (NO2) through an automated data reporting system from four monitoring stations in Lanzhou city (1088 km²). The 24-h average PM10 was measured for the period 1 April 2009 to 31 December 2012 for two stations, and for 1 January 2011 to 31 December 2012 for the two additional stations. The monitors were located in the southern part of Lanzhou in the metropolitan area with high population density (see Figure A3, available as Supplementary data at IJE online), the spread/density of the study population is similar to that of the total population (see Figure A4, available as Supplementary data at IJE online). Each subject’s residences throughout pregnancy, as measured by move-in and move-out dates, and work addresses were collected. We used the earth online sharing website provided by Google (www.earthol.com) to obtain longitude and latitude coordinates for each subject's home and work addresses. The mean and standard deviation of distance from maternal residence to each air monitor was 14.8(±10.4) km, 9.6(±10.7) km, 9.6(±12.1) km, and 9.4 (±12.4) km, respectively. The majority (93%) of the participants lived within 12 km from the nearest monitor. We calculated daily PM10 concentrations incorporating each subject’s home and work addresses, using all four monitors, or two monitors with the inverse distance weighting (IDW) approach or the nearest monitor. Subjects’ individual exposure levels were averaged: (i) from date of conception to the date of ultrasound examinations; (ii) 1 month before ultrasound examinations; (iii) 2 weeks before ultrasound examinations; and (iv) 1 week before ultrasound examinations. PM10 exposure levels for different exposure windows, estimated based on an average of the four monitors, two monitors or the nearest monitor, were similar. Therefore, we presented our results based on the four monitors. We calculated the correlations between average PM10 levels during the different exposure windows, and found that the PM10 levels averaged during exposure windows (ii), (iii) and (iv) were highly correlated (Pearson correlation coefficients ranging from 0.80 to 0.84), whereas the correlation between (i) and (ii) or (iii) or (iv) only ranged from 0.08 to 0.17. It suggested a temporal heterogeneity between long-term and short-term exposure windows. Statistical analysis All analyses were performed separately for each fetal parameter. The standard cutoffs were used to categorize under-growth (< 3rd centile, Z < −1.88), normal growth (3rd centile–97th centile, −1.88 ≤ Z ≤ 1.88) and over-growth (> 97th centile, Z > 1.88) (dotted horizontal lines in Figure 1). Similar results were obtained using other centiles for setting the normal range, which were the 5th and 95th centiles (Z = ±1.645), and the 10th and 90th centiles (Z = ±1.28) (results not shown). Thus, we presented more conservative results using the 3rd and 97th centiles as the boundaries of the normal range for the four fetal growth measurements. The subject-specific effect due to repeated measures across time during pregnancy was considered using mixed-effects models. Because ultrasound measures were recorded irregularly for each subject (one to four scans), the time intervals between measurements would be more or less unique to each subject. Under these circumstances, a continuous-time model is needed to describe the covariance among the errors; PROC MIXED with the spatial power covariance function (repeated/type = SP(POW) (gestational weeks at ultrasound)) was used for this purpose.41,42 The linear mixed model was fitted to calculate the mean change and the 95% confidence interval (CI) of the Z score of fetal growth measurements which was associated with maternal exposure to PM10. Then the generalized linear mixed model (mixed effects logistic regression) was fitted to calculate the odds ratio (OR) and 95% CI of the associations between maternal exposure to PM10 and risks of under-growth and over-growth. Normal growth was treated as the reference group for each of the analyses. Exposures of each subject to NO2 and SO2 were estimated in the same manner as PM10, and the average levels of SO2 (49.0 µg/m3) and NO2 (43.9 µg/m3) during pregnancy in our study area were lower than the China NAAQS standard (80 µg/m3 and 50 µg/m3),43 indicating these two pollutants were not the main components of air pollution in this city. Therefore, models were corrected for SO2, NO2 and daily temperature by using the averages as the continuous variables over the same exposure windows as for PM10. Exposure to PM10 was analysed as a continuous variable and, in a separate model, as a binary variable meeting or exceeding the US 24-h, health-based National Ambient Air Quality Standard (NAAQS, 150 µg/m3, equivalent to the China NAAQS Grade II level).43,44 Figure 1 View largeDownload slide The Scatter Plots of Associations Between Standardized Fetal Measurements (Z Scores) and PM10 Exposure Levels (μg/m3) During Pregnancy. Horizontal refernce lines at 3rd, 50th, and 97th centiles showed the cutoffs of undergrowth, normal growth, and overgrowth for fetal biparietal diameter (A), fetal femur length (B), fetal abdominal circumference (C), and fetal head circumference (D) measured by ultrasound according to gestational age. Dark black lines are the smooth nonparametric curves. Opens gray dots showed actual observations. Figure 1 View largeDownload slide The Scatter Plots of Associations Between Standardized Fetal Measurements (Z Scores) and PM10 Exposure Levels (μg/m3) During Pregnancy. Horizontal refernce lines at 3rd, 50th, and 97th centiles showed the cutoffs of undergrowth, normal growth, and overgrowth for fetal biparietal diameter (A), fetal femur length (B), fetal abdominal circumference (C), and fetal head circumference (D) measured by ultrasound according to gestational age. Dark black lines are the smooth nonparametric curves. Opens gray dots showed actual observations. All models were first adjusted for known determinants of fetal growth characteristics as shown in Table 1. For the final model selection, the Akaike information criterion was used with forward-selection procedure on covariates considered to be determinants of growth.45 We checked for the multi-collinearity of all variables in the model, especially for the effects of multiple pollutants including SO2 and NO2 exposures, as well as the daily temperature. The variation inflation factors varied from 1.02 to 2.57, demonstrating a lack of multi-collinearity. The same exposure windows were used for SO2, NO2 and temperature as for PM10 exposure in corresponding models. All analyses were performed using SAS software, version 9.4 (SAS Institute Inc., Cary, NC). Results There were 8770, 8537, 7733 and 8009 participants with ultrasound measurements of HC, AC, FL and BPD, respectively. The study included a total of 14 560, 18 583, 12 383 and 13 619 scans during the entire pregnancy for ultrasound measures of HC, AC, FL and BPD, respectively. According to the international standard fetal growth measurement cutoffs set at the 3rd and 97th centiles, for the standardized fetal growth measurements (Z scores): 515 HC (3.5%), 1316 AC (7.1%), 208 FL (1.7%) and 557 BPD (4.1%) scans were catergorized as under-growth; 11 012 HC (75.6%), 17 007 AC (91.5%), 9395 FL (75.9%) and 12 266 BPD (90.1%) scans were catergorized as normal growth; and 3033 HC (20.8%), 1576 AC (8.4%), 2779 FL (22.4%) and 796 BPD (5.8%) scans were catergorized as over-growth. Table 1 shows the distributions of selected characteristics of the study population. More than half of the women were younger than 30 years (63.5%), nulliparous (75.1%) and non-smokers during pregnancy (81.0%), and had higher education levels (62.7%) and normal pre-pregnancy body mass index (BMI) (68.9%). Table 2 presents the associations between PM10 exposures averaged by different exposure windows, and ultrasound measurement Z-scores. When average PM10 exposure from conception until the ultrasound examination exceeded 150 µg/m3, there were significant increases in standardized FL (β = 0.095, P = 0.0012) and HC (β = 0.090, P = 0.0078) measures, compared with PM10 < 150 µg/m3. When we treated the PM10 exposure as continuous variable, we found a significant decrease in BPD (β = −0.018, P = 0.0016) as per 10 µg/m3 increase in PM10. Table 2 Association between averaged PM10 exposure during the different exposure windows and the mean change in standardized fetal ultrasound measurements (Z scores) Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) βa 95% CI βa 95% CI βa 95% CI βa 95% CI Mean change comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound 0.016 −0.035, 0.067 0.095** 0.038, 0.153 0.001 −0.046, 0.049 0.090** 0.024, 0.156 One month before ultrasound −0.052 −0.097, 0.007 −0.007 −0.051, 0.038 −0.044 −0.082, 0.003 0.092* 0.020, 0.151 Two weeks before ultrasound −0.037 −0.083, 0.008 −0.043 −0.089, 0.002 −0.037 −0.076, 0.002 0.020 −0.040, 0.080 One week before ultrasound −0.034 −0.079, 0.011 −0.005 −0.050, 0.040 -0.025 −0.064, 0.014 0.070* 0.010, 0.130 Mean change per 10 µg/m3 increase in PM10 From conception to ultrasound −0.018** −0.030, -0.007 0.008 −0.006, 0.022 0.005 −0.016, 0.006 0.011 −0.006, 0.029 One month before ultrasound −0.004 −0.008, 0.000 −0.001 −0.005, 0.003 0.002 −0.007, 0.004 0.005* 0.000, 0.009 Two weeks before ultrasound −0.002 −0.006, 0.002 0.002 −0.006, 0.003 0.002 −0.006, 0.001 0.008* 0.001, 0.011 One week before ultrasound −0.002 −0.006, 0.002 0.000 −0.004, 0.004 −0.002 −0.005, 0.002 0.004* 0.000, 0.009 Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) βa 95% CI βa 95% CI βa 95% CI βa 95% CI Mean change comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound 0.016 −0.035, 0.067 0.095** 0.038, 0.153 0.001 −0.046, 0.049 0.090** 0.024, 0.156 One month before ultrasound −0.052 −0.097, 0.007 −0.007 −0.051, 0.038 −0.044 −0.082, 0.003 0.092* 0.020, 0.151 Two weeks before ultrasound −0.037 −0.083, 0.008 −0.043 −0.089, 0.002 −0.037 −0.076, 0.002 0.020 −0.040, 0.080 One week before ultrasound −0.034 −0.079, 0.011 −0.005 −0.050, 0.040 -0.025 −0.064, 0.014 0.070* 0.010, 0.130 Mean change per 10 µg/m3 increase in PM10 From conception to ultrasound −0.018** −0.030, -0.007 0.008 −0.006, 0.022 0.005 −0.016, 0.006 0.011 −0.006, 0.029 One month before ultrasound −0.004 −0.008, 0.000 −0.001 −0.005, 0.003 0.002 −0.007, 0.004 0.005* 0.000, 0.009 Two weeks before ultrasound −0.002 −0.006, 0.002 0.002 −0.006, 0.003 0.002 −0.006, 0.001 0.008* 0.001, 0.011 One week before ultrasound −0.002 −0.006, 0.002 0.000 −0.004, 0.004 −0.002 −0.005, 0.002 0.004* 0.000, 0.009 a Adjusted for maternal age (< 30, ≥ 30 years), education (< college, ≥ college), family monthly income per capita (< 3000, ≥ 3000 RMB), smoking during pregnancy (yes, no), pre-pregnancy BMI (≤ 18.5, 18.5–24.0, ≥ 24.0), weight gain du.ring pregnancy (≤ 15 kg, 15–18.5 kg, > 18.5 kg), parity (primiparous, multiparous), season of conception (spring/summer, autumn/winter), cooking fuel (gas/electricity, coal/biomass), estimated SO2, NO2 by different corresponding exposure windows, and averaged temperature during different corresponding exposure windows. * P < 0.05; ** P < 0.01. Table 2 Association between averaged PM10 exposure during the different exposure windows and the mean change in standardized fetal ultrasound measurements (Z scores) Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) βa 95% CI βa 95% CI βa 95% CI βa 95% CI Mean change comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound 0.016 −0.035, 0.067 0.095** 0.038, 0.153 0.001 −0.046, 0.049 0.090** 0.024, 0.156 One month before ultrasound −0.052 −0.097, 0.007 −0.007 −0.051, 0.038 −0.044 −0.082, 0.003 0.092* 0.020, 0.151 Two weeks before ultrasound −0.037 −0.083, 0.008 −0.043 −0.089, 0.002 −0.037 −0.076, 0.002 0.020 −0.040, 0.080 One week before ultrasound −0.034 −0.079, 0.011 −0.005 −0.050, 0.040 -0.025 −0.064, 0.014 0.070* 0.010, 0.130 Mean change per 10 µg/m3 increase in PM10 From conception to ultrasound −0.018** −0.030, -0.007 0.008 −0.006, 0.022 0.005 −0.016, 0.006 0.011 −0.006, 0.029 One month before ultrasound −0.004 −0.008, 0.000 −0.001 −0.005, 0.003 0.002 −0.007, 0.004 0.005* 0.000, 0.009 Two weeks before ultrasound −0.002 −0.006, 0.002 0.002 −0.006, 0.003 0.002 −0.006, 0.001 0.008* 0.001, 0.011 One week before ultrasound −0.002 −0.006, 0.002 0.000 −0.004, 0.004 −0.002 −0.005, 0.002 0.004* 0.000, 0.009 Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) βa 95% CI βa 95% CI βa 95% CI βa 95% CI Mean change comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound 0.016 −0.035, 0.067 0.095** 0.038, 0.153 0.001 −0.046, 0.049 0.090** 0.024, 0.156 One month before ultrasound −0.052 −0.097, 0.007 −0.007 −0.051, 0.038 −0.044 −0.082, 0.003 0.092* 0.020, 0.151 Two weeks before ultrasound −0.037 −0.083, 0.008 −0.043 −0.089, 0.002 −0.037 −0.076, 0.002 0.020 −0.040, 0.080 One week before ultrasound −0.034 −0.079, 0.011 −0.005 −0.050, 0.040 -0.025 −0.064, 0.014 0.070* 0.010, 0.130 Mean change per 10 µg/m3 increase in PM10 From conception to ultrasound −0.018** −0.030, -0.007 0.008 −0.006, 0.022 0.005 −0.016, 0.006 0.011 −0.006, 0.029 One month before ultrasound −0.004 −0.008, 0.000 −0.001 −0.005, 0.003 0.002 −0.007, 0.004 0.005* 0.000, 0.009 Two weeks before ultrasound −0.002 −0.006, 0.002 0.002 −0.006, 0.003 0.002 −0.006, 0.001 0.008* 0.001, 0.011 One week before ultrasound −0.002 −0.006, 0.002 0.000 −0.004, 0.004 −0.002 −0.005, 0.002 0.004* 0.000, 0.009 a Adjusted for maternal age (< 30, ≥ 30 years), education (< college, ≥ college), family monthly income per capita (< 3000, ≥ 3000 RMB), smoking during pregnancy (yes, no), pre-pregnancy BMI (≤ 18.5, 18.5–24.0, ≥ 24.0), weight gain du.ring pregnancy (≤ 15 kg, 15–18.5 kg, > 18.5 kg), parity (primiparous, multiparous), season of conception (spring/summer, autumn/winter), cooking fuel (gas/electricity, coal/biomass), estimated SO2, NO2 by different corresponding exposure windows, and averaged temperature during different corresponding exposure windows. * P < 0.05; ** P < 0.01. Table 3 shows the associations between PM10 exposures averaged by different exposure windows, and risks of under-growth, and over-growth. When PM10 exposure was estimated from conception until the ultrasound, compared with PM10 < 150 µ/m3, PM10 ≥ 150 µg/m3 was associated with an increased risk of overgrowth [odds ratio (OR), 1.22; 95% CI, 1.04, 1.42 for FL, and 1.19; 1.07, 1.33 for HC]. PM10 was associated with a reduced risk of BPD overgrowth when PM10 was treated as < 150 µg/m3 versus ≥ 150 µg/m3 or a continuous variable during 1 month or 2 weeks before ultrasound examinations. Table 3 Association between averaged PM10 during the different exposure windows and risks of fetal under-growth and over-growth Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) ORa 95% CI ORa 95% CI ORa 95% CI ORa 95% CI Number of pregnancies in each model Normal growth 7005 5662 6989 6378  Under-growth 438 189 393 367  Over-growth 566 1882 1155 1665 Risk comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound  Under-growth 1.13 0.89, 1.43 1.04 0.72, 1.50 0.86 0.73, 1.01 1.02 0.81, 1.30  Overg-rowth 1.00 0.81, 1.23 1.22* 1.04, 1.42 1.03 0.87, 1.21 1.19** 1.07, 1.33 One month before ultrasound  Under-growth 1.04 0.85, 1.26 0.87 0.62, 1.22 1.08 0.84, 1.39 0.98 0.78, 1.23  Over-growth 0.83* 0.69, 0.99 0.97 0.87, 1.08 0.95 0.83, 1.09 1.08* 1.00, 1.19 Two weeks before ultrasound  Under-growth 0.80 0.64, 1.01 1.08 0.78, 1.50 1.09 0.94, 1.26 0.97 0.76, 1.24  Over-growth 0.79** 0.66, 0.94 0.91 0.81, 1.02 0.91 0.79, 1.04 1.03 0.93, 1.14 One week before ultrasound  Under-growth 0.89 0.72, 1.09 0.86 0.61, 1.20 1.09 0.94, 1.26 0.99 0.77, 1.26  Over-growth 0.87 0.73, 1.04 0.96 0.86, 1.07 0.97 0.85, 1.11 1.11* 1.00, 1.23 Risk per 10 µg/m3 increase in PM10 From conception to ultrasound  Under-growth 1.03 0.98, 1.07 0.99 0.92, 1.07 0.98 0.93, 1.02 0.99 0.92, 1.06  Over-growth 0.98 0.94, 1.02 1.01 0.98, 1.04 1.01 0.98, 1.05 1.02 0.99, 1.05 One month before ultrasound  Under-growth 1.00 0.98, 1.02 0.99 0.96, 1.02 1.00 0.99, 1.02 1.00 0.98, 1.02  Over-growth 0.98* 0.97, 1.00 1.00 0.99, 1.01 0.99 0.98, 1.01 1.01* 1.00, 1.02 Two weeks before ultrasound  Under-growth 0.99 0.97, 1.01 1.00 0.97, 1.03 1.00 0.99, 1.02 1.00 0.97, 1.02  Over-growth 0.98** 0.96, 0.99 0.99 0.98, 1.01 1.00 0.99, 1.01 1.01* 1.00, 1.02 One week before ultrasound  Under-growth 0.99 0.97, 1.01 0.99 0.96, 1.02 1.00 0.99, 1.01 1.00 0.97, 1.02  Over-growth 0.99 0.97, 1.01 1.00 0.99, 1.01 1.00 0.98, 1.01 1.01* 1.00, 1.02 Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) ORa 95% CI ORa 95% CI ORa 95% CI ORa 95% CI Number of pregnancies in each model Normal growth 7005 5662 6989 6378  Under-growth 438 189 393 367  Over-growth 566 1882 1155 1665 Risk comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound  Under-growth 1.13 0.89, 1.43 1.04 0.72, 1.50 0.86 0.73, 1.01 1.02 0.81, 1.30  Overg-rowth 1.00 0.81, 1.23 1.22* 1.04, 1.42 1.03 0.87, 1.21 1.19** 1.07, 1.33 One month before ultrasound  Under-growth 1.04 0.85, 1.26 0.87 0.62, 1.22 1.08 0.84, 1.39 0.98 0.78, 1.23  Over-growth 0.83* 0.69, 0.99 0.97 0.87, 1.08 0.95 0.83, 1.09 1.08* 1.00, 1.19 Two weeks before ultrasound  Under-growth 0.80 0.64, 1.01 1.08 0.78, 1.50 1.09 0.94, 1.26 0.97 0.76, 1.24  Over-growth 0.79** 0.66, 0.94 0.91 0.81, 1.02 0.91 0.79, 1.04 1.03 0.93, 1.14 One week before ultrasound  Under-growth 0.89 0.72, 1.09 0.86 0.61, 1.20 1.09 0.94, 1.26 0.99 0.77, 1.26  Over-growth 0.87 0.73, 1.04 0.96 0.86, 1.07 0.97 0.85, 1.11 1.11* 1.00, 1.23 Risk per 10 µg/m3 increase in PM10 From conception to ultrasound  Under-growth 1.03 0.98, 1.07 0.99 0.92, 1.07 0.98 0.93, 1.02 0.99 0.92, 1.06  Over-growth 0.98 0.94, 1.02 1.01 0.98, 1.04 1.01 0.98, 1.05 1.02 0.99, 1.05 One month before ultrasound  Under-growth 1.00 0.98, 1.02 0.99 0.96, 1.02 1.00 0.99, 1.02 1.00 0.98, 1.02  Over-growth 0.98* 0.97, 1.00 1.00 0.99, 1.01 0.99 0.98, 1.01 1.01* 1.00, 1.02 Two weeks before ultrasound  Under-growth 0.99 0.97, 1.01 1.00 0.97, 1.03 1.00 0.99, 1.02 1.00 0.97, 1.02  Over-growth 0.98** 0.96, 0.99 0.99 0.98, 1.01 1.00 0.99, 1.01 1.01* 1.00, 1.02 One week before ultrasound  Under-growth 0.99 0.97, 1.01 0.99 0.96, 1.02 1.00 0.99, 1.01 1.00 0.97, 1.02  Over-growth 0.99 0.97, 1.01 1.00 0.99, 1.01 1.00 0.98, 1.01 1.01* 1.00, 1.02 a Adjusted for maternal age (< 30, 30 years), education (< college, college), family monthly income per capita (< 3000, ≥ 3000 RMB), smoking during pregnancy (yes, no), pre-pregnancy BMI (≤ 18.5, 18.5–24.0, ≥ 24.0), weight gain during pregnancy (≤ 15 kg, 15–18.5 kg, > 18.5 kg), parity (primiparous, multiparous), season of conception (spring/summer, autumn/winter), cooking fuel (gas/electricity, coal/biomass), estimated SO2, NO2 by different corresponding exposure windows, and averaged temperature during different corresponding exposure windows. * P ≤ 0.05; ** P < 0.01. Table 3 Association between averaged PM10 during the different exposure windows and risks of fetal under-growth and over-growth Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) ORa 95% CI ORa 95% CI ORa 95% CI ORa 95% CI Number of pregnancies in each model Normal growth 7005 5662 6989 6378  Under-growth 438 189 393 367  Over-growth 566 1882 1155 1665 Risk comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound  Under-growth 1.13 0.89, 1.43 1.04 0.72, 1.50 0.86 0.73, 1.01 1.02 0.81, 1.30  Overg-rowth 1.00 0.81, 1.23 1.22* 1.04, 1.42 1.03 0.87, 1.21 1.19** 1.07, 1.33 One month before ultrasound  Under-growth 1.04 0.85, 1.26 0.87 0.62, 1.22 1.08 0.84, 1.39 0.98 0.78, 1.23  Over-growth 0.83* 0.69, 0.99 0.97 0.87, 1.08 0.95 0.83, 1.09 1.08* 1.00, 1.19 Two weeks before ultrasound  Under-growth 0.80 0.64, 1.01 1.08 0.78, 1.50 1.09 0.94, 1.26 0.97 0.76, 1.24  Over-growth 0.79** 0.66, 0.94 0.91 0.81, 1.02 0.91 0.79, 1.04 1.03 0.93, 1.14 One week before ultrasound  Under-growth 0.89 0.72, 1.09 0.86 0.61, 1.20 1.09 0.94, 1.26 0.99 0.77, 1.26  Over-growth 0.87 0.73, 1.04 0.96 0.86, 1.07 0.97 0.85, 1.11 1.11* 1.00, 1.23 Risk per 10 µg/m3 increase in PM10 From conception to ultrasound  Under-growth 1.03 0.98, 1.07 0.99 0.92, 1.07 0.98 0.93, 1.02 0.99 0.92, 1.06  Over-growth 0.98 0.94, 1.02 1.01 0.98, 1.04 1.01 0.98, 1.05 1.02 0.99, 1.05 One month before ultrasound  Under-growth 1.00 0.98, 1.02 0.99 0.96, 1.02 1.00 0.99, 1.02 1.00 0.98, 1.02  Over-growth 0.98* 0.97, 1.00 1.00 0.99, 1.01 0.99 0.98, 1.01 1.01* 1.00, 1.02 Two weeks before ultrasound  Under-growth 0.99 0.97, 1.01 1.00 0.97, 1.03 1.00 0.99, 1.02 1.00 0.97, 1.02  Over-growth 0.98** 0.96, 0.99 0.99 0.98, 1.01 1.00 0.99, 1.01 1.01* 1.00, 1.02 One week before ultrasound  Under-growth 0.99 0.97, 1.01 0.99 0.96, 1.02 1.00 0.99, 1.01 1.00 0.97, 1.02  Over-growth 0.99 0.97, 1.01 1.00 0.99, 1.01 1.00 0.98, 1.01 1.01* 1.00, 1.02 Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) ORa 95% CI ORa 95% CI ORa 95% CI ORa 95% CI Number of pregnancies in each model Normal growth 7005 5662 6989 6378  Under-growth 438 189 393 367  Over-growth 566 1882 1155 1665 Risk comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound  Under-growth 1.13 0.89, 1.43 1.04 0.72, 1.50 0.86 0.73, 1.01 1.02 0.81, 1.30  Overg-rowth 1.00 0.81, 1.23 1.22* 1.04, 1.42 1.03 0.87, 1.21 1.19** 1.07, 1.33 One month before ultrasound  Under-growth 1.04 0.85, 1.26 0.87 0.62, 1.22 1.08 0.84, 1.39 0.98 0.78, 1.23  Over-growth 0.83* 0.69, 0.99 0.97 0.87, 1.08 0.95 0.83, 1.09 1.08* 1.00, 1.19 Two weeks before ultrasound  Under-growth 0.80 0.64, 1.01 1.08 0.78, 1.50 1.09 0.94, 1.26 0.97 0.76, 1.24  Over-growth 0.79** 0.66, 0.94 0.91 0.81, 1.02 0.91 0.79, 1.04 1.03 0.93, 1.14 One week before ultrasound  Under-growth 0.89 0.72, 1.09 0.86 0.61, 1.20 1.09 0.94, 1.26 0.99 0.77, 1.26  Over-growth 0.87 0.73, 1.04 0.96 0.86, 1.07 0.97 0.85, 1.11 1.11* 1.00, 1.23 Risk per 10 µg/m3 increase in PM10 From conception to ultrasound  Under-growth 1.03 0.98, 1.07 0.99 0.92, 1.07 0.98 0.93, 1.02 0.99 0.92, 1.06  Over-growth 0.98 0.94, 1.02 1.01 0.98, 1.04 1.01 0.98, 1.05 1.02 0.99, 1.05 One month before ultrasound  Under-growth 1.00 0.98, 1.02 0.99 0.96, 1.02 1.00 0.99, 1.02 1.00 0.98, 1.02  Over-growth 0.98* 0.97, 1.00 1.00 0.99, 1.01 0.99 0.98, 1.01 1.01* 1.00, 1.02 Two weeks before ultrasound  Under-growth 0.99 0.97, 1.01 1.00 0.97, 1.03 1.00 0.99, 1.02 1.00 0.97, 1.02  Over-growth 0.98** 0.96, 0.99 0.99 0.98, 1.01 1.00 0.99, 1.01 1.01* 1.00, 1.02 One week before ultrasound  Under-growth 0.99 0.97, 1.01 0.99 0.96, 1.02 1.00 0.99, 1.01 1.00 0.97, 1.02  Over-growth 0.99 0.97, 1.01 1.00 0.99, 1.01 1.00 0.98, 1.01 1.01* 1.00, 1.02 a Adjusted for maternal age (< 30, 30 years), education (< college, college), family monthly income per capita (< 3000, ≥ 3000 RMB), smoking during pregnancy (yes, no), pre-pregnancy BMI (≤ 18.5, 18.5–24.0, ≥ 24.0), weight gain during pregnancy (≤ 15 kg, 15–18.5 kg, > 18.5 kg), parity (primiparous, multiparous), season of conception (spring/summer, autumn/winter), cooking fuel (gas/electricity, coal/biomass), estimated SO2, NO2 by different corresponding exposure windows, and averaged temperature during different corresponding exposure windows. * P ≤ 0.05; ** P < 0.01. Compared with the results of cumulative exposure from conception to the ultrasound examination, similar positive associations were only observed for HC measures for other exposure windows, 1 month before ultrasound examinations and 1 or 2 weeks before ultrasound examinations, when PM10 was treated as < 150 µg/m3 versus ≥ 150 µg/m3 or a continuous variable (Tables 2 and 3). Discussion Our results support the hypothesis that prenatal exposure to high levels of ambient PM10 is associated with increased risk of fetal under- and over-growth. Our study also suggests a stronger and consistent association of over-growth for HC over the different exposure windows compared with other fetal growth parameters. To the best of our knowledge, our study was the first one to examine the associations between prenatal exposure to PM10 and risk of fetal over-growth. We found that exposure to higher levels of PM10 increased the risk of fetal HC over-growth. In early studies conducted in Australia, Europe and the USA, Hansen et al.5 found reductions of FL, AC and HC associated with increased exposure to PM10 during gestational days 91–120, and a reduction of FL measurement during 0–30 days of gestation; van den Hooven et al.22 observed reduction in HC in the third trimester; and Ritz et al.23 reported that no association was observed between PM10 exposure and HC, FL and AC. Although van den Hooven et al. did not highlight the findings that there were significant increases in HC measures during the second trimester in the second and third quartiles of PM10 exposure, and that there were significant increases in fetal weight in higher quartiles (second, third and fourth) of PM10 exposure, these findings were similar to ours. Although the underlying mechanisms are currently unclear, a growing body of literature suggests that increased oxidative stress and inflammatory response to PMs can influence energy expenditure, resulting in increased dietary intake.32,33 There was an increasing awareness that changes in placental metabolism and nutrient transport capacity might contribute to altered fetal growth.46 Human studies showed that higher cord blood leptin levels caused by greater weight gain were associated with increased fetal size,47 and altered expression of maternal insulin-like growth factor due to excessive gestational weight gain contributed to increased fetal growth through increased placental nutrient transporter expression.48,49 It is possible that the positive relationship between ambient air pollutants and fetal over-growth was mediated by maternal BMI/weight gain. In our study, pregnant women carrying an overgrown fetus were more likely to be in the highest category of maternal weight gain and to be pre-pregnancy obese. Comparing the study results among the published studies and ours was challenging. The outcome measures varied across different studies, including differences in ultrasound measurement methods and potential of inter-sonographer variations. The exposure assessment also varied across the studies. van den Hooven et al.22 performed dispersion modelling based on home addresses only, though allowing for residential mobility; Hansen et al.5 estimated monthly exposure to nearest monitors using IDW over the first 4 months and restricted analysis through four exposure windows (0–30, 31–60, 61–90, 91–120 days of gestation). Ritz et al.23 considered a 2-weeks exposure window around each measurement time (at 19.1, 28.8 and 36.7 weeks of gestation). In addition, all these published studies were conducted in low-air-pollution areas as compared with our study. The compositions of PM10 in different studies might also be different and could cause a different impact on fetal growth. Our study included large sample size (N = 8877) with a relatively large number of ultrasound scans for each fetal parameter (N ∼18 583), compared with previous studies.5,22,23 It allowed exploration of association by subgroups based on the growth status. Our study assessed exposure based on both home and work addresses with consideration of residential mobility, as well as PM10 exposure through different exposure windows, which minimized potential exposure misclassification and ability to explore critical exposure time windows on fetal growth. Detailed information on potential confounding factors such as smoking, cooking fuels and other co-exposure air pollutants were collected and included in our analyses. All participants were recruited from the same hospital and the information on ultrasound measurements was obtained from medical records, so potential misclassifications of fetal measurements were minimized. Additionally, with a potential U-shape relationship between air pollution and fetal growth, we employed the latest international fetal growth standard to examine the association with both under-growth and over-growth, in addition to treating growth measurements as continuous variables as done by earlier studies.5,22,23 This potential heterogeneity of PM10 exposure effects on fetal growth measurements still calls for future study with larger sample size, to replicate and confirm our findings. Limitations should be considered when interpreting the study results. The accuracy of exposure estimation might be limited by the lack of monitors in rural areas in Lanzhou city. However, more than 90% of the women lived within 5.5 km of a monitor. Sensitivity analyses using the data from different exposure assessment approaches (nearest monitors, IDW using two or four monitors, using data for the women living within 5.5 km or 12.9 km of a monitor) showed consistent results. PM10 exposure was estimated using data from government monitors which, though the most commonly used method in air pollution epidemiology, may not represent the actual individual exposure level which would include differences in indoor/outdoor activity patterns, and may not consider intra-urban gradients in pollutants. However, measurements through personal air monitors in this setting (pregnancy, in a large population) may be impractical as well as expensive, and numerous studies have used the above monitoring approach successfully. To evaluate whether the four monitors provided reasonable spatial coverage in our study, we calculated the coefficient of divergence, which provides the diversity between concentrations at sampling site-pairs.50 We found that coefficients of divergence for all site-pairs were lower than 0.20, indicating low spatial heterogeneity of PM10 in our study area. However, future studies might consider additional monitor coverage, in order to address the issues of spatial heterogeneity and improve estimates of exposure. Although the exact sources of PM10 in Lanzhou city are still unclear, a factory of the China Petroleum and Chemical Corporation (Sinopec Limited) has been located in the west, an upwind location of this city. Future work may investigate PM sources, which have different chemical structures and possibly different health impacts. The measurement errors with respect to the outcomes would be another concern both in previous large epidemiological studies and in the current study, because it is possible that the ultrasound examinations at different gestational weeks were performed by different clinicians/technicians. However, GPMCCH is the largest maternity and child care hospital in Lanzhou, China. The hospital performs internal quality control in order to provide high-quality ultrasound examinations, and the historical records of reproducibility also demonstrated that the measurement errors would be likely minimized. In conclusion, our study supports the hypothesis that prenatal exposure to high levels of ambient PM10 increases the risk of abnormal fetal growth, and stronger and consistent associations were mainly seen for HC over-growth by different exposure windows compared with other fetal growth measurements. This study suggests that the associations between maternal exposure to PM10 and fetal growth may vary by different fetal growth measurements (BPD, FL, AC and HC) and fetal growth patterns (under-/over-growth). Future multi-site studies with different types of air pollutants are needed to fully understand the impacts of air pollution combinations on fetal growth. Our study also calls for future studies to explore the underlying mechanisms and post-natal consequences of these findings Funding The work was supported by internal funding from the Gansu Provincial Maternity and Child Care Hospital, Gansu Provincial Science and Technology Department Grant (1204WCGA021), National Institutes of Health Grants (K02HD70324, R01ES019587) and the U.S. Environmental Protection Agency (EPA-G2014-STAR-J1). Author Contributions J.Q., Q.L. and Y.Z. designed the research; X.L., Z.T. and H.Z. conducted the birth cohort study; Y.Z. collected air pollution and meteorology data; N.Z. and H.H. managed the study and cleaned the database; N.Z., S.M., Y.H. and N.M. performed statistical analysis; N.Z. constructed the figures; N.Z., M.B. and Y.Z. wrote the first draft, and all authors contributed to the final draft and approved the manuscript. Conflict of interest: None declared. References 1 Lawn JE , Blencowe H , Pattinson R et al. Stillbirths: Where? When? Why? How to make the data count? Lancet 2011 ; 377 : 1448 – 63 . Google Scholar Crossref Search ADS PubMed 2 Liu L , Johnson HL , Cousens S et al. Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000 . 2012 ; 379 : 2151 – 61 . 3 World Health Organization . A Global Review of the Key Interventions Related to Reproductive, Maternal, Newborn and Child Health (RMNCH) . 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Birth outcomes and prenatal exposure to ozone, carbon monoxide, and particulate matter: results from the Children's Health Study . Environ Health Perspect 2005 ; 113 : 1638 – 44 . Google Scholar Crossref Search ADS PubMed 13 Hannam K , McNamee R , Baker P , Sibley C , Agius R. Air pollution exposure and adverse pregnancy outcomes in a large UK birth cohort: use of a novel spatio-temporal modelling technique . Scand J Work Environ Health 2014 ; 40 : 518 – 30 . Google Scholar Crossref Search ADS PubMed 14 Winckelmans E , Cox B , Martens E , Fierens F , Nemery B , Nawrot TS. Fetal growth and maternal exposure to particulate air pollution - More marked effects at lower exposure and modification by gestational duration . Environ Res 2015 ; 140 : 611 – 18 . Google Scholar Crossref Search ADS PubMed 15 Fleisch AF , Rifas-Shiman SL , Koutrakis P et al. Prenatal exposure to traffic pollution: associations with reduced fetal growth and rapid infant weight gain . Epidemiology 2015 ; 26 : 43 – 50 . Google Scholar Crossref Search ADS PubMed 16 Liu S , Krewski D , Shi Y , Chen Y , Burnett RT. Association between maternal exposure to ambient air pollutants during pregnancy and fetal growth restriction . J Expo Sci Environ Epidemiol 2007 ; 17 : 426 – 32 . Google Scholar Crossref Search ADS PubMed 17 Dejmek J , Selevan SG , Benes I , Solansky I , Sram RJ. Fetal growth and maternal exposure to particulate matter during pregnancy . Environ Health Perspect 1999 ; 107 : 475 – 80 . Google Scholar Crossref Search ADS PubMed 18 Smarr MM , Vadillo-Ortega F , Castillo-Castrejon M , O'Neill MS. The use of ultrasound measurements in environmental epidemiological studies of air pollution and fetal growth . Curr Opin Pediatr 2013 ; 25 : 240 – 46 . Google Scholar Crossref Search ADS PubMed 19 Gardosi J , Figueras F , Clausson B , Francis A. The customised growth potential: an international research tool to study the epidemiology of fetal growth . 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Prenatal air pollution exposure and ultrasound measures of fetal growth in Los Angeles, California . Environ Res 2014 ; 130 : 7 – 13 . Google Scholar Crossref Search ADS PubMed 24 Slama R , Thiebaugeorges O , Goua V et al. Maternal personal exposure to airborne benzene and intrauterine growth . Environ Health Perspect 2009 ; 117 : 1313 – 21 . Google Scholar Crossref Search ADS PubMed 25 Aguilera I , Garcia-Esteban R , Iniguez C et al. Prenatal exposure to traffic-related air pollution and ultrasound measures of fetal growth in the INMA Sabadell cohort . Environ Health Perspect 2010 ; 118 : 705 – 11 . Google Scholar Crossref Search ADS PubMed 26 Iniguez C , Ballester F , Estarlich M et al. Prenatal exposure to traffic-related air pollution and fetal growth in a cohort of pregnant women . Occup Environ Med 2012 ; 69 : 736 – 44 . Google Scholar Crossref Search ADS PubMed 27 Patelarou E , Kelly FJ. Indoor exposure and adverse birth outcomes related to fetal growth, miscarriage and prematurity - a systematic review . Int J Environ Res Public Health 2014 ; 11 : 5904 – 33 . Google Scholar Crossref Search ADS PubMed 28 Iniguez C , Esplugues A , Sunyer J et al. Prenatal exposure to NO2 and ultrasound measures of fetal growth in the Spanish INMA Cohort . Environ Health Perspect 2016 ; 124 : 235 – 42 . Google Scholar PubMed 29 Malmqvist E , Liew Z , Kallen K et al. Fetal growth and air pollution - A study on ultrasound and birth measures . Environ Res 2017 ; 152 : 73 – 80 . Google Scholar Crossref Search ADS PubMed 30 Langer O. Fetal macrosomia: etiologic factors . Clin Obstet Gynecol 2000 ; 43 : 283 – 97 . Google Scholar Crossref Search ADS PubMed 31 Hod M , Pardo J. Environmental rather than genetic fetal overgrowth: defining the difference and hints for diagnosis and management . Ultrasound Obstet Gynecolt 2009 ; 33 : 375 – 78 . Google Scholar Crossref Search ADS 32 Xu Z , Xu X , Zhong M et al. Ambient particulate air pollution induces oxidative stress and alterations of mitochondria and gene expression in brown and white adipose tissues . Particle Fibre Toxicol 2011 ; 8 : 20 . Google Scholar Crossref Search ADS 33 Bolton JL , Smith SH , Huff NC et al. Prenatal air pollution exposure induces neuroinflammation and predisposes offspring to weight gain in adulthood in a sex-specific manner . FASEB J 2012 ; 26 : 4743 – 54 . Google Scholar Crossref Search ADS PubMed 34 Thayer KA , Heindel JJ , Bucher JR , Gallo MA. Role of environmental chemicals in diabetes and obesity: a National Toxicology Program workshop review . Environ Health Perspect 2012 ; 120 : 779 – 89 . Google Scholar Crossref Search ADS PubMed 35 Qiu J , He X , Cui H et al. Passive smoking and preterm birth in urban China . Am J Epidemiol 2014 ; 180 : 94 – 102 . Google Scholar Crossref Search ADS PubMed 36 Zhao N , Qiu J , Zhang Y et al. Ambient air pollutant PM10 and risk of preterm birth in Lanzhou, China . Environ Int 2015 ; 76 : 71 – 77 . Google Scholar Crossref Search ADS PubMed 37 Jin L , Qiu J , Zhang Y et al. . Ambient air pollution and congenital heart defects in Lanzhou, China . Environ Res Lett 2015 ; 10 : 074005 . Google Scholar Crossref Search ADS 38 Huang X , Qiu J , Zhang Y et al. Ambient air pollutant PM10 and risk of pregnancy-induced hypertension in urban China . Environ Res Lett 2015 ; 10 : 084025 . Google Scholar Crossref Search ADS 39 Jiang M , Qiu J , Zhou M et al. Exposure to cooking fuels and birthweight in Lanzhou, China: a birth cohort study . BMC Public Health 2015 ; 15 : 712 . Google Scholar Crossref Search ADS PubMed 40 Papageorghiou AT , Ohuma EO , Altman DG et al. International standards for fetal growth based on serial ultrasound measurements: the Fetal Growth Longitudinal Study of the INTERGROWTH-21st Project . Lancet 2014 ; 384 : 869 – 79 . Google Scholar Crossref Search ADS PubMed 41 Pinheiro JC , Bates DM. Mixed-Effects Models in S and S-Plus New York, NY : Springer , 2000 . 42 Moser EB. Repeated Measures Modeling With PROC MIXED . Louisiana State University, Baton Rouge, LA : SAS Institute , 2004 . 43 Zhang Y , Li M , Bravo M et al. Air quality in Lanzhou, a major industrial city in China: characteristics of air pollution and review of existing evidence from air pollution and health studies . Water, Air, & Soil Pollution 2014 ; 225 : 1 – 17 . 44 Environmental Protection Agency . National Ambient Air Quality Standards (NAAQS) 2015. https://www.epa.gov/pm-pollution/table-historical-particulate-matter-pm-national-ambient-air-quality-standards-naaqs (15 January 2013, date last accessed). 45 Aho K , Derryberry D , Peterson T. Model selection for ecologists: the worldviews of AIC and BIC . Ecology 2014 ; 95 : 631 – 36 . Google Scholar Crossref Search ADS PubMed 46 Higgins L , Greenwood SL , Wareing M , Sibley CP , Mills TA. Obesity and the placenta: A consideration of nutrient exchange mechanisms in relation to aberrant fetal growth . Placenta 2011 ; 32 : 1 – 7 . Google Scholar Crossref Search ADS PubMed 47 Karakosta P , Georgiou V , Fthenou E et al. Maternal weight status, cord blood leptin and fetal growth: a prospective mother-child cohort study (Rhea study) . Paediatr Perinat Epidemiol 2013 ; 27 : 461 – 71 . Google Scholar Crossref Search ADS PubMed 48 Ferraro ZM , Qiu Q , Gruslin A , Adamo KB. Excessive gestational weight gain and obesity contribute to altered expression of maternal insulin-like growth factor binding protein-3 . Int J Womens Health 2013 ; 5 : 657 – 65 . Google Scholar Crossref Search ADS PubMed 49 Tung E , Roberts CT , Heinemann GK et al. Increased placental nutrient transporter expression at midgestation after maternal growth hormone treatment in pigs: a placental mechanism for increased fetal growth . Biol Reprod 2012 ; 87 : 126 . Google Scholar Crossref Search ADS PubMed 50 Pinto JP , Lefohn AS , Shadwick DS. Spatial variability of PM2.5 in urban areas in the United States . Journal of the Air & Waste Management Association 2004 ; 54 : 440 – 49 . Google Scholar Crossref Search ADS © The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Epidemiology Oxford University Press

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Oxford University Press
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© The Author(s) 2018; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
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0300-5771
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1464-3685
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10.1093/ije/dyy019
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Abstract

Abstract Background Limited epidemiological studies have investigated the relationship between prenatal exposure to ambient particulate matter and risk of abnormal fetal growth, and have reached inconclusive results. No study has been conducted in areas with very high air pollution levels. We investigated the hypothesis that exposure to high levels of particulate matter with aerodynamic diameter no larger than 10 µm (PM10) during pregnancy increases the risk of abnormal fetal growth. Methods A birth cohort study was performed in Lanzhou, China, 2010–12, including 8877 pregnant women with 18 583 ultrasound measurements of four fetal growth parameters during pregnancy, including biparietal diameter (BPD), femur length (FL), head circumference (HC) and abdominal circumference (AC). Mixed-effects modelling was used to examine the associations between PM10 exposure and risk of abnormal fetal growth. Results When average PM10 exposure from conception until the ultrasound examination exceeded 150 µg/m3, there were significant increases in standardized FL (β = 0.095, P = 0.0012) and HC (β = 0.090, P = 0.0078) measures. When average PM10 exposure was treated as continuous variable, we found a significant decrease in standardized BPD (β = −0.018, P = 0.0016) as per 10 µg/m3 increase in PM10. After examining the associations by various exposure windows, positive associations between higher levels of PM10 and fetal overgrowth were consistently seen for HC measures. Conclusions Our study suggested that prenatal exposure to high levels of ambient PM10 increased the risk of abnormal fetal growth. The findings from our study have important public health implications and also call for future studies to explore the underlying mechanisms and post-natal consequences of these findings. Fetal growth, PM10, air pollution, ultrasound measures, pregnancy Key Messages No study has been conducted in areas with very high air pollution levels. Exposure to high levels of ambient PM10 increases the risk of abnormal fetal growth. The risk may vary by different fetal growth measurements and fetal growth patterns. Positive associations between higher levels of PM10 and fetal over- growth were consistently seen for head circumference measures by various exposure windows. Introduction A growing body of evidence indicates that fetal growth restriction (FGR), also known as intrauterine growth restriction or fetal under-growth, is an important predictor of neonatal death.1–4 Over the past decade, numerous studies have indicated that ambient air pollution during pregnancy influences fetal growth.5,6 A majority of these previous epidemiological studies have associated maternal air pollution exposure with fetal growth by using various neonatal anthropometric measurements at birth, such as birthweight and length, size for gestational age, head circumference (HC) and abdominal circumference (AC).7–17 However, the late assessment of growth restriction potentially introduces bias,18 and patterns of fetal growth during pregnancy cannot be assessed in these studies.5 In addition, clinical studies have suggested that birthweight as a proxy for fetal growth may not be the best indicator, and the conditions for growth restriction and small size for gestational age are not synonymous.19,20 Furthermore, others suggest that birthweight poorly reflects fetal under-growth during the first two trimesters, because growth impairment and imbalance that may have occurred during early period may be compensated for in the remaining pregnancy period.21 Assessing fetal growth in utero using ultrasound measures could provide more accurate classification of restricted or excessive growth by reducing the time between exposure and outcome assessment.18,22 A small number of studies have investigated the association between exposure to various air pollutants [i.e. nitrogen monoxide, nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone, carbon monoxide and particulate matter with aerodynamic diameter < 10 µm (PM10)] during pregnancy and fetal growth measured by ultrasound.5,22–29 Only three of these have focused on the effects of exposure to PM10 during pregnancy, and these studies reached inconsistent results.5,22,23 All of these studies investigated different exposure time windows. In addition, all of them were conducted in Europe or the USA, where the concentrations of ambient PM10 were lower than in Asia, with mean levels of PM10 ranging from 17.8 µg/m3 to 40.7 µg/m35,22,23 versus 140.8 µg/m3 in the current study (Table 1; and Table A1, available as Supplementary data at IJE online). Table 1 Distributions of selected characteristics of the study population (N = 8877) Characteristics N Percent (%) Maternal age (years)  < 30 5636 63.5  ≥ 30 3239 36.5 Highest education level  < College 3309 37.3  ≥ College 5568 62.7 Family monthly income (RMB per capita)  < 3000 5111 57.6  ≥ 3000 3766 42.4 Employment during pregnancy  No 4101 46.2  Yes 4776 53.8 Smoking (active and passive) during pregnancy  No 7189 81.0  Yes 1688 19.0 Alcohol consumption during pregnancy  No 8857 99.8  Yes 20 0.2 Birthweight (g)  < 2500 438 4.9  2500–4000 7806 87.9  ≥ 4000 633 7.1 Pre-pregnancy BMIa  ≤ 18.5 1822 20.5  18.5–24.0 6118 68.9  ≥ 24.0 937 10.6 Weight gain during pregnancy (kg)  ≤ 15.0 3247 36.6  15–18.5 2089 23.5  > 18.5 3248 36.6 Parity  Primiparous 6669 75.1  Multiparous 2208 24.9 Caesarean section  No 5599 63.1  Yes 3278 36.9 Preeclampsia  No 8673 97.7  Yes 204 2.3 Maternal diabetes  No 8774 98.8  Yes 103 1.2 Season of conceptionb  Autumn 2431 27.4  Winter 2025 22.8  Spring 1854 20.9  Summer 2567 28.9 Cooking fuel  Gas or electricity 7635 86.0  Biomass or coal 1242 14.0 Newborn gender  Boys 4656 52.5  Girls 4221 47.5 Continuous variable Mean (SD) Range Gestational age (weeks) 38.7 (1.7) 22–44 Averaged daily PM10 during pregnancy (µg/m3) 140.8 (24.8) 66.5–242.6 Averaged daily SO2 during pregnancy (µg/m3) 49.0 (13.1) 14.7–87.1 Averaged daily NO2 during pregnancy (µg/m3) 43.9 (6.0) 26.0–64.6 Averaged daily temperature during pregnancy (°C) 10.3 (3.1) 1.7–20.9 Birth AC (mm) 324.0 (25.2) 200–530 Birth HC (mm) 343.6 (19.0) 250–510 Characteristics N Percent (%) Maternal age (years)  < 30 5636 63.5  ≥ 30 3239 36.5 Highest education level  < College 3309 37.3  ≥ College 5568 62.7 Family monthly income (RMB per capita)  < 3000 5111 57.6  ≥ 3000 3766 42.4 Employment during pregnancy  No 4101 46.2  Yes 4776 53.8 Smoking (active and passive) during pregnancy  No 7189 81.0  Yes 1688 19.0 Alcohol consumption during pregnancy  No 8857 99.8  Yes 20 0.2 Birthweight (g)  < 2500 438 4.9  2500–4000 7806 87.9  ≥ 4000 633 7.1 Pre-pregnancy BMIa  ≤ 18.5 1822 20.5  18.5–24.0 6118 68.9  ≥ 24.0 937 10.6 Weight gain during pregnancy (kg)  ≤ 15.0 3247 36.6  15–18.5 2089 23.5  > 18.5 3248 36.6 Parity  Primiparous 6669 75.1  Multiparous 2208 24.9 Caesarean section  No 5599 63.1  Yes 3278 36.9 Preeclampsia  No 8673 97.7  Yes 204 2.3 Maternal diabetes  No 8774 98.8  Yes 103 1.2 Season of conceptionb  Autumn 2431 27.4  Winter 2025 22.8  Spring 1854 20.9  Summer 2567 28.9 Cooking fuel  Gas or electricity 7635 86.0  Biomass or coal 1242 14.0 Newborn gender  Boys 4656 52.5  Girls 4221 47.5 Continuous variable Mean (SD) Range Gestational age (weeks) 38.7 (1.7) 22–44 Averaged daily PM10 during pregnancy (µg/m3) 140.8 (24.8) 66.5–242.6 Averaged daily SO2 during pregnancy (µg/m3) 49.0 (13.1) 14.7–87.1 Averaged daily NO2 during pregnancy (µg/m3) 43.9 (6.0) 26.0–64.6 Averaged daily temperature during pregnancy (°C) 10.3 (3.1) 1.7–20.9 Birth AC (mm) 324.0 (25.2) 200–530 Birth HC (mm) 343.6 (19.0) 250–510 a BMI, body mass index. b Autumn: September, October and November; winter: December, January and February; spring: March, April and May; summer: June, July, and August. Table 1 Distributions of selected characteristics of the study population (N = 8877) Characteristics N Percent (%) Maternal age (years)  < 30 5636 63.5  ≥ 30 3239 36.5 Highest education level  < College 3309 37.3  ≥ College 5568 62.7 Family monthly income (RMB per capita)  < 3000 5111 57.6  ≥ 3000 3766 42.4 Employment during pregnancy  No 4101 46.2  Yes 4776 53.8 Smoking (active and passive) during pregnancy  No 7189 81.0  Yes 1688 19.0 Alcohol consumption during pregnancy  No 8857 99.8  Yes 20 0.2 Birthweight (g)  < 2500 438 4.9  2500–4000 7806 87.9  ≥ 4000 633 7.1 Pre-pregnancy BMIa  ≤ 18.5 1822 20.5  18.5–24.0 6118 68.9  ≥ 24.0 937 10.6 Weight gain during pregnancy (kg)  ≤ 15.0 3247 36.6  15–18.5 2089 23.5  > 18.5 3248 36.6 Parity  Primiparous 6669 75.1  Multiparous 2208 24.9 Caesarean section  No 5599 63.1  Yes 3278 36.9 Preeclampsia  No 8673 97.7  Yes 204 2.3 Maternal diabetes  No 8774 98.8  Yes 103 1.2 Season of conceptionb  Autumn 2431 27.4  Winter 2025 22.8  Spring 1854 20.9  Summer 2567 28.9 Cooking fuel  Gas or electricity 7635 86.0  Biomass or coal 1242 14.0 Newborn gender  Boys 4656 52.5  Girls 4221 47.5 Continuous variable Mean (SD) Range Gestational age (weeks) 38.7 (1.7) 22–44 Averaged daily PM10 during pregnancy (µg/m3) 140.8 (24.8) 66.5–242.6 Averaged daily SO2 during pregnancy (µg/m3) 49.0 (13.1) 14.7–87.1 Averaged daily NO2 during pregnancy (µg/m3) 43.9 (6.0) 26.0–64.6 Averaged daily temperature during pregnancy (°C) 10.3 (3.1) 1.7–20.9 Birth AC (mm) 324.0 (25.2) 200–530 Birth HC (mm) 343.6 (19.0) 250–510 Characteristics N Percent (%) Maternal age (years)  < 30 5636 63.5  ≥ 30 3239 36.5 Highest education level  < College 3309 37.3  ≥ College 5568 62.7 Family monthly income (RMB per capita)  < 3000 5111 57.6  ≥ 3000 3766 42.4 Employment during pregnancy  No 4101 46.2  Yes 4776 53.8 Smoking (active and passive) during pregnancy  No 7189 81.0  Yes 1688 19.0 Alcohol consumption during pregnancy  No 8857 99.8  Yes 20 0.2 Birthweight (g)  < 2500 438 4.9  2500–4000 7806 87.9  ≥ 4000 633 7.1 Pre-pregnancy BMIa  ≤ 18.5 1822 20.5  18.5–24.0 6118 68.9  ≥ 24.0 937 10.6 Weight gain during pregnancy (kg)  ≤ 15.0 3247 36.6  15–18.5 2089 23.5  > 18.5 3248 36.6 Parity  Primiparous 6669 75.1  Multiparous 2208 24.9 Caesarean section  No 5599 63.1  Yes 3278 36.9 Preeclampsia  No 8673 97.7  Yes 204 2.3 Maternal diabetes  No 8774 98.8  Yes 103 1.2 Season of conceptionb  Autumn 2431 27.4  Winter 2025 22.8  Spring 1854 20.9  Summer 2567 28.9 Cooking fuel  Gas or electricity 7635 86.0  Biomass or coal 1242 14.0 Newborn gender  Boys 4656 52.5  Girls 4221 47.5 Continuous variable Mean (SD) Range Gestational age (weeks) 38.7 (1.7) 22–44 Averaged daily PM10 during pregnancy (µg/m3) 140.8 (24.8) 66.5–242.6 Averaged daily SO2 during pregnancy (µg/m3) 49.0 (13.1) 14.7–87.1 Averaged daily NO2 during pregnancy (µg/m3) 43.9 (6.0) 26.0–64.6 Averaged daily temperature during pregnancy (°C) 10.3 (3.1) 1.7–20.9 Birth AC (mm) 324.0 (25.2) 200–530 Birth HC (mm) 343.6 (19.0) 250–510 a BMI, body mass index. b Autumn: September, October and November; winter: December, January and February; spring: March, April and May; summer: June, July, and August. Abnormal fetal growth includes both under-growth and over-growth. There is accumulating evidence that macrosomia, or being large-for-gestational age, as a result of excessive and unbalanced intrauterine growth places the individual at increased risk for metabolic short-term complications and can influence metabolic disorders in adulthood.30,31 It has been suggested that air pollution causes weight gain and obesity through oxidative stress and inflammatory response.32–34 However, to the best of our knowledge, no study has explored the effect of air pollution on fetal over-growth. In light of the limited studies investigating prenatal exposure to PM10 and risk of fetal under-growth, and the lack of studies investigating the association between air pollution and fetal over-growth, we analysed data from the Lanzhou Birth Cohort Study to investigate the hypothesis that exposure to high levels of PM10 during pregnancy increases the risk of abnormal fetal growth. Methods Study population The study population has been described previously.35–39 In brief, pregnant women who came to the Gansu Provincial Maternity and Child Care Hospital (GPMCCH) in Lanzhou, China, for delivery in 2010–12, and who were 18 years or older with a gestational age of ≥ 20 weeks, were eligible. Individuals with mental illness would have limited ability to answer questions. During the study period, a total of 13 pregnant women had a diagnosis of mental illness (including autism, bipolar disorder, depression, obsessive-compulsive disorder) and were excluded from the beginning of the study recruitment. A total of 10 542 (73.4%) women participated in the study. All study procedures were approved by the Human Investigation Committees at the GPMCCH and Yale University. After obtaining written consent, an in-person interview was conducted at the hospital by trained study interviewers using a standardized and structured questionnaire to collect information on demographics, reproductive and medical history, lifestyle factors, occupation and residential history. Information on birth outcomes and maternal complications were abstracted from the medical records. Gestational age was estimated using the date of the last menstrual period reported at recruitment and confirmed by the first ultrasound examination.39 Fetal growth characteristics The repeated ultrasound measurements of HC, AC, femur length (FL) and biparietal diameter (BPD) during pregnancy were abstracted from the medical records. All readings were recorded in millimetres. Since an ultrasound examination was not routinely performed during each of the three trimesters and not all examination records contained four fetal growth parameters, we restricted our analyses to pregnant women who had at least one ultrasound measurement. Of 8969 subjects who had singleton live births and lived in Lanzhou City,36 we excluded participants: who had no records of ultrasound measures; whose records were considered as unreasonable [values below or above 3 standard deviations (SD) from the normal ranges of fetal growth standards, N = 62, 23, 41 and 33 for HC, AC, FL and BPD, respectively];40 or who had babies with birthweights < 500 g (N = 38) or gestational age ≥ 309 days (N = 1); which yielded a final sample size of 8877. Similar distributions of maternal characteristics were observed between pregnant women with and without ultrasound measurement information (data not shown). Across different times during pregnancy, study participants had up to three ultrasound measurements available for FL and BPD, and up to four ultrasound measurements available for HC and AC, because HC and AC were also measured by ultrasound just before birth. The time of ultrasound examination for each fetal growth parameter was presented as week of gestation and was commonly performed after the first trimester (see Figure A1, available as Supplementary data at IJE online). We used the SGPLOT procedure to generate the smooth nonparamatric curves; the fetal growth patterns of BPD, FL and HC during pregnancy against the ultrasound examination time showed a nonlinear growth pattern (see Figure A2, available as Supplementary data at IJE online), which was consistent with the standard fetal growth curves worldwide.40 To avoid results that cannot be compared for each gestational week, the standardized fetal parameters (Z scores) were used as an outcome variable based on recently updated international standards for all four fetal growth measurements.40 The Z scores were calculated through the formula (actual value-mean)SD, where actual value was the observed ultrasound measures, and mean and SD were the standard values in the same weeks of gestation provided by Papageorghiou et al.40 Exposure assessment As described in previous studies,36–38 data on ambient air pollutants were obtained from the Gansu Provincial Environmental Monitoring Central Station, which collects 24-h average concentration for PM10, sulphur dioxide (SO2) and nitrogen dioxide (NO2) through an automated data reporting system from four monitoring stations in Lanzhou city (1088 km²). The 24-h average PM10 was measured for the period 1 April 2009 to 31 December 2012 for two stations, and for 1 January 2011 to 31 December 2012 for the two additional stations. The monitors were located in the southern part of Lanzhou in the metropolitan area with high population density (see Figure A3, available as Supplementary data at IJE online), the spread/density of the study population is similar to that of the total population (see Figure A4, available as Supplementary data at IJE online). Each subject’s residences throughout pregnancy, as measured by move-in and move-out dates, and work addresses were collected. We used the earth online sharing website provided by Google (www.earthol.com) to obtain longitude and latitude coordinates for each subject's home and work addresses. The mean and standard deviation of distance from maternal residence to each air monitor was 14.8(±10.4) km, 9.6(±10.7) km, 9.6(±12.1) km, and 9.4 (±12.4) km, respectively. The majority (93%) of the participants lived within 12 km from the nearest monitor. We calculated daily PM10 concentrations incorporating each subject’s home and work addresses, using all four monitors, or two monitors with the inverse distance weighting (IDW) approach or the nearest monitor. Subjects’ individual exposure levels were averaged: (i) from date of conception to the date of ultrasound examinations; (ii) 1 month before ultrasound examinations; (iii) 2 weeks before ultrasound examinations; and (iv) 1 week before ultrasound examinations. PM10 exposure levels for different exposure windows, estimated based on an average of the four monitors, two monitors or the nearest monitor, were similar. Therefore, we presented our results based on the four monitors. We calculated the correlations between average PM10 levels during the different exposure windows, and found that the PM10 levels averaged during exposure windows (ii), (iii) and (iv) were highly correlated (Pearson correlation coefficients ranging from 0.80 to 0.84), whereas the correlation between (i) and (ii) or (iii) or (iv) only ranged from 0.08 to 0.17. It suggested a temporal heterogeneity between long-term and short-term exposure windows. Statistical analysis All analyses were performed separately for each fetal parameter. The standard cutoffs were used to categorize under-growth (< 3rd centile, Z < −1.88), normal growth (3rd centile–97th centile, −1.88 ≤ Z ≤ 1.88) and over-growth (> 97th centile, Z > 1.88) (dotted horizontal lines in Figure 1). Similar results were obtained using other centiles for setting the normal range, which were the 5th and 95th centiles (Z = ±1.645), and the 10th and 90th centiles (Z = ±1.28) (results not shown). Thus, we presented more conservative results using the 3rd and 97th centiles as the boundaries of the normal range for the four fetal growth measurements. The subject-specific effect due to repeated measures across time during pregnancy was considered using mixed-effects models. Because ultrasound measures were recorded irregularly for each subject (one to four scans), the time intervals between measurements would be more or less unique to each subject. Under these circumstances, a continuous-time model is needed to describe the covariance among the errors; PROC MIXED with the spatial power covariance function (repeated/type = SP(POW) (gestational weeks at ultrasound)) was used for this purpose.41,42 The linear mixed model was fitted to calculate the mean change and the 95% confidence interval (CI) of the Z score of fetal growth measurements which was associated with maternal exposure to PM10. Then the generalized linear mixed model (mixed effects logistic regression) was fitted to calculate the odds ratio (OR) and 95% CI of the associations between maternal exposure to PM10 and risks of under-growth and over-growth. Normal growth was treated as the reference group for each of the analyses. Exposures of each subject to NO2 and SO2 were estimated in the same manner as PM10, and the average levels of SO2 (49.0 µg/m3) and NO2 (43.9 µg/m3) during pregnancy in our study area were lower than the China NAAQS standard (80 µg/m3 and 50 µg/m3),43 indicating these two pollutants were not the main components of air pollution in this city. Therefore, models were corrected for SO2, NO2 and daily temperature by using the averages as the continuous variables over the same exposure windows as for PM10. Exposure to PM10 was analysed as a continuous variable and, in a separate model, as a binary variable meeting or exceeding the US 24-h, health-based National Ambient Air Quality Standard (NAAQS, 150 µg/m3, equivalent to the China NAAQS Grade II level).43,44 Figure 1 View largeDownload slide The Scatter Plots of Associations Between Standardized Fetal Measurements (Z Scores) and PM10 Exposure Levels (μg/m3) During Pregnancy. Horizontal refernce lines at 3rd, 50th, and 97th centiles showed the cutoffs of undergrowth, normal growth, and overgrowth for fetal biparietal diameter (A), fetal femur length (B), fetal abdominal circumference (C), and fetal head circumference (D) measured by ultrasound according to gestational age. Dark black lines are the smooth nonparametric curves. Opens gray dots showed actual observations. Figure 1 View largeDownload slide The Scatter Plots of Associations Between Standardized Fetal Measurements (Z Scores) and PM10 Exposure Levels (μg/m3) During Pregnancy. Horizontal refernce lines at 3rd, 50th, and 97th centiles showed the cutoffs of undergrowth, normal growth, and overgrowth for fetal biparietal diameter (A), fetal femur length (B), fetal abdominal circumference (C), and fetal head circumference (D) measured by ultrasound according to gestational age. Dark black lines are the smooth nonparametric curves. Opens gray dots showed actual observations. All models were first adjusted for known determinants of fetal growth characteristics as shown in Table 1. For the final model selection, the Akaike information criterion was used with forward-selection procedure on covariates considered to be determinants of growth.45 We checked for the multi-collinearity of all variables in the model, especially for the effects of multiple pollutants including SO2 and NO2 exposures, as well as the daily temperature. The variation inflation factors varied from 1.02 to 2.57, demonstrating a lack of multi-collinearity. The same exposure windows were used for SO2, NO2 and temperature as for PM10 exposure in corresponding models. All analyses were performed using SAS software, version 9.4 (SAS Institute Inc., Cary, NC). Results There were 8770, 8537, 7733 and 8009 participants with ultrasound measurements of HC, AC, FL and BPD, respectively. The study included a total of 14 560, 18 583, 12 383 and 13 619 scans during the entire pregnancy for ultrasound measures of HC, AC, FL and BPD, respectively. According to the international standard fetal growth measurement cutoffs set at the 3rd and 97th centiles, for the standardized fetal growth measurements (Z scores): 515 HC (3.5%), 1316 AC (7.1%), 208 FL (1.7%) and 557 BPD (4.1%) scans were catergorized as under-growth; 11 012 HC (75.6%), 17 007 AC (91.5%), 9395 FL (75.9%) and 12 266 BPD (90.1%) scans were catergorized as normal growth; and 3033 HC (20.8%), 1576 AC (8.4%), 2779 FL (22.4%) and 796 BPD (5.8%) scans were catergorized as over-growth. Table 1 shows the distributions of selected characteristics of the study population. More than half of the women were younger than 30 years (63.5%), nulliparous (75.1%) and non-smokers during pregnancy (81.0%), and had higher education levels (62.7%) and normal pre-pregnancy body mass index (BMI) (68.9%). Table 2 presents the associations between PM10 exposures averaged by different exposure windows, and ultrasound measurement Z-scores. When average PM10 exposure from conception until the ultrasound examination exceeded 150 µg/m3, there were significant increases in standardized FL (β = 0.095, P = 0.0012) and HC (β = 0.090, P = 0.0078) measures, compared with PM10 < 150 µg/m3. When we treated the PM10 exposure as continuous variable, we found a significant decrease in BPD (β = −0.018, P = 0.0016) as per 10 µg/m3 increase in PM10. Table 2 Association between averaged PM10 exposure during the different exposure windows and the mean change in standardized fetal ultrasound measurements (Z scores) Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) βa 95% CI βa 95% CI βa 95% CI βa 95% CI Mean change comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound 0.016 −0.035, 0.067 0.095** 0.038, 0.153 0.001 −0.046, 0.049 0.090** 0.024, 0.156 One month before ultrasound −0.052 −0.097, 0.007 −0.007 −0.051, 0.038 −0.044 −0.082, 0.003 0.092* 0.020, 0.151 Two weeks before ultrasound −0.037 −0.083, 0.008 −0.043 −0.089, 0.002 −0.037 −0.076, 0.002 0.020 −0.040, 0.080 One week before ultrasound −0.034 −0.079, 0.011 −0.005 −0.050, 0.040 -0.025 −0.064, 0.014 0.070* 0.010, 0.130 Mean change per 10 µg/m3 increase in PM10 From conception to ultrasound −0.018** −0.030, -0.007 0.008 −0.006, 0.022 0.005 −0.016, 0.006 0.011 −0.006, 0.029 One month before ultrasound −0.004 −0.008, 0.000 −0.001 −0.005, 0.003 0.002 −0.007, 0.004 0.005* 0.000, 0.009 Two weeks before ultrasound −0.002 −0.006, 0.002 0.002 −0.006, 0.003 0.002 −0.006, 0.001 0.008* 0.001, 0.011 One week before ultrasound −0.002 −0.006, 0.002 0.000 −0.004, 0.004 −0.002 −0.005, 0.002 0.004* 0.000, 0.009 Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) βa 95% CI βa 95% CI βa 95% CI βa 95% CI Mean change comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound 0.016 −0.035, 0.067 0.095** 0.038, 0.153 0.001 −0.046, 0.049 0.090** 0.024, 0.156 One month before ultrasound −0.052 −0.097, 0.007 −0.007 −0.051, 0.038 −0.044 −0.082, 0.003 0.092* 0.020, 0.151 Two weeks before ultrasound −0.037 −0.083, 0.008 −0.043 −0.089, 0.002 −0.037 −0.076, 0.002 0.020 −0.040, 0.080 One week before ultrasound −0.034 −0.079, 0.011 −0.005 −0.050, 0.040 -0.025 −0.064, 0.014 0.070* 0.010, 0.130 Mean change per 10 µg/m3 increase in PM10 From conception to ultrasound −0.018** −0.030, -0.007 0.008 −0.006, 0.022 0.005 −0.016, 0.006 0.011 −0.006, 0.029 One month before ultrasound −0.004 −0.008, 0.000 −0.001 −0.005, 0.003 0.002 −0.007, 0.004 0.005* 0.000, 0.009 Two weeks before ultrasound −0.002 −0.006, 0.002 0.002 −0.006, 0.003 0.002 −0.006, 0.001 0.008* 0.001, 0.011 One week before ultrasound −0.002 −0.006, 0.002 0.000 −0.004, 0.004 −0.002 −0.005, 0.002 0.004* 0.000, 0.009 a Adjusted for maternal age (< 30, ≥ 30 years), education (< college, ≥ college), family monthly income per capita (< 3000, ≥ 3000 RMB), smoking during pregnancy (yes, no), pre-pregnancy BMI (≤ 18.5, 18.5–24.0, ≥ 24.0), weight gain du.ring pregnancy (≤ 15 kg, 15–18.5 kg, > 18.5 kg), parity (primiparous, multiparous), season of conception (spring/summer, autumn/winter), cooking fuel (gas/electricity, coal/biomass), estimated SO2, NO2 by different corresponding exposure windows, and averaged temperature during different corresponding exposure windows. * P < 0.05; ** P < 0.01. Table 2 Association between averaged PM10 exposure during the different exposure windows and the mean change in standardized fetal ultrasound measurements (Z scores) Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) βa 95% CI βa 95% CI βa 95% CI βa 95% CI Mean change comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound 0.016 −0.035, 0.067 0.095** 0.038, 0.153 0.001 −0.046, 0.049 0.090** 0.024, 0.156 One month before ultrasound −0.052 −0.097, 0.007 −0.007 −0.051, 0.038 −0.044 −0.082, 0.003 0.092* 0.020, 0.151 Two weeks before ultrasound −0.037 −0.083, 0.008 −0.043 −0.089, 0.002 −0.037 −0.076, 0.002 0.020 −0.040, 0.080 One week before ultrasound −0.034 −0.079, 0.011 −0.005 −0.050, 0.040 -0.025 −0.064, 0.014 0.070* 0.010, 0.130 Mean change per 10 µg/m3 increase in PM10 From conception to ultrasound −0.018** −0.030, -0.007 0.008 −0.006, 0.022 0.005 −0.016, 0.006 0.011 −0.006, 0.029 One month before ultrasound −0.004 −0.008, 0.000 −0.001 −0.005, 0.003 0.002 −0.007, 0.004 0.005* 0.000, 0.009 Two weeks before ultrasound −0.002 −0.006, 0.002 0.002 −0.006, 0.003 0.002 −0.006, 0.001 0.008* 0.001, 0.011 One week before ultrasound −0.002 −0.006, 0.002 0.000 −0.004, 0.004 −0.002 −0.005, 0.002 0.004* 0.000, 0.009 Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) βa 95% CI βa 95% CI βa 95% CI βa 95% CI Mean change comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound 0.016 −0.035, 0.067 0.095** 0.038, 0.153 0.001 −0.046, 0.049 0.090** 0.024, 0.156 One month before ultrasound −0.052 −0.097, 0.007 −0.007 −0.051, 0.038 −0.044 −0.082, 0.003 0.092* 0.020, 0.151 Two weeks before ultrasound −0.037 −0.083, 0.008 −0.043 −0.089, 0.002 −0.037 −0.076, 0.002 0.020 −0.040, 0.080 One week before ultrasound −0.034 −0.079, 0.011 −0.005 −0.050, 0.040 -0.025 −0.064, 0.014 0.070* 0.010, 0.130 Mean change per 10 µg/m3 increase in PM10 From conception to ultrasound −0.018** −0.030, -0.007 0.008 −0.006, 0.022 0.005 −0.016, 0.006 0.011 −0.006, 0.029 One month before ultrasound −0.004 −0.008, 0.000 −0.001 −0.005, 0.003 0.002 −0.007, 0.004 0.005* 0.000, 0.009 Two weeks before ultrasound −0.002 −0.006, 0.002 0.002 −0.006, 0.003 0.002 −0.006, 0.001 0.008* 0.001, 0.011 One week before ultrasound −0.002 −0.006, 0.002 0.000 −0.004, 0.004 −0.002 −0.005, 0.002 0.004* 0.000, 0.009 a Adjusted for maternal age (< 30, ≥ 30 years), education (< college, ≥ college), family monthly income per capita (< 3000, ≥ 3000 RMB), smoking during pregnancy (yes, no), pre-pregnancy BMI (≤ 18.5, 18.5–24.0, ≥ 24.0), weight gain du.ring pregnancy (≤ 15 kg, 15–18.5 kg, > 18.5 kg), parity (primiparous, multiparous), season of conception (spring/summer, autumn/winter), cooking fuel (gas/electricity, coal/biomass), estimated SO2, NO2 by different corresponding exposure windows, and averaged temperature during different corresponding exposure windows. * P < 0.05; ** P < 0.01. Table 3 shows the associations between PM10 exposures averaged by different exposure windows, and risks of under-growth, and over-growth. When PM10 exposure was estimated from conception until the ultrasound, compared with PM10 < 150 µ/m3, PM10 ≥ 150 µg/m3 was associated with an increased risk of overgrowth [odds ratio (OR), 1.22; 95% CI, 1.04, 1.42 for FL, and 1.19; 1.07, 1.33 for HC]. PM10 was associated with a reduced risk of BPD overgrowth when PM10 was treated as < 150 µg/m3 versus ≥ 150 µg/m3 or a continuous variable during 1 month or 2 weeks before ultrasound examinations. Table 3 Association between averaged PM10 during the different exposure windows and risks of fetal under-growth and over-growth Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) ORa 95% CI ORa 95% CI ORa 95% CI ORa 95% CI Number of pregnancies in each model Normal growth 7005 5662 6989 6378  Under-growth 438 189 393 367  Over-growth 566 1882 1155 1665 Risk comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound  Under-growth 1.13 0.89, 1.43 1.04 0.72, 1.50 0.86 0.73, 1.01 1.02 0.81, 1.30  Overg-rowth 1.00 0.81, 1.23 1.22* 1.04, 1.42 1.03 0.87, 1.21 1.19** 1.07, 1.33 One month before ultrasound  Under-growth 1.04 0.85, 1.26 0.87 0.62, 1.22 1.08 0.84, 1.39 0.98 0.78, 1.23  Over-growth 0.83* 0.69, 0.99 0.97 0.87, 1.08 0.95 0.83, 1.09 1.08* 1.00, 1.19 Two weeks before ultrasound  Under-growth 0.80 0.64, 1.01 1.08 0.78, 1.50 1.09 0.94, 1.26 0.97 0.76, 1.24  Over-growth 0.79** 0.66, 0.94 0.91 0.81, 1.02 0.91 0.79, 1.04 1.03 0.93, 1.14 One week before ultrasound  Under-growth 0.89 0.72, 1.09 0.86 0.61, 1.20 1.09 0.94, 1.26 0.99 0.77, 1.26  Over-growth 0.87 0.73, 1.04 0.96 0.86, 1.07 0.97 0.85, 1.11 1.11* 1.00, 1.23 Risk per 10 µg/m3 increase in PM10 From conception to ultrasound  Under-growth 1.03 0.98, 1.07 0.99 0.92, 1.07 0.98 0.93, 1.02 0.99 0.92, 1.06  Over-growth 0.98 0.94, 1.02 1.01 0.98, 1.04 1.01 0.98, 1.05 1.02 0.99, 1.05 One month before ultrasound  Under-growth 1.00 0.98, 1.02 0.99 0.96, 1.02 1.00 0.99, 1.02 1.00 0.98, 1.02  Over-growth 0.98* 0.97, 1.00 1.00 0.99, 1.01 0.99 0.98, 1.01 1.01* 1.00, 1.02 Two weeks before ultrasound  Under-growth 0.99 0.97, 1.01 1.00 0.97, 1.03 1.00 0.99, 1.02 1.00 0.97, 1.02  Over-growth 0.98** 0.96, 0.99 0.99 0.98, 1.01 1.00 0.99, 1.01 1.01* 1.00, 1.02 One week before ultrasound  Under-growth 0.99 0.97, 1.01 0.99 0.96, 1.02 1.00 0.99, 1.01 1.00 0.97, 1.02  Over-growth 0.99 0.97, 1.01 1.00 0.99, 1.01 1.00 0.98, 1.01 1.01* 1.00, 1.02 Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) ORa 95% CI ORa 95% CI ORa 95% CI ORa 95% CI Number of pregnancies in each model Normal growth 7005 5662 6989 6378  Under-growth 438 189 393 367  Over-growth 566 1882 1155 1665 Risk comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound  Under-growth 1.13 0.89, 1.43 1.04 0.72, 1.50 0.86 0.73, 1.01 1.02 0.81, 1.30  Overg-rowth 1.00 0.81, 1.23 1.22* 1.04, 1.42 1.03 0.87, 1.21 1.19** 1.07, 1.33 One month before ultrasound  Under-growth 1.04 0.85, 1.26 0.87 0.62, 1.22 1.08 0.84, 1.39 0.98 0.78, 1.23  Over-growth 0.83* 0.69, 0.99 0.97 0.87, 1.08 0.95 0.83, 1.09 1.08* 1.00, 1.19 Two weeks before ultrasound  Under-growth 0.80 0.64, 1.01 1.08 0.78, 1.50 1.09 0.94, 1.26 0.97 0.76, 1.24  Over-growth 0.79** 0.66, 0.94 0.91 0.81, 1.02 0.91 0.79, 1.04 1.03 0.93, 1.14 One week before ultrasound  Under-growth 0.89 0.72, 1.09 0.86 0.61, 1.20 1.09 0.94, 1.26 0.99 0.77, 1.26  Over-growth 0.87 0.73, 1.04 0.96 0.86, 1.07 0.97 0.85, 1.11 1.11* 1.00, 1.23 Risk per 10 µg/m3 increase in PM10 From conception to ultrasound  Under-growth 1.03 0.98, 1.07 0.99 0.92, 1.07 0.98 0.93, 1.02 0.99 0.92, 1.06  Over-growth 0.98 0.94, 1.02 1.01 0.98, 1.04 1.01 0.98, 1.05 1.02 0.99, 1.05 One month before ultrasound  Under-growth 1.00 0.98, 1.02 0.99 0.96, 1.02 1.00 0.99, 1.02 1.00 0.98, 1.02  Over-growth 0.98* 0.97, 1.00 1.00 0.99, 1.01 0.99 0.98, 1.01 1.01* 1.00, 1.02 Two weeks before ultrasound  Under-growth 0.99 0.97, 1.01 1.00 0.97, 1.03 1.00 0.99, 1.02 1.00 0.97, 1.02  Over-growth 0.98** 0.96, 0.99 0.99 0.98, 1.01 1.00 0.99, 1.01 1.01* 1.00, 1.02 One week before ultrasound  Under-growth 0.99 0.97, 1.01 0.99 0.96, 1.02 1.00 0.99, 1.01 1.00 0.97, 1.02  Over-growth 0.99 0.97, 1.01 1.00 0.99, 1.01 1.00 0.98, 1.01 1.01* 1.00, 1.02 a Adjusted for maternal age (< 30, 30 years), education (< college, college), family monthly income per capita (< 3000, ≥ 3000 RMB), smoking during pregnancy (yes, no), pre-pregnancy BMI (≤ 18.5, 18.5–24.0, ≥ 24.0), weight gain during pregnancy (≤ 15 kg, 15–18.5 kg, > 18.5 kg), parity (primiparous, multiparous), season of conception (spring/summer, autumn/winter), cooking fuel (gas/electricity, coal/biomass), estimated SO2, NO2 by different corresponding exposure windows, and averaged temperature during different corresponding exposure windows. * P ≤ 0.05; ** P < 0.01. Table 3 Association between averaged PM10 during the different exposure windows and risks of fetal under-growth and over-growth Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) ORa 95% CI ORa 95% CI ORa 95% CI ORa 95% CI Number of pregnancies in each model Normal growth 7005 5662 6989 6378  Under-growth 438 189 393 367  Over-growth 566 1882 1155 1665 Risk comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound  Under-growth 1.13 0.89, 1.43 1.04 0.72, 1.50 0.86 0.73, 1.01 1.02 0.81, 1.30  Overg-rowth 1.00 0.81, 1.23 1.22* 1.04, 1.42 1.03 0.87, 1.21 1.19** 1.07, 1.33 One month before ultrasound  Under-growth 1.04 0.85, 1.26 0.87 0.62, 1.22 1.08 0.84, 1.39 0.98 0.78, 1.23  Over-growth 0.83* 0.69, 0.99 0.97 0.87, 1.08 0.95 0.83, 1.09 1.08* 1.00, 1.19 Two weeks before ultrasound  Under-growth 0.80 0.64, 1.01 1.08 0.78, 1.50 1.09 0.94, 1.26 0.97 0.76, 1.24  Over-growth 0.79** 0.66, 0.94 0.91 0.81, 1.02 0.91 0.79, 1.04 1.03 0.93, 1.14 One week before ultrasound  Under-growth 0.89 0.72, 1.09 0.86 0.61, 1.20 1.09 0.94, 1.26 0.99 0.77, 1.26  Over-growth 0.87 0.73, 1.04 0.96 0.86, 1.07 0.97 0.85, 1.11 1.11* 1.00, 1.23 Risk per 10 µg/m3 increase in PM10 From conception to ultrasound  Under-growth 1.03 0.98, 1.07 0.99 0.92, 1.07 0.98 0.93, 1.02 0.99 0.92, 1.06  Over-growth 0.98 0.94, 1.02 1.01 0.98, 1.04 1.01 0.98, 1.05 1.02 0.99, 1.05 One month before ultrasound  Under-growth 1.00 0.98, 1.02 0.99 0.96, 1.02 1.00 0.99, 1.02 1.00 0.98, 1.02  Over-growth 0.98* 0.97, 1.00 1.00 0.99, 1.01 0.99 0.98, 1.01 1.01* 1.00, 1.02 Two weeks before ultrasound  Under-growth 0.99 0.97, 1.01 1.00 0.97, 1.03 1.00 0.99, 1.02 1.00 0.97, 1.02  Over-growth 0.98** 0.96, 0.99 0.99 0.98, 1.01 1.00 0.99, 1.01 1.01* 1.00, 1.02 One week before ultrasound  Under-growth 0.99 0.97, 1.01 0.99 0.96, 1.02 1.00 0.99, 1.01 1.00 0.97, 1.02  Over-growth 0.99 0.97, 1.01 1.00 0.99, 1.01 1.00 0.98, 1.01 1.01* 1.00, 1.02 Exposure windows BPD (n = 8009) FL (n = 7733) AC (n = 8537) HC (n = 8770) ORa 95% CI ORa 95% CI ORa 95% CI ORa 95% CI Number of pregnancies in each model Normal growth 7005 5662 6989 6378  Under-growth 438 189 393 367  Over-growth 566 1882 1155 1665 Risk comparing exposures ≥ 150 µg/m3 PM10 with those < 150 μg/m3 From conception to ultrasound  Under-growth 1.13 0.89, 1.43 1.04 0.72, 1.50 0.86 0.73, 1.01 1.02 0.81, 1.30  Overg-rowth 1.00 0.81, 1.23 1.22* 1.04, 1.42 1.03 0.87, 1.21 1.19** 1.07, 1.33 One month before ultrasound  Under-growth 1.04 0.85, 1.26 0.87 0.62, 1.22 1.08 0.84, 1.39 0.98 0.78, 1.23  Over-growth 0.83* 0.69, 0.99 0.97 0.87, 1.08 0.95 0.83, 1.09 1.08* 1.00, 1.19 Two weeks before ultrasound  Under-growth 0.80 0.64, 1.01 1.08 0.78, 1.50 1.09 0.94, 1.26 0.97 0.76, 1.24  Over-growth 0.79** 0.66, 0.94 0.91 0.81, 1.02 0.91 0.79, 1.04 1.03 0.93, 1.14 One week before ultrasound  Under-growth 0.89 0.72, 1.09 0.86 0.61, 1.20 1.09 0.94, 1.26 0.99 0.77, 1.26  Over-growth 0.87 0.73, 1.04 0.96 0.86, 1.07 0.97 0.85, 1.11 1.11* 1.00, 1.23 Risk per 10 µg/m3 increase in PM10 From conception to ultrasound  Under-growth 1.03 0.98, 1.07 0.99 0.92, 1.07 0.98 0.93, 1.02 0.99 0.92, 1.06  Over-growth 0.98 0.94, 1.02 1.01 0.98, 1.04 1.01 0.98, 1.05 1.02 0.99, 1.05 One month before ultrasound  Under-growth 1.00 0.98, 1.02 0.99 0.96, 1.02 1.00 0.99, 1.02 1.00 0.98, 1.02  Over-growth 0.98* 0.97, 1.00 1.00 0.99, 1.01 0.99 0.98, 1.01 1.01* 1.00, 1.02 Two weeks before ultrasound  Under-growth 0.99 0.97, 1.01 1.00 0.97, 1.03 1.00 0.99, 1.02 1.00 0.97, 1.02  Over-growth 0.98** 0.96, 0.99 0.99 0.98, 1.01 1.00 0.99, 1.01 1.01* 1.00, 1.02 One week before ultrasound  Under-growth 0.99 0.97, 1.01 0.99 0.96, 1.02 1.00 0.99, 1.01 1.00 0.97, 1.02  Over-growth 0.99 0.97, 1.01 1.00 0.99, 1.01 1.00 0.98, 1.01 1.01* 1.00, 1.02 a Adjusted for maternal age (< 30, 30 years), education (< college, college), family monthly income per capita (< 3000, ≥ 3000 RMB), smoking during pregnancy (yes, no), pre-pregnancy BMI (≤ 18.5, 18.5–24.0, ≥ 24.0), weight gain during pregnancy (≤ 15 kg, 15–18.5 kg, > 18.5 kg), parity (primiparous, multiparous), season of conception (spring/summer, autumn/winter), cooking fuel (gas/electricity, coal/biomass), estimated SO2, NO2 by different corresponding exposure windows, and averaged temperature during different corresponding exposure windows. * P ≤ 0.05; ** P < 0.01. Compared with the results of cumulative exposure from conception to the ultrasound examination, similar positive associations were only observed for HC measures for other exposure windows, 1 month before ultrasound examinations and 1 or 2 weeks before ultrasound examinations, when PM10 was treated as < 150 µg/m3 versus ≥ 150 µg/m3 or a continuous variable (Tables 2 and 3). Discussion Our results support the hypothesis that prenatal exposure to high levels of ambient PM10 is associated with increased risk of fetal under- and over-growth. Our study also suggests a stronger and consistent association of over-growth for HC over the different exposure windows compared with other fetal growth parameters. To the best of our knowledge, our study was the first one to examine the associations between prenatal exposure to PM10 and risk of fetal over-growth. We found that exposure to higher levels of PM10 increased the risk of fetal HC over-growth. In early studies conducted in Australia, Europe and the USA, Hansen et al.5 found reductions of FL, AC and HC associated with increased exposure to PM10 during gestational days 91–120, and a reduction of FL measurement during 0–30 days of gestation; van den Hooven et al.22 observed reduction in HC in the third trimester; and Ritz et al.23 reported that no association was observed between PM10 exposure and HC, FL and AC. Although van den Hooven et al. did not highlight the findings that there were significant increases in HC measures during the second trimester in the second and third quartiles of PM10 exposure, and that there were significant increases in fetal weight in higher quartiles (second, third and fourth) of PM10 exposure, these findings were similar to ours. Although the underlying mechanisms are currently unclear, a growing body of literature suggests that increased oxidative stress and inflammatory response to PMs can influence energy expenditure, resulting in increased dietary intake.32,33 There was an increasing awareness that changes in placental metabolism and nutrient transport capacity might contribute to altered fetal growth.46 Human studies showed that higher cord blood leptin levels caused by greater weight gain were associated with increased fetal size,47 and altered expression of maternal insulin-like growth factor due to excessive gestational weight gain contributed to increased fetal growth through increased placental nutrient transporter expression.48,49 It is possible that the positive relationship between ambient air pollutants and fetal over-growth was mediated by maternal BMI/weight gain. In our study, pregnant women carrying an overgrown fetus were more likely to be in the highest category of maternal weight gain and to be pre-pregnancy obese. Comparing the study results among the published studies and ours was challenging. The outcome measures varied across different studies, including differences in ultrasound measurement methods and potential of inter-sonographer variations. The exposure assessment also varied across the studies. van den Hooven et al.22 performed dispersion modelling based on home addresses only, though allowing for residential mobility; Hansen et al.5 estimated monthly exposure to nearest monitors using IDW over the first 4 months and restricted analysis through four exposure windows (0–30, 31–60, 61–90, 91–120 days of gestation). Ritz et al.23 considered a 2-weeks exposure window around each measurement time (at 19.1, 28.8 and 36.7 weeks of gestation). In addition, all these published studies were conducted in low-air-pollution areas as compared with our study. The compositions of PM10 in different studies might also be different and could cause a different impact on fetal growth. Our study included large sample size (N = 8877) with a relatively large number of ultrasound scans for each fetal parameter (N ∼18 583), compared with previous studies.5,22,23 It allowed exploration of association by subgroups based on the growth status. Our study assessed exposure based on both home and work addresses with consideration of residential mobility, as well as PM10 exposure through different exposure windows, which minimized potential exposure misclassification and ability to explore critical exposure time windows on fetal growth. Detailed information on potential confounding factors such as smoking, cooking fuels and other co-exposure air pollutants were collected and included in our analyses. All participants were recruited from the same hospital and the information on ultrasound measurements was obtained from medical records, so potential misclassifications of fetal measurements were minimized. Additionally, with a potential U-shape relationship between air pollution and fetal growth, we employed the latest international fetal growth standard to examine the association with both under-growth and over-growth, in addition to treating growth measurements as continuous variables as done by earlier studies.5,22,23 This potential heterogeneity of PM10 exposure effects on fetal growth measurements still calls for future study with larger sample size, to replicate and confirm our findings. Limitations should be considered when interpreting the study results. The accuracy of exposure estimation might be limited by the lack of monitors in rural areas in Lanzhou city. However, more than 90% of the women lived within 5.5 km of a monitor. Sensitivity analyses using the data from different exposure assessment approaches (nearest monitors, IDW using two or four monitors, using data for the women living within 5.5 km or 12.9 km of a monitor) showed consistent results. PM10 exposure was estimated using data from government monitors which, though the most commonly used method in air pollution epidemiology, may not represent the actual individual exposure level which would include differences in indoor/outdoor activity patterns, and may not consider intra-urban gradients in pollutants. However, measurements through personal air monitors in this setting (pregnancy, in a large population) may be impractical as well as expensive, and numerous studies have used the above monitoring approach successfully. To evaluate whether the four monitors provided reasonable spatial coverage in our study, we calculated the coefficient of divergence, which provides the diversity between concentrations at sampling site-pairs.50 We found that coefficients of divergence for all site-pairs were lower than 0.20, indicating low spatial heterogeneity of PM10 in our study area. However, future studies might consider additional monitor coverage, in order to address the issues of spatial heterogeneity and improve estimates of exposure. Although the exact sources of PM10 in Lanzhou city are still unclear, a factory of the China Petroleum and Chemical Corporation (Sinopec Limited) has been located in the west, an upwind location of this city. Future work may investigate PM sources, which have different chemical structures and possibly different health impacts. The measurement errors with respect to the outcomes would be another concern both in previous large epidemiological studies and in the current study, because it is possible that the ultrasound examinations at different gestational weeks were performed by different clinicians/technicians. However, GPMCCH is the largest maternity and child care hospital in Lanzhou, China. The hospital performs internal quality control in order to provide high-quality ultrasound examinations, and the historical records of reproducibility also demonstrated that the measurement errors would be likely minimized. In conclusion, our study supports the hypothesis that prenatal exposure to high levels of ambient PM10 increases the risk of abnormal fetal growth, and stronger and consistent associations were mainly seen for HC over-growth by different exposure windows compared with other fetal growth measurements. This study suggests that the associations between maternal exposure to PM10 and fetal growth may vary by different fetal growth measurements (BPD, FL, AC and HC) and fetal growth patterns (under-/over-growth). Future multi-site studies with different types of air pollutants are needed to fully understand the impacts of air pollution combinations on fetal growth. Our study also calls for future studies to explore the underlying mechanisms and post-natal consequences of these findings Funding The work was supported by internal funding from the Gansu Provincial Maternity and Child Care Hospital, Gansu Provincial Science and Technology Department Grant (1204WCGA021), National Institutes of Health Grants (K02HD70324, R01ES019587) and the U.S. Environmental Protection Agency (EPA-G2014-STAR-J1). 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Journal

International Journal of EpidemiologyOxford University Press

Published: Aug 1, 2018

References

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