Particulate matter and early childhood body weight

Particulate matter and early childhood body weight

Environment International 94 (2016) 591–599 Contents lists available at ScienceDirect Environment International journal homepage: www.elsevier.com/l...

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Environment International 94 (2016) 591–599

Contents lists available at ScienceDirect

Environment International journal homepage: www.elsevier.com/locate/envint

Particulate matter and early childhood body weight Eunjeong Kim a, Hyesook Park b, Eun Ae Park c, Yun-Chul Hong d, Mina Ha e, Hwan-Cheol Kim f, Eun-Hee Ha g,⁎ a

Department of Occupational and Environmental Medicine, Ewha Womans University Mokdong Hospital, Seoul, South Korea Department of Preventive Medicine and Ewha Medical Research Center, School of Medicine, Ewha Womans University, Seoul, South Korea c Department of Pediatrics, School of Medicine, Ewha Womans University, Seoul, South Korea d Department of Preventive Medicine, College of Medicine, Seoul National University College of Medicine, Seoul, South Korea e Department of Preventive Medicine, College of Medicine, Dankook University, Cheonan, South Korea f Department of Occupational & Environmental Medicine, Inha University School of Medicine, Incheon, South Korea g Department of Occupational and Environmental Medicine, Ewha Medical Research Center, School of Medicine, Ewha Womans University, Seoul, South Korea b

a r t i c l e

i n f o

Article history: Received 19 January 2016 Received in revised form 16 June 2016 Accepted 16 June 2016 Available online 22 June 2016 Keywords: Air pollution Particulate matter Children growth Weight

a b s t r a c t Concerns over adverse effects of air pollution on children's health have been rapidly rising. However, the effects of air pollution on childhood growth remain to be poorly studied. We investigated the association between prenatal and postnatal exposure to PM10 and children's weight from birth to 60 months of age. This birth cohort study evaluated 1129 mother-child pairs in South Korea. Children's weight was measured at birth and at six, 12, 24, 36, and 60 months. The average levels of children's exposure to particulate matter up to 10 μm in diameter (PM10) were estimated during pregnancy and during the period between each visit until 60 months of age. Exposure to PM10 during pregnancy lowered children's weight at 12 months. PM10 exposure from seven to 12 months negatively affected weight at 12, 36, and 60 months. Repeated measures of PM10 and weight from 12 to 60 months revealed a negative association between postnatal exposure to PM10 and children's weight. Children continuously exposed to a high level of PM10 (N 50 μg/m3) from pregnancy to 24 months of age had weight z-scores of 60 that were 0.44 times lower than in children constantly exposed to a lower level of PM10 (≤50 μg/m3) for the same period. Furthermore, growth was more vulnerable to PM10 exposure in children with birth weight b 3.3 kg than in children with birth weight N3.3 kg. Air pollution may delay growth in early childhood and exposure to air pollution may be more harmful to children when their birth weight is low. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction Ambient air pollution is considered as an unavoidable environmental exposure and serves as a source of oxidative stress and inflammation inducing factors with effects on cardiovascular and pulmonary systems (Brook et al., 2010; Götschi et al., 2008; Perez et al., 2010). Children are especially vulnerable to air pollution because their respiratory and immune systems are immature and are at the stage of rapid development (Landrigan and Etzel, 2014). Air pollution increases infant mortality and is related to risks of respiratory infection, asthma, and low respiratory function (Landrigan and Etzel, 2014). Furthermore, exposure to air pollutants during pregnancy has been related to adverse birth outcomes, including preterm birth and low birth weight, as well as other health Abbreviations: PM10, particulate matter up to 10 μm in diameter; IDW, inverse distance weighting; MOCEH study, Mothers and Children's Environmental Health study; BMI, body mass index; GLM, generalized linear model; GEE, generalized estimating equations. ⁎ Corresponding author at: Department of Occupational and Environmental Medicine, Ewha Medical Research Center, School of Medicine, 911-1 Mok-5 dong, Yangcheon-ku, Seoul 158-056, Korea. E-mail address: [email protected] (E.-H. Ha).

http://dx.doi.org/10.1016/j.envint.2016.06.021 0160-4120/© 2016 Elsevier Ltd. All rights reserved.

problems (Kim et al., 2014; Osmond and Barker, 2000; Pope et al., 2010; Srám et al., 2005). A wide spectrum of birth weight and growth patterns occurs during childhood. Low birth weight has been associated with cardiovascular and metabolic diseases in subsequent adulthood (Osmond and Barker, 2000). Growth restriction in utero can sometimes result in accelerated growth above the normal range for at least 1 year of age, which later leads to catch-up growth (Finkielstain et al., 2013; Wit and Boersma, 2002). Variations in growth patterns in early childhood can therefore affect health and development later in life. For example, rapid catch-up weight gain after birth has been linked with obesity and high blood pressure (Lei et al., 2015; Ong and Loos, 2006; Salgin et al., 2015). On the contrary, a failure in catch-up growth in early childhood has been associated with low intelligence (Lei et al., 2015). Postnatal growth restriction and subsequent catch-up growth may also reduce insulin sensitivity (Alexeev et al., 2015). Children with low birth weight also might have more varied growth patterns than the patterns seen in children with normal birth status. Children with low birth weight can also be more vulnerable to environmental hazards than other children; therefore, more studies should be conducted with respect to low birth weight population (Fleisch et al., 2015; Lei et al., 2015).

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Some evidences support an association between prenatal exposure to air pollution and birth outcomes, including birth weight, but the relationship between air pollution and children's growth is still unclear. Furthermore, numerous studies have measured only prenatal exposure to air pollution in an effort to investigate the relationship between both the issues or to perform a comparative analysis between the groups living in areas with different levels of exposure (Ghosh et al., 2011; Jerrett et al., 2010; Sram et al., 2013; Tielsch et al., 2009). In other words, previous studies either carried out observations only during a certain period of time or failed to measure individual exposure levels to air pollution to identify association between air pollution and growth in children. The aim of this study was to investigate the association between prenatal and postnatal exposure to air pollution and children's weight at the time of birth and at six, 12, 24, 36, and 60 months with repeated measures of individual exposure level of particulate matter up to 10 μm in diameter (PM10). We used a longitudinal study with repeated measures of exposure level for air pollution and child growth at each follow-up to provide an opportunity to understand the relationship and to reveal the periods of susceptibility to air pollution exposure in child growth. We also investigated whether children's birth weight might have impact on the vulnerability to air pollution by comparing the impact of PM10 exposure on child growth in two groups of children (lower birth weight and higher birth weight than the mean birth weight).

728, 648, 545, and 387 at 6, 12, 24, 36, and 60 months, respectively (Fig. 1). At the time of enrolment, the sociodemographic covariates of maternal age, maternal education, and family income were collected, the body mass index (BMI) was calculated based on maternal pre-pregnancy weight and height, and cotinine levels in urine was measured. Data on pregnancy health results, such as hypertension or diabetes during pregnancy, and weight gain during pregnancy were collected at delivery, and birth outcomes, such as birth weight, sex, and gestational age, were obtained from perinatal medical records. At the time of six month visit, information about the feeding method of infants was obtained. The variables in the model were categorized and used as categorical variables, except for maternal pre-pregnancy BMI, gestational age, maternal age, and maternal weight gain during pregnancy, which were used as continuous variables. Maternal cotinine levels in urine were categorized by 95 percentiles of the total level, which was 14.9 μg/g creatinine. Children were divided into two groups with a cut-off point of mean birth weight (3.3 kg) in this study and were examined for weight growth until 60 months of age to investigate whether vulnerability to

2. Methods 2.1. Study population This research was conducted as a part of the Mothers and Children's Environmental Health (MOCEH) study, a multiregional prospective birth cohort study in South Korea established in 2006 to investigate the effects of environmental hazards on the health of mothers and their children. The regional centers of the study (Seoul, Cheonan, and Ulsan) each feature a community-based network of a university hospital, local clinics, and public health centers. Pregnant women from these cities and who fulfilled the inclusion criterion of being N 18 years of age were enrolled before they were into 20 weeks of their pregnancies. All enrolled participants provided written informed consent at the initial visit. The study protocols and design were approved by the Institutional Review Boards of Ewha Womans University, Dankook University Hospital, and Ulsan University Hospital (Kim et al., 2009). Among the 1751 enrolled participants in this study, exposure levels of PM10 were estimated for 1484 mother–child pairs, who provided all required information for the exposure modeling method, including residential address and birth date. Of these 1484 pairs, 1454 remained after excluding children for whom growth measurements were not assessed at the time of birth and at six, 12, 24, 36, and 60 months visits. Children with birth weight b2500 g or preterm births at gestational ages under 37 weeks (n = 79) were excluded from this study with regard to other considerable health determinants or problematic conditions, such as birth defects and maternal malnutrition; furthermore, it was assumed that other health complications from adverse birth condition might have confounded the results of the association between PM10 and child growth follow-up. Table S1 shows weight distribution for the babies excluded from the study because of low birth weight (birth weight b2500 g) and preterm birth. An analysis of the association between prenatal PM10 exposure and adverse birth outcomes revealed no significant relationship between prenatal PM10 exposure and preterm birth, small for gestational age, or low birth weight (Table S2). Another 246 mother-infant pairs were excluded from the study because of missing covariates data. Ultimately, 1129 mother-child pairs were included in the analysis. However, some children were lost for follow-up or missed measurements at some visits, so the number of children included in the association analysis was 1129 at birth, and 812,

Fig. 1. Flow chart of the study population. ⁎Total number of enrolled mother–child pairs in the MOCEH study.

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PM10 exposure was greater in children with lower birth weight than those with higher birth weight. 2.2. Growth assessment Weight at birth was assessed by a trained nurse at the time of delivery. The birth weight was measured twice for calculation of average birth weight. The newborn was placed on the infant tray of an AD-15T Infantometer (CAS Korea, Seoul, Korea) and weight was read to two decimal places (0.01 kg). Weight at six, 12, 24, 36, and 60 months was assessed by a trained study technician at each follow-up visit. Weight at six and 12 months was measured using a model DS-B02 Infantometer (Dong Sahn Jenix Co. LTD, Seoul, Korea) by laying infants on the center of the scale, and reading the weight measurement to one decimal place (0.1 kg). At 24, 36, and 60 months, weight was obtained employing a model DS-102 automatic measuring station (Dong Sahn Jenix Co. LTD, Seoul, Korea) by allowing the child to stand on the center of the scale on both the feet, and placing heels, bottom, back, and posterior head against the measuring rod. Weight was measured while the participant was clad only in light clothes; socks, shoes, and any other accessories were removed. Weight was also converted into age- and gender-specific standard scores (weight-for-age z scores) based on norms from the Korean population (Korea Centers for Disease Control and Prevention et al., 2007). 2.3. Measurement of air pollution exposure The exposure level of PM10 at each participant's residence was predicted using the inverse distance weighting (IDW) modeling method, a common modeling method used for spatial interpolation to model a distribution from collected data points (Zou et al., 2009). The number of national air quality monitoring stations in South Korea in 2006 was 202 and had slightly increased by 2012 to 246. Fig. 2 describes the locations of the monitoring stations, the enrolled mothers' locations, and the distance to the nearest monitoring station from the participants' home addresses, as described in Table S3. The monthly exposure levels of PM10 from 2006 to 2012 were obtained from the monitoring stations and collected data were used for estimating the monthly exposure

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levels at the participants' addresses employing a modeling method with a geographic information system (ArcGIS 9.3, Arc Map 9.3; ESRI Inc., Redlands, CA, USA). We evaluated the quality of the values predicted from IDW using a cross-validation technique, whereby each monitoring station was removed, one at a time, and the concentration at each omitted station was predicted using the concentration values observed at other monitoring stations. The observed (measured) concentrations at the ambient monitoring sites were subsequently compared with the values predicted by IDW. The root mean square prediction error (RMSPE) was calculated during cross-validation. The adjusted R2 value of the models was 32% and RMSPE was 9.13 μg/m3 for PM10, which suggested that the predicted values fitted moderately well with the measured values. Each participant's gestational age and birth date were used to compute the average level of exposure during pregnancy and the average level of exposure in children between each visit (at birth to six, seven to 12, 13 to 24, 25 to 36, and 37 to 60 months). For example, if the birth date was January 15, the average level of exposure from birth to six months was computed as (average level in January × 16 + February × 28 + March × 31 + April × 30 + May × 31 + June × 15) / (16 + 28 + 31 + 30 + 31 + 15). 2.4. Statistical analyses A generalized linear model (GLM) was used to estimate linear associations between each periods of prenatal and postnatal exposure levels to PM10 and children's weight at birth and at six, 12, 24, 36, and 60 months of age. Covariates were selected based on the literature related to children's growth. The final analysis model included sex, maternal age, gestational age, maternal education, family income, birth weight, and maternal BMI (model 1). Birth weight was also used as a covariate in the model to assess the effect of PM10 on children's weight at each visit (except for the weight at birth). The feeding method at six months was added from model 1 as a covariate in the sub-analysis (model 2). The association between PM10 exposure and children's weight in low birth weight and in preterm birth babies was also evaluated in sensitivity analysis (Table S4). Other possible determinants of child

Fig. 2. Locations of the monitoring stations and of the enrolled mothers.

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development were tested in the sensitivity analysis and are presented in supplementary Table S5. Generalized estimating equations (GEE) were used to estimate the effect of exposure level of PM10 on children's weight, considering the correlation between repeated measures of PM10 and children's weight (Jennrich and Schluchter, 1986). The average exposure level of PM10 was treated in the GEE model as follows: average level during pregnancy to time zero, average level of zero to six months to time six, seven to 12 months to time 12, 13 to 24 months to time 24, 25 to 36 months to time 36, and 37 to 60 months to time 60. Four different models of GEE were evaluated in the analysis. The first model included PM10 exposure during pregnancy and repeated measures of children's weight from birth to 60 months. The second and third models included repeated measures of postnatal exposure and weight from six to 60 months and 12 to 60 months, respectively. The last model included all repeated measures of exposure and weight (Table 5). The exposure levels and the weight index were both treated as continuous variables with the same covariates as GLM. Among different working correlation structures, an autoregressive and an exchangeable structure were considered as the appropriate correlation structures to explain our data. The model fit criterion showed little difference between the two correlation structures, so an autoregressive structure was chosen for the working correlation in GEE model, based on its consideration as a better correlation structure to explain within-the-subject correlation in our data (Twisk, 2005). We investigated the differences in growth pattern in children living in environments with levels of PM10 higher and lower than standard levels by obtaining measurements from the time of the mother's pregnancy to early childhood. We divided the children into two groups as children continuously exposed to PM10 levels below the annual standards for PM10 in South Korea (50 μg/m3) (Environment, 2015) and children exposed to PM10 over 50 μg/m3 from pregnancy to 24 months and their weight was compared from birth to 60 months (Fig. 3). We also investigated the difference in vulnerability to PM10 exposure by dividing the children into two groups with a cut-off point of mean birth weight (3.3 kg) and by conducting the same analysis, as shown in Fig. 3 (Fig. 4). All statistical analyses were performed using SAS version 9.3 (SAS Institute Inc. Cary, NC). 3. Results Characteristics of the participants are presented in Table 1. The mean age of the mothers was 30.3 years. The average gestational age of the children was 39.1 weeks and 51.7% of the children were males. Males

had a higher average birth weight than females. Children from women with higher BMIs (≥ 23 kg/m2) had higher birth weight as compared to other children. The average weight of children at birth and at six, 12, 24, 36, and 60 months of age was 3.3, 8.5, 10.2, 12.5, 14.5, and 19.1 kg, respectively, and the average weight-for-age z scores was −0.14, 0.35, −0.08, −0.24, −0.08, and 0.10, respectively (Table 2). Table 3 presents average concentration of PM10 during pregnancy and from birth to six months, seven to 12 months, 13 to 24 months, 25 to 36 months, and 37 to 60 months of age. Linear regression analyses revealed that exposure to PM10 during pregnancy only demonstrated significant effects on the child's weight-for-age z scores at six months in model 2. Analyses of postnatal exposure to PM10 revealed significantly adverse impacts from seven to 12 months on children's weight at 12, 36, and 60 months of age (Table 4). The association was robust when low birth weight and preterm birth babies were included in the analysis and a significant relationship was additionally observed between PM10 at 13 to 24 months and body weight at 24 months of age (Table S4). The sensitivity analysis shown in Table S5 has model 3 additionally adjusted for maternal cotinine level in the urine and for maternal hypertension or diabetes during pregnancy (from model 1 in Table 4) and the results were found to be robust. Addition of maternal weight gain during pregnancy as a covariate from model 3 (model 4) revealed significant association between prenatal PM10 exposure and children's weight at 60 months of age. The GEE analysis considering repeated measurements at six, 12, 24, 36, and 60 months revealed negative association between PM10 and children's weight (β = − 0.07, 95% CI − 0.13–0.003) from 12 to 60 months of age as demonstrated in model 2 (Table 5). We evaluated the growth patterns of children, who were born and had lived in areas with high exposure to air pollution in early life, by designing two subgroups of children based on levels of PM10 exposure from pregnancy to 24 months of age. The high exposure group included the children, who were continuously exposed to PM10 over 50 μg/m3 from pregnancy to 24 months of age. The reference group comprised of children, who were continuously exposed to PM10 under 50 μg/m3 during the same time period. Fig. 3 presents the average weight-forage z scores and weight of the two subgroups from birth to 60 months of age. The weight of the high exposure group of children was significantly lower than the reference group at birth and at 12, 36, and 60 months. The difference in weight between the two groups at 60 months was 0.44 for weight-for-age z scores and 1.11 kg for weight. We also divided the children into two groups based on mean birth weight (3.3 kg) and compared the growth pattern between children exposed to high PM10 levels and the reference group (Fig. 4). Both the

Fig. 3. Difference in average weight-for-age z scores (left) and weight (right) between children exposed to PM10 under 50 μg/m3 and children exposed to PM10 over 50 μg/m3 from pregnancy to 24 monthsa. aTotal number of participants in the analysis (the number of participants in high exposure group). At birth: 380 (278), 6 months: 238 (152), 12 months: 213 (137), 24 months: 199 (124), 36 months: 179 (113), 60 months: 124 (79).

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Fig. 4. Difference in average weight-for-age z scores between children exposed to PM10 under 50 μg/m3 and children exposed to PM10 over 50 μg/m3 from pregnancy to 24 months in children with birth weight under 3.3 kg (left) and over 3.3 kg (right)a. aTotal number of participants in the analysis (the number of participants in higher exposure group). Left - At birth: 183 (132), 6 months: 114 (72), 12 months: 102 (67), 24 months: 102 (65), 36 months: 87 (56), 60 months: 62 (42). Right - At birth: 197 (146), 6 months: 124 (80), 12 months: 111 (70), 24 months: 97 (59), 36 months: 97 (57), 60 months: 62 (37).

groups of children (birth weight b 3.3 kg and ≥3.3 kg) showed average z scores that were significantly lower in the high exposure groups at 12 months than in the reference group, but the difference was larger in children with birth weight b 3.3 kg (β = −0.39, p = 0.05). By 36 months, a significant difference was only found in children with birth weight b 3.3 kg (β = −0.53, p = 0.02) and the difference in average z score was also larger in children with birth weight b3.3 kg as compared to children with birth weight ≥3.3 kg. At 60 months, the difference continued to be larger in children with birth weight b3.3 kg; however, it was no longer statistically significant (β = −0.51, p = 0.06).

4. Discussion The effects of exposure to PM10 during pregnancy and after birth on child weight in early childhood were evaluated in the present study. Prenatal exposure to PM10 was associated with children's weight at six months in the sub-analysis (model 2). Postnatal exposure from seven to 12 months resulted in lower body weight at 12, 36, and 60 months of age. These findings indicate that both prenatal and postnatal exposure to air pollution affects children's growth. Furthermore, it is hypothesized that the period before one year of age might be an

Table 1 Characteristics of participants (n = 1129)a. Characteristics

N (%) or mean (SD)b

Birth weight (mean (SD))

Maternal characteristics Maternal age (years) ≤30 years N30 years Maternal education ≤High school ≥University Family income b$2000 $2000–$4000 b$4000 Pre-pregnancy BMI b23 ≥23 Hypertension or diabetes during pregnancy Yes No Cotinine level in urine N14.9 ≤14.9 Pregnancy weight gain N12.9 ≤12.9

Characteristics

N (%) or mean (SD)b

Birth weight (mean (SD))

39.1 (1.1) 3.31 (0.37)

N/A N/A

584 (51.7) 545 (48.3)

3·36 (0·37)⁎ 3·26 (0·37) N/A

Child characteristics 30.3 (3.6) 651 (57.7) 478 (42.3)

3.30 (0.37) 3.32 (0.38)

296 (26.2) 833 (73.8)

3.34 (0.41) 3.30 (0.36)

304 (26.9) 602 (53.3) 223 (19.8) 21.2 (3.0) 870 (77.1) 259 (22.9)

3.30 (0.41) 3.29 (0.36) 3.31 (0.36)

27 (2.4) 1075 (97.6)

3.32 (0.47) 3.31 (0.37)

52 (4.9) 1003 (95.1) 12.9 (4.6) 437 (49.6) 444 (50.4)

3.20 (0.38)⁎ 3.31 (0.38)

3.28 (0.36)⁎ 3.39 (0.40)

3.26 (0.35)⁎ 3.38 (0.39)

⁎ p b 0.05 by analysis of variance or chi-square test. a Children with birth weight lower than 2500 g or gestational age under 37 weeks were excluded. b Percentages for categorical variables and means (standard deviation) for continuous variables. c Feeding method at 6 months of age.

Gestational age (weeks) Birth weight Sex Male Female Feeding methodc Breast milk only Mixed or formula only

542 (63.2) 316 (36.8)

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Table 2 Distribution of children's weight and weight-for-age z scores. Weight (kg)

Weight-for-age z scores

Age at visits (months)

Mina

Mean (SD)

Maxa

Mina

Mean (SD)

Maxa

At birth 6 12 24 36 60

2.50 6.00 7.40 9.00 10.20 12.00

3.31 (0.37) 8.54 (0.97) 10.18 (1.06) 12.47 (1.36) 14.53 (1.60) 19.11 (2.55)

4.65 13.00 15.00 21.50 21.00 32.50

−2.10 −2.19 −2.74 −3.07 −3.36 −4.60

−0.14 (0.73) 0.35 (0.95) −0.08 (0.94) −0.24 (0.95) −0.08 (0.97) 0.10 (0.98)

1.98 4.36 3.71 4.60 3.08 3.48

a

Min: minimum/Max: maximum.

that rats with postnatal growth retardation in weight had poorer neurodevelopment (Alexeev et al., 2015). More interestingly, the same study showed that catch-up growth following postnatal growth restriction was linked with increases in body adiposity and impaired insulin sensitivity. Taken together, these results indicate that delayed growth in early childhood caused by air pollution exposure could lead to impaired neurodevelopment as well as an increased risk of metabolism-related diseases if catch-up growth is inappropriate. The children with lower-than-mean birth weight in our study showed a greater vulnerability to PM10 than those with higher-than-mean birth weight, and considering that children with lower-than-mean birth weight may have more difficulty in catching up on their growth, additional long-term studies are needed to observe their growth patterns as well as the health effects related to exposure to environmental pollutants. The biological mechanisms underlying the effects of air pollution on children's growth remain to be uncertain until date. It is hypothesized that particulate matter and air pollution possibly trigger systemic inflammation responses and cause oxidative stress in organ tissues, such as cardiovascular, respiratory, and central nervous system organs, and this adverse systemic response might affect a child's growth after birth (Brook et al., 2010; Freire et al., 2010; Rückerl et al., 2011). The evidence is limited, but indoor airborne particulate matter has been reported to significantly interfere with thyroxine binding to transthyretin, a plasma carrier protein, thereby possibly inhibiting the functioning of thyroid hormone, which is important for growth in children (Alink et al., 1994; Smyczynska et al., 2010). Induction of cell apoptosis due to DNA damage caused by air pollutants might also result in delayed growth in early childhood (Nikolić et al., 2014). The association between air pollution and growth during childhood could also be an indirect secondary affect arising from other adverse health effects of air pollution. A high level of air pollution increases the risk of asthma, respiratory related diseases, and other health problems in children. The increased risk of these adverse health conditions could delay growth in children. The susceptibility to PM10 at infancy may be related to development of the respiratory system in children. The process of growth and maturation of the respiratory system continues after birth (Buchdahl et al., 1996; Zeltner and Burri, 1987). In postnatal lung development, differentiation of detoxification systems is time-dependent and infant's lungs may not get properly repaired following acute epithelial injury

especially susceptible period for exposure to air pollution in terms of growth in early childhood. Few studies have focused on presence of association between air pollution and children's growth. One study reported that children born in 1946 in Britain and living in households using coal-burning sources were shorter in height than other children at seven years of age (Bobak et al., 2004). A study in the Czech Republic also suggested an adverse relationship between pollution from indoor coal use and height in early childhood (Ghosh et al., 2011). Another study that analyzed data from seven developing countries also revealed an association between bio-fuel smoke and stunting among children from birth to 59 months of age (Kyu et al., 2009). Children born near a coal-fired power plant showed presence of higher polycyclic aromatic hydrocarbon-DNA adduct levels in their cord blood when compared with children born after shutdown of the plant, and a negative association was found between the adduct levels and body weight at 18, 24, and 30 months of age in children born before the shutdown (Tang et al., 2008). A study in Serbia that compared the growth of children, who lived in a high air pollution area with children in a comparison group (Nikolić et al., 2014) reported an interesting finding of a significantly lower average weight in children in the high exposure area at seven and eight years of age, but a higher weight (although not statistically significant) in the same children at nine and ten years of age. Two other studies suggested that air pollution is related to an increase in risk of being overweight or high BMI in children (Jerrett et al., 2010, 2014). Considering that the age of children in these studies was five to 18 years, the effect of air pollution associated with children's growth might possibly be complex, with differences arising in each growth period. A study with long-term follow-up observation is required to enhance understanding of the relationship between exposure to air pollution and children's growth. Lei et al. studied the health effects of term small for gestational age babies divided into groups that showed different growth patterns (Lei et al., 2015). These babies, when they had appropriate catch-up growth, did not display any increased risk of adverse outcomes when compared to appropriate for gestational age babies. However, babies with slow or no catch-up growth had higher risks of growth restriction and low intelligence quotient (IQ) at seven years of age, while those with excessive catch-up growth had higher risks of being overweight and developing higher blood pressure at same age. One experimental study showed

Table 3 Distribution of PM10 at measured periods. Distribution of PM10 (μg/㎥)a Exposure Period Prenatal Postnatal

a

During pregnancy 0–6 months 7–12 months 13–24 months 25–36 months 37–60 months

The levels were estimated by IDW modeling method.

Minimum

Mean (SD)

Maximum

38.7 36.9 32.4 35.1 32.9 36.0

52.8 (6.2) 53.2 (7.5) 51.6 (7.7) 50.2 (5.0) 47.9 (5.3) 50.0 (4.3)

75.2 76.2 75.6 67.8 66.6 65.2

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Table 4 Association of PM10 exposure with children's weight-for-age z-scores at each measured period. Measured period (months)

Change in weight-for-age z-scores (95% confidence intervals)

PM10 (10 μg/m3)

Weight

N

Model 1a

p-value

N

Model 2a

p-value

Prenatal

At birth 6 12 24 36 60 6 12 24 36 60 12 24 36 60 24 36 60 36 60 60

1129 812 728 648 545 387 812 728 648 545 387 728 648 545 387 648 545 387 545 387 286

−0.004 (−0.07, 0.06) −0.02 (−0.12, 0.09) −0.09 (−0.19, 0.007) −0.06 (−0.17, 0.05) −0.06 (−0.19, 0.07) −0.12 (−0.29, 0.06) −0.07 (−0.15, 0.01) 0.02 (−0.06, 0.10) −0.01 (−0.10, 0.09) 0.08 (−0.02, 0.19) −0.04 (−0.17, 0.08) −0.13 (−0.21, −0.05) −0.08 (−0.18, 0.01) −0.15 (−0.25, −0.04) −0.17 (−0.29, −0.04) −0.12 (−0.26, 0.02) −0.03 (−0.19, 0.13) −0.14 (−0.33, 0.06) −0.02 (−0.17, 0.14) −0.17 (−0.37, 0.04) −0.19 (−0.45, 0.07)

0.90 0.73 0.07 0.30 0.34 0.18 0.10 0.61 0.90 0.12 0.51 0.002 0.08 0.006 0.008 0.10 0.70 0.16 0.82 0.12 0.15

N/A 740 638 595 481 340 740 638 595 481 340 638 595 481 340 595 481 340 481 340 248

N/A −0.03 (−0.14, 0.08) −0.11 (−0.21, −0.002) −0.03 (−0.14, 0.09) −0.06 (−0.20, 0.07) −0.16 (−0.35, 0.03) −0.08 (−0.17, 0.006) 0.002 (−0.08, 0.08) 0.004 (−0.09, 0.10) 0.01 (−0.01, 0.21) −0.08 (−0.22, 0.06) −0.14 (−0.22, −0.05) −0.06 (−0.15, 0.04) −0.16 (−0.37, −0.05) −0.19 (−0.34, −0.06) −0.07 (−0.23, 0.08) −0.02 (−0.29, 0.16) −0.17 (−0.38, 0.05) −0.02 (−0.19, 0.16) −0.20 (−0.45, 0.04) −0.22 (−0.51, −0.08)

N/A 0.63 0.05 0.65 0.36 0.10 0.07 0.96 0.93 0.08 0.28 0.003 0.24 0.005 0.006 0.35 0.84 0.13 0.86 0.10 0.15

0–6

7–12

13–24

25–36 37–60

Model 2 included feeding method at 6 months as a covariate in model 1. a Model 1 was adjusted for sex, maternal age, gestational age, maternal education, family income, birth weight (except for the period at birth) and maternal body mass index.

(Dezateux et al., 1999; Fanucchi, 2010; Fanucchi et al., 2000; Smiley-Jewell et al., 2000). From birth to one year of age, children undergo rapid growth and development; consequently, organs undergoing rapid development, such as the brain, might be more vulnerable to toxicants (Zeltner and Burri, 1987). Furthermore, the vulnerability to toxicants may be particularly greater in children with lower-thanmean birth weight because of immaturity of their organ development. This study has multiple strengths. First, it is a prospective birth cohort study and the exposure level of PM10 and children's weight were measured independently. Second, we estimated an individual level of exposure to PM10 during pregnancy and in early childhood using a modeling method; we also repeatedly measured children's weight from birth to 60 months of age to investigate the windows of growth susceptibility to air pollution. To the best of our knowledge, this study is the first to assess the effects of postnatal exposure to ambient air pollution on children's growth starting right from the time of their birth, with repeated measures during the follow-up time. It was ultimately found that children with lower-than-mean birth weight were more vulnerable to PM10 exposure than those with higher-than-mean birth weight, even though the birth weight was not clinically low as under 2500 g. The present study is also associated with certain limitations. No information regarding nutrition intake was considered in the analysis,

except for the feeding method at six months of age in model 2. However, children's diets or food intake have been strongly associated with their parents' socioeconomic status; therefore, socioeconomic variables used in the analysis might be able to partially reflect the effect from children's nutrition intake in the results (Drewnowski, 2007; Kim and Lee, 2014). Second, as the estimation for PM10 exposure was based on participants' home address and did not consider the individual's time based activity patterns, the measure of PM10 could be inaccurate for the participants, who stayed outside of their living area for most of the time. For example, 39% of the mothers among the enrolled women worked outside their place of dwelling during early pregnancy; hence, their average level of actual exposure during pregnancy would be different from our estimation as the exposure to PM10 at their working area was not considered in the exposure modeling. Additionally, when an address change occurred between two visits of participants, the estimation was derived by considering half the period at the previous address and the other half from the new address, rather than the period from exact moving date. Third, the number of participants was reduced during the follow-up period. To use all available data for the analysis, we included all children who participated in visits for weight more than once after their birth, and the analysis of each visit period was carried out. The GEE longitudinal analysis also included all measured values. Finally, indoor sources of air pollution were not considered in the study model. In

Table 5 Analysis of association between repeated measures of PM10 and children's weight-for-age z-scores. Measured period PM10 (10 μg/m3) b

Prenatal Postnatald Postnatald All periode

Change in weight-for-age z-scores (95% confidence intervals) Weight c

0–60 months or 6–60 months 6–60 months 12–60 months 0–60 months

Model 1a

p-value

Model 2a

p-value

−0.03 (−0.10, 0.03) −0.04 (−0.08, 0.01) −0.06 (−0.12, 0.003) −0.02 (−0.06, 0.03)

0.33 0.13 0.06 0.44

−0.07 (−0.16, 0.03) −0.04 (−0.09, 0.01) −0.07 (−0.13, −0.003) −0.03 (−0.08, 0.02)

0.09 0.12 0.04 0.23

Model 2 was adjusted for the same covariates in model 1 and additionally included feeding method at 6 months. a Model 1 was adjusted for sex, maternal age, gestational age, maternal education, family income, and maternal body mass index. b Only average exposure level of PM10 during pregnancy was used in the analysis. c 0–60 months for model 1 and 6–60 months for model 2. d Repeated exposure measures of postnatal PM10 in children were used in the analysis. e All repeated exposure measures of PM10 from pregnancy to 60 months of age were used in the analysis.

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our study, most of the participants (98.6%) used gas cookers for home cooking, but exposure to the amount of indoor air pollution from gas cookers was not estimated and hence not considered in the final model. 5. Conclusion Air pollution may affect growth in early childhood, and children less than one year of age may be especially susceptible to the adverse effects of air pollution. Children with lower-than-mean birth weight may also be more vulnerable to exposure to air pollution. Further long-term follow-up studies are necessitated to investigate consistency of association between the two discussed issues, and national effects should be explored to reduce the level of exposure to air pollution as well as to minimize the adverse effects in vulnerable populations.

Financial disclosure All authors disclose no financial relationships relevant to this article. Conflict statement The authors declare that there is no conflict of interest in this manuscript.

Contributors' statement Eunjeong Kim: Dr. Kim conceptualized and designed the study, conducted the analyses, drafted the initial manuscript, and approved the final manuscript as appropriate for submission. Mina Ha, Hyesook Park, and Yun-Chul Hong: Dr. Ha, Dr. Park, and Dr. Hong coordinated and supervised the data collection, reviewed and revised the manuscript, and approved the final manuscript for submission. Eunae Park: Dr. Park supervised the assessment of children's growth, reviewed and revised the manuscript, and approved the final manuscript for submission. Hwan-Cheol Kim: Dr. Kim managed the evaluation of air pollution exposure data, reviewed and revised the manuscript, and approved the final manuscript for submission. Eun\\Hee Ha: Dr. Ha conceptualized and designed the study, reviewed and revised the manuscript, and approved the final manuscript for submission.

Funding source This study was supported by Mothers and Children's Environmental Health project of the National Institute of Environmental Research, Republic of Korea (http://www.nier.go.kr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Acknowledgement This study was supported by Mothers and Children's Environmental Health project of the National Institute of Environmental Research, Republic of Korea.

Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.envint.2016.06.021.

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