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山东大学学报 (医学版) ›› 2021, Vol. 59 ›› Issue (8): 99-106.doi: 10.6040/j.issn.1671-7554.0.2021.0301

• 公共卫生与管理学 • 上一篇    下一篇

孕期PM10和PM2.5暴露对新生儿出生体质量的影响

翟一凡1,王兆军2,白硕鑫1,林少倩3,王方怡1,杜爽1,王志萍1   

  1. 1.山东大学齐鲁医学院公共卫生学院, 山东 济南 250012;2.山东省济南生态环境监测中心, 山东 济南 250000;3.济南市疾病预防控制中心, 山东 济南 250021
  • 发布日期:2021-09-16
  • 通讯作者: 王志萍. E-mail:zhipingw@sdu.edu.cn
  • 基金资助:
    国家自然科学基金(81773386)

Effect of maternal PM10 and PM2.5 exposure during pregnancy on neonatal birth weight

ZHAI Yifan1, WANG Zhaojun2, BAI Shuoxin1, LIN Shaoqian3, WANG Fangyi1, DU Shuang1, WANG Zhiping1   

  1. 1. School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, Shandong, China;
    2. Shandong Jinan Ecological Environment Monitoring Center, Jinan 250000, Shandong, China;
    3. Jinan Municipal Center for Disease Control and Prevention, Jinan 250021, Shandong, China
  • Published:2021-09-16

摘要: 目的 探讨母亲孕期PM10和PM2.5暴露与新生儿出生体质量的关系,并进一步确定孕期PM10和PM2.5暴露对新生儿出生体质量影响的关键窗口期。 方法 以济南市婴幼儿哮喘出生队列基线研究为基础,研究区域内的新生儿为研究对象,获取新生儿出生体质量。结合济南市区17个监测站的颗粒物监测数据、孕期母亲的居住地址经纬度及监测站地址经纬度,使用反距离权重法得到每位母亲孕期的逐日个体暴露浓度,获得每位母亲整个孕期和孕早期、孕中期、孕晚期的暴露浓度。结合广义相加模型与阴性对照暴露分析的方法探索孕期PM10和PM2.5暴露与新生儿出生体质量之间的关系,并将孕早期、孕中期和孕晚期的暴露情况同时纳入模型寻找关键窗口期。 结果 (1) 纳入4 602名研究对象,平均出生体质量为(3 420.98±465.27)g,母亲孕期PM10和PM2.5平均暴露浓度分别为114.15 μg/m3和54.71 μg/m3;(2) 整个孕期PM10和PM2.5暴露浓度每升高10 μg/m3,新生儿出生体质量分别减少30.46 g(P=0.013)和51.81 g(P=0.028);其阴性暴露对照期PM10和PM2.5暴露浓度与新生儿出生体质量变化之间关系均无统计学意义(PM10: P=0.166; PM2.5: P=0.650)。(3) 进一步分析结果显示,孕早期PM10和PM2.5暴露浓度每升高10 μg/m3,出生体质量分别降低11.56 g(P=0.004)和43.85 g(P<0.001)。孕晚期PM10和PM2.5暴露浓度每升高10 μg/m3,出生体质量分别降低13.09 g(P=0.001)和44.04 g(P<0.001)。 结论 母亲孕期大气PM10和PM2.5暴露会降低新生儿出生体质量,孕早期与孕晚期是其关键暴露窗口期。

关键词: 孕期, 可吸入颗粒物, 细颗粒物, 出生体质量, 阴性对照暴露分析

Abstract: Objective To explore the association between maternal PM10 and PM2.5 exposure during pregnancy and neonatal birth weight, and further determine the critical window period of maternal PM10 and PM2.5 exposure on neonatal birth weight. Methods The study was based on the baseline study of infant asthma birth cohort in Jinan City, the newborns in the study area were selected as the research subjects, and the birth weight of newborns was obtained. Combined with the particulate matter monitoring data of 17 monitoring stations in Jinan City, the longitude and latitude of the mothers residential address during pregnancy and the longitude and latitude of the monitoring station address, the daily individual exposure concentration of each mother during pregnancy was obtained by using the inverse distance weight method, so as to obtain the exposure dose of each mother during the whole pregnancy, the first trimester, the second trimester and the third trimester. The association between maternal PM10 and PM2.5 exposure during pregnancy and neonatal birth weight was explored by using generalized additive model and negative control exposures analysis method, and the exposure dose of three pregnancy periods were included in the model to find the key window period. Results (1) Totally, 4 602 subjects were included in this study, with an average birth weight of(3 420.98±465.27)g. The average exposure concentrations of PM10 and PM2.5 during pregnancy were 114.15 μg/m3 and 54.71 μg/m3, respectively. (2) For every 10 μg/m3 increase of PM10 and PM2.5 exposure during pregnancy, the birth weight of newborn decreased by 30.46 g (P=0.013)and 51.81 g(P=0.028), respectively; there was no significant association between PM10 and PM2.5 exposure and neonatal birth weight in negative control exposure period(PM10: P=0.166; PM2.5: P=0.650). (3) The results of critical window period analysis showed that the birth weight decreased by 11.56 g(P=0.004)and 43.85 g(P<0.001)for every 10 μg/m3 increase of PM10 and PM2.5 exposure concentration during the first trimester, and decreased by 13.09 g(P=0.001)and 44.04 g(P<0.001)during the third trimester. Conclusion Maternal exposure to PM10 and PM2.5 during pregnancy can reduce neonatal birth weight, and the first trimester and the third trimester are the critical exposure window periods.

Key words: Pregnancy, Inhalable particulate matter, Fine particulate matter, Birth weight, Negative control exposures analysis

中图分类号: 

  • R122.7
[1] Badshah S, Mason L, McKelvie K, et al. Risk factors for low birthweight in the public-hospitals at Peshawar, NWFP-Pakistan[J]. BMC Public Health, 2008, 8(1): 197. doi: 10.1186/1471-2458-8-197.
[2] Risnes KR, Vatten LJ, Baker JL, et al. Birthweight and mortality in adulthood: a systematic review and meta-analysis[J]. Int J Epidemiol, 2011, 40(3): 647-661.
[3] 华琦, 谭静, 刘朝晖,等. 出生体质量与青少年期单纯性肥胖及血脂、血糖、血压相关关系的队列研究[J].中华内科杂志, 2007, 46(11): 923-925. HUA Qi, TAN Jing, LIU Zhaohui, et al. A cohort study on the correlation between birth weight, simple obesity, blood lipids, blood glucose and blood pressure from childhood to adolescence[J]. Chinese Journal of Internal Medicine, 2007, 46(11): 923-925.
[4] Ornoy A. Prenatal origin of obesity and their complications: gestational diabetes, maternal overweight and the paradoxical effects of fetal growth restriction and macrosomia[J]. Reprod Toxicol, 2011, 32(2): 205-212.
[5] Salgado CM, Brandao Veiga Jardim PC, Goncalves Teles FB, et al. Low birth weight as a marker of changes in ambulatory blood pressure monitoring[J]. Arq Bras Cardiol, 2009, 92(2): 113-121.
[6] Zhao Y, Wang SF, Mu M, et al. Birth weight and overweight/obesity in adults: a meta-analysis[J]. Eur J Pediatr, 2012, 171(12): 1737-1746.
[7] Fan CF, Huang TT, Cui FF, et al. Paternal factors to the offspring birth weight: the 829 birth cohort study[J]. Int J Clin Exp Med, 2015, 8(7): 11370-11378.
[8] Slemming W, Bello B, Saloojee H, et al. Maternal risk exposure during pregnancy and infant birth weight[J]. Early Hum Dev, 2016, 99: 31-36. doi: 10.1016/j.earlhumdev.2016.03.012.
[9] Gray SC, Edwards SE, Miranda ML. Assessing exposure metrics for PM and birth weight models[J]. J Expo Sci Environ Epidemiol, 2010, 20(5): 469-477.
[10] Ines Balsa A, Caffera M, Bloomfield J. Exposures to particulate matter from the eruptions of the puyehue volcano and birth outcomes in Montevideo, Uruguay[J]. Environ Health Persp, 2016, 124(11): 1816-1822.
[11] Han YY, Ji YW, Kang SY, et al. Effects of particulate matter exposure during pregnancy on birth weight: a retrospective cohort study in Suzhou, China[J]. Sci Total Environ, 2018, 615: 369-374. doi: 10.1016/j.scitotenv.2017.09.236.
[12] Ye L, Ji YW, Lv W, et al. Associations between maternal exposure to air pollution and birth outcomes: a retrospective cohort study in Taizhou, China[J]. Environ Sci Pollut R, 2018, 25(22): 21927-21936.
[13] Lin LZ, Li Q, Yang J, et al. The associations of particulate matters with fetal growth in utero and birth weight: a birth cohort study in Beijing, China[J]. Sci Total Environ, 2020, 709: 136246. doi: 10.1016/j.scitotenv.2019.136246.
[14] Yang Y, Lin Q, Liang Y, et al. The mediation effect of maternal glucose on the association between ambient air pollution and birth weight in Foshan, China[J]. Environ Pollut, 2020, 266(Pt 1): 115128.. doi: 10.1016/j.envpol.2020.115128.
[15] Lipsitch M, Tchetgen ET, Cohen T. Negative controls a tool for detecting confounding and bias in observational studies[J]. Epidemiology, 2010, 21(3): 383-388.
[16] 阚慧,张淼,郑英杰. 阴性对照法:原理、方法及应用[J].中华流行病学杂志, 2020(4): 594-598. doi: 10.3760/cma.j.cn112338-20191109-00796. KAN Hui, ZHANG Miao, ZHENG Yingjie. On ‘negative control methods’: related principles, methods and applications[J]. Chinese Journal of Epidemiology, 2020(4): 594-598. doi: 10.3760/cma.j.cn112338-20191109-00796.
[17] Liew Z, Kioumourtzoglou MA, Roberts AL, et al. Use of negative control exposure analysis to evaluate confounding: an example of acetaminophen exposure and attention-deficit/hyperactivity disorder in nurses health study II[J]. Am J Epidemiol, 2019, 188(4): 768-775.
[18] Villar J, Ismail LC, Victora CG, et al. International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-sectional Study of the INTERGROWTH-21st Project[J]. Lancet, 2014, 384(9946): 857-868.
[19] Jo H, Eckel SP, Chen JC, et al. Associations of gestational diabetes mellitus with residential air pollution exposure in a large Southern California pregnancy cohort[J]. Environ Int, 2019, 130: 104933. doi: 10.1016/j.envint.2019.104933.
[20] Wilcox AJ, Weinberg CR, Basso O. On the pitfalls of adjusting for gestational age at birth[J]. Am J Epidemiol, 2011, 174(9): 1062-1068.
[21] Shang L, Huang LY, Yang WF, et al. Maternal exposure to PM2.5 may increase the risk of congenital hypothyroidism in the offspring: a national database based study in China[J]. BMC Public Health, 2019, 19(1): 1412. doi: 10.1186/s12889-019-7790-1.
[22] 贾梦唯,赵天良,张祥志,等. 南京主要大气污染物季节变化及相关气象分析[J].中国环境科学,2016, 36(9): 2567-2577. JIA Mengwei, ZHAO Tianliang, ZHANG Xiangzhi, et al. Seasonal variations in major air pollutants in Nanjing and their meteorological correlation analyses[J]. China Environmental Science, 2016, 36(9): 2567-2577.
[23] Yu YY, Li HK, Sun XR, et al. Identification and estimation of causal effects using a negative-control exposure in time-series studies with applications to environmental epidemiology [J]. Am J Epidemiol, 2021, 190(3): 468-476.
[24] Roth C, Magnus P, Schjolberg S, et al. Folic acid supplements in pregnancy and severe language delay in children[J]. JAMA, 2011, 306(14): 1566-1573.
[25] Suren P, Roth C, Bresnahan M, et al. Association between maternal use of folic acid supplements and risk of autism spectrum disorders in children[J]. JAMA, 2013, 309(6): 570-577.
[26] Easey KE, Timpson NJ, Munafo MR. Association of prenatal alcohol exposure and offspring depression: a negative control analysis of maternal and partner consumption[J]. Alcohol Clin Exp Res, 2020, 44(5): 1132-1140.
[27] Wilson A, Chiu YHM, Hsu HHL, et al. Potential for bias when estimating critical windows for air pollution in childrens health[J]. Am J Epidemiol, 2017, 186(11): 1281-1289.
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