{
  "abstract": "Background Evidence regarding the association between maternal exposure to PM 2.5 and its major components and the risk of gestational diabetes mellitus (GDM) is still limited. Our study aimed to fill this research gap with a case–control study in Southern China.Methods 191 cases and 764 controls were enrolled during 2013–2020. Daily mean PM 2.5 and component concentrations were obtained from the ChinaHighAirPollutants. GDM was diagnosed using the WHO criteria. We used logistic regression integrated with inverse probability weighting and generalised weighted quantile sum regression models to estimate the association between PM2.5 components during multiple trimesters and GDM risk.Results A positive association was observed between PM 2.5 exposure and GDM risk, with ORs of 1.36 (95% CI 1.11 to 1.66) and 1.34 (95% CI 1.08 to 1.67) per IQR increase in the first (18.8 µg/m³) and second (20.6 µg/m³) trimesters, respectively. Women with low income and education levels were particularly vulnerable to GDM following particulate exposure. Black carbon (weight: 0.33), nitrate (weight: 0.22) and sulfate (weight: 0.20) contributed most of the overall PM2.5 mixture effect in the first trimester, while in the second trimester, sulfate (weight: 0.38) made the most significant contribution, followed by black carbon (weight: 0.22) and nitrate (weight: 0.20).Conclusions The risk of GDM was significantly associated with PM 2.5 exposure in the first and second trimesters. The mixture impact in the first trimester was mainly attributed to black carbon, nitrate and sulfate, while that in the second trimester was mainly attributed to sulfate, black carbon and nitrate.",
  "authors": [
    {
      "affiliations": [
        "Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China"
      ],
      "name": "Xian Lin"
    },
    {
      "affiliations": [
        "Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China"
      ],
      "name": "Yanji Qu"
    },
    {
      "affiliations": [
        "Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China"
      ],
      "name": "Xiaoru Wu"
    },
    {
      "affiliations": [
        "Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China"
      ],
      "name": "Ximeng Wang"
    },
    {
      "affiliations": [
        "Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China"
      ],
      "name": "Xiangmin Gao"
    },
    {
      "affiliations": [
        "Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China"
      ],
      "name": "Yong Wu"
    },
    {
      "affiliations": [
        "Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China"
      ],
      "name": "Xinli Zhou"
    },
    {
      "affiliations": [
        "Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China"
      ],
      "name": "Xiaoqing Liu"
    },
    {
      "affiliations": [
        "Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China"
      ],
      "name": "Shirui Chen"
    },
    {
      "affiliations": [
        "Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China"
      ],
      "name": "Shanidewuhaxi Tuohetasen"
    },
    {
      "affiliations": [
        "Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China"
      ],
      "name": "Zhibing Chen"
    },
    {
      "affiliations": [
        "Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China"
      ],
      "name": "Dan Chen"
    },
    {
      "affiliations": [
        "Global Health Research Center, Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong, China"
      ],
      "name": "Jimei Chen"
    },
    {
      "affiliations": [
        "Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, Guangdong, China"
      ],
      "name": "Wangjian Zhang"
    }
  ],
  "title": "Maternal exposure to PM2.5 and its major components and the risk of gestational diabetes mellitus: evidence from a case–control study in Southern China",
  "uid": "d75b6263-19c4-569b-9801-0d6b804095e8"
}