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Heat exposure induced risks of preterm birth mediated by maternal hypertension

Nature Medicine (2024)Cite this article

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Abstract

Heat exposure is associated with an increased risk of preterm birth (PTB), with previous work suggesting that maternal blood pressure may play a role in these associations. Here we conducted a cohort study of 197,080 singleton live births across 8 provinces in China from 2015 to 2018. The study first estimated the associations between heat exposure, maternal hypertension and clinical subtypes of PTB, and then quantified the role of maternal hypertension in heat and PTB using mediation analyses. We show that heat exposure (>85th, 90th and 95th percentiles of local temperature distributions) spanning from conception to the 20th gestational week was associated with a 15–21% increase in PTB, and a 20–22% increase in medically indicated PTB. Heat exposure is likely to increase the risk of maternal hypertension and elevated blood pressure. Maternal hypertension mediated 15.7% and 33.9% of the effects of heat exposure (>90th percentile) on PTB and medically indicated PTB, respectively. Based on this large-population study, we found that exposure to heat in early pregnancy can increase the risk of maternal hypertension, thereby affecting the incidence of PTB.

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Fig. 1: Associations of preterm birth and its subtypes with heat exposure.
Fig. 2: Associations of maternal hypertension after 20 weeks of gestation or SBP trajectory patterns with heat exposure.
Fig. 3: Mediation effect of maternal hypertension on the association between heat exposure and preterm birth during 1–20 weeks of gestation.

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Data availability

Meteorological data can be accessed from the China Meteorological Data Service Center by researcher application (http://data.cma.cn). Air pollution data can be accessed at the China National Environmental Monitoring Centre by application (www.cnemc.cn/). The individual, de-identified participant data that underline the results reported in this study (text, tables, figures and appendices) are available via figshare at https://doi.org/10.6084/m9.figshare.25393195 (ref. 77). Source data are provided with this paper.

Code availability

Codes to reproduce the statistical analysis are made available at https://github.com/liyunn11/mediation-analysis-BP.

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Acknowledgements

This study was supported by grants from the National Natural Science Foundation of China (42175183), the National Key R&D Program of China (2018YFA0606200) and the Sanming Project of Medicine in Shenzhen, China (number SZSM202111001). We appreciate the efforts of all staff in data collection, data entry and reporting in the monitoring counties (Xinhua, Zhengding, Lishan, Tiedong, Macheng, Luotian, Yueyanglou, Yueyang, Haicang, Jimei, Zijin, Longchuan, Gongjing, Rong, Tonghai, Huaning). We acknowledge and thank the managers of the Maternal and Newborn Health Monitoring Program in the above monitoring areas. T. Benmarhnia, Department of Family Medicine and Public Health and Scripps Institution of Oceanography, University of California San Diego; L. Knibbs, School of Public Health, the University of Sydney; and M.S. Bloom, Department of Global and Community Health, George Mason University, contributed to the revising of an early draft of the paper.

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  1. These authors contributed equally: Liyun Wang, Jiangli Di.

Authors and Affiliations

  1. School of Public Health, Sun Yat-sen University, Guangzhou, China

    Liyun Wang & Qiong Wang

  2. Vanke School of Public Health, Tsinghua University, Beijing, China

    Liyun Wang, Qian Di, John S. Ji, Wannian Liang & Cunrui Huang

  3. National Center for Women and Children’s Health, Chinese Center for Disease Control and Prevention, Beijing, China

    Jiangli Di & Wei Zhao

  4. School of Public Health, Zhengzhou University, Zhengzhou, China

    Huanhuan Zhang

  5. National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, China

    Xiaoming Shi

  6. Institute of Healthy China, Tsinghua University, Beijing, China

    Wannian Liang & Cunrui Huang

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Contributions

C.H. initiated the study and contributed to the methodological design and research coordination. L.W., J.D. and W.Z. collected and managed the data. L.W. performed the statistical analysis and drafted the paper. C.H., J.D., Q.W., H.Z., W.Z., X.S, Q.D., J.S.J. and W.L. provided an expert review of the paper. C.H. supervised the study. All authors have read the final version of the paper and approved the submission.

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Correspondence to Wei Zhao or Cunrui Huang.

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Extended data

Extended Data Fig. 1 Adjusted HRs and 95% CIs of preterm birth associated with weekly-specific heat exposure during the 20 weeks following conception.

a, heat exposure (85th percentile), reference (50th percentile); b, heat exposure (95th percentile), reference (50th percentile). Distributed lag nonlinear models (DLNMs) incorporated with Cox regression models were adjusted for maternal age, pre–pregnancy BMI, mother’s education, residential area, year of birth, season of conception, behavioral risk factors, parity, adequacy of prenatal care utilization, infant sex, average temperature after 20 weeks of gestation, cold exposure during 1–20 weeks, relative humidity, PM2.5 and O3 exposure during the entire pregnancy (n=197,080). The hatched section represents 95% CI. The red dashed line that lies within the shaded region represents the hazard ratio (HR). The horizontal solid line represents the HR equal to 1, which is indicative of no effect.

Source data

Extended Data Fig. 2 The frequency of heat exposure days experienced by pregnant women during weeks 1–20 of gestation.

The number of heat exposure days of pregnant women during 1–20 weeks of gestation represented the number of days when the daily mean temperature was higher than the 85th percentiles of local temperature distribution (n=197,080). The blue column represents that pregnant women were not exposed to heat.

Source data

Extended Data Fig. 3 The diastolic blood pressure trajectories after 20 weeks of gestation in 16 counties, China.

DBP trajectory patterns of the study participants after 20th gestational week (n=133,515).

Source data

Extended Data Fig. 4 Geographical distribution of the study subjects.

Geographical spread of the study participants in 16 counties across 8 provinces in China. There are Lishan County and Tiedong County, Anshan City, Liaoning Province; Xinhua County and Zhengding County, Shijiazhuang City, Hebei Province; Macheng County and Luotian County, Huanggang City, Hubei Province; Gongjing County and Rong County, Zigong City, Sichuan Province; Yueyanglou County and Yueyang County, Yueyang City, Hunan Province; Haicang County and Jimei County, Xiamen City, Fujian Province; Tonghai County and Huaning County, Yuxi City, Yunnan Province; Zijin County and Longchuan County, Heyuan City, Guangdong Province. The figure was drawn using ArcGIS 10.2, data from: https://www.webmap.cn/main.do?method=index.

Extended Data Fig. 5 The inclusion and exclusion criteria of study subjects.

After excluding stillbirths (n=314), multiple births (n= 6,803), women with gestational age <20 or >44 weeks (n=123) and women aged <13 years or >50 years (n=4,409), women that not measured BP at least once after 20 weeks’ gestation (n=111,40), women with hypertension before 20 weeks’ gestation (n=2,578), a total of 197,080 participants were eventually included in the analyses. 133,515 pregnant women with more than four BP measurements after 20 weeks’ gestation were included in BP trajectories study.

Extended Data Fig. 6 Frequency of preterm births in different gestational weeks in the birth cohort.

MI–PTB, medically induced preterm birth; S–PTB, spontaneous preterm birth. There were 3,600 medically induced preterm birth and 3,915 spontaneous preterm birth. (n=7515).

Source data

Extended Data Fig. 7 Directed acyclic graph (DAG) modelling the causal framework between heat exposure and preterm birth.

Maternal hypertension (M) are affected by heat exposure (X) and influence preterm birth risk (Y), and thus act as the mediators of interest in this analysis. All confounding variables, including the listed baseline covariates and time-varying confounder (C) are assumed to influence the exposure, mediator and outcome.

Supplementary information

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Supplementary Tables 1–21.

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Source data

Source Data Fig. 1

Statistical source data for Fig. 1.

Source Data Fig. 2

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Source Data Fig. 3

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Source Data Extended Data Fig. 1

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Source Data Extended Data Fig. 2

Statistical source data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Statistical source data for Extended Data Fig. 3.

Source Data Extended Data Fig. 6

Statistical source data for Extended Data Fig. 6.

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Wang, L., Di, J., Wang, Q. et al. Heat exposure induced risks of preterm birth mediated by maternal hypertension. Nat Med (2024). https://doi.org/10.1038/s41591-024-03002-w

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