Long-term Exposure to Fine Particulate Matter and Climate factors and The Risk of Circulatory system Disease in Women with Gestational Hypertension: A Nationwide Cohort Study in South Korea.

Long-term Exposure to Fine Particulate Matter and Climate factors and The Risk of Circulatory system Disease in Women with Gestational Hypertension: A Nationwide Cohort Study in South Korea. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term Exposure to Fine Particulate Matter and Climate factors and The Risk of Circulatory system Disease in Women with Gestational Hypertension: A Nationwide Cohort Study in South Korea. Seyedehmahla Hosseini, Youngrin Kwag, Min-Ho Kim, Yoonkyung Chang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7783470/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background: Gestational hypertension (GHTN) is a known risk factor for long-term circulatory system conditions, encompassing both cardiovascular and other vascular disorders (CSDs). Exposure to fine particulate matter (PM2.5) from air pollution and adverse climate factors may exacerbate this risk through inflammation and oxidative stress. This study investigated the associations between long-term exposure to PM2.5 and climate factors and the risk of CSD in pregnant women with GHTN. Methods: We conducted a retrospective cohort study using Korean National Health Insurance data from 2015–2019, including 307,938 first-time pregnant women after exclusions. Environmental exposures (PM₂.₅, high temperature, and atmospheric pressure) were measured during pregnancy and throughout the two-year postpartum period. GHTN and CSD outcomes were identified via ICD-10 codes. Propensity score matching (1:4) was applied, followed by logistic regression to estimate odds ratios (ORs) for CSD, stratified by GHTN status. Results: GHTN significantly increased the risk of postpartum CSD, with an OR of up to 2.78 (95% CI: 2.63–2.94) within two years after delivery. PM₂.₅ exposure was associated with increased CSD risk, increasing from an OR of 1.07 (95% CI: 1.05–1.08) during pregnancy to 1.33 (95% CI: 1.29–1.36) postpartum. High temperature and atmospheric pressure also had positive associations with CSD risk. These effects intensified with longer exposure durations and were more pronounced among women with GHTN, suggesting a potential interaction. Additionally, CSD risk varies by socioeconomic status and region. Conclusions: Both gestational hypertension and prolonged environmental exposure significantly increase the risk of postpartum circulatory system diseases. These findings highlight the importance of postpartum monitoring and targeted environmental health interventions, especially for women with hypertensive disorders during pregnancy. Gestational hypertension (GHTN) circulatory system diseases (CSDs) PM₂.₅ postpartum health air pollution high temperature environmental exposure Figures Figure 1 Figure 2 1. Introduction Circulatory system diseases (CSDs), encompassing both cardiovascular conditions (e.g., ischemic heart disease, stroke, heart failure) and other vascular disorders such as peripheral, venous, and lymphatic diseases, contribute substantially to morbidity worldwide. Nearly half of the global burden is concentrated in the Asia–Pacific region, highlighting significant regional health disparities[ 1 – 3 ]. Women who experience hypertensive disorders during pregnancy, such as preeclampsia and gestational hypertension, are at a significantly greater risk of developing CSD later in life[ 4 – 8 ]. Pregnancy places substantial physiological stress on the cardiovascular system, potentially unmasking latent vulnerabilities that may contribute to future circulatory disease[ 9 ]. Hypertensive disorders of pregnancy (HDP) affect approximately 5% to 10% of pregnancies worldwide and represent a major cause of maternal morbidity and mortality [ 10 ]. Among these, gestational hypertension (GHTN) is the most common subtype, occurring in 5% to 8% of otherwise healthy pregnant women [ 11 – 15 ]. GHTN is defined as new-onset hypertension (systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg, measured on two occasions at least four hours apart) that develops after 20 weeks of gestation in women with previously normal blood pressure and without proteinuria [ 16 , 17 ]. Environmental stressors such as fine particulate matter (PM2.5), high temperature, and fluctuations in atmospheric pressure are increasingly recognized as significant risk factors for cardiovascular morbidity and mortality [ 18 , 19 ]. Long-term exposure to PM 2.5 has shown particularly strong associations with CSD outcomes, especially among vulnerable populations such as pregnant women, older adults, and children [ 20 , 21 ]. Notably, approximately 72% of deaths attributed to PM exposure are due to ischemic heart disease and stroke [ 22 ]. In addition, recent studies have identified non-optimal ambient temperatures, including both extreme heat and cold, as major contributors to excess mortality from cardiovascular diseases, which constitute the majority of circulatory system disease deaths worldwide[ 23 – 25 ]. Despite growing evidence connecting environmental exposure to cardiovascular outcomes, pregnancy represents a uniquely sensitive period during which these exposures may have amplified effects. Women with GHTN are especially vulnerable, and face both immediate pregnancy-related risks and elevated long-term CSD risk postpartum. However, few studies have examined the combined impact of hypertensive pregnancy disorders and environmental stressors. In particular, the interaction between GHTN and prolonged exposure to PM₂.₅, high temperature, and atmospheric pressure remains understudied in large-scale population-based cohorts. Most existing research has focused narrowly on cardiovascular disease outcomes such as ischemic heart disease and stroke. In contrast, this study adopts a broader lens by evaluating all circulatory system diseases (CSDs), including arterial, venous, and lymphatic conditions, to offer a more comprehensive understanding of maternal vascular health. Therefore, this study aimed to investigate the associations between long-term exposure to PM₂.₅, high temperature, and atmospheric pressure during pregnancy and the risk of subsequent CSD in women diagnosed with GHTN. By focusing on this physiologically vulnerable period, we assess whether environmental stressors further increase postpartum CSD risk beyond that associated with GHTN alone. Exposures are evaluated across three key windows: during pregnancy, from delivery to two years postpartum, and cumulatively from conception to two years after delivery. This approach offers novel insights into how environmental factors shape maternal circulatory health trajectories and may inform targeted prevention strategies. 2. Methods 2.1 Data sources This retrospective cohort study utilized data from the Korean National Health Insurance Service (NHIS), a comprehensive nationwide database that includes medical claims for nearly the entire Korean population [ 26 ]. The fine particulate matter (PM₂.₅ ; particles ≤ 2.5 µm in diameter) data were obtained from the Korea Environment Corporation [ 27 ], whereas the climate exposure data, including daily maximum temperature and average atmospheric pressure, were obtained from the Korea Meteorological Administration [ 28 ]. 2.2 Study population The study population included all women whose first pregnancy records were recorded between 2012 and 2019 in the NHIS database. Participants were eligible if they had valid pregnancy episodes and corresponding environmental exposure data. Individuals were excluded if they (1) relocated outside the seven major metropolitan areas during the follow-up period; (2) had a diagnosis of cardiovascular disease prior to pregnancy; or (3) were lost to follow-up due to death, emigration, or missing key information (e.g., age, income), which was due to non-recording in the original NHIS database. Cardiovascular disease prior to pregnancy was defined via a targeted set of ICD-10 codes representing major chronic conditions likely to confound postpartum circulatory outcomes. Specifically, we excluded individuals with diagnoses of hypertension (I10–I15), heart failure (I50), myocardial infarction (I21–I22), or stroke (I60–I64). These exclusions were selected to minimize reverse causality and ensure that the outcome reflected incident CSD beyond the hypertensive spectrum already captured by the exposure variable. The final analytic sample was determined after applying these exclusion criteria, as illustrated in Fig. 1 . 2.3 Variables and definitions The variables included demographic factors such as age at pregnancy, income level (categorized as low [levels 1–5], middle [levels 6–10], mid-high [levels 11–15], and high [levels 16–20], on the basis of the NHIS income classification system, which ranks individuals into 20 levels according to income and assets), and residential cities which are defined as the seven major metropolitan cities in South Korea (including Seoul, Busan, Incheon, Daegu, Daejeon, Gwangju, and Ulsan). Gestational hypertension (GHTN) was identified via the ICD-10 codes O13 and O16. While O13 specifically captures gestational hypertension without significant proteinuria, O16 may include other hypertensive disorders. Both codes were included to maximize case identification. Circulatory system disease (CSD) outcomes were defined as any diagnosis coded under ICD-10 I00–I99, excluding I10–I15 (hypertensive diseases) to prevent overlap with the exposure variable and minimize outcome misclassification. This approach captures a broad spectrum of arterial, venous, and lymphatic conditions relevant to systemic circulatory health. Environmental exposures were measured over specified time windows and included the average concentration of PM₂.₅, daily maximum temperature (defined as the highest recorded temperature within a 24-hour period), and average atmospheric pressure. 2.4 Statistical analysis To minimize confounding, 1:4 propensity score (PS) matching was performed on the basis of gestational hypertension (GHTN) status, maternal age at pregnancy, income level, and residential area. Propensity scores were estimated via logistic regression, and each exposed participant was matched to four unexposed individuals via nearest neighbor matching with replacement. Covariate balance after matching was evaluated via standardized mean differences (SMDs). The associations between environmental and climate exposures and the risk of CSDs were evaluated across three distinct exposure periods: (1) from the start of pregnancy to delivery, (2) from delivery to two years postpartum, and (3) from pregnancy through two years postpartum. The analyses were stratified by GHTN status. Descriptive statistics summarized the baseline characteristics. Group differences were assessed via chi-square tests for categorical variables and t-tests for continuous variables. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between exposure and circulatory system outcomes. All the statistical analyses were conducted via SAS software (version 9.4, SAS Institute, Inc., Cary, NC, USA), and data visualization, including forest plots, was performed via RStudio (version 2025.05.01, Posit Software, Boston, MA, USA). 3. Results A total of 386,378 women with a first recorded pregnancy between 2015 and 2019 in the seven major metropolitan cities of South Korea were initially identified. After 78,149 participants with a history of cardiovascular disease or pre-existing hypertension prior to pregnancy (based on ICD-10 codes) were excluded, 308,129 women remained. A further 291 women were excluded due to relocation, death, or missing key data (e.g., age, income), yielding a final analytic sample of 307,938 women. Among this population, 8,062 (2.62%) were diagnosed with GHTN, and 299,876 (97.38%) were not. To reduce confounding factors, 1:4 propensity score matching was conducted on the basis of GHTN status, age at pregnancy, income level, and residential city. After matching, 8,051 women with GHTN were successfully matched to 32,204 women without GHTN, yielding a matched cohort of 40,255 participants (Fig. 1 ). The baseline characteristics after matching are presented in Table 1 , and the pre-matching characteristics are presented in Supplementary Table S1 . Prior to PS matching, significant differences were observed between women with and without GHTN in terms of age, income level, residential city, and CSD status (Supplementary Table S1 ). For example, women with GHTN had a greater mean age (33.12 ± 4.03 vs. 32.66 ± 3.91 years, p < 0.001), a greater proportion in the low-income group (14.97% vs. 12.76%, p < 0.001), and a notably greater prevalence of CSD (34.74% vs. 16.91%, p < 0.001). Table 1 Participants and exposure factors characteristics after PS Matching. Variables Total population Without 1 GHTN With GHTN 3 SMDs Total 40,255 32,204 (80.00%) 8,051 (20.00%) Age(years) 33.12 ± 4.01 33.12 ± 4.01 33.12 ± 4.01 0.000 Income group Low level 6,035 (14.99%) 4,828 (14.99%) 1,207 (14.99%) 0.000 Middle level 9,230 (22.93%) 7,384 (22.93%) 1,846 (22.93%) 0.000 Mid-high level 14,231 (35.35%) 11,384 (35.35%) 2,847 (35.36%) 0.00026 High level 10,759 (26.73%) 8,608 (26.73%) 2,151 (26.72%) 0.00028 City Gwangju 280 (0.70%) 224 (0.70%) 56 (0.70%) 0.000 Daegu 725 (1.80%) 580 (1.80%) 145 (1.80%) 0.000 Daejeon 410 (1.02%) 328 (1.02%) 82 (1.02%) 0.000 Busan 9,325 (23.16%) 7,460 (23.16%) 1,865 (23.16%) 0.000 Seoul 28,800 (71.54%) 23,040 (71.54%) 5,762 (71.47%) 0.001 Ulsan 171 (0.42%) 136 (0.42%) 35 (0.43%) 0.002 Incheon 4,505 (1.46%) 436 (1.35%) 108 (1.34%) 0.001 2 CSD status With CSD 8,359 (20.77%) 5,563 (17.27%) 2,796 (34.73%) <0.001* 1 Gestatioal Hypertension 2 Circulatory system disease, 3 Standardized mean differences, * p-value calculated by chi-square test. Characteristics of 40,255 participants after 1:4 propensity score matching by gestational hypertension (GHTN) status. Values are presented as mean ± standard deviation or number (percentage). The standardized mean differences (SMDs) used to evaluate the covariate balance after PS matching. Matching variables (age, income, city) were well balanced between groups (SMDs ≈ 0.000). Circulatory system disease(CSD) prevalence differed significantly between groups (p < 0.001). After applying 1:4 propensity score matching on the basis of GHTN status, maternal age, income level, and residential city, a total of 40,255 participants were included in the matched cohort—comprising 8,051 women with GHTN and 32,204 without GHTN (Table 1 ). The baseline characteristics were well balanced between the two groups. The mean age was identical (33.12 ± 4.01 years; SMD = 0.000), and the income distribution showed perfect alignment across all categories (SMDs < 0.001). Residential city proportions were also closely matched, with SMDs ranging from 0.000 to 0.002, confirming successful covariate balance. Despite this balance, the prevalence of CSD remained significantly greater in the GHTN group than in the non-GHTN group (34.73% vs. 17.27%, p < 0.001). This difference underscores the strong association between gestational hypertension and postpartum CSD risk, independent of demographic and regional factors. Among the total population, the most prevalent categories of circulatory system diseases were venous and lymphatic disorders (42.43%; 23,121 cases), arterial diseases (23.24%; 12,656 cases), and other heart conditions (13.93%; 7,585 cases). Among women diagnosed with gestational hypertension (GHTN; 3,773 cases), venous and lymphatic diseases were overwhelmingly predominant (69.81%; 2,634 cases), followed by arterial diseases (11.02%; 416 cases), other heart conditions (9.09%; 343 cases), and cerebrovascular diseases (3.84%; 145 cases) (Supplementary Table S3). These findings underscore the disproportionate burden of venous and vascular disorders among women with GHTN, suggesting a potential link between hypertensive pregnancy complications and vascular health outcomes. The environmental and climate exposure characteristics are summarized in Table 2 . The average concentration of PM 2.5 across the seven major cities was 22.996 µg/m³ (± 12.9), ranging from 1.863 to 135.285 µg/m³. The mean high temperature exposure was 19.06°C (± 9.5), ranging from − 11.9 to 39.6°C. The average atmospheric pressure exposure was 1007.9 hPa (± 7.9), ranging from 979.2 to 1030.0 hPa. These findings underscore the substantial variability in the environmental conditions experienced by the study population. Table 2 Distribution of total concentration of PM 2.5 and Climate factors in 7 major cities in Korea. 1 PM2.5(µg/m³) 2 H-Tem(°C) 3 Ave-Pressure(hPa) Minimum 1.863 -11.9 979.2 10th percentile 9.415 5.7 997.9 25th percentile 13.672 11.2 1001.8 Median 20.504 20.4 1008.0 Mean 22.996 ± 12.9 19.06 ± 9.5 1007.9 ± 7.9 75th percentile 29.321 27.0 1014.1 90th percentile 39.877 30.6 1018.3 Maximum 135.285 39.6 1030.0 1 Particular matter < 2.5(µg/m³), 2 High temperature exposure, 3 Average Atmospheric Pressure exposure. Distribution of total concentrations of PM2.5 and climate factors in seven major cities in Korea. Values represent the minimum, 10th percentile, 25th percentile, median, mean (± standard deviation), 75th percentile, 90th percentile, and maximum for each variable. Table 3 presents the associations between GHTN, environmental and climate exposures, and the risk of CSD across three distinct exposure windows: (1) from the start of pregnancy until delivery, (2) from delivery until two years postpartum, and (3) from pregnancy until two years postpartum. Logistic regression models were applied using propensity score–matched data, including the Model 1(adjusting for GHTN and PM₂.₅), Model 2 (adjusting for GHTN, PM₂.₅, high daily temperature, and average atmospheric pressure), and a Model 3 (additionally adjusting for age, income level, and residential area). Table 3 Association of gestational hypertension and environmental exposures with circulatory system disease. Variables From the start of pregnancy until delivery From delivery until 2 years later From pregnancy until 2 years after delivery Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Odds ratio/95% CI Odds ratio/95% CI Odds ratio/95% CI Odds ratio/95% CI Odds ratio/95% CI Odds ratio/95% CI Odds ratio/95% CI Odds ratio/95% CI Odds ratio/95% CI 1 GHTN 2.628(2.489–2.775) 2.653(2.512–2.802) 2.658(2.516–2.807) 2.712(2.567–2.864) 2.759(2.611–2.915) 2.783(2.633–2.941) 2.609(2.471–2.756) 2.669(2.526–2.819) 2.695(2.550–2.847) 2 PM 2.5 (µg/m³) 1.033(1.021–1.045) 1.067(1.053–1.082) 1.066(1.051–1.082) 1.122(1.104–1.141) 1.199(1.176–1.222) 1.231(1.206–1.258) 1.137(1.112–1.162) 1.282(1.247–1.317) 1.331(1.294–1.362) 3 H-Tem(°C) 1.076(1.059–1.092) 1.086(1.054–1.119) 1.166(1.132–1.201) 1.238(1.091–1.405) 1.251(1.205–1.299) 1.376(1.171–1.617) 4 Ave-Pressure(hPa) 1.059(1.038–1.079) 1.080(1.036–1.127) 1.119(1.086–1.154) 1.226(1.020–1.472) 1.107(1.071–1.145) 1.309(1.028–1.667) Age (years) 1.020(1.013–1.026) 1.022(1.016–1.029) 1.021(1.014–1.027) Income Low level 1.128(1.041–1.222) 1.098(1.013–1.191) 1.049(0.967–1.137) Middle level 1.146(1.068–1.230) 1.146(1.067–1.230) 1.102(1.027–1.183) Mid-high level 1.058(0.993–1.129) 1.054(0.988–1.124) 1.063(0.998–1.133) High level Ref Ref Ref Area Gwangju Ref Ref Ref Daegu 0.952(0.667–1.360) 0.620(0.384–1.001) 0.504(0.292–0.868) Daejeon 1.088(0.737–1.605) 1.110(0.748–1.645) 1.488(1.017–2.178) Busan 1.221(0.901–1.665) 1.369(0.975–1.922) 1.453(1.019–2.072) Seoul 1.227(0.887–1.699) 1.408(0.754–2.631) 1.739(0.797–3.796) Ulsan 1.177(0.718–1.930) 2.298(1.187–4.451) 2.906(1.401–6.031) Incheon 1.039(0.706–1.527) 1.956(1.136–3.368) 2.231(1.190–4.184) 1 Gestatioal Hypertension, 2 Particular matter < 2.5(µg/m³), 3 High temperature exposure, 4 Average Atmospheric Pressure exposure, CI: confidence interval. Logistic regression models were conducted after propensity score matching. Model 1 included gestational hypertension (GHTN) and PM₂.₅; Model 2 included GHTN, PM₂.₅, high daily temperature, and average atmospheric pressure; Model 3 included all variables, including age, income level, and residential area. For income, the high level was used as the reference group, and for residential area, Gwangju was the reference city. Three exposure periods were analyzed. Across all the models and exposure periods, GHTN was strongly and significantly associated with an elevated risk of CSD. Notably, the magnitude of the association increased as the exposure window lengthened. In the Model 3, the odds ratio (OR) for GHTN was 2.658 (95% CI: 2.516–2.807) from the start of pregnancy until delivery, increased to 2.783 (95% CI: 2.633–2.941) from delivery to two years postpartum, and remained elevated at 2.695 (95% CI: 2.550–2.847) over the entire two-year postpartum period. This trend indicates a cumulative effect of GHTN on long-term circulatory system outcomes, with a particularly strong impact in the extended postpartum period. PM₂.₅ exposure also demonstrated a clear dose-response pattern with increasing odds of CSD across longer exposure durations and more comprehensively adjusted models. For example, in the Model 3, the OR for PM₂.₅ increased from 1.066 (95% CI: 1.051–1.082) during pregnancy, to 1.231 (95% CI: 1.206–1.258) from delivery to two years postpartum, and reached 1.331 (95% CI: 1.294–1.362) when exposure from pregnancy through the entire two-year postpartum period was considered. These results suggest that prolonged exposure to fine particulate matter significantly increases the risk of developing CSD. Similarly, high temperature and average atmospheric pressure were positively associated with CSD risk. For example, in Model 3, which covered the full exposure period, high temperature was associated with an OR of 1.376 (95% CI: 1.171–1.617), and average pressure was associated with an OR of 1.309 (95% CI: 1.028–1.667), suggesting that chronic exposure to adverse climate conditions may exacerbate CSD risk in this population. Sociodemographic variables also contributed to risk differentials. Increasing age was consistently associated with increased odds of CSD. Compared with the high-income reference group, individuals in the low- and middle-income categories had increased risk, with the middle-income group showing a significant association in the full-period model (OR: 1.102; 95% CI: 1.027–1.183). Regional variations were also evident. Using Gwangju as the reference, significantly higher odds of CSD were observed in Incheon (OR: 2.231; 95% CI: 1.190–4.184), Ulsan (OR: 2.906; 95% CI: 1.401–6.031), and Busan (OR: 1.453; 95% CI: 1.019–2.072) in the full-period model. These regional disparities may reflect differences in local environmental exposure or healthcare access. Taken together, these findings demonstrate that GHTN substantially increases the risk of CSD, particularly as the follow-up duration increases. Moreover, long-term exposure to air pollution and extreme climate conditions further amplifies this risk, and the impact is modified by socioeconomic status and residential area. Supplementary Table S2 summarizes the associations between environmental and climate exposures and the risk of circulatory system disease (CSD) across three defined exposure windows. The analysis was conducted via logistic regression models with PS matching to control for potential confounders. Gestational hypertension (GHTN) was not included in these models to isolate the independent impact of environmental exposure on CSD risk. Across all exposure windows and models, PM₂.₅ exposure was consistently significantly associated with increased CSD risk. The effect size became stronger with longer exposure durations. For example, in the Model 3, the odds ratio (OR) for PM₂.₅ increased from 1.053 (95% CI: 1.038–1.069) during pregnancy, to 1.193 (95% CI: 1.169–1.218) during the post-delivery period, and reached 1.273 (95% CI: 1.238–1.309) when exposure was assessed over the full period from pregnancy to two years postpartum. High temperature exposure and average atmospheric pressure also showed significant positive associations with CSD, particularly in models 2 and 3. In Model 3 for the full exposure period, high temperature was associated with an OR of 1.354 (95% CI: 1.155–1.587), and average pressure was associated with an OR of 1.300 (95% CI: 1.025–1.649), suggesting that cumulative exposure to high daily temperatures and atmospheric fluctuations may play a role in CSD risk. Figure 2 shows the Model 3 results across the three exposure windows, providing a visual summary of the estimated associations. Sociodemographic covariates also played a role. Increasing age was associated with increased CSD risk. Compared with high-income individuals, low or middle-income individuals were more likely to have CSD. For example, the middle-income group showed a significant association in the full-period model (OR: 1.098; 95% CI: 1.025–1.177). In terms of geographic variation, several cities presented elevated CSD risk relative to Gwangju. Notably, in the full-period model, Ulsan (OR: 2.719; 95% CI: 1.326–5.572), Incheon (OR: 2.119; 95% CI: 1.141–3.935), and Busan (OR: 1.424; 95% CI: 1.005–2.019) were significantly associated with increased risk. These findings suggest that prolonged exposure to fine particulate matter and adverse climatic factors contribute to an increased risk of CSD, even when GHTN is not accounted, and that the risk intensifies with longer exposure durations. We investigated the interaction effects between environmental and climate exposures and GHTN on CSD risk across three exposure periods after propensity score matching (Table 4 ). The analyses were stratified by GHTN status to evaluate whether the associations between PM₂.₅, high temperature, and average atmospheric pressure exposures and CSD risk differ between women with and without GHTN. Table 4 Interaction between environmental exposures and gestational hypertension on circulatory system disease risk. Variables From the start of pregnancy until delivery From delivery until 2 years later From pregnancy until 2 years after delivery OR 95% CI p-value OR 95% CI p-value OR 95% CI p-value 1 PM2.5 at GHTN group 1.096 (1.080–1.113) < 0.001 1.271 (1.244–1.298) < 0.001 1.372 (1.333–1.413 ) < 0.001 PM2.5 at non-GHTN group 1.054 (1.039–1.070) 1.213 (1.188–1.239) 1.313 (1.277–1.351) 2 H-Tem at GHTN group 1.127 (1.094–1.162) < 0.001 1.285 (1.132–1.458) < 0.001 1.430 (1.217–1.680) < 0.001 H-Tem at non-GHTN group 1.072 (1.040–1.104) 1.217 (1.072–1.381) 1.356 (1.154–1.594) 3 Ave-Pressure at GHTN group 1.081 (1.037–1.127) < 0.001 1.226 (1.021–1.473) < 0.001 1.310 (1.028–1.668) < 0.001 Ave-Pressure at non-GHTN group 1.080 (1.036–1.126) 1.225 (1.020–1.472) 1.308 (1.027–1.666) 1 Particular matter < 2.5(µg/m³), 2 High temperature exposure, 3 Average Atmospheric Pressure exposure, 4 Circulatory system disease. Odds ratios (ORs), 95% confidence intervals (CIs), and p-values are presented for each environmental factor (PM₂.₅, high temperature, and average atmospheric pressure), stratified by gestational hypertension (GHTN) status. Results are shown across three exposure periods: (1) from the start of pregnancy until delivery, (2) from delivery until two years postpartum, and (3) from pregnancy through two years postpartum. All models were adjusted for matched covariates, and interaction terms between GHTN status and each environmental variable were assessed to examine effect modification. Across all exposure periods, both women with and without GHTN showed significant positive associations between environmental exposures and CSD risk; however, the magnitude of these associations was consistently greater in the GHTN group. For PM₂.₅ exposure, the odds ratio (OR) in women with GHTN increased from 1.096 (95% CI: 1.080–1.113, p < 0.001) during pregnancy to 1.372 (95% CI: 1.333–1.413, p < 0.001) over the full period from pregnancy through two years postpartum. The corresponding ORs for women without GHTN were lower but also increased over time, from 1.054 (95% CI: 1.039–1.070) to 1.313 (95% CI: 1.277–1.351). Similarly, high temperature exposure demonstrated stronger associations with CSD risk in the GHTN group, with ORs increasing from 1.127 (95% CI: 1.094–1.162, p < 0.001) during pregnancy to 1.430 (95% CI: 1.217–1.680, p < 0.001) over the full period, whereas ORs increased from 1.072 (95% CI: 1.040–1.104) to 1.356 (95% CI: 1.154–1.594) in women without GHTN. The average atmospheric pressure exposure showed a comparable pattern, with the GHTN group having slightly higher ORs throughout the exposure window and statistically significant interaction p-values. These results indicate that the adverse circulatory system effects of environmental and climate exposures are more pronounced when such exposures occur during pregnancy in women who develop GHTN. This finding reinforces the concept that pregnancy serves as a circulatory system “stress test,” during which environmental insults may trigger long-term consequences that extend into the postpartum period. Moreover, the risk associated with these exposures progressively increases as the exposure duration increases from pregnancy through the postpartum period, suggesting a cumulative interaction effect between GHTN and environmental factors on CSD risk. The associations between air pollution, especially PM 2.5 , and CSD morbidity and mortality are well established. PM 2.5 can induce systemic inflammation, oxidative stress, and endothelial dysfunction, which are key mechanisms underlying atherosclerosis and hypertension. Our findings corroborate these mechanisms, showing that prolonged exposure to PM 2.5 significantly increases CSD risk in pregnant women, a population that has been underrepresented in the epidemiology of air pollution. 4. Discussion This study investigated the associations between long-term exposure to environmental factors, including fine particulate matter (PM 2.5 ), high temperature, and average atmospheric pressure, and the risk of CSD among pregnant women in South Korea. We also examined the potential modifying role of GHTN. Our findings showed that environmental exposure was significantly associated with an increased risk of CSD, regardless of GHTN status. However, the magnitude of this association was greatest when exposures occurred during pregnancy in women who developed GHTN, indicating a synergistic effect between gestational hypertensive disorders and environmental stressors. These results suggest that environmental exposures may contribute to CSD risk on their own but can have more pronounced and lasting effects when experienced during the physiologically vulnerable period of pregnancy, especially in the presence of GHTN. Pregnancy represents a uniquely vulnerable period during which environmental exposures may exert stronger effects on circulatory health than at other life stages. This vulnerability may be explained by the substantial physiological changes that accompany pregnancy, including increased blood volume, elevated cardiac output, altered endothelial function, and shifts in blood pressure regulation. Such adaptations may heighten the circulatory system’s sensitivity to environmental stressors. In addition, pregnancy is characterized by dynamic changes in placental vascular development and immune modulation [ 29 , 30 ], which may further increase susceptibility. Environmental stressors such as air pollution and heat may interfere with these processes, potentially contributing to vascular dysfunction and heightened susceptibility to hypertensive disorders [ 30 , 31 ]. These mechanisms may help explain the observed synergistic effects between GHTN and environmental exposures in our study. The link between air pollution, particularly PM 2.5 , and CSD morbidity and mortality has been well documented in previous research [ 32 – 35 ]. PM 2.5 can trigger systemic inflammation, oxidative stress, and endothelial dysfunction, which are key mechanisms in the development of atherosclerosis and hypertension [ 36 – 38 ]. Our findings support these mechanisms, showing that prolonged exposure to PM 2.5 during pregnancy significantly increases CSD risk. This highlights the vulnerability of the pregnancy period, a physiologically sensitive time that has often been underrepresented in air pollution epidemiologic studies. Similarly, exposure to high temperatures is associated with negative effects on circulatory system health. These effects were observed during both pregnancy and the postpartum period. High temperatures may increase cardiovascular stress by increasing the cardiac workload, promoting dehydration, and triggering inflammatory responses [ 39 – 41 ]. During pregnancy, these heat-related effects may be amplified by concurrent hemodynamic changes, which further emphasizes the need to protect maternal circulatory system health during environmental stress. Several studies conducted in East Asia have also reported increased heat-related hospitalization rates, particularly among women and the elderly [ 42 , 43 ]. Our study builds upon this by highlighting the heightened vulnerability during pregnancy, a physiologically unique life stage[ 44 ]. Gestational hypertension is a well-established risk factor for future CSD [ 45 , 46 ]. In our study, environmental exposure was also significantly associated with an increased risk of CSD, even in the absence of GHTN. Furthermore, this risk was substantially greater among individuals with GHTN, suggesting a potential synergistic effect between environmental stressors and gestational hypertensive conditions. Consistent with this, our supplementary analysis (Table S3) revealed that venous and lymphatic diseases accounted for the majority of CSD cases among women with GHTN. This findings supports the literature suggesting that hypertensive disorders during pregnancy may have a "priming effect" on the circulatory system, increasing the sensitivity of the circulatory system to environmental stressors [ 47 , 48 ]. The higher odds ratios observed in women with GHTN exposed to PM2.5 and extreme heat suggest a possible interaction effect. These findings emphasize that minimizing environmental exposure during pregnancy, a physiologically critical transitional period, may be a key strategy for safeguarding long-term circulatory system health in mothers. As climate change leads to rising levels of fine particulate matter and more frequent extreme heat events, populations experiencing significant physiological changes, such as pregnant and postpartum women, are at greater risk [ 49 , 50 ]. Therefore, future efforts should focus on strengthening both policy and clinical interventions that address environmental exposures during pregnancy. Prenatal and postpartum care programs could incorporate environmental risk assessments, whereas community-level strategies to improve air quality and mitigate heatwaves should be designed with pregnant women in mind. Such measures may not only help prevent pregnancy-related complications but also contribute to broader public health goals by reducing long-term CSD risks in mothers. Building on these findings, future research should aim to integrate environmental, clinical, and behavioral data to better capture the multifactorial nature of CSD risk during and after pregnancy. Linking health insurance databases with obstetric registries and health examination records would enable more comprehensive analyses that account for lifestyle factors, delivery characteristics, and preexisting conditions. Interdisciplinary collaboration across epidemiology, obstetrics, and environmental sciences will be essential for developing targeted interventions and refining risk prediction models for maternal cardiovascular health. By advancing both preventive strategies and research integration, these efforts can help protect women during the physiologically vulnerable period of pregnancy and reduce long-term circulatory system risks across the life course. Strengths This study benefits from a large, nationally representative cohort with robust linkage to high-resolution environmental exposure datasets. Its longitudinal design enables the tracking of exposures across key time windows, from pregnancy through two years postpartum, allowing for temporal assessment of circulatory system disease risk. Additionally, by employing models with and without adjustment for GHTN, we were able to evaluate the effects of environmental exposures with consideration of GHTN as a potential modifier. Limitations This study has several limitations. First, residual confounding by unmeasured lifestyle or genetic factors cannot be excluded, despite adjustment for key covariates. Second, environmental exposures were estimated at the city level via outdoor monitoring data, which may not accurately capture individual-level exposures, particularly for indoor environments, leading to potential misclassification. Third, the NHIS dataset lacked detailed information on time-varying behavioral factors such as smoking, alcohol consumption, physical activity, and body mass index (BMI), which limited our ability to account for these important confounders. Fourth, although the dataset captured whether CSD occurred within the two-year follow-up period, it did not provide precise information on the timing of disease onset, limiting our ability to evaluate temporal patterns or progression. Fifth, our definition of gestational hypertension relied on the ICD-10 codes O13 and O16. While O13 specifically captures gestational hypertension without significant proteinuria, O16 is broader and may include chronic hypertension or preeclampsia/eclampsia. Thus, some degree of misclassification cannot be excluded, which may have affected the precision of our gestational hypertension classification. Finally, because the study cohort consisted exclusively of urban Korean women, the generalizability of our findings to rural populations or other ethnic groups may be limited. 5. Conclusion This nationwide cohort study highlights the significant and compounding risks of CSD in women with a history of GHTN, especially when it is combined with prolonged exposure to environmental stressors such as PM 2.5 , high temperature, and atmospheric pressure. These findings suggest that GHTN is associated with an elevated risk of maternal CSD in the postpartum period. Environmental exposure during pregnancy, when the circulatory system is under heightened stress, appears to exacerbate this risk. Women who experience GHTN during pregnancy may constitute a key population for targeted environmental health interventions aimed at preventing long-term circulatory system outcomes. Notably, the effects of environmental exposure were also observed among women without GHTN, highlighting the broader role of air pollution and climatic stress in influencing long-term circulatory system outcomes in reproductive-aged women. These risks appeared to intensify over time, suggesting a cumulative impact of environmental and hypertensive exposures. As climate change and air pollution continue to worsen globally, public health systems must proactively address these environmental determinants of maternal circulatory system health. Integrating environmental risk factors into postpartum CSD risk screening, especially for women with GHTN, may enable more targeted prevention strategies. Future research should aim to refine individual-level exposure assessments and explore mechanistic pathways linking pregnancy-related complications, environmental stressors, and CSD. Abbreviations CSD Circulatory system disease GHTN Gestational hypertension HDP Hypertensive disorders of pregnancy PM2.5 Particulate matter ≤ 2.5 µm in diameter NHIS Korean National Health Insurance Service PS Propensity score SMDs Standardized mean differences CI Confidence interval OR Odds ratio Ave-Pressure Average atmospheric pressure (hPa) H-Tem High temperature (°C) Declarations Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board (IRB) of Ewha Womans University College of Medicine (IRB file number: 2022-10-046) and was conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived because the study used de-identified secondary data from the National Health Insurance Service (NHIS) of Korea. Consent for publication Not applicable. Availability of data and materials The data that support the findings of this study are available from the National Health Insurance Service (NHIS) of Korea, but restrictions apply to the availability of these data, which were used under license for the current study and are therefore not publicly available. Data are, however, available from the NHIS for researchers who meet the criteria for access to confidential data (https://nhiss.nhis.or.kr) Competing interests The authors declare that they have no competing interests. Funding This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(No. 2022R1A6A3A01085789). Authors' contributions S.H. and Y.K. contributed equally to this work and are co–first authors. They jointly conceptualized the study, performed the statistical analyses, interpreted the findings, and drafted the manuscript. M.-H.K. contributed to data management and supported software analysis. J.O. assisted with data visualization and software development. Y.C. and K.A.L. provided clinical expertise in obstetrics and gynecology and contributed to the interpretation of results. W.B.P. contributed to methodological design and critically reviewed the manuscript. T.-J.S. checked the analysis results, reviewed the manuscript and supervised the study. E.H. conceived and supervised the overall project, guided the methodological framework, and provided critical revisions to the manuscript. E.H. and T.-J.S. are co–corresponding authors. All authors read and approved of the final manuscript. Acknowledgements We thank the National Health Insurance Service (NHIS) of Korea for providing access to the data used in this study. We also acknowledge the support of the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2022R1A6A3A01085789). 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07:11:42","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159699,"visible":true,"origin":"","legend":"","description":"","filename":"982479cd2a5346f5bd7d59175dc382f31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7783470/v1/ef0c0c3bfaf9a1fd42796309.xml"},{"id":95510275,"identity":"82b80792-993a-46e6-8d28-3373160af9c4","added_by":"auto","created_at":"2025-11-10 07:11:43","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":168207,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7783470/v1/0ffe5e2ac2884c7a374477b7.html"},{"id":95510258,"identity":"6b2f9cd3-b1d4-4afc-a87d-a95e4dbb7675","added_by":"auto","created_at":"2025-11-10 07:11:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":406749,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart showing the derivation of the study.\u003c/p\u003e\n\u003cp\u003eThis flowchart shows the selection and derivation of the study population, including the inclusion and exclusion criteria, and the final number of participants included in the analysis.\u003c/p\u003e","description":"","filename":"figure1BMC.png","url":"https://assets-eu.researchsquare.com/files/rs-7783470/v1/fea13ecc0e5a75357f1a350b.png"},{"id":95510264,"identity":"67f9d2ab-95f7-4680-907e-18e27b4ce47d","added_by":"auto","created_at":"2025-11-10 07:11:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213485,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of Environmental and Climate Exposure with Circulatory System Disease Risk (Without the Gestational Hypertension Effect).\u003c/p\u003e\n\u003cp\u003eOdds ratios (ORs) and 95% confidence intervals (CIs) for the association between environmental exposure and incidence of circulatory system disease(CSD) without considering the effect of gestational hypertension. This forest plot shows the associations of three environmental factors , PM2.5 (µg/m³), high temperature (°C) (H-Tem), and average atmospheric pressure (hPa) (Ave-Pressure), with the risk of CSD across three exposure periods: during pregnancy, after delivery (up to 2 years post-partum), and the entire period from pregnancy through 2 years post-partum. All models were adjusted for maternal age at the start of pregnancy, income level, and region (Model 3). The complete results are provided in Supplementary Table S2.\u003c/p\u003e","description":"","filename":"figure2BMC.png","url":"https://assets-eu.researchsquare.com/files/rs-7783470/v1/6e2c50cc4a52776020fd3f20.png"},{"id":95531773,"identity":"0d01d3d2-a90a-4dcf-8411-9555f7ab9807","added_by":"auto","created_at":"2025-11-10 10:24:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1546727,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7783470/v1/9de9e231-671b-42c6-ac22-d0c186089cd7.pdf"},{"id":95510259,"identity":"0cf3dc3e-04e0-4eda-97cb-9aa314e05773","added_by":"auto","created_at":"2025-11-10 07:11:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":42913,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7783470/v1/168e539df2b86f7057787042.docx"},{"id":95528778,"identity":"a2b496d3-0ead-4782-a8ac-e8ee4d56997e","added_by":"auto","created_at":"2025-11-10 10:16:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14961,"visible":true,"origin":"","legend":"","description":"","filename":"SuppTablesLegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-7783470/v1/1e9bba3ffd3553f1c0840806.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term Exposure to Fine Particulate Matter and Climate factors and The Risk of Circulatory system Disease in Women with Gestational Hypertension: A Nationwide Cohort Study in South Korea.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCirculatory system diseases (CSDs), encompassing both cardiovascular conditions (e.g., ischemic heart disease, stroke, heart failure) and other vascular disorders such as peripheral, venous, and lymphatic diseases, contribute substantially to morbidity worldwide. Nearly half of the global burden is concentrated in the Asia\u0026ndash;Pacific region, highlighting significant regional health disparities[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Women who experience hypertensive disorders during pregnancy, such as preeclampsia and gestational hypertension, are at a significantly greater risk of developing CSD later in life[\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Pregnancy places substantial physiological stress on the cardiovascular system, potentially unmasking latent vulnerabilities that may contribute to future circulatory disease[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHypertensive disorders of pregnancy (HDP) affect approximately 5% to 10% of pregnancies worldwide and represent a major cause of maternal morbidity and mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Among these, gestational hypertension (GHTN) is the most common subtype, occurring in 5% to 8% of otherwise healthy pregnant women [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. GHTN is defined as new-onset hypertension (systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mm Hg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg, measured on two occasions at least four hours apart) that develops after 20 weeks of gestation in women with previously normal blood pressure and without proteinuria [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eEnvironmental stressors such as fine particulate matter (PM2.5), high temperature, and fluctuations in atmospheric pressure are increasingly recognized as significant risk factors for cardiovascular morbidity and mortality [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Long-term exposure to PM\u003csub\u003e2.5\u003c/sub\u003e has shown particularly strong associations with CSD outcomes, especially among vulnerable populations such as pregnant women, older adults, and children [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Notably, approximately 72% of deaths attributed to PM exposure are due to ischemic heart disease and stroke [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In addition, recent studies have identified non-optimal ambient temperatures, including both extreme heat and cold, as major contributors to excess mortality from cardiovascular diseases, which constitute the majority of circulatory system disease deaths worldwide[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDespite growing evidence connecting environmental exposure to cardiovascular outcomes, pregnancy represents a uniquely sensitive period during which these exposures may have amplified effects. Women with GHTN are especially vulnerable, and face both immediate pregnancy-related risks and elevated long-term CSD risk postpartum. However, few studies have examined the combined impact of hypertensive pregnancy disorders and environmental stressors. In particular, the interaction between GHTN and prolonged exposure to PM₂.₅, high temperature, and atmospheric pressure remains understudied in large-scale population-based cohorts. Most existing research has focused narrowly on cardiovascular disease outcomes such as ischemic heart disease and stroke. In contrast, this study adopts a broader lens by evaluating all circulatory system diseases (CSDs), including arterial, venous, and lymphatic conditions, to offer a more comprehensive understanding of maternal vascular health.\u003c/p\u003e\u003cp\u003eTherefore, this study aimed to investigate the associations between long-term exposure to PM₂.₅, high temperature, and atmospheric pressure during pregnancy and the risk of subsequent CSD in women diagnosed with GHTN. By focusing on this physiologically vulnerable period, we assess whether environmental stressors further increase postpartum CSD risk beyond that associated with GHTN alone. Exposures are evaluated across three key windows: during pregnancy, from delivery to two years postpartum, and cumulatively from conception to two years after delivery. This approach offers novel insights into how environmental factors shape maternal circulatory health trajectories and may inform targeted prevention strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data sources\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study utilized data from the Korean National Health Insurance Service (NHIS), a comprehensive nationwide database that includes medical claims for nearly the entire Korean population [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The fine particulate matter (PM₂.₅ ; particles\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m in diameter) data were obtained from the Korea Environment Corporation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], whereas the climate exposure data, including daily maximum temperature and average atmospheric pressure, were obtained from the Korea Meteorological Administration [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Study population\u003c/h2\u003e\u003cp\u003eThe study population included all women whose first pregnancy records were recorded between 2012 and 2019 in the NHIS database. Participants were eligible if they had valid pregnancy episodes and corresponding environmental exposure data. Individuals were excluded if they (1) relocated outside the seven major metropolitan areas during the follow-up period; (2) had a diagnosis of cardiovascular disease prior to pregnancy; or (3) were lost to follow-up due to death, emigration, or missing key information (e.g., age, income), which was due to non-recording in the original NHIS database.\u003c/p\u003e\u003cp\u003eCardiovascular disease prior to pregnancy was defined via a targeted set of ICD-10 codes representing major chronic conditions likely to confound postpartum circulatory outcomes. Specifically, we excluded individuals with diagnoses of hypertension (I10\u0026ndash;I15), heart failure (I50), myocardial infarction (I21\u0026ndash;I22), or stroke (I60\u0026ndash;I64). These exclusions were selected to minimize reverse causality and ensure that the outcome reflected incident CSD beyond the hypertensive spectrum already captured by the exposure variable. The final analytic sample was determined after applying these exclusion criteria, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Variables and definitions\u003c/h2\u003e\u003cp\u003eThe variables included demographic factors such as age at pregnancy, income level (categorized as low [levels 1\u0026ndash;5], middle [levels 6\u0026ndash;10], mid-high [levels 11\u0026ndash;15], and high [levels 16\u0026ndash;20], on the basis of the NHIS income classification system, which ranks individuals into 20 levels according to income and assets), and residential cities which are defined as the seven major metropolitan cities in South Korea (including Seoul, Busan, Incheon, Daegu, Daejeon, Gwangju, and Ulsan). Gestational hypertension (GHTN) was identified via the ICD-10 codes O13 and O16. While O13 specifically captures gestational hypertension without significant proteinuria, O16 may include other hypertensive disorders. Both codes were included to maximize case identification. Circulatory system disease (CSD) outcomes were defined as any diagnosis coded under ICD-10 I00\u0026ndash;I99, excluding I10\u0026ndash;I15 (hypertensive diseases) to prevent overlap with the exposure variable and minimize outcome misclassification. This approach captures a broad spectrum of arterial, venous, and lymphatic conditions relevant to systemic circulatory health. Environmental exposures were measured over specified time windows and included the average concentration of PM₂.₅, daily maximum temperature (defined as the highest recorded temperature within a 24-hour period), and average atmospheric pressure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\u003cp\u003eTo minimize confounding, 1:4 propensity score (PS) matching was performed on the basis of gestational hypertension (GHTN) status, maternal age at pregnancy, income level, and residential area. Propensity scores were estimated via logistic regression, and each exposed participant was matched to four unexposed individuals via nearest neighbor matching with replacement. Covariate balance after matching was evaluated via standardized mean differences (SMDs).\u003c/p\u003e\u003cp\u003eThe associations between environmental and climate exposures and the risk of CSDs were evaluated across three distinct exposure periods: (1) from the start of pregnancy to delivery, (2) from delivery to two years postpartum, and (3) from pregnancy through two years postpartum. The analyses were stratified by GHTN status.\u003c/p\u003e\u003cp\u003eDescriptive statistics summarized the baseline characteristics. Group differences were assessed via chi-square tests for categorical variables and t-tests for continuous variables. Logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for associations between exposure and circulatory system outcomes. All the statistical analyses were conducted via SAS software (version 9.4, SAS Institute, Inc., Cary, NC, USA), and data visualization, including forest plots, was performed via RStudio (version 2025.05.01, Posit Software, Boston, MA, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 386,378 women with a first recorded pregnancy between 2015 and 2019 in the seven major metropolitan cities of South Korea were initially identified. After 78,149 participants with a history of cardiovascular disease or pre-existing hypertension prior to pregnancy (based on ICD-10 codes) were excluded, 308,129 women remained. A further 291 women were excluded due to relocation, death, or missing key data (e.g., age, income), yielding a final analytic sample of 307,938 women. Among this population, 8,062 (2.62%) were diagnosed with GHTN, and 299,876 (97.38%) were not. To reduce confounding factors, 1:4 propensity score matching was conducted on the basis of GHTN status, age at pregnancy, income level, and residential city. After matching, 8,051 women with GHTN were successfully matched to 32,204 women without GHTN, yielding a matched cohort of 40,255 participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe baseline characteristics after matching are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and the pre-matching characteristics are presented in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Prior to PS matching, significant differences were observed between women with and without GHTN in terms of age, income level, residential city, and CSD status (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). For example, women with GHTN had a greater mean age (33.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.03 vs. 32.66\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), a greater proportion in the low-income group (14.97% vs. 12.76%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a notably greater prevalence of CSD (34.74% vs. 16.91%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eParticipants and exposure factors characteristics after PS Matching.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal population\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWithout \u003csup\u003e1\u003c/sup\u003eGHTN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWith GHTN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eSMDs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40,255\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e32,204 (80.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e8,051 (20.00%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e33.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,035 (14.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4,828 (14.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,207 (14.99%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,230 (22.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7,384 (22.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,846 (22.93%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMid-high level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14,231 (35.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11,384 (35.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,847 (35.36%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,759 (26.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8,608 (26.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,151 (26.72%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCity\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGwangju\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e280 (0.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e224 (0.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e56 (0.70%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaegu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e725 (1.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e580 (1.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e145 (1.80%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaejeon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e410 (1.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e328 (1.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e82 (1.02%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,325 (23.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7,460 (23.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1,865 (23.16%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeoul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28,800 (71.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23,040 (71.54%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5,762 (71.47%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUlsan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e171 (0.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e136 (0.42%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e35 (0.43%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncheon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,505 (1.46%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e436 (1.35%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e108 (1.34%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eCSD status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWith CSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,359 (20.77%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5,563 (17.27%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2,796 (34.73%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e1\u003c/sup\u003eGestatioal Hypertension \u003csup\u003e2\u003c/sup\u003eCirculatory system disease,\u003csup\u003e3\u003c/sup\u003e Standardized mean differences, * p-value calculated by chi-square test.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eCharacteristics of 40,255 participants after 1:4 propensity score matching by gestational hypertension (GHTN) status. Values are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or number (percentage). The standardized mean differences (SMDs) used to evaluate the covariate balance after PS matching. Matching variables (age, income, city) were well balanced between groups (SMDs\u0026thinsp;\u0026asymp;\u0026thinsp;0.000). Circulatory system disease(CSD) prevalence differed significantly between groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter applying 1:4 propensity score matching on the basis of GHTN status, maternal age, income level, and residential city, a total of 40,255 participants were included in the matched cohort\u0026mdash;comprising 8,051 women with GHTN and 32,204 without GHTN (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The baseline characteristics were well balanced between the two groups. The mean age was identical (33.12\u0026thinsp;\u0026plusmn;\u0026thinsp;4.01 years; SMD\u0026thinsp;=\u0026thinsp;0.000), and the income distribution showed perfect alignment across all categories (SMDs\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Residential city proportions were also closely matched, with SMDs ranging from 0.000 to 0.002, confirming successful covariate balance. Despite this balance, the prevalence of CSD remained significantly greater in the GHTN group than in the non-GHTN group (34.73% vs. 17.27%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This difference underscores the strong association between gestational hypertension and postpartum CSD risk, independent of demographic and regional factors.\u003c/p\u003e\u003cp\u003eAmong the total population, the most prevalent categories of circulatory system diseases were venous and lymphatic disorders (42.43%; 23,121 cases), arterial diseases (23.24%; 12,656 cases), and other heart conditions (13.93%; 7,585 cases). Among women diagnosed with gestational hypertension (GHTN; 3,773 cases), venous and lymphatic diseases were overwhelmingly predominant (69.81%; 2,634 cases), followed by arterial diseases (11.02%; 416 cases), other heart conditions (9.09%; 343 cases), and cerebrovascular diseases (3.84%; 145 cases) (Supplementary Table S3). These findings underscore the disproportionate burden of venous and vascular disorders among women with GHTN, suggesting a potential link between hypertensive pregnancy complications and vascular health outcomes.\u003c/p\u003e\u003cp\u003eThe environmental and climate exposure characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average concentration of PM\u003csub\u003e2.5\u003c/sub\u003e across the seven major cities was 22.996 \u0026micro;g/m\u0026sup3; (\u0026plusmn;\u0026thinsp;12.9), ranging from 1.863 to 135.285 \u0026micro;g/m\u0026sup3;. The mean high temperature exposure was 19.06\u0026deg;C (\u0026plusmn;\u0026thinsp;9.5), ranging from \u0026minus;\u0026thinsp;11.9 to 39.6\u0026deg;C. The average atmospheric pressure exposure was 1007.9 hPa (\u0026plusmn;\u0026thinsp;7.9), ranging from 979.2 to 1030.0 hPa. These findings underscore the substantial variability in the environmental conditions experienced by the study population.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of total concentration of PM\u003csub\u003e2.5\u003c/sub\u003e and Climate factors in 7 major cities in Korea.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003ePM2.5(\u0026micro;g/m\u0026sup3;)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eH-Tem(\u0026deg;C)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eAve-Pressure(hPa)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMinimum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.863\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e979.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10th percentile\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9.415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e997.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e25th percentile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.672\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1001.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e20.504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1008.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.996\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1007.9\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e75th percentile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1014.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e90th percentile\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1018.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaximum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e135.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e39.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1030.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003csup\u003e1\u003c/sup\u003e Particular matter\u0026thinsp;\u0026lt;\u0026thinsp;2.5(\u0026micro;g/m\u0026sup3;), \u003csup\u003e2\u003c/sup\u003e High temperature exposure, \u003csup\u003e3\u003c/sup\u003e Average Atmospheric Pressure exposure.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eDistribution of total concentrations of PM2.5 and climate factors in seven major cities in Korea. Values represent the minimum, 10th percentile, 25th percentile, median, mean (\u0026plusmn;\u0026thinsp;standard deviation), 75th percentile, 90th percentile, and maximum for each variable.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the associations between GHTN, environmental and climate exposures, and the risk of CSD across three distinct exposure windows: (1) from the start of pregnancy until delivery, (2) from delivery until two years postpartum, and (3) from pregnancy until two years postpartum. Logistic regression models were applied using propensity score\u0026ndash;matched data, including the Model 1(adjusting for GHTN and PM₂.₅), Model 2 (adjusting for GHTN, PM₂.₅, high daily temperature, and average atmospheric pressure), and a Model 3 (additionally adjusting for age, income level, and residential area).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation of gestational hypertension and environmental exposures with circulatory system disease.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eFrom the start of pregnancy until delivery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eFrom delivery until 2 years later\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eFrom pregnancy until 2 years after delivery\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eOdds ratio/95% CI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eGHTN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.628(2.489\u0026ndash;2.775)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.653(2.512\u0026ndash;2.802)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.658(2.516\u0026ndash;2.807)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.712(2.567\u0026ndash;2.864)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.759(2.611\u0026ndash;2.915)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.783(2.633\u0026ndash;2.941)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.609(2.471\u0026ndash;2.756)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.669(2.526\u0026ndash;2.819)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.695(2.550\u0026ndash;2.847)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e2\u003c/sup\u003ePM\u003csub\u003e2.5\u003c/sub\u003e(\u0026micro;g/m\u0026sup3;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.033(1.021\u0026ndash;1.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.067(1.053\u0026ndash;1.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.066(1.051\u0026ndash;1.082)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.122(1.104\u0026ndash;1.141)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.199(1.176\u0026ndash;1.222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.231(1.206\u0026ndash;1.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.137(1.112\u0026ndash;1.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.282(1.247\u0026ndash;1.317)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.331(1.294\u0026ndash;1.362)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eH-Tem(\u0026deg;C)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.076(1.059\u0026ndash;1.092)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.086(1.054\u0026ndash;1.119)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.166(1.132\u0026ndash;1.201)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.238(1.091\u0026ndash;1.405)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.251(1.205\u0026ndash;1.299)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.376(1.171\u0026ndash;1.617)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eAve-Pressure(hPa)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.059(1.038\u0026ndash;1.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.080(1.036\u0026ndash;1.127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.119(1.086\u0026ndash;1.154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.226(1.020\u0026ndash;1.472)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.107(1.071\u0026ndash;1.145)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.309(1.028\u0026ndash;1.667)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.020(1.013\u0026ndash;1.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.022(1.016\u0026ndash;1.029)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.021(1.014\u0026ndash;1.027)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.128(1.041\u0026ndash;1.222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.098(1.013\u0026ndash;1.191)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.049(0.967\u0026ndash;1.137)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.146(1.068\u0026ndash;1.230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.146(1.067\u0026ndash;1.230)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.102(1.027\u0026ndash;1.183)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMid-high level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.058(0.993\u0026ndash;1.129)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.054(0.988\u0026ndash;1.124)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.063(0.998\u0026ndash;1.133)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGwangju\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaegu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.952(0.667\u0026ndash;1.360)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.620(0.384\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.504(0.292\u0026ndash;0.868)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDaejeon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.088(0.737\u0026ndash;1.605)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.110(0.748\u0026ndash;1.645)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.488(1.017\u0026ndash;2.178)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.221(0.901\u0026ndash;1.665)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.369(0.975\u0026ndash;1.922)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.453(1.019\u0026ndash;2.072)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeoul\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.227(0.887\u0026ndash;1.699)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.408(0.754\u0026ndash;2.631)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.739(0.797\u0026ndash;3.796)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUlsan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.177(0.718\u0026ndash;1.930)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.298(1.187\u0026ndash;4.451)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.906(1.401\u0026ndash;6.031)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIncheon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.039(0.706\u0026ndash;1.527)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.956(1.136\u0026ndash;3.368)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e2.231(1.190\u0026ndash;4.184)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e1\u003c/sup\u003eGestatioal Hypertension, \u003csup\u003e2\u003c/sup\u003e Particular matter\u0026thinsp;\u0026lt;\u0026thinsp;2.5(\u0026micro;g/m\u0026sup3;), \u003csup\u003e3\u003c/sup\u003e High temperature exposure, \u003csup\u003e4\u003c/sup\u003e Average Atmospheric Pressure exposure, CI: confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eLogistic regression models were conducted after propensity score matching. Model 1 included gestational hypertension (GHTN) and PM₂.₅; Model 2 included GHTN, PM₂.₅, high daily temperature, and average atmospheric pressure; Model 3 included all variables, including age, income level, and residential area. For income, the high level was used as the reference group, and for residential area, Gwangju was the reference city. Three exposure periods were analyzed.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross all the models and exposure periods, GHTN was strongly and significantly associated with an elevated risk of CSD. Notably, the magnitude of the association increased as the exposure window lengthened. In the Model 3, the odds ratio (OR) for GHTN was 2.658 (95% CI: 2.516\u0026ndash;2.807) from the start of pregnancy until delivery, increased to 2.783 (95% CI: 2.633\u0026ndash;2.941) from delivery to two years postpartum, and remained elevated at 2.695 (95% CI: 2.550\u0026ndash;2.847) over the entire two-year postpartum period. This trend indicates a cumulative effect of GHTN on long-term circulatory system outcomes, with a particularly strong impact in the extended postpartum period.\u003c/p\u003e\u003cp\u003ePM₂.₅ exposure also demonstrated a clear dose-response pattern with increasing odds of CSD across longer exposure durations and more comprehensively adjusted models. For example, in the Model 3, the OR for PM₂.₅ increased from 1.066 (95% CI: 1.051\u0026ndash;1.082) during pregnancy, to 1.231 (95% CI: 1.206\u0026ndash;1.258) from delivery to two years postpartum, and reached 1.331 (95% CI: 1.294\u0026ndash;1.362) when exposure from pregnancy through the entire two-year postpartum period was considered. These results suggest that prolonged exposure to fine particulate matter significantly increases the risk of developing CSD. Similarly, high temperature and average atmospheric pressure were positively associated with CSD risk. For example, in Model 3, which covered the full exposure period, high temperature was associated with an OR of 1.376 (95% CI: 1.171\u0026ndash;1.617), and average pressure was associated with an OR of 1.309 (95% CI: 1.028\u0026ndash;1.667), suggesting that chronic exposure to adverse climate conditions may exacerbate CSD risk in this population.\u003c/p\u003e\u003cp\u003eSociodemographic variables also contributed to risk differentials. Increasing age was consistently associated with increased odds of CSD. Compared with the high-income reference group, individuals in the low- and middle-income categories had increased risk, with the middle-income group showing a significant association in the full-period model (OR: 1.102; 95% CI: 1.027\u0026ndash;1.183). Regional variations were also evident. Using Gwangju as the reference, significantly higher odds of CSD were observed in Incheon (OR: 2.231; 95% CI: 1.190\u0026ndash;4.184), Ulsan (OR: 2.906; 95% CI: 1.401\u0026ndash;6.031), and Busan (OR: 1.453; 95% CI: 1.019\u0026ndash;2.072) in the full-period model. These regional disparities may reflect differences in local environmental exposure or healthcare access.\u003c/p\u003e\u003cp\u003eTaken together, these findings demonstrate that GHTN substantially increases the risk of CSD, particularly as the follow-up duration increases. Moreover, long-term exposure to air pollution and extreme climate conditions further amplifies this risk, and the impact is modified by socioeconomic status and residential area.\u003c/p\u003e\u003cp\u003eSupplementary Table S2 summarizes the associations between environmental and climate exposures and the risk of circulatory system disease (CSD) across three defined exposure windows. The analysis was conducted via logistic regression models with PS matching to control for potential confounders. Gestational hypertension (GHTN) was not included in these models to isolate the independent impact of environmental exposure on CSD risk.\u003c/p\u003e\u003cp\u003eAcross all exposure windows and models, PM₂.₅ exposure was consistently significantly associated with increased CSD risk. The effect size became stronger with longer exposure durations. For example, in the Model 3, the odds ratio (OR) for PM₂.₅ increased from 1.053 (95% CI: 1.038\u0026ndash;1.069) during pregnancy, to 1.193 (95% CI: 1.169\u0026ndash;1.218) during the post-delivery period, and reached 1.273 (95% CI: 1.238\u0026ndash;1.309) when exposure was assessed over the full period from pregnancy to two years postpartum. High temperature exposure and average atmospheric pressure also showed significant positive associations with CSD, particularly in models 2 and 3. In Model 3 for the full exposure period, high temperature was associated with an OR of 1.354 (95% CI: 1.155\u0026ndash;1.587), and average pressure was associated with an OR of 1.300 (95% CI: 1.025\u0026ndash;1.649), suggesting that cumulative exposure to high daily temperatures and atmospheric fluctuations may play a role in CSD risk. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Model 3 results across the three exposure windows, providing a visual summary of the estimated associations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSociodemographic covariates also played a role. Increasing age was associated with increased CSD risk. Compared with high-income individuals, low or middle-income individuals were more likely to have CSD. For example, the middle-income group showed a significant association in the full-period model (OR: 1.098; 95% CI: 1.025\u0026ndash;1.177). In terms of geographic variation, several cities presented elevated CSD risk relative to Gwangju. Notably, in the full-period model, Ulsan (OR: 2.719; 95% CI: 1.326\u0026ndash;5.572), Incheon (OR: 2.119; 95% CI: 1.141\u0026ndash;3.935), and Busan (OR: 1.424; 95% CI: 1.005\u0026ndash;2.019) were significantly associated with increased risk. These findings suggest that prolonged exposure to fine particulate matter and adverse climatic factors contribute to an increased risk of CSD, even when GHTN is not accounted, and that the risk intensifies with longer exposure durations.\u003c/p\u003e\u003cp\u003eWe investigated the interaction effects between environmental and climate exposures and GHTN on CSD risk across three exposure periods after propensity score matching (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The analyses were stratified by GHTN status to evaluate whether the associations between PM₂.₅, high temperature, and average atmospheric pressure exposures and CSD risk differ between women with and without GHTN.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInteraction between environmental exposures and gestational hypertension on circulatory system disease risk.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eFrom the start of pregnancy until delivery\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eFrom delivery until 2 years later\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e\u003cp\u003eFrom pregnancy until 2 years after delivery\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003ePM2.5 at GHTN group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.080\u0026ndash;1.113)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(1.244\u0026ndash;1.298)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.372\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.333\u0026ndash;1.413\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePM2.5 at non-GHTN group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.054\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.039\u0026ndash;1.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(1.188\u0026ndash;1.239)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.313\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.277\u0026ndash;1.351)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eH-Tem at GHTN group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.094\u0026ndash;1.162)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(1.132\u0026ndash;1.458)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.430\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.217\u0026ndash;1.680)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eH-Tem at non-GHTN group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.040\u0026ndash;1.104)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(1.072\u0026ndash;1.381)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.356\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.154\u0026ndash;1.594)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003eAve-Pressure at GHTN group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.037\u0026ndash;1.127)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(1.021\u0026ndash;1.473)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.310\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.028\u0026ndash;1.668)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAve-Pressure at non-GHTN group\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(1.036\u0026ndash;1.126)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e(1.020\u0026ndash;1.472)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.308\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e(1.027\u0026ndash;1.666)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e1\u003c/sup\u003e Particular matter\u0026thinsp;\u0026lt;\u0026thinsp;2.5(\u0026micro;g/m\u0026sup3;), \u003csup\u003e2\u003c/sup\u003e High temperature exposure, \u003csup\u003e3\u003c/sup\u003e Average Atmospheric Pressure exposure,\u003csup\u003e4\u003c/sup\u003e Circulatory system disease.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"10\"\u003eOdds ratios (ORs), 95% confidence intervals (CIs), and p-values are presented for each environmental factor (PM₂.₅, high temperature, and average atmospheric pressure), stratified by gestational hypertension (GHTN) status. Results are shown across three exposure periods: (1) from the start of pregnancy until delivery, (2) from delivery until two years postpartum, and (3) from pregnancy through two years postpartum. All models were adjusted for matched covariates, and interaction terms between GHTN status and each environmental variable were assessed to examine effect modification.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAcross all exposure periods, both women with and without GHTN showed significant positive associations between environmental exposures and CSD risk; however, the magnitude of these associations was consistently greater in the GHTN group. For PM₂.₅ exposure, the odds ratio (OR) in women with GHTN increased from 1.096 (95% CI: 1.080\u0026ndash;1.113, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) during pregnancy to 1.372 (95% CI: 1.333\u0026ndash;1.413, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) over the full period from pregnancy through two years postpartum. The corresponding ORs for women without GHTN were lower but also increased over time, from 1.054 (95% CI: 1.039\u0026ndash;1.070) to 1.313 (95% CI: 1.277\u0026ndash;1.351). Similarly, high temperature exposure demonstrated stronger associations with CSD risk in the GHTN group, with ORs increasing from 1.127 (95% CI: 1.094\u0026ndash;1.162, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) during pregnancy to 1.430 (95% CI: 1.217\u0026ndash;1.680, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) over the full period, whereas ORs increased from 1.072 (95% CI: 1.040\u0026ndash;1.104) to 1.356 (95% CI: 1.154\u0026ndash;1.594) in women without GHTN. The average atmospheric pressure exposure showed a comparable pattern, with the GHTN group having slightly higher ORs throughout the exposure window and statistically significant interaction p-values.\u003c/p\u003e\u003cp\u003eThese results indicate that the adverse circulatory system effects of environmental and climate exposures are more pronounced when such exposures occur during pregnancy in women who develop GHTN. This finding reinforces the concept that pregnancy serves as a circulatory system \u0026ldquo;stress test,\u0026rdquo; during which environmental insults may trigger long-term consequences that extend into the postpartum period. Moreover, the risk associated with these exposures progressively increases as the exposure duration increases from pregnancy through the postpartum period, suggesting a cumulative interaction effect between GHTN and environmental factors on CSD risk.\u003c/p\u003e\u003cp\u003eThe associations between air pollution, especially PM\u003csub\u003e2.5\u003c/sub\u003e, and CSD morbidity and mortality are well established. PM\u003csub\u003e2.5\u003c/sub\u003e can induce systemic inflammation, oxidative stress, and endothelial dysfunction, which are key mechanisms underlying atherosclerosis and hypertension. Our findings corroborate these mechanisms, showing that prolonged exposure to PM\u003csub\u003e2.5\u003c/sub\u003e significantly increases CSD risk in pregnant women, a population that has been underrepresented in the epidemiology of air pollution.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study investigated the associations between long-term exposure to environmental factors, including fine particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e), high temperature, and average atmospheric pressure, and the risk of CSD among pregnant women in South Korea. We also examined the potential modifying role of GHTN. Our findings showed that environmental exposure was significantly associated with an increased risk of CSD, regardless of GHTN status. However, the magnitude of this association was greatest when exposures occurred during pregnancy in women who developed GHTN, indicating a synergistic effect between gestational hypertensive disorders and environmental stressors. These results suggest that environmental exposures may contribute to CSD risk on their own but can have more pronounced and lasting effects when experienced during the physiologically vulnerable period of pregnancy, especially in the presence of GHTN.\u003c/p\u003e\u003cp\u003ePregnancy represents a uniquely vulnerable period during which environmental exposures may exert stronger effects on circulatory health than at other life stages. This vulnerability may be explained by the substantial physiological changes that accompany pregnancy, including increased blood volume, elevated cardiac output, altered endothelial function, and shifts in blood pressure regulation. Such adaptations may heighten the circulatory system\u0026rsquo;s sensitivity to environmental stressors. In addition, pregnancy is characterized by dynamic changes in placental vascular development and immune modulation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which may further increase susceptibility. Environmental stressors such as air pollution and heat may interfere with these processes, potentially contributing to vascular dysfunction and heightened susceptibility to hypertensive disorders [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These mechanisms may help explain the observed synergistic effects between GHTN and environmental exposures in our study.\u003c/p\u003e\u003cp\u003eThe link between air pollution, particularly PM\u003csub\u003e2.5\u003c/sub\u003e, and CSD morbidity and mortality has been well documented in previous research [\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. PM\u003csub\u003e2.5\u003c/sub\u003e can trigger systemic inflammation, oxidative stress, and endothelial dysfunction, which are key mechanisms in the development of atherosclerosis and hypertension [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Our findings support these mechanisms, showing that prolonged exposure to PM\u003csub\u003e2.5\u003c/sub\u003e during pregnancy significantly increases CSD risk. This highlights the vulnerability of the pregnancy period, a physiologically sensitive time that has often been underrepresented in air pollution epidemiologic studies. Similarly, exposure to high temperatures is associated with negative effects on circulatory system health. These effects were observed during both pregnancy and the postpartum period. High temperatures may increase cardiovascular stress by increasing the cardiac workload, promoting dehydration, and triggering inflammatory responses [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. During pregnancy, these heat-related effects may be amplified by concurrent hemodynamic changes, which further emphasizes the need to protect maternal circulatory system health during environmental stress.\u003c/p\u003e\u003cp\u003eSeveral studies conducted in East Asia have also reported increased heat-related hospitalization rates, particularly among women and the elderly [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Our study builds upon this by highlighting the heightened vulnerability during pregnancy, a physiologically unique life stage[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Gestational hypertension is a well-established risk factor for future CSD [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. In our study, environmental exposure was also significantly associated with an increased risk of CSD, even in the absence of GHTN. Furthermore, this risk was substantially greater among individuals with GHTN, suggesting a potential synergistic effect between environmental stressors and gestational hypertensive conditions. Consistent with this, our supplementary analysis (Table S3) revealed that venous and lymphatic diseases accounted for the majority of CSD cases among women with GHTN.\u003c/p\u003e\u003cp\u003eThis findings supports the literature suggesting that hypertensive disorders during pregnancy may have a \"priming effect\" on the circulatory system, increasing the sensitivity of the circulatory system to environmental stressors [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The higher odds ratios observed in women with GHTN exposed to PM2.5 and extreme heat suggest a possible interaction effect. These findings emphasize that minimizing environmental exposure during pregnancy, a physiologically critical transitional period, may be a key strategy for safeguarding long-term circulatory system health in mothers. As climate change leads to rising levels of fine particulate matter and more frequent extreme heat events, populations experiencing significant physiological changes, such as pregnant and postpartum women, are at greater risk [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, future efforts should focus on strengthening both policy and clinical interventions that address environmental exposures during pregnancy. Prenatal and postpartum care programs could incorporate environmental risk assessments, whereas community-level strategies to improve air quality and mitigate heatwaves should be designed with pregnant women in mind. Such measures may not only help prevent pregnancy-related complications but also contribute to broader public health goals by reducing long-term CSD risks in mothers.\u003c/p\u003e\u003cp\u003eBuilding on these findings, future research should aim to integrate environmental, clinical, and behavioral data to better capture the multifactorial nature of CSD risk during and after pregnancy. Linking health insurance databases with obstetric registries and health examination records would enable more comprehensive analyses that account for lifestyle factors, delivery characteristics, and preexisting conditions. Interdisciplinary collaboration across epidemiology, obstetrics, and environmental sciences will be essential for developing targeted interventions and refining risk prediction models for maternal cardiovascular health.\u003c/p\u003e\u003cp\u003eBy advancing both preventive strategies and research integration, these efforts can help protect women during the physiologically vulnerable period of pregnancy and reduce long-term circulatory system risks across the life course.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study benefits from a large, nationally representative cohort with robust linkage to high-resolution environmental exposure datasets. Its longitudinal design enables the tracking of exposures across key time windows, from pregnancy through two years postpartum, allowing for temporal assessment of circulatory system disease risk. Additionally, by employing models with and without adjustment for GHTN, we were able to evaluate the effects of environmental exposures with consideration of GHTN as a potential modifier.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study has several limitations. First, residual confounding by unmeasured lifestyle or genetic factors cannot be excluded, despite adjustment for key covariates. Second, environmental exposures were estimated at the city level via outdoor monitoring data, which may not accurately capture individual-level exposures, particularly for indoor environments, leading to potential misclassification. Third, the NHIS dataset lacked detailed information on time-varying behavioral factors such as smoking, alcohol consumption, physical activity, and body mass index (BMI), which limited our ability to account for these important confounders. Fourth, although the dataset captured whether CSD occurred within the two-year follow-up period, it did not provide precise information on the timing of disease onset, limiting our ability to evaluate temporal patterns or progression. Fifth, our definition of gestational hypertension relied on the ICD-10 codes O13 and O16. While O13 specifically captures gestational hypertension without significant proteinuria, O16 is broader and may include chronic hypertension or preeclampsia/eclampsia. Thus, some degree of misclassification cannot be excluded, which may have affected the precision of our gestational hypertension classification. Finally, because the study cohort consisted exclusively of urban Korean women, the generalizability of our findings to rural populations or other ethnic groups may be limited.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis nationwide cohort study highlights the significant and compounding risks of CSD in women with a history of GHTN, especially when it is combined with prolonged exposure to environmental stressors such as PM\u003csub\u003e2.5\u003c/sub\u003e, high temperature, and atmospheric pressure. These findings suggest that GHTN is associated with an elevated risk of maternal CSD in the postpartum period. Environmental exposure during pregnancy, when the circulatory system is under heightened stress, appears to exacerbate this risk. Women who experience GHTN during pregnancy may constitute a key population for targeted environmental health interventions aimed at preventing long-term circulatory system outcomes. Notably, the effects of environmental exposure were also observed among women without GHTN, highlighting the broader role of air pollution and climatic stress in influencing long-term circulatory system outcomes in reproductive-aged women. These risks appeared to intensify over time, suggesting a cumulative impact of environmental and hypertensive exposures.\u003c/p\u003e\u003cp\u003eAs climate change and air pollution continue to worsen globally, public health systems must proactively address these environmental determinants of maternal circulatory system health. Integrating environmental risk factors into postpartum CSD risk screening, especially for women with GHTN, may enable more targeted prevention strategies. Future research should aim to refine individual-level exposure assessments and explore mechanistic pathways linking pregnancy-related complications, environmental stressors, and CSD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCSD\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCirculatory system disease\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGHTN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGestational hypertension\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHDP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHypertensive disorders of pregnancy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePM2.5\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eParticulate matter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m in diameter\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNHIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKorean National Health Insurance Service\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePropensity score\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eSMDs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eStandardized mean differences\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eConfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOdds ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAve-Pressure\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAverage atmospheric pressure (hPa)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eH-Tem\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHigh temperature (\u0026deg;C)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board (IRB) of Ewha Womans University College of Medicine (IRB file number: 2022-10-046) and was conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived because the study used de-identified secondary data from the National Health Insurance Service (NHIS) of Korea.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the National Health Insurance Service (NHIS) of Korea, but restrictions apply to the availability of these data, which were used under license for the current study and are therefore not publicly available. Data are, however, available from the NHIS for researchers who meet the criteria for access to confidential data (https://nhiss.nhis.or.kr)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(No. 2022R1A6A3A01085789).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eS.H. and Y.K. contributed equally to this work and are co\u0026ndash;first authors. They jointly conceptualized the study, performed the statistical analyses, interpreted the findings, and drafted the manuscript. M.-H.K. contributed to data management and supported software analysis. J.O. assisted with data visualization and software development. Y.C. and K.A.L. provided clinical expertise in obstetrics and gynecology and contributed to the interpretation of results. W.B.P. contributed to methodological design and critically reviewed the manuscript. T.-J.S. checked the analysis results, reviewed the manuscript and supervised the study. E.H. conceived and supervised the overall project, guided the methodological framework, and provided critical revisions to the manuscript.\u003c/p\u003e\n\u003cp\u003eE.H. and T.-J.S. are co\u0026ndash;corresponding authors. All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the National Health Insurance Service (NHIS) of Korea for providing access to the data used in this study. We also acknowledge the support of the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2022R1A6A3A01085789). The authors are grateful to colleagues at Ewha Womans University College of Medicine for their helpful comments and administrative support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFoundation BH. Global Heart \u0026amp; Circulatory Diseases Factsheet. In: J Am Coll Cardiol. 2025.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMensah GAFV, Murray CJL, Roth GA, Apostol GLC, et al. Global burden of cardiovascular diseases and risks, 1990\u0026ndash;2022. J Am Coll Cardiol. 2023;82(25):2350\u0026ndash;473.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan SCWZB, Tang ML, Chu H, Zhao YT, Weng C. Global burden of cardiovascular diseases and its risk factors, 1990\u0026ndash;2021: A systematic analysis for the Global Burden of Disease Study 2021. 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C.: Hypertensive Disorders in Pregnancy and Preeclampsia and the Effect of Environmental Chemical Exposures. Elsevier; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStaff ACD, Jacobsen R. D.P.: Hypertensive Disorders of Pregnancy and Cardiovascular Disease. Risk: Springer; 2024.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eVeenema RJH, Geer LA. Climate Change-Related Environmental Exposures and Perinatal and Maternal Health Outcomes in the U.S. Int J Environ Res Public Health. 2023;20(3):1662.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eConway FP, Filippi A, Chou V, Kovats D. Climate change, air pollution and maternal and newborn health: An overview of reviews of health outcomes. J Global Health. 2024;14:04128.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gestational hypertension (GHTN), circulatory system diseases (CSDs), PM₂.₅, postpartum health, air pollution, high temperature, environmental exposure","lastPublishedDoi":"10.21203/rs.3.rs-7783470/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7783470/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eGestational hypertension (GHTN) is a known risk factor for long-term circulatory system conditions, encompassing both cardiovascular and other vascular disorders (CSDs). Exposure to fine particulate matter (PM2.5) from air pollution and adverse climate factors may exacerbate this risk through inflammation and oxidative stress. This study investigated the associations between long-term exposure to PM2.5 and climate factors and the risk of CSD in pregnant women with GHTN.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eWe conducted a retrospective cohort study using Korean National Health Insurance data from 2015\u0026ndash;2019, including 307,938 first-time pregnant women after exclusions. Environmental exposures (PM₂.₅, high temperature, and atmospheric pressure) were measured during pregnancy and throughout the two-year postpartum period. GHTN and CSD outcomes were identified via ICD-10 codes. Propensity score matching (1:4) was applied, followed by logistic regression to estimate odds ratios (ORs) for CSD, stratified by GHTN status.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eGHTN significantly increased the risk of postpartum CSD, with an OR of up to 2.78 (95% CI: 2.63\u0026ndash;2.94) within two years after delivery. PM₂.₅ exposure was associated with increased CSD risk, increasing from an OR of 1.07 (95% CI: 1.05\u0026ndash;1.08) during pregnancy to 1.33 (95% CI: 1.29\u0026ndash;1.36) postpartum. High temperature and atmospheric pressure also had positive associations with CSD risk. These effects intensified with longer exposure durations and were more pronounced among women with GHTN, suggesting a potential interaction. Additionally, CSD risk varies by socioeconomic status and region.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eBoth gestational hypertension and prolonged environmental exposure significantly increase the risk of postpartum circulatory system diseases. These findings highlight the importance of postpartum monitoring and targeted environmental health interventions, especially for women with hypertensive disorders during pregnancy.\u003c/p\u003e","manuscriptTitle":"Long-term Exposure to Fine Particulate Matter and Climate factors and The Risk of Circulatory system Disease in Women with Gestational Hypertension: A Nationwide Cohort Study in South Korea.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-10 07:11:37","doi":"10.21203/rs.3.rs-7783470/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T17:21:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T13:30:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T08:32:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"201503983480347719843435222272698278415","date":"2025-11-04T07:53:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250841897364986153843042048759887186002","date":"2025-10-29T13:14:16+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-29T12:58:52+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-07T12:04:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-06T00:41:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-06T00:40:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2025-10-05T07:51:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1ff58155-7753-4413-b57e-538b039766f5","owner":[],"postedDate":"November 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T17:24:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-10 07:11:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7783470","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7783470","identity":"rs-7783470","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}