{"paper_id":"8b5f9b25-a035-4c4b-9bfa-f0d9d944c622","body_text":"Assessing air pollution as a risk factor for early menopause in 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing air pollution as a risk factor for early menopause in Korea Joyce Mary Kim, Jieun Min, Jungsil Lee, Kyungah Jeong, Eun-Hee Ha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3930338/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Backgrounds Ambient air pollution has become a serious public health issue that affects fertility rates in women worldwide. Therefore, there is a need to evaluate the risk factors associated with menopause to be able to inform women of the associated health risks. Methods We collected data from KHANES (The Korea National Health and Nutrition Examination Survey) between 2010 and 2020, from the Korean Center for Disease Control and Prevention, Ministry of Health and Welfare, and linked it with summary pollution data from AiMS-CREATE (AI-Machine Learning and Statistics Collaborative Research Ensemble for Air Pollution, Temperature, and All Types of Environmental Exposures) from 2002 to 2020. This summary data encapsulates the monthly average air pollution predictions for 226 si-gun-gu (cities, counties, and districts) in Korea. A total of 8,616 participants who had experienced menopause (early menopause: 20–45 years, N = 679; normal menopause: 46–60 years, N = 7,937) between 2002 and 2020 were included in the analysis. We employed survey logistic regression analyses to determine the associations between ambient air pollution and menopause after adjusting for covariates. Results There was an association between particulate matter 2.5 (PM 2.5 ) and early menopause (adjusted odds ratio [aOR]: 1.27, 95% confidence interval [CI]: 1.23–1.32), between particulate matter 10 (PM 10 ) and early menopause (aOR: 1.17, 95% confidence interval [CI]: 1.15–1.20), and between nitrogen dioxide (NO 2 ) and early menopause (aOR: 1.05, 95% confidence interval [CI]: 1.02–1.09). Conclusion Our results are consistent with the proposed hypothesis regarding an association between exposure to ambient air pollution and early menopause. This study provides substantial quantitative evidence that further supports the need for public health interventions to improve air quality, which is a risk in promoting early menopause. air pollution particulate matter early menopause environment epidemiology women’s health Figures Figure 1 Figure 2 Introduction Menopause marks a significant transition in a woman’s life, signifying the end of her reproductive period. Typically, menopause occurs in the late forties to early fifties. However, there is increasing concern regarding the incidence of early menopause, characterized by the cessation of menstruation before the age of forty-five. This study aims to probe the critical issues surrounding the rise in early menopause and its possible connection with environmental factors, specifically air pollution. It highlights the vital role research plays in clarifying how air pollutants may affect women's reproductive health (Hassan et al., 2023 ). Menopause indicates the end of natural fertility through a cessation of ovulation, which leads to a decrease in the production of essential hormones, particularly estrogen. The resulting hormonal shifts induce a spectrum of physical and psychological changes, including irregular menstrual cycles, the emergence of hot flashes, changes in mood, and an elevated risk of developing osteoporosis and cardiovascular diseases (Handy et al., 2022 ; Nash et al., 2022 ). The phenomenon of early menopause presents additional complexities (Afaya et al., 2022 ). Women experiencing early menopause also face a heightened risk of developing osteoporosis and cardiovascular diseases following the earlier decline in estrogen levels (LeBoff et al., 2022 ). Notably, the incidence of early menopause is on the rise globally, with numerous countries indicating an upward trend (Miyahira and Soule, 2022 ). In this research, we focused on South Korea, where pertinent research has detected a decline in the age of menopause. Recent statistics from South Korea have noted a reduction in the typical age of menopause, from approximately forty-nine years in the 1970s to around forty-eight years in the 2000s (Voedisch et al., 2021 ). While this decrease may seem modest, it is statistically enormous and warrants additional scrutiny. An intricate interplay of determinants precipitates the phenomenon of early menopause (Roheel et al., 2023 ). Predominantly, lifestyle behaviors, such as tobacco use and elevated alcohol consumption, are recognized as contributory factors, hastening the onset of menopause before the normative age. Genetic predilections also significantly influence; notably, females with a familial lineage of early menopause exhibit an augmented susceptibility (Giandalia et al., 2021 ). Emergent in the literature is the exploration of environmental contributors, emphasizing air pollution. Recent scholarly investigations have underscored the potential correlation between atmospheric pollutants and the increasing trend of premature menopause, thereby enriching our understanding of its etiological factors (Skakkebaek et al., 2022 ). A rigorous, multifactorial analysis is required to elucidate the complexities underpinning this phenomenon comprehensively (Zhu et al., 2021 ). Air pollution, originating from sources such as industrial effluents, vehicular emissions, and fossil fuel combustion, represents a ubiquitous global concern. This environmental contaminant encompasses deleterious constituents, including fine particulate matter (PM 2.5 ) and volatile organic compounds (VOCs), which pose significant risks to human health (Liu et al., 2022 ). Remarkably, despite the significant implications for public health, a pronounced lacuna persists in the literature concerning the investigation into the association between early menopause onset and air pollution (Guo et al., 2022 ). This deficit in knowledge warrants urgent attention to elucidate the extent of the effects of air pollution on the reproductive health outcomes of women. This research aimed to investigate the association between early menopause and air pollution. Particulate matter 2.5 (PM 2.5 ) has emerged as a significant global public health concern, which impacts the global fertility rates of women. While numerous studies have examined the separate effects of PM 2.5 and menopause, the association between PM 2.5 and the occurrence of early menopause has received limited attention. Therefore, our research aimed to investigate this specific association. Thus, a comprehensive analysis of the Korea National Health and Nutrition Examination Survey (KNHANES) was conducted in South Korea. The importance of this investigation is considerable, highlighting the critical need to understand the impacts of environmental variables on reproductive health outcomes (Skakkebaek et al., 2022 ). This knowledge transcends individual health, providing policymakers with the empirical foundation necessary to develop and enact robust policies to mitigate air pollution and preserve public health (Raimi, 2020 ). Moreover, the comprehensive exploration of the causes and potential treatments for early-onset menopause is of paramount importance (Guo et al., 2022 ). Such research has the potential to markedly enhance the well-being of individuals experiencing menopause and support their pursuit of a healthier life during the aging process. By exploring the associations between environmental factors and the early onset of menopause, the scope of this study extends beyond the immediate concerns of reproductive health; it also significantly contributes to more comprehensive public health initiatives (Davis et al., 2015 ). Method 2.1 Data sources The National Health and Nutrition Examination Surveys (NHANES) are annual national screenings that incorporate a self-survey component. For our research, we sourced data from the Korea National Health and Nutrition Examination Survey (KHANES) from 2010 to 2020, which was linked with summary pollution data from AiMS-CREATE (AI-Machine Learning and Statistics Collaborative Research Ensemble for Air Pollution, Temperature, and All Types of Environmental Exposures) (Figure S1). This summary data encapsulates the monthly average air pollution predictions for 226 si-gun-gu (cities, counties, and districts) in Korea from 2002 to 2020. These predictions were developed using machine learning techniques by the Graduate School of Public Health at Seoul National University and the School of Biomedical Convergence Engineering at Pusan National University (Kwon et al., 2002 ; Park et al., 2023 ). 2.2 Study population For the investigation, our primary sample consisted of 88,220 participants selected from 2010 to 2020. We implemented specific eligibility criteria by excluding individuals under the age of 20, thereby focusing on those aged between 20 and 80. Additionally, males were also excluded, as were females who were pregnant. Furthermore, the study did not consider women who failed to answer the menopause-related questions. This included those who did not report their age at menopause onset, provided ambiguous responses, such as 'do not know', or had incomplete responses. After implementing these exclusion criteria, a total of 17,712 female participants remained. Of these participants, 9096 were excluded because they had not experienced menopause between 2002 and 2020, as were the individuals who were not within the following age ranges: early menopause 20–45 and normal menopause 46–60. Subsequently, a total of 8,616 women who experienced menopause between 2010 and 2020 were included in the analysis (Fig. 1 ). 2.3 Covariates The variables considered were as follows: address districts of each participant were used as covariates. The ages of the participants ranged from 20 to 80 years. Survey-reported current ages were categorized into four groups: 20–29, 30–39, 40–49, and above 50 years. The marital status of each participant was classified as married, separated, widowed, or divorced. Additionally, educational backgrounds were categorized as below high school, high school, and college or higher. Household income levels were classified as low, mid-low, mid-high, or high. In terms of smoking status, participants were categorized as current, occasional, former, never, or unknown smokers. Drinking statuses were grouped as never, current, or unknown. We also assessed body mass index (BMI), categorizing it as underweight, normal, overweight, obese I, obese II, or obese III. For conditions such as hypertension, diabetes, and anemia, participants were identified as either having or not having the condition. 2.4 Statistical analysis We performed a survey logistic regression analysis, considering the survey weights from the complex, stratified, and probability-cluster sampling procedures of the KHANES data. This was conducted to analyze the associations between ambient air pollution (PM 2.5 , PM 10 , and NO 2 ) and early menopause while adjusting for the covariates. In the statistical analysis, the covariates included age, district, marital status, educational level, household income level, smoking status, drinking habits, BMI, hypertension, diabetes, and anemia. To estimate the year menopause occurred for the participants, we subtracted the number of years since they reached menopause from the survey year. The calculation is provided below: Air pollution year = year - (survey age-menopause age) All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R 4.3.1 (R Development Core Team, Vienna, Austria). Results As shown in Table 1, a total of 8616 participants were included in the study, with 679 (9.28%) in the early menopause age group (20–45 years old) and 7937 (90.72%) in the normal menopause age group (46–60 years old). Additionally, the distribution of menopause occurrences across the respective age groups is as follows: 20–29 (N = 15, 0.35%), 30–39 (N = 24, 0.34%), 40–49 (N = 538, 7.82%), ≥ 50 (N = 8039, 91.49%). The marital status for all participants included 7109 (83.17%) who were married, 76 (0.97%) were separated, 763 (8.07%) were widowed, and 668 (7.79%) were divorced. Additionally, 6883 (78.85%) had received education to high school level or below, while 1733 (21.15%) had a college education or higher. The household incomes for the participants were: 1321 (15.34%) in the low-income category, 2339 (26.40%) had mid–low income, 2294 (26.55%) had middle–high income, and 2662 (31.71%) had high income. Smoking status: 7968 (91.60%) had never smoked, 266 (3.57%) were current smokers, 302 (3.69%) were former smokers, 58 (0.84%) were occasional smokers, and 22 (0.30%) responded with 'do not know'. Alcohol consumption: 6992 (83.00%) were current drinkers, 1603 (16.71%) were non-drinkers, and 21 (0.29%) responded with 'do not know'. For BMI, 162 (1.91%) were underweight, 3319 (38.86%) were of normal weight, 2109 (23.96%) were overweight, 2546 (30.05%) were in the obese class I category, 421 (4.66%) were in the obese class II category, and 59 (0.56%) were in the obese class III category. The majority of participants reported not having hypertension, diabetes, or anemia. Specifically, for hypertension, 6300 (74.89%) responded 'no' and 2316 (25.11%) responded 'yes'. Regarding diabetes, 7839 (91.46%) answered 'no', while 777 (8.54%) answered 'yes'. For anemia, 8109 (94.10%) responded 'no' and 507 (5.90%) responded 'yes'. Figure 2 shows the geographical distributions of menopause and air pollution across 229 districts in South Korea between 2002 and 2020. Table 1. General characteristics of the study population. Variables Menopause Total N = 8616 (%) Early menopause (age 20–45) N = 679 (9.28) Normal menopause (age 46–60) N = 7937 (90.72) P -value Age* 20–29 15 (0.35) 15 (0.35) 0 (0.00) < 0.0001 30–39 24 (0.34) 24 (0.34) 0 (0.00) 40–49 538 (7.82) 281 (4.01) 257 (3.81) ≥ 50 8039 (91.49) 359 (4.59) 7680 (86.91) Marital status Married 7109 (83.17) 564 (7.79) 6545 (75.38) < 0.0001 Separated 76 (0.97) 6 (0.07) 70 (0.90) Widowed 763 (8.07) 32 (0.33) 731 (7.74) Divorced 668 (7.79) 77 (1.09) 591 (6.70) Educational level ≤ Highschool 6883 (78.85) 489 (6.68) 6394 (72.17) 0.0158 ≥ College 1733 (21.15) 190 (2.61) 1543 (18.54) Household income level Low 1321 (15.34) 68 (0.88) 1253 (14.46) 0.0054 Middle–low 2339 (26.40) 184 (2.69) 2155 (23.71) Middle–high 2294 (26.55) 179 (2.28) 2115 (24.27) High 2662 (31.71) 248 (3.43) 2414 (28.28) Smoking Status Current 266 (3.57) 39 (0.54) 227 (3.03) 0.0023 Sometimes 58 (0.84) 14 (0.23) 44 (0.61) Former 302 (3.69) 44 (0.66) 258 (3.03) Never 7968 (91.60) 580 (7.82) 7388 (83.78) Do not know 22 (0.30) 2 (0.03) 20 (0.27) Drinking Never 1603 (16.71) 87 (1.13) 1516(15.57) 0.0142 Current 6992 (83.00) 590 (8.12) 6402 (74.88) Do not know 21 (0.29) 2 (0.03) 19 (0.26) BMI** Underweight (<18.5 kg/m 2 ) 162 (1.91) 15 (0.15) 147 (1.76) 0.1005 Normal (18.5–23 kg/m 2 ) 3319 (38.86) 300 (4.09) 3019 (34.77) Overweight (23–25 kg/m 2 ) 2109 (23.96) 159 (2.25) 1950 (21.71) Obese class I (25–30 kg/m 2 ) 2546 (30.05) 166 (2.25) 2380 (27.80) Obese class II (30–35 kg/m 2 ) 421 (4.66) 33 (0.45) 388 (4.21) Obese class III (≥ 35 kg/m 2 ) 59 (0.56) 6 (0.09) 53 (0.47) Hypertension No 6300 (74.89) 561 (7.70) 5739 (67.19) < 0.0001 Yes 2,316 (25.11) 118 (1.58) 2198 (23.53) Diabetes No 7839 (91.46) 637 (8.69) 7202 (82.77) 0.0822 Yes 777 (8.54) 42 (0.59) 735 (7.95) Anemia No 8109 (94.10) 647 (8.83) 7462 (85.27) 0.3698 Yes 507 (5.90) 32 (0.45) 475 (5.45) Abbreviation: *\"age\" denotes current age at the time of survey. **\"BMI\" denotes body mass index. In Table 2 —the adjusted model—the odds ratios (ORs) were controlled for factors including district, age, marital status, educational levels, household income, smoking status, drinking habits, BMI, hypertension, diabetes, and anemia. There was an association between the exposure to PM 2.5 and early menopause (adjusted odds ratio [aOR]: 1.27, 95% confidence interval [CI]: 1.23–1.32), particulate matter 10 (PM 10 ) and early menopause (adjusted odds ratio [aOR]: 1.17, 95% confidence interval [CI] 1.15–1.20), and nitrogen dioxide (NO 2 ) and early menopause (adjusted odds ratio [aOR]: 1.05, 95% confidence interval [CI] 1.02–1.09). Table 2. Odds ratios (and 95% confidence intervals) from survey logistic regression analyses identifying associations between air pollution and early menopause. The stratified analysis of particulate matter exposure, specifically to PM 2.5 , in relation to menopausal status and smoking habits, is presented in Table 3 . The exposure to PM 2.5 was assessed at two levels: The national ambient air quality standard and high exposure, with the former being ≤ 15 µg/m³ and the latter being > 15 µg/m³ (Korea, 2022 ). The standards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO). The total number of subjects across all categories and exposure levels was 8616. There were no cases of early menopause being associated with the standard level PM 2.5 ambient air quality set by Air Korea for either non-smokers or current smokers. However, in the high ambient air quality PM 2.5 set by Air Korea, there were 626 cases of early menopause in the never-smokers and 53 in the current smokers, totaling 679 cases. For those in the normal menopause age group, four cases were reported in never-smokers at the standard air quality level established by Air Korea, with no instances in current smokers. A significant number of cases were reported at the high exposure level: 7,662 for never-smokers and 271 for current smokers, totaling 7,933 cases. Overall, the dataset clearly indicates a significant difference in PM 2.5 exposure when comparing the Air Korea PM 2.5 national ambient air quality standard levels to those considered high exposure—a trend that is observed regardless of the menopausal status of the individuals. Additionally, within each exposure category, the proportion of never-smokers was notably higher than for current smokers. Table 3 Stratification comparing menopause and smoking status in relation to PM2.5 exposure. Variable Smoking status Never Current Total Early Menopause x standard Air Korea PM 2.5 national ambient air quality 0 0 0 Early Menopause x high exposure Air Korea PM 2.5 national ambient air quality 626 53 679 Normal Menopause x standard Air Korea PM 2.5 national ambient air quality 4 0 4 Normal Menopause x high exposure Air Korea PM 2.5 national ambient air quality 7662 271 7933 Total 8292 324 8616 Classification of PM 2.5 (particulate matter with a diameter of 5 micrometers or less) annual average concentration into \"standard,\" and \"high\". Standard Air Korea PM 2.5 national ambient air quality is 15 µg/m³. High exposure Air Korea PM 2.5 national ambient air quality is above 15 µg/m³. Standards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO). Table 4 shows stratified data comparing menopause status and age in relation to PM 2.5 exposure. For the age group of 20–29, there were no cases of early menopause associated with either standard or high exposure to PM 2.5 according to the national ambient air quality standards set by Air Korea. In the 30–39 age group, 15 cases of early menopause were associated with high exposure, while no cases were related to standard exposure. Among women aged 40–49, 28 cases of early menopause were associated with high exposure, and four cases with standard exposure. For the age group above 50, 35 cases of early menopause were associated with high exposure, while no cases were associated with the standard exposure. For cases associated with the normal onset of menopause, 253 cases in the over-50 age group were associated with high exposure. In contrast, no cases were associated with standard exposure for any age group. The total number of cases of early menopause associated with high exposure was 67, whereas for standard exposure, it was four. The total number of cases relating to normal menopause and high exposure was 7933. Table 4 Stratification comparing menopause and age in relation to PM 2.5 exposure. Variable Age a 20–29 30–39 40–49 ≥ 50 Total Early menopause x standard Air Korea PM 2.5 national ambient air quality 0 0 0 0 0 Early menopause x high exposure Air Korea PM 2.5 national ambient air quality 15 24 281 359 679 Normal menopause x standard Air Korea PM 2.5 national ambient air quality 0 0 4 0 4 Normal menopause x high exposure Air Korea PM 2.5 national ambient air quality 0 0 253 7680 7933 Total 15 24 538 8039 8616 Abbreviation: a = current age at the time of the examination survey. Classification of PM 2.5 (particulate matter with a diameter of 5 micrometers or less) annual average concentration into \"standard,\" and \"high\". Standard Air Korea PM 2.5 national ambient air quality is 15 µg/m³. High exposure Air Korea PM 2.5 national ambient air quality is above 15 µg/m³. Standards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO). Table 5 presents the association between menopause, smoking status, and PM 10 exposure for 8616 individuals. In the early menopause category, a total of 271 individuals who had never smoked and 27 current smokers were identified as being exposed to PM 10 levels compliant with national ambient air quality standards. In stark contrast, 355 never-smokers and 26 current smokers in the early menopause stage were exposed to elevated PM 10 levels. For those individuals experiencing normal menopause, 4079 never-smokers and 135 current smokers were exposed to PM 10 levels within the standard range. Meanwhile, 3587 never-smokers and 136 current smokers encountered elevated PM 10 levels. It is noteworthy that a consistently higher prevalence of never-smokers was observed across all categories of menopause and PM 10 exposure levels compared to current smokers—the well-established relationship between smoking and menopause risk. Our analysis further underscores the remarkable similarity in the proportions of current smokers adhering to PM 10 standards and those facing elevated PM 10 . However, individuals who have never smoked may exhibit a heightened susceptibility to the adverse effects of PM 10 high exposure at the onset of early menopause. Furthermore, it is worth noting that the proportion of individuals experiencing early menopause without a history of smoking and exposure to high PM 10 levels is significantly higher than in those undergoing normal menopause. Table 5 Stratification comparing menopause and smoking status in relation to PM 10 exposure. Variable Smoking status Never Current Total Early menopause x standard Air Korea PM 10 national ambient air quality 271 27 298 Early menopause x high exposure Air Korea PM 10 national ambient air quality 355 26 381 Normal menopause x standard Air Korea PM 10 national ambient air quality 4079 135 4214 Normal menopause x high exposure Air Korea PM 10 national ambient air quality 3587 136 3723 Total 8292 324 8616 Classification of PM 10 (particulate matter with a diameter of 10 micrometers or less) annual average concentration into \"standard,\" and \"high\". Standard PM 10 national ambient air quality is 50 µg/m³. High exposure PM 10 national ambient air quality is above 50 µg/m³. Standards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO). In Table 6, a total of 15 early menopause individuals aged between 20 and 29 were shown to be subjected to standard national ambient air quality levels of PM 10 . Conversely, high PM 10 exposure was not reported in any individuals in the early menopause age group of 20–29. In contrast, significant exposure to PM 10 was noted in the normal menopause group, particularly in individuals aged above 50. Within this age category, 3995 individuals were exposed to standard PM 10 levels and 3685 to high PM 10 levels. The aggregated data across all age groups in the study revealed that out of the 8616 participants, those in the normal menopause stage, especially in the older age spectrum, experienced a higher incidence of exposure to PM 10 . This trend highlights the intersection of menopause status, age, and environmental exposure within the study population, indicating a potential age-related vulnerability to air quality issues among women undergoing menopause. Table 6. Stratification comparing menopause and age in relation to PM 10 exposure. Variable Age a 20–29 30–39 40–49 ≥ 50 Total Early menopause x standard Air Korea PM 10 national ambient air quality 15 15 166 102 298 Early menopause x high exposure Air Korea PM 10 national ambient air quality 0 9 115 257 381 Normal menopause x standard Air Korea PM 10 national ambient air quality 0 0 219 3995 4214 Normal menopause x high exposure Air Korea PM 10 national ambient air quality 0 0 38 3685 3723 Total 15 24 538 8039 8616 Abbreviation: a = current age on the date of the examination survey. Classification of PM 10 (particulate matter with a diameter of 10 micrometers or less) annual average concentration into \"standard,\" and \"high\". Standard PM 10 National ambient air quality is 50 µg/m³. High exposure PM 10 National ambient air quality is above 50 µg/m³. Standards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO). Discussion This study investigated the association between ambient air pollution and menopausal age by adjusting for other factors that could impact menopause, such as marital status, drinking patterns, household income, educational level, body mass index (BMI), hypertension, and anemia (Morris et al., 2012 ). As such, it surveyed around 17,712 female participants, of which 8616 responded comprehensively with their details, meaning their data was included in the final analysis and used to calculate the adjusted odds ratio (aOR) regarding an association with the four primary air pollutants. The study setting involved Korean cities, countries, and districts. The logistic regression analyzed the association between the ambient air pollution levels at the noted geographical locations provided by AiMS-CREATE and women experiencing menopause from 2002 to 2020. Compared to a previous study, the prevalence rate of early menopause in Korean women was reported to be 7.2% in KHANES conducted from 2007 to 2012, which is higher than the rate of 3.4% in US women (Choe SA, 2020 ). The current study reveals a notable increase, with a prevalence rate of 9.28% observed in KHANES conducted from 2010 to 2020. The final results revealed that ambient air pollution was linked positively and significantly with early menopause in women, with PM 2.5 presenting the highest odds in this association. PM 10 had an aOR of 1.17, while NO 2 had an adjusted aOR of 1.05. Each aOR reflects a 95% confidence interval, meaning one can be 95% sure about these associations in the given intensity. In short, this study found that air pollutants, such as PM 2.5 , PM 10 , and NO 2 , were positively associated with early menopause in women. These findings prove the hypothesis that there is a strong association between ambient air pollution and early menopause in women. Furthermore, these findings are similar to Guo et al. ( 2018 ); however, the current levels associate more significance to the connection between PM 2.5 and NO 2 and early menopause since the respective aORs were found to be 1.27 and 1.17, respectively, vs. 1.16 and 1.04, respectively, which were found by Guo et al. ( 2018 ) in Taiwan. Again, geographical differences play a focal role, indicating the differences in climate and pollution levels between Taiwan and Korea. The biological mechanism underlying these findings is limited in the literature (Li et al., 2021 ). However, one can match different findings presented by various scholars to begin to understand why there is such a strong association between three air pollutants and age at menopause (Zhang et al., 2017 ). First, one must consider what causes early menopause in women. Studies by Harlow and Signorello ( 2000 ), Shuster et al. ( 2010 ), Soules and Bremner ( 1982 ), and Thomford et al. ( 1987 ) have previously explained the characterization of early menopause. Their findings suggest that early menopause occurs due to the loss of ovarian follicular function, the early loss of which occurs following a more significant number of depleted oocytes and a high rate of ovarian follicle atresia. This points to the factors or agents associated with these biological realities causing early menopause in women. Alternatively, studies show the biological and hormonal impact of air pollution on the bodies of females. Thurston et al. ( 2000 ) showed that extreme exposure to air pollution can impact the length of the menstrual cycle. Abareshi et al. ( 2020 ) found that females under extreme exposure to air pollutants exhibited significantly reduced hormones related to ovarian reserve levels. Maluf et al. ( 2009 ), Gaskins et al. ( 2019 ), and Veras et al. ( 2009 ) discovered that it was possible to have ovarian follicular function loss due to exposure to toxins and air pollutants. Expanding on these findings, Carré et al. ( 2017 ) found that females could suffer ovarian follicular function loss due to exposure to air pollution because these pollutants disturbed the inflammatory response system and led to epigenetic modifications, oxidative stress, and cell DNA alteration (Shi et al., 2016 ). Through these studies, one can see a pattern regarding how air pollution can cause early menopause; air pollution disturbs the lowest levels of biological functions, causes ovarian follicular function loss, and reduces levels of pro-menstrual hormones. Regarding the study's strengths and limitations, it is well-established that the study focused on the Korean population. Geographical factors implicate climate, which, in turn, involves the probabilistic realities under observation. The study includes a large sample size, as Namvar et al. ( 2022 ) previously recommended the inclusion of a large sample size to achieve any level of significance in the result. Therefore, this study followed their recommendation and included 8616 women in the final analysis. Our study also had limitations. Firstly, verifying the responses of the participants in the study was not possible. Therefore, potential biases may affect the results due to the possibility of including incorrect answers. Additionally, it may include recall bias because it collected menopause-related information through a self-administered questionnaire. Secondly, the accuracy of the addresses was assessed based on the examination date for women who had experienced menopause. Moreover, this study ignores the potential impact of household air pollution on menopause age. Conclusion Our results are consistent with the proposed hypothesis about the association between exposure to PM 2.5, PM 10 , and NO 2 and early menopause. This study provides substantial quantitative evidence, further supporting the need for public health interventions to improve air quality since it can affect the onset of early menopause. Declarations Author Contribution Author’s contributionsJoyce Mary Kim: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Writing—original draftJieun Min: Methodology, Writing—original draftJungsil Lee: Methodology, Writing—original draftKyungah Jeong: Conceptualization, Methodology, Supervision Writing—original draft Eunhee Ha: Conceptualization, Methodology, Supervision Writing—original draft Acknowledgment Joyce Kim was supported as a trainee in the environmental health training program provided by the Environmental Health Centre of the Catholic University of Korea and funded by the Ministry of Environment, Republic of Korea (2023). References Abareshi F, et al. Association of exposure to air pollution and green space with ovarian reserve hormones levels. Environ Res. 2020;184:109342. Afaya A, et al. Health system barriers influencing timely breast cancer diagnosis and treatment among women in low and middle-income Asian countries: evidence from a mixed-methods systematic review. BMC Health Serv Res. 2022;22:1–17. Carré J, et al. Does air pollution play a role in infertility? a systematic review. Environ Health. 2017;16:1–16. Choe SA, S. J. 2020. Trends of Premature and Early Menopause: a Comparative Study of the US National Health and Nutrition Examination Survey and the Korea National Health and Nutrition Examination Survey. J Korean Med Sci 35, e97. Davis SR et al. 2015. Menopause (Primer). Nature Reviews: Disease Primers. 1. Gaskins AJ, et al. Time-varying exposure to air pollution and outcomes of in vitro fertilization among couples from a fertility clinic. Environ Health Perspect. 2019;127:077002. Giandalia A, et al. Gender differences in diabetic kidney disease: focus on hormonal, genetic and clinical factors. Int J Mol Sci. 2021;22:5808. Guo C, et al. Associations between long-term exposure to multiple air pollutants and age at menopause: a longitudinal cohort study. Ann Epidemiol. 2022;76:68–76. Guo C, et al. Effect of long-term exposure to fine particulate matter on lung function decline and risk of chronic obstructive pulmonary disease in Taiwan: a longitudinal, cohort study. Lancet Planet Health. 2018;2:e114–25. Handy AB, et al. Psychiatric symptoms across the menstrual cycle in adult women: a comprehensive review. Harv Rev Psychiatry. 2022;30:100. Harlow BL, Signorello LB. Factors associated with early menopause. Maturitas. 2000;35:3–9. Hassan S et al. 2023. Endocrine disruptors: Unravelling the link between chemical exposure and Women's reproductive health. Environ Res. 117385. Korea A. Air Quality Standards. Ministry of Environment; 2022. Kwon D et al. 2002. Estimation of High-Spatial Resolution of Ground-Level Ozone, Nitrogen Dioxide, and Carbon Monoxide in South Korea During 2002–2020 Using Machine-Learning Based Ensemble Model. Nitrogen Dioxide, and Carbon Monoxide in South Korea During. 2020. LeBoff M, et al. The clinician’s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2022;33:2049–102. Li H, et al. Long-term exposure to particulate matter and roadway proximity with age at natural menopause in the Nurses’ Health Study II Cohort. Environ Pollut. 2021;269:116216. Liu B, et al. Catalytic ozonation of VOCs at low temperature: A comprehensive review. J Hazard Mater. 2022;422:126847. Maluf M, et al. In vitro fertilization, embryo development, and cell lineage segregation after pre-and/or postnatal exposure of female mice to ambient fine particulate matter. Fertil Steril. 2009;92:1725–35. Miyahira AK, Soule HR. The 28th Annual Prostate Cancer Foundation Scientific Retreat report. Prostate. 2022;82:1346–77. Morris DH, et al. Body mass index, exercise, and other lifestyle factors in relation to age at natural menopause: analyses from the breakthrough generations study. Am J Epidemiol. 2012;175:998–1005. Namvar Z, et al. Association of ambient air pollution and age at menopause: a population-based cohort study in Tehran. Iran Air Qual Atmos Health. 2022;15:2231–8. Nash Z, et al. Bone and heart health in menopause. Best Pract Res Clin Obstet Gynecol. 2022;81:61–8. Park J, et al. Association of long-term exposure to air pollution with chronic sleep deprivation in South Korea: A community-level longitudinal study, 2008–2018. Environ Res. 2023;228:115812. Raimi MO, A Critical Review of Health Impact Assessment. Towards Strengthening the Knowledge of Decision Makers Understand Sustainable Development Goals in the Twenty-First Century: Necessity Today; Essentiality Tomorrow. Research and Advances: Environmental Sciences; 2020. pp. 2652–3655. Roheel A, et al. Global epidemiology of breast cancer based on risk factors: a systematic review. Front Oncol. 2023;13:1240098. Shi L et al. 2016. Long-term moderate oxidative stress decreased ovarian reproductive function by reducing follicle quality and progesterone production. PLoS ONE 11, e0162194. Shuster LT, et al. Premature menopause or early menopause: long-term health consequences. Maturitas. 2010;65:161–6. Skakkebaek NE, et al. Environmental factors in declining human fertility. Nat Reviews Endocrinol. 2022;18:139–57. Soules MR, Bremner WJ. The menopause and climacteric: endocrinologic basis and associated symptomatology. J Am Geriatr Soc. 1982;30:547–61. Thomford PJ, et al. Effect of oocyte number and rate of atresia on the age of menopause. Reprod Toxicol. 1987;1:41–51. Thurston SW, et al. Petrochemical exposure and menstrual disturbances. Am J Ind Med. 2000;38:555–64. Veras MM, et al. Chronic exposure to fine particulate matter emitted by traffic affects reproductive and fetal outcomes in mice. Environ Res. 2009;109:536–43. Voedisch AJ, et al. Menopause: a global perspective and clinical guide for practice. Clin Obstet Gynecol. 2021;64:528–54. Zhang Z, et al. Satellite-based estimates of long-term exposure to fine particulate matter are associated with C-reactive protein in 30 034 Taiwanese adults. Int J Epidemiol. 2017;46:1126–36. Zhu Y, et al. Epidemiology and genomics of prostate cancer in Asian men. Nat Reviews Urol. 2021;18:282–301. Additional Declarations No competing interests reported. Supplementary Files FigureS1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3930338\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":271348732,\"identity\":\"b4a30d83-455e-44c7-aa5b-176c94306d19\",\"order_by\":0,\"name\":\"Joyce Mary Kim\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Ewha Womans University College of Medicine\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Joyce\",\"middleName\":\"Mary\",\"lastName\":\"Kim\",\"suffix\":\"\"},{\"id\":271348733,\"identity\":\"7f7e3765-4ce1-4126-bdda-3c1c3c914aa8\",\"order_by\":1,\"name\":\"Jieun 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Chart\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3930338/v1/1dc846a35ed715b14d80f746.png\"},{\"id\":50815770,\"identity\":\"1e591af8-fa13-4ecd-84f2-7d0124f535dc\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 19:43:22\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":455322,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGeographical distribution of the average proportion of air pollution across 299 districts in South Korea from 2002 to 2020.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3930338/v1/22f4b8b3abe12e226e9156b9.png\"},{\"id\":53312801,\"identity\":\"15e2d688-5c02-4a2d-b690-792f24d1f3d1\",\"added_by\":\"auto\",\"created_at\":\"2024-03-23 15:40:30\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":847220,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3930338/v1/47778084-fee2-4789-b0dc-4320483e9485.pdf\"},{\"id\":50816965,\"identity\":\"cccf47a6-dcfa-445a-81c8-a38b340bec34\",\"added_by\":\"auto\",\"created_at\":\"2024-02-07 19:51:22\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":78107,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"FigureS1.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3930338/v1/ecaeffbd3b480941f2715834.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Assessing air pollution as a risk factor for early menopause in Korea\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eMenopause marks a significant transition in a woman\\u0026rsquo;s life, signifying the end of her reproductive period. Typically, menopause occurs in the late forties to early fifties. However, there is increasing concern regarding the incidence of early menopause, characterized by the cessation of menstruation before the age of forty-five. This study aims to probe the critical issues surrounding the rise in early menopause and its possible connection with environmental factors, specifically air pollution. It highlights the vital role research plays in clarifying how air pollutants may affect women's reproductive health (Hassan et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eMenopause indicates the end of natural fertility through a cessation of ovulation, which leads to a decrease in the production of essential hormones, particularly estrogen. The resulting hormonal shifts induce a spectrum of physical and psychological changes, including irregular menstrual cycles, the emergence of hot flashes, changes in mood, and an elevated risk of developing osteoporosis and cardiovascular diseases (Handy et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Nash et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe phenomenon of early menopause presents additional complexities (Afaya et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Women experiencing early menopause also face a heightened risk of developing osteoporosis and cardiovascular diseases following the earlier decline in estrogen levels (LeBoff et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eNotably, the incidence of early menopause is on the rise globally, with numerous countries indicating an upward trend (Miyahira and Soule, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In this research, we focused on South Korea, where pertinent research has detected a decline in the age of menopause. Recent statistics from South Korea have noted a reduction in the typical age of menopause, from approximately forty-nine years in the 1970s to around forty-eight years in the 2000s (Voedisch et al., \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). While this decrease may seem modest, it is statistically enormous and warrants additional scrutiny.\\u003c/p\\u003e \\u003cp\\u003eAn intricate interplay of determinants precipitates the phenomenon of early menopause (Roheel et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Predominantly, lifestyle behaviors, such as tobacco use and elevated alcohol consumption, are recognized as contributory factors, hastening the onset of menopause before the normative age. Genetic predilections also significantly influence; notably, females with a familial lineage of early menopause exhibit an augmented susceptibility (Giandalia et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Emergent in the literature is the exploration of environmental contributors, emphasizing air pollution. Recent scholarly investigations have underscored the potential correlation between atmospheric pollutants and the increasing trend of premature menopause, thereby enriching our understanding of its etiological factors (Skakkebaek et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). A rigorous, multifactorial analysis is required to elucidate the complexities underpinning this phenomenon comprehensively (Zhu et al., \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAir pollution, originating from sources such as industrial effluents, vehicular emissions, and fossil fuel combustion, represents a ubiquitous global concern. This environmental contaminant encompasses deleterious constituents, including fine particulate matter (PM\\u003csub\\u003e2.5\\u003c/sub\\u003e) and volatile organic compounds (VOCs), which pose significant risks to human health (Liu et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRemarkably, despite the significant implications for public health, a pronounced lacuna persists in the literature concerning the investigation into the association between early menopause onset and air pollution (Guo et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). This deficit in knowledge warrants urgent attention to elucidate the extent of the effects of air pollution on the reproductive health outcomes of women.\\u003c/p\\u003e \\u003cp\\u003eThis research aimed to investigate the association between early menopause and air pollution. Particulate matter 2.5 (PM\\u003csub\\u003e2.5\\u003c/sub\\u003e) has emerged as a significant global public health concern, which impacts the global fertility rates of women. While numerous studies have examined the separate effects of PM\\u003csub\\u003e2.5\\u003c/sub\\u003e and menopause, the association between PM\\u003csub\\u003e2.5\\u003c/sub\\u003e and the occurrence of early menopause has received limited attention. Therefore, our research aimed to investigate this specific association. Thus, a comprehensive analysis of the Korea National Health and Nutrition Examination Survey (KNHANES) was conducted in South Korea.\\u003c/p\\u003e \\u003cp\\u003eThe importance of this investigation is considerable, highlighting the critical need to understand the impacts of environmental variables on reproductive health outcomes (Skakkebaek et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). This knowledge transcends individual health, providing policymakers with the empirical foundation necessary to develop and enact robust policies to mitigate air pollution and preserve public health (Raimi, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Moreover, the comprehensive exploration of the causes and potential treatments for early-onset menopause is of paramount importance (Guo et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Such research has the potential to markedly enhance the well-being of individuals experiencing menopause and support their pursuit of a healthier life during the aging process. By exploring the associations between environmental factors and the early onset of menopause, the scope of this study extends beyond the immediate concerns of reproductive health; it also significantly contributes to more comprehensive public health initiatives (Davis et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e).\\u003c/p\\u003e\"},{\"header\":\"Method\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Data sources\\u003c/h2\\u003e \\u003cp\\u003eThe National Health and Nutrition Examination Surveys (NHANES) are annual national screenings that incorporate a self-survey component. For our research, we sourced data from the Korea National Health and Nutrition Examination Survey (KHANES) from 2010 to 2020, which was linked with summary pollution data from AiMS-CREATE (AI-Machine Learning and Statistics Collaborative Research Ensemble for Air Pollution, Temperature, and All Types of Environmental Exposures) (Figure S1). This summary data encapsulates the monthly average air pollution predictions for 226 si-gun-gu (cities, counties, and districts) in Korea from 2002 to 2020. These predictions were developed using machine learning techniques by the Graduate School of Public Health at Seoul National University and the School of Biomedical Convergence Engineering at Pusan National University (Kwon et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Park et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Study population\\u003c/h2\\u003e \\u003cp\\u003e For the investigation, our primary sample consisted of 88,220 participants selected from 2010 to 2020. We implemented specific eligibility criteria by excluding individuals under the age of 20, thereby focusing on those aged between 20 and 80. Additionally, males were also excluded, as were females who were pregnant. Furthermore, the study did not consider women who failed to answer the menopause-related questions. This included those who did not report their age at menopause onset, provided ambiguous responses, such as 'do not know', or had incomplete responses. After implementing these exclusion criteria, a total of 17,712 female participants remained. Of these participants, 9096 were excluded because they had not experienced menopause between 2002 and 2020, as were the individuals who were not within the following age ranges: early menopause 20\\u0026ndash;45 and normal menopause 46\\u0026ndash;60. Subsequently, a total of 8,616 women who experienced menopause between 2010 and 2020 were included in the analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" 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 Covariates\\u003c/h2\\u003e \\u003cp\\u003eThe variables considered were as follows: address districts of each participant were used as covariates. The ages of the participants ranged from 20 to 80 years. Survey-reported current ages were categorized into four groups: 20\\u0026ndash;29, 30\\u0026ndash;39, 40\\u0026ndash;49, and above 50 years. The marital status of each participant was classified as married, separated, widowed, or divorced. Additionally, educational backgrounds were categorized as below high school, high school, and college or higher. Household income levels were classified as low, mid-low, mid-high, or high. In terms of smoking status, participants were categorized as current, occasional, former, never, or unknown smokers. Drinking statuses were grouped as never, current, or unknown. We also assessed body mass index (BMI), categorizing it as underweight, normal, overweight, obese I, obese II, or obese III. For conditions such as hypertension, diabetes, and anemia, participants were identified as either having or not having the condition.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Statistical analysis\\u003c/h2\\u003e \\u003cp\\u003eWe performed a survey logistic regression analysis, considering the survey weights from the complex, stratified, and probability-cluster sampling procedures of the KHANES data. This was conducted to analyze the associations between ambient air pollution (PM\\u003csub\\u003e2.5\\u003c/sub\\u003e, PM\\u003csub\\u003e10\\u003c/sub\\u003e, and NO\\u003csub\\u003e2\\u003c/sub\\u003e) and early menopause while adjusting for the covariates. In the statistical analysis, the covariates included age, district, marital status, educational level, household income level, smoking status, drinking habits, BMI, hypertension, diabetes, and anemia. To estimate the year menopause occurred for the participants, we subtracted the number of years since they reached menopause from the survey year.\\u003c/p\\u003e \\u003cp\\u003eThe calculation is provided below:\\u003c/p\\u003e \\u003cp\\u003eAir pollution year\\u0026thinsp;=\\u0026thinsp;year - (survey age-menopause age)\\u003c/p\\u003e \\u003cp\\u003eAll analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) and R 4.3.1 (R Development Core Team, Vienna, Austria).\\u003c/p\\u003e \"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eAs shown in Table 1, a total of 8616 participants were included in the study, with 679 (9.28%) in the early menopause age group (20\\u0026ndash;45 years old) and 7937 (90.72%) in the normal menopause age group (46\\u0026ndash;60 years old). Additionally, the distribution of menopause occurrences across the respective age groups is as follows: 20\\u0026ndash;29 (N\\u0026thinsp;=\\u0026thinsp;15, 0.35%), 30\\u0026ndash;39 (N\\u0026thinsp;=\\u0026thinsp;24, 0.34%), 40\\u0026ndash;49 (N\\u0026thinsp;=\\u0026thinsp;538, 7.82%), \\u0026ge; 50 (N\\u0026thinsp;=\\u0026thinsp;8039, 91.49%). The marital status for all participants included 7109 (83.17%) who were married, 76 (0.97%) were separated, 763 (8.07%) were widowed, and 668 (7.79%) were divorced. Additionally, 6883 (78.85%) had received education to high school level or below, while 1733 (21.15%) had a college education or higher. The household incomes for the participants were: 1321 (15.34%) in the low-income category, 2339 (26.40%) had mid\\u0026ndash;low income, 2294 (26.55%) had middle\\u0026ndash;high income, and 2662 (31.71%) had high income. Smoking status: 7968 (91.60%) had never smoked, 266 (3.57%) were current smokers, 302 (3.69%) were former smokers, 58 (0.84%) were occasional smokers, and 22 (0.30%) responded with \\u0026apos;do not know\\u0026apos;. Alcohol consumption: 6992 (83.00%) were current drinkers, 1603 (16.71%) were non-drinkers, and 21 (0.29%) responded with \\u0026apos;do not know\\u0026apos;. For BMI, 162 (1.91%) were underweight, 3319 (38.86%) were of normal weight, 2109 (23.96%) were overweight, 2546 (30.05%) were in the obese class I category, 421 (4.66%) were in the obese class II category, and 59 (0.56%) were in the obese class III category. The majority of participants reported not having hypertension, diabetes, or anemia. Specifically, for hypertension, 6300 (74.89%) responded \\u0026apos;no\\u0026apos; and 2316 (25.11%) responded \\u0026apos;yes\\u0026apos;. Regarding diabetes, 7839 (91.46%) answered \\u0026apos;no\\u0026apos;, while 777 (8.54%) answered \\u0026apos;yes\\u0026apos;. For anemia, 8109 (94.10%) responded \\u0026apos;no\\u0026apos; and 507 (5.90%) responded \\u0026apos;yes\\u0026apos;. Figure \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e shows the geographical distributions of menopause and air pollution across 229 districts in South Korea between 2002 and 2020.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003eTable 1. General characteristics of the study population.\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003ctable id=\\\"Taba\\\" border=\\\"1\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth colspan=\\\"5\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVariables\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth colspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMenopause\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTotal\\u003c/p\\u003e\\n \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;8616 (%)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly menopause\\u003c/p\\u003e\\n \\u003cp\\u003e(age 20\\u0026ndash;45)\\u003c/p\\u003e\\n \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;679 (9.28)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal menopause\\u003c/p\\u003e\\n \\u003cp\\u003e(age 46\\u0026ndash;60)\\u003c/p\\u003e\\n \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;7937 (90.72)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e-value\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAge*\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20\\u0026ndash;29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15 (0.35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15 (0.35)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u0026ndash;39\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24 (0.34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24 (0.34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0 (0.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u0026ndash;49\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e538 (7.82)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e281 (4.01)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e257 (3.81)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;\\u0026thinsp;50\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8039 (91.49)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e359 (4.59)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7680 (86.91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMarital status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMarried\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7109 (83.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e564 (7.79)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6545 (75.38)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSeparated\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e76 (0.97)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (0.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e70 (0.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eWidowed\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e763 (8.07)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e32 (0.33)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e731 (7.74)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDivorced\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e668 (7.79)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e77 (1.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e591 (6.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eEducational level\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026le; Highschool\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6883 (78.85)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e489 (6.68)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6394 (72.17)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0158\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge; College\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1733 (21.15)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e190 (2.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1543 (18.54)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHousehold income level\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLow\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1321 (15.34)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e68 (0.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1253 (14.46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0054\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMiddle\\u0026ndash;low\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2339 (26.40)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e184 (2.69)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2155 (23.71)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eMiddle\\u0026ndash;high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2294 (26.55)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e179 (2.28)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2115 (24.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHigh\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2662 (31.71)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e248 (3.43)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2414 (28.28)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eSmoking Status\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCurrent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e266 (3.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e39 (0.54)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e227 (3.03)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"5\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0023\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSometimes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e58 (0.84)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14 (0.23)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44 (0.61)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eFormer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e302 (3.69)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44 (0.66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e258 (3.03)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7968 (91.60)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e580 (7.82)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7388 (83.78)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDo not know\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e22 (0.30)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0.03)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20 (0.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDrinking\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1603 (16.71)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e87 (1.13)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1516(15.57)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"3\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0142\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCurrent\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6992 (83.00)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e590 (8.12)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6402 (74.88)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eDo not know\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e21 (0.29)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2 (0.03)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e19 (0.26)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBMI**\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eUnderweight (\\u0026lt;18.5 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e162 (1.91)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15 (0.15)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e147 (1.76)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"6\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.1005\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal (18.5\\u0026ndash;23 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3319 (38.86)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e300 (4.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3019 (34.77)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eOverweight (23\\u0026ndash;25 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2109 (23.96)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e159 (2.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e1950 (21.71)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObese class I (25\\u0026ndash;30 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2546 (30.05)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e166 (2.25)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2380 (27.80)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObese class II (30\\u0026ndash;35 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e421 (4.66)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e33 (0.45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e388 (4.21)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eObese class III (\\u0026ge;\\u0026thinsp;35 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e59 (0.56)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6 (0.09)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e53 (0.47)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eHypertension\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e6300 (74.89)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e561 (7.70)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e5739 (67.19)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.0001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2,316 (25.11)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e118 (1.58)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e2198 (23.53)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eDiabetes\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7839 (91.46)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e637 (8.69)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7202 (82.77)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.0822\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e777 (8.54)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e42 (0.59)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e735 (7.95)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eAnemia\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNo\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8109 (94.10)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e647 (8.83)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7462 (85.27)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0.3698\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eYes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e507 (5.90)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e32 (0.45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e475 (5.45)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAbbreviation: *\\u0026quot;age\\u0026quot; denotes current age at the time of survey. **\\u0026quot;BMI\\u0026quot; denotes body mass index.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eIn Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e\\u0026mdash;the adjusted model\\u0026mdash;the odds ratios (ORs) were controlled for factors including district, age, marital status, educational levels, household income, smoking status, drinking habits, BMI, hypertension, diabetes, and anemia. There was an association between the exposure to PM\\u003csub\\u003e2.5\\u003c/sub\\u003e and early menopause (adjusted odds ratio [aOR]: 1.27, 95% confidence interval [CI]: 1.23\\u0026ndash;1.32), particulate matter 10 (PM\\u003csub\\u003e10\\u003c/sub\\u003e) and early menopause (adjusted odds ratio [aOR]: 1.17, 95% confidence interval [CI] 1.15\\u0026ndash;1.20), and nitrogen dioxide (NO\\u003csub\\u003e2\\u003c/sub\\u003e) and early menopause (adjusted odds ratio [aOR]: 1.05, 95% confidence interval [CI] 1.02\\u0026ndash;1.09).\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTable 2. Odds ratios (and 95% confidence intervals) from survey logistic regression analyses identifying associations between air pollution and early menopause.\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cimg 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\\\" alt=\\\"\\\"\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eThe stratified analysis of particulate matter exposure, specifically to PM\\u003csub\\u003e2.5\\u003c/sub\\u003e, in relation to menopausal status and smoking habits, is presented in Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. The exposure to PM\\u003csub\\u003e2.5\\u003c/sub\\u003e was assessed at two levels: The national ambient air quality standard and high exposure, with the former being \\u0026le;\\u0026thinsp;15 \\u0026micro;g/m\\u0026sup3; and the latter being \\u0026gt;\\u0026thinsp;15 \\u0026micro;g/m\\u0026sup3; (Korea, \\u003cspan class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). The standards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO).\\u003c/p\\u003e\\n\\u003cp\\u003eThe total number of subjects across all categories and exposure levels was 8616. There were no cases of early menopause being associated with the standard level PM\\u003csub\\u003e2.5\\u003c/sub\\u003e ambient air quality set by Air Korea for either non-smokers or current smokers. However, in the high ambient air quality PM\\u003csub\\u003e2.5\\u003c/sub\\u003e set by Air Korea, there were 626 cases of early menopause in the never-smokers and 53 in the current smokers, totaling 679 cases. For those in the normal menopause age group, four cases were reported in never-smokers at the standard air quality level established by Air Korea, with no instances in current smokers. A significant number of cases were reported at the high exposure level: 7,662 for never-smokers and 271 for current smokers, totaling 7,933 cases. Overall, the dataset clearly indicates a significant difference in PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure when comparing the Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality standard levels to those considered high exposure\\u0026mdash;a trend that is observed regardless of the menopausal status of the individuals. Additionally, within each exposure category, the proportion of never-smokers was notably higher than for current smokers.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003eStratification comparing menopause and smoking status in relation to PM2.5 exposure.\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth colspan=\\\"3\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoking status\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCurrent\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTotal\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly Menopause x standard Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly Menopause x high exposure Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e626\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e53\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e679\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal Menopause x standard Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal Menopause x high exposure Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7662\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e271\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7933\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8292\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e324\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8616\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClassification of PM\\u003csub\\u003e2.5\\u003c/sub\\u003e (particulate matter with a diameter of 5 micrometers or less) annual average concentration into \\u0026quot;standard,\\u0026quot; and \\u0026quot;high\\u0026quot;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStandard Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality is 15 \\u0026micro;g/m\\u0026sup3;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHigh exposure Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality is above 15 \\u0026micro;g/m\\u0026sup3;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStandards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO).\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cp\\u003eTable 4 shows stratified data comparing menopause status and age in relation to PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eexposure. For the age group of 20\\u0026ndash;29, there were no cases of early menopause associated with either standard or high exposure to PM\\u003csub\\u003e2.5\\u0026nbsp;\\u003c/sub\\u003eaccording to the national ambient air quality standards set by Air Korea. In the 30\\u0026ndash;39 age group, 15 cases of early menopause were associated with high exposure, while no cases were related to standard exposure. Among women aged 40\\u0026ndash;49, 28 cases of early menopause were associated with high exposure, and four cases with standard exposure. For the age group above 50, 35 cases of early menopause were associated with high exposure, while no cases were associated with the standard exposure. For cases associated with the normal onset of menopause, 253 cases in the over-50 age group were associated with high exposure. In contrast, no cases were associated with standard exposure for any age group. The total number of cases of early menopause associated with high exposure was 67, whereas for standard exposure, it was four. The total number of cases relating to normal menopause and high exposure was 7933.\\u003c/p\\u003e\\u003cbr\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab3\\\" style=\\\"width: 887px;\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eStratification comparing menopause and age in relation to PM\\u003csub\\u003e2.5\\u003c/sub\\u003e exposure.\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth style=\\\"width: 586px;\\\" rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 235px;\\\" colspan=\\\"5\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20\\u0026ndash;29\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u0026ndash;39\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u0026ndash;49\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;\\u0026thinsp;50\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTotal\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 586px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly menopause x standard Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 586px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly menopause x high exposure Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e281\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e359\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e679\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 586px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal menopause x standard Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 586px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal menopause x high exposure Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e253\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7680\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e7933\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 586px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e538\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8039\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8616\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 586px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAbbreviation: \\u003csup\\u003ea\\u003c/sup\\u003e = current age at the time of the examination survey.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 833.299px;\\\" colspan=\\\"6\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClassification of PM\\u003csub\\u003e2.5\\u003c/sub\\u003e (particulate matter with a diameter of 5 micrometers or less) annual average concentration into \\u0026quot;standard,\\u0026quot; and \\u0026quot;high\\u0026quot;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 742px;\\\" colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStandard Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality is 15 \\u0026micro;g/m\\u0026sup3;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 586px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHigh exposure Air Korea PM\\u003csub\\u003e2.5\\u003c/sub\\u003e national ambient air quality is above 15 \\u0026micro;g/m\\u0026sup3;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 52px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 37px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 42px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 843.299px;\\\" colspan=\\\"6\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStandards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO).\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003cp\\u003eTable 5 presents the association between menopause, smoking status, and PM\\u003csub\\u003e10\\u003c/sub\\u003e exposure for 8616 individuals. In the early menopause category, a total of 271 individuals who had never smoked and 27 current smokers were identified as being exposed to PM\\u003csub\\u003e10\\u003c/sub\\u003e levels compliant with national ambient air quality standards. In stark contrast, 355 never-smokers and 26 current smokers in the early menopause stage were exposed to elevated PM\\u003csub\\u003e10\\u003c/sub\\u003e levels. For those individuals experiencing normal menopause, 4079 never-smokers and 135 current smokers were exposed to PM\\u003csub\\u003e10\\u003c/sub\\u003e levels within the standard range. Meanwhile, 3587 never-smokers and 136 current smokers encountered elevated PM\\u003csub\\u003e10\\u003c/sub\\u003e levels. It is noteworthy that a consistently higher prevalence of never-smokers was observed across all categories of menopause and PM\\u003csub\\u003e10\\u003c/sub\\u003e exposure levels compared to current smokers\\u0026mdash;the well-established relationship between smoking and menopause risk. Our analysis further underscores the remarkable similarity in the proportions of current smokers adhering to PM\\u003csub\\u003e10\\u003c/sub\\u003e standards and those facing elevated PM\\u003csub\\u003e10\\u003c/sub\\u003e. However, individuals who have never smoked may exhibit a heightened susceptibility to the adverse effects of PM\\u003csub\\u003e10\\u003c/sub\\u003e high exposure at the onset of early menopause. Furthermore, it is worth noting that the proportion of individuals experiencing early menopause without a history of smoking and exposure to high PM\\u003csub\\u003e10\\u003c/sub\\u003e levels is significantly higher than in those undergoing normal menopause.\\u003c/p\\u003e\\u003csub\\u003e\\u003cbr\\u003e\\u003c/sub\\u003e\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption\\u003e\\n \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eStratification comparing menopause and smoking status in relation to PM\\u003csub\\u003e10\\u003c/sub\\u003e exposure.\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth colspan=\\\"3\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoking status\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNever\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCurrent\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTotal\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly menopause x standard Air Korea PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e271\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e27\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e298\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly menopause x high exposure Air Korea PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e355\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e26\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e381\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal menopause x standard Air Korea PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4079\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e135\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4214\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal menopause x high exposure Air Korea PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3587\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e136\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3723\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8292\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e324\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8616\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClassification of PM\\u003csub\\u003e10\\u003c/sub\\u003e (particulate matter with a diameter of 10 micrometers or less) annual average concentration into \\u0026quot;standard,\\u0026quot; and \\u0026quot;high\\u0026quot;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStandard PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality is 50 \\u0026micro;g/m\\u0026sup3;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHigh exposure PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality is above 50 \\u0026micro;g/m\\u0026sup3;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStandards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO).\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eIn Table\\u0026nbsp;6, a total of 15 early menopause individuals aged between 20 and 29 were shown to be subjected to standard national ambient air quality levels of PM\\u003csub\\u003e10\\u003c/sub\\u003e. Conversely, high PM\\u003csub\\u003e10\\u003c/sub\\u003e exposure was not reported in any individuals in the early menopause age group of 20\\u0026ndash;29. In contrast, significant exposure to PM\\u003csub\\u003e10\\u003c/sub\\u003e was noted in the normal menopause group, particularly in individuals aged above 50. Within this age category, 3995 individuals were exposed to standard PM\\u003csub\\u003e10\\u003c/sub\\u003e levels and 3685 to high PM\\u003csub\\u003e10\\u003c/sub\\u003e levels. The aggregated data across all age groups in the study revealed that out of the 8616 participants, those in the normal menopause stage, especially in the older age spectrum, experienced a higher incidence of exposure to PM\\u003csub\\u003e10\\u003c/sub\\u003e. This trend highlights the intersection of menopause status, age, and environmental exposure within the study population, indicating a potential age-related vulnerability to air quality issues among women undergoing menopause.\\u003c/p\\u003e\\n\\u003cp\\u003eTable\\u0026nbsp;6. Stratification comparing menopause and age in relation to PM\\u003csub\\u003e10\\u003c/sub\\u003e exposure.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003cdiv class=\\\"colspec\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n \\u003ctable id=\\\"Tabb\\\" style=\\\"width: 942.37px;\\\" border=\\\"1\\\"\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth style=\\\"width: 625px;\\\" rowspan=\\\"2\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eVariable\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 227px;\\\" colspan=\\\"5\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAge\\u003csup\\u003ea\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20\\u0026ndash;29\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30\\u0026ndash;39\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40\\u0026ndash;49\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026ge;\\u0026thinsp;50\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTotal\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 625px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly menopause x standard Air Korea PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e102\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e298\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 625px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eEarly menopause x high exposure Air Korea PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e9\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e115\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e257\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e381\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 625px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal menopause x standard Air Korea PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e219\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3995\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e4214\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 625px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNormal menopause x high exposure Air Korea PM\\u003csub\\u003e10\\u003c/sub\\u003e national ambient air quality\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e0\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e38\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3685\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e3723\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 625px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTotal\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e24\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e538\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8039\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e8616\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 625px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eAbbreviation: \\u003csup\\u003ea\\u003c/sup\\u003e = current age on the date of the examination survey.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 793px;\\\" colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClassification of PM\\u003csub\\u003e10\\u003c/sub\\u003e (particulate matter with a diameter of 10 micrometers or less) annual average concentration into \\u0026quot;standard,\\u0026quot; and \\u0026quot;high\\u0026quot;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 793px;\\\" colspan=\\\"4\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStandard PM\\u003csub\\u003e10\\u003c/sub\\u003e National ambient air quality is 50 \\u0026micro;g/m\\u0026sup3;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 625px;\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHigh exposure PM\\u003csub\\u003e10\\u003c/sub\\u003e National ambient air quality is above 50 \\u0026micro;g/m\\u0026sup3;.\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 56px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 28px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 31px;\\\" align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 852px;\\\" colspan=\\\"6\\\" align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eStandards for fine dust were selected based on the criteria set by Air Korea rather than the average values proposed by the World Health Organization (WHO).\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study investigated the association between ambient air pollution and menopausal age by adjusting for other factors that could impact menopause, such as marital status, drinking patterns, household income, educational level, body mass index (BMI), hypertension, and anemia (Morris et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). As such, it surveyed around 17,712 female participants, of which 8616 responded comprehensively with their details, meaning their data was included in the final analysis and used to calculate the adjusted odds ratio (aOR) regarding an association with the four primary air pollutants. The study setting involved Korean cities, countries, and districts. The logistic regression analyzed the association between the ambient air pollution levels at the noted geographical locations provided by AiMS-CREATE and women experiencing menopause from 2002 to 2020. Compared to a previous study, the prevalence rate of early menopause in Korean women was reported to be 7.2% in KHANES conducted from 2007 to 2012, which is higher than the rate of 3.4% in US women (Choe SA, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The current study reveals a notable increase, with a prevalence rate of 9.28% observed in KHANES conducted from 2010 to 2020. The final results revealed that ambient air pollution was linked positively and significantly with early menopause in women, with PM\\u003csub\\u003e2.5\\u003c/sub\\u003e presenting the highest odds in this association. PM\\u003csub\\u003e10\\u003c/sub\\u003e had an aOR of 1.17, while NO\\u003csub\\u003e2\\u003c/sub\\u003e had an adjusted aOR of 1.05. Each aOR reflects a 95% confidence interval, meaning one can be 95% sure about these associations in the given intensity. In short, this study found that air pollutants, such as PM\\u003csub\\u003e2.5\\u003c/sub\\u003e, PM\\u003csub\\u003e10\\u003c/sub\\u003e, and NO\\u003csub\\u003e2\\u003c/sub\\u003e, were positively associated with early menopause in women. These findings prove the hypothesis that there is a strong association between ambient air pollution and early menopause in women. Furthermore, these findings are similar to Guo et al. (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e); however, the current levels associate more significance to the connection between PM\\u003csub\\u003e2.5\\u003c/sub\\u003e and NO\\u003csub\\u003e2\\u003c/sub\\u003e and early menopause since the respective aORs were found to be 1.27 and 1.17, respectively, vs. 1.16 and 1.04, respectively, which were found by Guo et al. (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) in Taiwan. Again, geographical differences play a focal role, indicating the differences in climate and pollution levels between Taiwan and Korea.\\u003c/p\\u003e \\u003cp\\u003eThe biological mechanism underlying these findings is limited in the literature (Li et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, one can match different findings presented by various scholars to begin to understand why there is such a strong association between three air pollutants and age at menopause (Zhang et al., \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). First, one must consider what causes early menopause in women. Studies by Harlow and Signorello (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e), Shuster et al. (\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e), Soules and Bremner (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e1982\\u003c/span\\u003e), and Thomford et al. (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e1987\\u003c/span\\u003e) have previously explained the characterization of early menopause. Their findings suggest that early menopause occurs due to the loss of ovarian follicular function, the early loss of which occurs following a more significant number of depleted oocytes and a high rate of ovarian follicle atresia. This points to the factors or agents associated with these biological realities causing early menopause in women. Alternatively, studies show the biological and hormonal impact of air pollution on the bodies of females. Thurston et al. (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e) showed that extreme exposure to air pollution can impact the length of the menstrual cycle. Abareshi et al. (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) found that females under extreme exposure to air pollutants exhibited significantly reduced hormones related to ovarian reserve levels. Maluf et al. (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e), Gaskins et al. (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), and Veras et al. (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e) discovered that it was possible to have ovarian follicular function loss due to exposure to toxins and air pollutants. Expanding on these findings, Carr\\u0026eacute; et al. (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) found that females could suffer ovarian follicular function loss due to exposure to air pollution because these pollutants disturbed the inflammatory response system and led to epigenetic modifications, oxidative stress, and cell DNA alteration (Shi et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Through these studies, one can see a pattern regarding how air pollution can cause early menopause; air pollution disturbs the lowest levels of biological functions, causes ovarian follicular function loss, and reduces levels of pro-menstrual hormones.\\u003c/p\\u003e \\u003cp\\u003eRegarding the study's strengths and limitations, it is well-established that the study focused on the Korean population. Geographical factors implicate climate, which, in turn, involves the probabilistic realities under observation. The study includes a large sample size, as Namvar et al. (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) previously recommended the inclusion of a large sample size to achieve any level of significance in the result. Therefore, this study followed their recommendation and included 8616 women in the final analysis. Our study also had limitations. Firstly, verifying the responses of the participants in the study was not possible. Therefore, potential biases may affect the results due to the possibility of including incorrect answers. Additionally, it may include recall bias because it collected menopause-related information through a self-administered questionnaire. Secondly, the accuracy of the addresses was assessed based on the examination date for women who had experienced menopause. Moreover, this study ignores the potential impact of household air pollution on menopause age.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eOur results are consistent with the proposed hypothesis about the association between exposure to PM\\u003csub\\u003e2.5,\\u003c/sub\\u003e PM\\u003csub\\u003e10\\u003c/sub\\u003e, and NO\\u003csub\\u003e2\\u003c/sub\\u003e and early menopause. This study provides substantial quantitative evidence, further supporting the need for public health interventions to improve air quality since it can affect the onset of early menopause.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eAuthor\\u0026rsquo;s contributionsJoyce Mary Kim: Conceptualization, Methodology, Investigation, Formal analysis, Data curation, Writing\\u0026mdash;original draftJieun Min: Methodology, Writing\\u0026mdash;original draftJungsil Lee: Methodology, Writing\\u0026mdash;original draftKyungah Jeong: Conceptualization, Methodology, Supervision Writing\\u0026mdash;original draft Eunhee Ha: Conceptualization, Methodology, Supervision Writing\\u0026mdash;original draft\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgment\\u003c/h2\\u003e \\u003cp\\u003e Joyce Kim was supported as a trainee in the environmental health training program provided by the Environmental Health Centre of the Catholic University of Korea and funded by the Ministry of Environment, Republic of Korea (2023).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbareshi F, et al. Association of exposure to air pollution and green space with ovarian reserve hormones levels. Environ Res. 2020;184:109342.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAfaya A, et al. Health system barriers influencing timely breast cancer diagnosis and treatment among women in low and middle-income Asian countries: evidence from a mixed-methods systematic review. BMC Health Serv Res. 2022;22:1\\u0026ndash;17.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCarr\\u0026eacute; J, et al. Does air pollution play a role in infertility? a systematic review. Environ Health. 2017;16:1\\u0026ndash;16.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChoe SA, S. J. 2020. Trends of Premature and Early Menopause: a Comparative Study of the US National Health and Nutrition Examination Survey and the Korea National Health and Nutrition Examination Survey. J Korean Med Sci 35, e97.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDavis SR et al. 2015. Menopause (Primer). Nature Reviews: Disease Primers. 1.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGaskins AJ, et al. Time-varying exposure to air pollution and outcomes of in vitro fertilization among couples from a fertility clinic. Environ Health Perspect. 2019;127:077002.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGiandalia A, et al. Gender differences in diabetic kidney disease: focus on hormonal, genetic and clinical factors. Int J Mol Sci. 2021;22:5808.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGuo C, et al. Associations between long-term exposure to multiple air pollutants and age at menopause: a longitudinal cohort study. Ann Epidemiol. 2022;76:68\\u0026ndash;76.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGuo C, et al. Effect of long-term exposure to fine particulate matter on lung function decline and risk of chronic obstructive pulmonary disease in Taiwan: a longitudinal, cohort study. Lancet Planet Health. 2018;2:e114\\u0026ndash;25.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHandy AB, et al. Psychiatric symptoms across the menstrual cycle in adult women: a comprehensive review. Harv Rev Psychiatry. 2022;30:100.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHarlow BL, Signorello LB. Factors associated with early menopause. Maturitas. 2000;35:3\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHassan S et al. 2023. Endocrine disruptors: Unravelling the link between chemical exposure and Women's reproductive health. Environ Res. 117385.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKorea A. Air Quality Standards. Ministry of Environment; 2022.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKwon D et al. 2002. Estimation of High-Spatial Resolution of Ground-Level Ozone, Nitrogen Dioxide, and Carbon Monoxide in South Korea During 2002\\u0026ndash;2020 Using Machine-Learning Based Ensemble Model. Nitrogen Dioxide, and Carbon Monoxide in South Korea During. 2020.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLeBoff M, et al. The clinician\\u0026rsquo;s guide to prevention and treatment of osteoporosis. Osteoporos Int. 2022;33:2049\\u0026ndash;102.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi H, et al. Long-term exposure to particulate matter and roadway proximity with age at natural menopause in the Nurses\\u0026rsquo; Health Study II Cohort. Environ Pollut. 2021;269:116216.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu B, et al. Catalytic ozonation of VOCs at low temperature: A comprehensive review. J Hazard Mater. 2022;422:126847.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMaluf M, et al. In vitro fertilization, embryo development, and cell lineage segregation after pre-and/or postnatal exposure of female mice to ambient fine particulate matter. Fertil Steril. 2009;92:1725\\u0026ndash;35.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMiyahira AK, Soule HR. The 28th Annual Prostate Cancer Foundation Scientific Retreat report. Prostate. 2022;82:1346\\u0026ndash;77.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMorris DH, et al. Body mass index, exercise, and other lifestyle factors in relation to age at natural menopause: analyses from the breakthrough generations study. Am J Epidemiol. 2012;175:998\\u0026ndash;1005.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNamvar Z, et al. Association of ambient air pollution and age at menopause: a population-based cohort study in Tehran. Iran Air Qual Atmos Health. 2022;15:2231\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNash Z, et al. Bone and heart health in menopause. Best Pract Res Clin Obstet Gynecol. 2022;81:61\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePark J, et al. Association of long-term exposure to air pollution with chronic sleep deprivation in South Korea: A community-level longitudinal study, 2008\\u0026ndash;2018. Environ Res. 2023;228:115812.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRaimi MO, A Critical Review of Health Impact Assessment. Towards Strengthening the Knowledge of Decision Makers Understand Sustainable Development Goals in the Twenty-First Century: Necessity Today; Essentiality Tomorrow. Research and Advances: Environmental Sciences; 2020. pp. 2652\\u0026ndash;3655.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRoheel A, et al. Global epidemiology of breast cancer based on risk factors: a systematic review. Front Oncol. 2023;13:1240098.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShi L et al. 2016. Long-term moderate oxidative stress decreased ovarian reproductive function by reducing follicle quality and progesterone production. PLoS ONE 11, e0162194.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShuster LT, et al. Premature menopause or early menopause: long-term health consequences. Maturitas. 2010;65:161\\u0026ndash;6.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSkakkebaek NE, et al. Environmental factors in declining human fertility. Nat Reviews Endocrinol. 2022;18:139\\u0026ndash;57.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSoules MR, Bremner WJ. The menopause and climacteric: endocrinologic basis and associated symptomatology. J Am Geriatr Soc. 1982;30:547\\u0026ndash;61.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThomford PJ, et al. Effect of oocyte number and rate of atresia on the age of menopause. Reprod Toxicol. 1987;1:41\\u0026ndash;51.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eThurston SW, et al. Petrochemical exposure and menstrual disturbances. Am J Ind Med. 2000;38:555\\u0026ndash;64.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVeras MM, et al. Chronic exposure to fine particulate matter emitted by traffic affects reproductive and fetal outcomes in mice. Environ Res. 2009;109:536\\u0026ndash;43.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVoedisch AJ, et al. Menopause: a global perspective and clinical guide for practice. Clin Obstet Gynecol. 2021;64:528\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang Z, et al. Satellite-based estimates of long-term exposure to fine particulate matter are associated with C-reactive protein in 30 034 Taiwanese adults. Int J Epidemiol. 2017;46:1126\\u0026ndash;36.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhu Y, et al. Epidemiology and genomics of prostate cancer in Asian men. Nat Reviews Urol. 2021;18:282\\u0026ndash;301.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"air pollution, particulate matter, early menopause, environment epidemiology, women’s health\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3930338/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3930338/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackgrounds\\u003c/h2\\u003e \\u003cp\\u003eAmbient air pollution has become a serious public health issue that affects fertility rates in women worldwide. Therefore, there is a need to evaluate the risk factors associated with menopause to be able to inform women of the associated health risks.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eWe collected data from KHANES (The Korea National Health and Nutrition Examination Survey) between 2010 and 2020, from the Korean Center for Disease Control and Prevention, Ministry of Health and Welfare, and linked it with summary pollution data from AiMS-CREATE (AI-Machine Learning and Statistics Collaborative Research Ensemble for Air Pollution, Temperature, and All Types of Environmental Exposures) from 2002 to 2020. This summary data encapsulates the monthly average air pollution predictions for 226 si-gun-gu (cities, counties, and districts) in Korea. A total of 8,616 participants who had experienced menopause (early menopause: 20\\u0026ndash;45 years, N\\u0026thinsp;=\\u0026thinsp;679; normal menopause: 46\\u0026ndash;60 years, N\\u0026thinsp;=\\u0026thinsp;7,937) between 2002 and 2020 were included in the analysis. We employed survey logistic regression analyses to determine the associations between ambient air pollution and menopause after adjusting for covariates.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eThere was an association between particulate matter 2.5 (PM\\u003csub\\u003e2.5\\u003c/sub\\u003e) and early menopause (adjusted odds ratio [aOR]: 1.27, 95% confidence interval [CI]: 1.23\\u0026ndash;1.32), between particulate matter 10 (PM\\u003csub\\u003e10\\u003c/sub\\u003e) and early menopause (aOR: 1.17, 95% confidence interval [CI]: 1.15\\u0026ndash;1.20), and between nitrogen dioxide (NO\\u003csub\\u003e2\\u003c/sub\\u003e) and early menopause (aOR: 1.05, 95% confidence interval [CI]: 1.02\\u0026ndash;1.09).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eOur results are consistent with the proposed hypothesis regarding an association between exposure to ambient air pollution and early menopause. This study provides substantial quantitative evidence that further supports the need for public health interventions to improve air quality, which is a risk in promoting early menopause.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Assessing air pollution as a risk factor for early menopause in Korea\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-02-07 19:43:18\",\"doi\":\"10.21203/rs.3.rs-3930338/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"1ff58155-7753-4413-b57e-538b039766f5\",\"owner\":[],\"postedDate\":\"February 7th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-03-23T15:32:23+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-02-07 19:43:18\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3930338\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3930338\",\"identity\":\"rs-3930338\",\"version\":[\"v1\"]},\"buildId\":\"CiT4i_kKBbxQbnFL0ufpk\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}