Cumulative abdominal obesity exposure and risk of endometrial cancer in young women: a population-based cohort study

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This study aimed to examine whether cumulative abdominal obesity exposure in young women was associated with the development of endometrial cancer. METHODS We used data from the South Korean National Health Insurance Service for women aged 20–39 years who had completed four consecutive annual health examinations between 2009 and 2015 and had no history of cancer at baseline. Participants were categorized into five groups based on the number of abdominal obesity exposures (waist circumference ≥ 85 cm). Exposure numbers ranged from 0 to 4, indicating the frequency of abdominal obesity across the four health examinations over 4 years. The primary outcome was newly diagnosed endometrial cancer during a follow-up period of 7.12 years. RESULTS Among the 445,791 young women (mean [SD] age 30.82 [4.55] years), 302 (mean [SD], 32.79 [4.53] years) developed endometrial cancer. The cumulative incidence of endometrial cancer differed significantly according to the number of abdominal obesity exposures (log-rank test, P < .001). The incidence of endometrial cancer has progressively increased with abdominal obesity exposure. The multivariable-adjusted HRs for incident endometrial cancer were 1.480 (95% CI, 0.970–2.258), 2.361 (95% CI, 1.391–4.008), 4.114 (95% CI, 2.546–6.647), and 6.215 (95% CI, 4.250–9.088) for participants with exposure numbers of 1–4, respectively, compared with those with an exposure number of 0. CONCLUSION In this population-based nationwide cohort study of young women, we observed a progressive increase in the risk of endometrial cancer with cumulative abdominal obesity exposure. Health sciences/Risk factors Health sciences/Diseases/Cancer Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity Figures Figure 1 Figure 2 What’s new? Abdominal obesity is currently being investigated as an indicator of adiposity and cancer risk, and its prevalence is increasing in young women. In this population-based longitudinal cohort study, we observed a progressive increase in the risk of endometrial cancer with cumulative abdominal obesity exposure in young women aged 20–39 years. In addition, abdominal obesity showed a stronger association with the risk of endometrial cancer than did general obesity, as cumulative exposure increased. These findings underscore the importance of identifying abdominal obesity as a potential risk factor for endometrial cancer in young women. INTRODUCTION Abdominal obesity, which is characterized by the accumulation of visceral fat, is currently being investigated as an indicator of adiposity and cancer risk [1]. Although the precise mechanism is not fully understood, obesity-related abnormalities such as impaired glucose tolerance, insulin resistance, and systemic inflammation are expected to contribute to cancer development [2]. Apart from body mass index (BMI), other anthropometric measurements, such as waist-to-hip ratio or waist circumference (WC), are associated with an increased risk of endometrial cancer [3]. However, previous studies have indicated that most endometrial cancers in patients younger than 40 years are associated with general obesity [4]. The role of abdominal obesity in the risk of endometrial cancer, particularly in young women, requires comprehensive exploration. Endometrial cancer, a gynecological malignancy arising from the inner lining of the uterus, is the most common gynecologic malignancy in developed countries, and its incidence has steadily increased in recent decades [5,6]. The majority of cases occur predominately in women who are postmenopausal; 2‒14% of cases develop in women aged 40 years and younger [7]. The incidence of endometrial cancer continues to increase sharply in younger generations [8]. This demographic shift highlights the need to identify the risk factors that contribute to endometrial cancer development among young women. The faster rise in endometrial cancer occurring before the age of 40 years mirrors similar observations in other cancers, pointing to a possible link with the rising obesity epidemic in younger generations [9]. This population-based nationwide cohort study of young Korean women aimed to assess the risk of endometrial cancer associated with cumulative exposure to abdominal obesity. This study provides valuable insights into the role of abdominal obesity in the development of endometrial cancer in young women, with implications for preventive strategies and public health interventions aimed at reducing the burden of this disease in this vulnerable population. METHODS Data sources In this large-scale epidemiological study of a Korean cohort, we used the Korean National Health Information Database (NHID), which combines information obtained from the National Health Insurance Service (NHIS) and general health examinations provided to all Korean adults [10]. The NHIS is the only insurer managing the health insurance system in Korea and enrolls approximately 97% of the Korean population. The general health screening program involves annual or biennial health examinations for the entire population of Korean adults aged ≥ 40 years and for regional household members and dependents of people aged ≥ 20 years. The NHID includes claims data, health screening information, detailed lifestyle questionnaires, laboratory results, and anthropometric measurements. Study population From the NHID, we identified 471,376 women aged 20–39 years at baseline who underwent at least one health examination between 2009 and 2012 and had three additional consecutive annual health examinations. The index date was defined as the date of the fourth (latest) health examination. Among them, we excluded 20,804 participants with missing data and 3,549 who had been diagnosed with cancer before the index date. We set a 1-year lag period and excluded 1,232 patients diagnosed with uterine corpus cancer to minimize the risk of reverse causality. Consequently, the final study population consisted of 445,791 women, divided into four groups according to their cumulative exposure to abdominal obesity (Fig. 1 ). This study was approved by the Institutional Review Board of the Hanyang University of Korea (No. HYU-2023-010). Informed consent was waived because anonymous and de-identified information was used for the analysis. This study was conducted in compliance with the principles of the Declaration of Helsinki. Cumulative abdominal obesity exposure The WC, weight, and height of the participants were measured in a standardized manner by trained nurses during each examination. We defined abdominal obesity as a WC ≥ 85 cm for Korean women according to the WC cut-off point in the Asian-specific population [11]. WC was measured by trained examiners at the midpoint between the inferior border of the lower rib and the iliac crest on the mid-axillary line after exhalation. General obesity was defined as a BMI ≥ 25 kg/m 2 according to the Asia-Pacific criteria of the World Health Organization Guidelines [12]. BMI was calculated as weight (kg) divided by height (m) squared. To estimate cumulative exposure to abdominal obesity, we assessed the frequency of abdominal obesity (WC ≥ 85 cm) across four consecutive annual health examinations over 4 years and counted it as abdominal obesity exposure. In this classification, 0 indicated no abdominal obesity and 1–4 indicated the frequency of abdominal obesity. Additionally, the number of individuals with obesity ranged from 0 to 4, indicating the frequency of obesity (BMI ≥ 25 kg/m 2 ) over the four health examinations. The participants were categorized into five groups based on their abdominal obesity scores: 0, 1, 2, 3, and 4. Outcome and follow-up The primary outcome of this study was newly diagnosed cases of endometrial cancer. For this study, we created a customized database by merging the NHIS Medical Check-up database, which contains the 2009 NHIS health examination and cancer screening questionnaire results, and the NHIS claims database. Patients diagnosed with endometrial cancer were identified using the International Classification of Diseases, 10th Revision (ICD-10) code C54–55. The study population was monitored from baseline to the date of diagnosis, death, or until the end of the study period (December 31, 2020), whichever occurred first. The median follow-up duration was 7.12 (5.59 –7 .49) years. Measurements and definitions of variables Comorbidities were defined using a combination of ICD-10-CM codes and self-reported medication histories. Metabolic syndrome was defined as a combination of abdominal obesity, impaired fasting glucose, high triglyceride levels, low high-density lipoprotein (HDL) cholesterol levels, and elevated blood pressure (BP), according to the revised National Cholesterol Education Program Adult Treatment Panel III criteria [13]. Hypertension was defined as taking antihypertensive medications for at least one claim per year under ICD-10 codes I10–11, with systolic BP ≥ 140 mmHg or diastolic BP ≥ 90 mmHg. Dyslipidemia was defined as having at least one claim per year under ICD-10 code E78 for lipid-lowering agents or total cholesterol ≥ 240 mg/dL. Diabetes mellitus was defined as a fasting blood glucose level of ≥ 126 mg/dL or the presence of one or more claims per year for antihyperglycemic medications with ICD-10 code E10–14. Polycystic ovarian syndrome (PCOS) was defined as the presence of at least one claim per year under ICD-10 code E28.0–28.9 in this study. Parity was defined as childbirth using the diagnosis and procedure codes for pregnancy. Smoking status was classified as current or non-current. Heavy alcohol consumption was defined as weekly alcohol consumption of > 28 standard drinks (210 g alcohol). Regular physical activity was defined as moderate-to-high-intensity activity ≥ 3 times/week. Income levels were defined based on health insurance premiums and categorized into quartiles, with the first quartile representing the low-income group. BP was measured during regular medical checkups using standardized methods. Blood samples were collected after overnight fasting, and the parameters measured included fasting plasma glucose (FPG), total cholesterol, triglycerides, HDL cholesterol, and low-density lipoprotein (LDL) cholesterol (calculated using the Friedewald formula). The hospitals where these health examinations were performed were certified by the NHIS and subjected to regular quality control. Statistical analysis Baseline characteristics of the study population, divided by the number of abdominal obesity exposures, are represented as mean ± standard deviation (SD) or proportions (%). Geometric means are used for heavily skewed distributions. The disease-free probability of endometrial cancer was calculated using Kaplan–Meier curves, and the differences in the numbers of abdominal obesity exposures and general obesity exposures were analyzed using a log-rank test. The incidence rate per 1,000 person-years was estimated as the number of newly diagnosed cases of endometrial cancer during the follow-up period. Survival analyses were performed using Cox proportional hazards regression to estimate the HRs and 95% CIs for incident endometrial cancer. Adjusted models were used to adjust for potential confounding variables. Model 1 was crude. Model 2 was adjusted for age. Model 3 was adjusted for age, hypertension, dyslipidemia, diabetes, PCOS, parity, smoking status, alcohol consumption, physical activity, and low-income status. All statistical tests were two-sided at P < .05 statistical significance level. These estimations were performed using the Statistical Analysis System (SAS) statistical software package (version 9.4; SAS Institute Inc., Cary, NC, US). The data analysis was performed between February 2023 and November 2023. RESULTS Baseline characteristics of the study population The baseline characteristics of the study population, stratified by the number of abdominal obesity exposures during the four health examinations, are represented in Table 1 . Among the total study population of 445,791 young women (mean [SD] age, 34.08 [4.98] years), 386,323 (86.7%) had no abdominal obesity. A total of 31,467 (7.1%) participants had a one-time occurrence of abdominal obesity, 11,090 (2.5%) had two occurrences, 7,631 (1.7%) had three occurrences, and 9,280 (2.1%) had four consecutive occurrences of abdominal obesity. The mean WC for participants with exposure numbers of 0–4 were 69.01 ± 5.92, 80.88 ± 8.67, 85.77 ± 6.95, 89.35 ± 7, and 96.47 ± 8.16 cm, respectively. Additionally, there was a trend toward a higher prevalence of metabolic syndrome, hypertension, dyslipidemia, diabetes, PCOS, current smokers, heavy alcohol drinkers, and participants with low-income levels with increased exposure numbers. The parity decreased as the number of exposures increased. As the number of exposures increased, BMI, BP, LDL-cholesterol, triglyceride, and FPG levels showed a gradual increase, whereas HDL cholesterol levels exhibited a decrease. Table 1 Baseline Characteristics of Young Women Stratified by Cumulative Exposure to Abdominal Obesity Exposure number for abdominal obesity (waist circumference ≥ 85 cm) P for trend 0 1 2 3 4 No. of participants 386323 31467 11090 7631 9280 < .0001 Age, years 30.68 ± 4.55 31.55 ± 4.42 31.7 ± 4.77 31.84 ± 4.7 32 ± 4.69 < .0001 Waist circumference, cm 69.01 ± 5.92 80.88 ± 8.67 85.77 ± 6.95 89.35 ± 7 96.47 ± 8.16 < .0001 Body mass index, kg/m² 20.68 ± 2.38 24.37 ± 3.1 27.12 ± 3.06 29.07 ± 3.23 32.38 ± 4.17 < .0001 Abdominal obesity, No. (%) 0 (0) 11940 (37.94) 6911 (62.32) 6100 (79.94) 9280 (100) < .0001 Obesity, No. (%) 20603 (5.33) 13074 (41.55) 8393 (75.68) 6979 (91.46) 9149 (98.59) < .0001 Metabolic syndrome, No. (%) 3622 (0.94) 2745 (8.72) 2326 (20.97) 2341 (30.68) 4589 (49.45) < .0001 Hypertension, No. (%) 4942 (1.28) 980 (3.11) 686 (6.19) 763 (10) 1691 (18.22) < .0001 Dyslipidemia, No. (%) 13656 (3.53) 3862 (12.27) 1128 (10.17) 893 (11.7) 1426 (15.37) < .0001 Diabetes, No. (%) 2245 (0.58) 466 (1.48) 369 (3.33) 419 (5.49) 1033 (11.13) < .0001 Polycystic ovarian syndrome, No. (%) 3705 (0.96) 329 (1.05) 136 (1.23) 100 (1.31) 158 (1.7) < .0001 Parity, No. (%) 29399 (7.61) 7877 (25.03) 1733 (15.63) 806 (10.56) 628 (6.77) < .0001 Current Smoker, No. (%) 14566 (3.77) 1403 (4.46) 772 (6.96) 632 (8.28) 978 (10.54) < .0001 Heavy alcohol drinker, No. (%) 8492 (2.2) 755 (2.4) 389 (3.51) 274 (3.59) 362 (3.9) < .0001 Regular exercise, No. (%) 53603 (13.88) 4240 (13.47) 1768 (15.94) 1322 (17.32) 1519 (16.37) < .0001 Low-income level, No. (%) 33108 (8.57) 3334 (10.6) 1533 (13.82) 1158 (15.17) 1545 (16.65) < .0001 Systolic blood pressure, mmHg 110.4 ± 10.58 113.48 ± 11.62 117.49 ± 12.18 120.15 ± 12.62 124.5 ± 13.98 < .0001 Diastolic blood pressure, mmHg 69.56 ± 7.95 71.39 ± 8.61 74.08 ± 8.97 75.91 ± 9.3 78.81 ± 10.33 < .0001 Total Cholesterol, mg/dL 178.65 ± 30.05 192.06 ± 40.58 191.77 ± 35.81 193.8 ± 35.22 198.94 ± 36.18 < .0001 HDL-Cholesterol, mg/dL 65.23 ± 18.67 62 ± 18.72 57.7 ± 15.99 55.45 ± 14.26 53.02 ± 14.29 < .0001 LDL-Cholesterol, mg/dL 98.18 ± 27.65 108.4 ± 32.53 111.52 ± 33.13 114.19 ± 32.36 118.82 ± 33.36 < .0001 Triglyceride level, mg/dL * 69.14 (69.04 – 69.24) 92.92 (92.33 – 93.5) 99.08 (98.09 – 100.08) 106.54 (105.27 – 107.82) 120.72 (119.45 – 122.01) < .0001 Fasting plasma glucose, mmol/L 87.67 ± 10.77 89.2 ± 14.65 92.99 ± 21.01 95.86 ± 23.77 102.56 ± 33.84 < .0001 Median follow-up duration, years 7.13 (5.6 – 7.5) 7.13 (5.64 – 7.5) 7.07 (5.52 – 7.48) 7.03 (5.43 – 7.45) 7.01 (5.28 – 7.44) < .0001 Data are represented as mean ± standard deviation (SD) for continuous variables and as proportions (%) for categorical variables. * Geometric mean (95% confidence interval [CI]) Risk of endometrial cancer based on exposure numbers to abdominal obesity in young women During a median follow-up period of 7.12 years, 302 participants were newly diagnosed with endometrial cancer. In Fig. 2 (A), the cumulative incidence of endometrial cancer differed significantly according to the number of abdominal obesity exposures (log-rank test, P < .001). The incidence and HRs of endometrial cancer progressively increased with exposure to abdominal obesity (Table 2 ). The incidence rates of endometrial cancer were 0.08048, 0.12086, 0.20876, 0.38767, and 0.66182 per 1,000 person-years for participants with 0–4 exposures to abdominal obesity, respectively. After adjusting for age, hypertension, dyslipidemia, diabetes, PCOS, parity, smoking status, alcohol consumption, physical activity, and low income, the multivariable-adjusted HRs for incident endometrial cancer were 1.480 (95% CI, 0.970–2.258), 2.361 (95% CI, 1.391–4.008), 4.114 (95% CI, 2.546–6.647), and 6.215 (95% CI, 4.250–9.088) for participants with numbers of 1–4, respectively, compared to those with an exposure number of 0 (Table 2 ). Table 2 Multivariable-adjusted Hazard Ratios for Endometrial Cancer According to Abdominal Obesity Exposure in Young Women Exposure number Number at risk Incidence case Follow-up duration (person-years) Incidence rate (per 1,000 person-years) HR (95% CI) Model 1 Model 2 Model 3 0 386323 204 2534678.47 0.08048 1 (Ref.) 1 (Ref.) 1 (Ref.) 1 31467 25 206854.47 0.12086 1.500 (0.990, 2.273) 1.417 (0.935, 2.147) 1.480 (0.970, 2.258) 2 11090 15 71851.28 0.20876 2.615 (1.548, 4.417) 2.417 (1.430, 4.085) 2.361 (1.391, 4.008) 3 7631 19 49010.5 0.38767 4.889 (3.055, 7.823) 4.482 (2.799, 7.178) 4.114 (2.546, 6.647) 4 9280 39 58928.78 0.66182 8.398 (5.962, 11.829) 7.581 (5.375, 10.693) 6.215 (4.250, 9.088) Model 1 was crude. Model 2 is adjusted for age. Model 3 was adjusted for age, hypertension, dyslipidemia, diabetes, polycystic ovarian syndrome, parity, smoking status, alcohol consumption, physical activity, and income level. Risk of endometrial cancer based on exposure numbers to obesity in young women Figure 2 (B) depicts a significant difference in the cumulative incidence of endometrial cancer according to the number of obesity exposures (log-rank test, P < .001). Table 3 represents the association between the number of exposures to obesity and the risk of endometrial cancer. The incidence rates of endometrial cancer were 0.07289, 0.15727, 0.15363, 0.18126, and 0.38188 per 1,000 person-years for participants with 0–4 obesity exposures. The multivariable-adjusted HRs for incident endometrial cancer were 2.136 (95% CI, 1.354–3.371), 2.006 (95% CI, 1.116–3.605), 2.269 (95% CI, 1.313–3.919), and 4.182 (95% CI, 3.133–5.582) for participants with numbers of 1–4, respectively, compared to those with an exposure number of 0 (Table 3 ). Table 3 Multivariable-adjusted Hazard Ratios for Endometrial Cancer According to Obesity Exposure in Young Women Exposure number Number at risk Incidence case Follow-up duration (person-years) Incidence rate (per 1,000 person-years) HR (95% CI) Model 1 Model 2 Model 3 0 369789 177 2428196.49 0.07289 1 (Ref.) 1 (Ref.) 1 (Ref.) 1 20385 21 133525.44 0.15727 2.163 (1.376, 3.400) 2.071 (1.317, 3.256) 2.136 (1.354, 3.371) 2 12023 12 78110.4 0.15363 2.124 (1.183, 3.810) 2.010 (1.120, 3.608) 2.006 (1.116, 3.605) 3 11911 14 77237.2 0.18126 2.508 (1.455, 4.322) 2.340 (1.357, 4.034) 2.269 (1.313, 3.919) 4 31683 78 204253.97 0.38188 5.305 (4.064, 6.925) 4.811 (3.679, 6.291) 4.182 (3.133, 5.582) Model 1 was crude. Model 2 is adjusted for age. Model 3 was adjusted for age, hypertension, dyslipidemia, diabetes, polycystic ovarian syndrome, parity, smoking status, alcohol consumption, physical activity, and income level. DISCUSSION In this population-based longitudinal study, we observed a significant association between cumulative abdominal obesity exposures and the risk of endometrial cancer in young women. The analysis revealed a dose-response relationship, in which the incidence rates of endometrial cancer progressively increased with higher abdominal obesity exposures in women aged < 40 years. Furthermore, the risk of endometrial cancer based on the number of abdominal obesity exposure cases was generally higher than that based on the number of individuals with obesity. This difference became more pronounced as the number of exposures increased. These findings suggest that abdominal obesity may be a potential risk factor for the development of endometrial cancer in young women, emphasizing the need for targeted preventive interventions in at-risk populations. Endometrial cancer in women aged < 40 is strongly associated with obesity [14,15]. Many studies have consistently demonstrated a positive correlation between elevated BMI and the risk of developing endometrial cancer [16]. However, the BMI, a crude parameter of body size, does not account for body fat composition [17]. Several studies have reported that body fat distribution confers an additional risk of endometrial cancer [18]. A previous study showed that upper body and visceral fat distributions are more closely associated with endometrial cancer risk than overall adiposity [19]. In addition, a recent epidemiological study reported that WC was more strongly associated with endometrial cancer in younger women, independent of BMI [20]. Consistent with previous research, our study indicated the cumulative impact of prolonged abdominal obesity exposure on the risk of endometrial cancer, particularly among young women, which appeared to be higher than that associated with prolonged general obesity exposure. These findings provide additional insights into the role of adiposity in the pathogenesis of endometrial cancer. Abdominal obesity is a strong predictor of the risk of all cancers, including endometrial cancer [21]. This condition may affect the pathogenesis of endometrial cancer more than that using BMI through various mechanisms, including alterations in hormonal profiles, chronic inflammation, and insulin resistance [22]. Abdominal obesity, often accompanied by metabolic disturbances such as insulin resistance and dyslipidemia, can promote a pro-inflammatory state and alter hormone levels, particularly estrogen, thereby increasing the risk of endometrial cancer [23]. Moreover, the dysregulated secretion of adipokines and cytokines by visceral fat in individuals with abdominal obesity may contribute to inflammation, angiogenesis, cell growth, and the proliferation and progression of endometrial carcinogenesis [24]. Furthermore, lifestyle factors associated with abdominal obesity exposure, such as a high-calorie diet and sedentary behavior, may exacerbate the risk of endometrial cancer by promoting obesity-related metabolic abnormalities and chronic inflammation. Our study showed that various health conditions and lifestyle factors, such as metabolic syndrome, hypertension, dyslipidemia, diabetes, PCOS, current smoking, heavy alcohol consumption, and low-income levels, were more prevalent among young women who had higher cumulative abdominal obesity exposure. We observed the multifaceted nature of persistent abdominal obesity exposure as a risk factor for endometrial cancer, reflecting its interplay with metabolic dysfunction and unhealthy lifestyle behaviors. Further elucidation of these mechanisms could provide insights into early detection and management strategies. The relationship between abdominal obesity and endometrial cancer is primarily attributed to hormonal and metabolic mechanisms, particularly in young adults [25]. Most young women who develop endometrial cancer have obesity, and the rate of obesity is higher among younger women than among women who are postmenopausal [26]. Prolonged exposure to unopposed estrogen, which is linked to obesity, has been implicated in the development of endometrial cancer in young women [27]. This suggests a possible link to the rising obesity epidemic in younger generations, reflecting similar trends observed in other cancers [28]. The results of the current study underscore the importance of risk stratification and preventive interventions for endometrial cancer in young women with prolonged abdominal obesity exposure. Although the incidence of endometrial cancer is lower in younger women than those who are postmenopausal, the increasing trend of endometrial cancer among the younger generation is expected to continue owing to economic development and lifestyle transitions [29]. Further etiological studies focusing on modifiable risk factors in early life are required to elucidate the causes of these emerging trends. Despite the strengths of our study, including its population-based longitudinal design and identification of a dose-response relationship, several limitations should be considered. First, as an observational study, we examined the association between abdominal obesity and endometrial cancer but could not definitively establish reverse causality or completely explain the effects of unmeasured confounding factors such as estrogen exposure, family history, and genetic predisposition. Second, abdominal obesity was diagnosed based on a single annual health examination. However, this method is commonly used in other epidemiological studies. We addressed this limitation by counting the frequency of abdominal obesity across four consecutive health examinations. Third, we investigated uterine corpus cancer identified through the ICD-10 code as a single entity and did not further differentiate histopathologically; however, endometrial cancer accounts for approximately 90% of uterine cancers [30]. Finally, although the focus on young women is a strength, the findings may not be generalizable to populations with different demographic, ethnic, or socioeconomic characteristics, limiting the applicability of the results to diverse groups. Future prospective studies are needed to validate our findings and assess the potential effect of abdominal obesity on different subtypes of uterine cancer. CONCLUSION Cumulative abdominal obesity exposure in young women is associated with a progressively increased risk of endometrial cancer. In addition, abdominal obesity exposure showed a stronger association with the risk of endometrial cancer than did general obesity exposure, as cumulative exposure increased. These findings underscore the importance of identifying abdominal obesity as a potential risk factor for endometrial cancer in young women. Future research should investigate the underlying mechanisms and the potential impact of abdominal obesity exposure on different subtypes of uterine cancer to enhance risk stratification and develop effective preventive strategies. Declarations ACKNOWLEDGEMENTS This work was supported by the Technology Innovation Program (20008924), funded by the Ministry of Trade, Industry & Energy (MOTIE, Korea) and the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (2022R1F1A061069). The funders had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. AUTHOR CONTRIBUTIONS M.K.L. and K.D.H. contributed to the study design, analysis, and data interpretation. M.K.L. drafted and edited the manuscript. K.D.H. performed the statistical analysis of the data. M.K.L. and Y.S.S. supervised and revised the manuscript. All the authors have approved the final manuscript. M.K.L. and K.D.H. were the guarantors of this study and responsible for the integrity of the data and the accuracy of the data analysis. COMPETING INTERESTS The authors declare no competing financial interests in relation interests in in relation to the work. DATA AVAILABILITY STATEMENT Professor Han had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. The authors are restricted from sharing the data underlying this study because the Korean National Health Insurance Service (NHIS) owns the data. Researchers can request access to the NHIS website (https://nhiss.nhis.or.kr). The details of this process and a provisional guide are available at http://nhiss.nhis.or.kr/bd/ab/bdaba000eng.do. References Lee KR, Seo MH, Do Han K, Jung J, Hwang IC, on behalf of the Taskforce Team of the Obesity Fact Sheet of the Korean Society for the Study of O. Waist circumference and risk of 23 site-specific cancers: a population-based cohort study of Korean adults. British Journal of Cancer 2018;119:1018-27. https://doi.org/10.1038/s41416-018-0214-7. Ramos-Nino ME. The role of chronic inflammation in obesity-associated cancers. 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Jenabi E, Poorolajal J. The effect of body mass index on endometrial cancer: a meta-analysis. Public Health 2015;129:872-80. https://doi.org/10.1016/j.puhe.2015.04.017. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. Body Fatness and Cancer--Viewpoint of the IARC Working Group. N Engl J Med 2016;375:794-8. https://doi.org/10.1056/NEJMsr1606602. Friedenreich C, Cust A, Lahmann PH, Steindorf K, Boutron-Ruault MC, Clavel-Chapelon F, et al. Anthropometric factors and risk of endometrial cancer: the European prospective investigation into cancer and nutrition. Cancer Causes Control 2007;18:399-413. https://doi.org/10.1007/s10552-006-0113-8. Iemura A, Douchi T, Yamamoto S, Yoshimitsu N, Nagata Y. Body fat distribution as a risk factor of endometrial cancer. J Obstet Gynaecol Res 2000;26:421-5. https://doi.org/10.1111/j.1447-0756.2000.tb01352.x. Xu WH, Matthews CE, Xiang YB, Zheng W, Ruan ZX, Cheng JR, et al. Effect of Adiposity and Fat Distribution on Endometrial Cancer Risk in Shanghai Women. American Journal of Epidemiology 2005;161:939-47. https://doi.org/10.1093/aje/kwi127. Barberio AM, Alareeki A, Viner B, Pader J, Vena JE, Arora P, et al. Central body fatness is a stronger predictor of cancer risk than overall body size. Nat Commun 2019;10:383. https://doi.org/10.1038/s41467-018-08159-w. Onstad MA, Schmandt RE, Lu KH. Addressing the Role of Obesity in Endometrial Cancer Risk, Prevention, and Treatment. J Clin Oncol 2016;34:4225-30. https://doi.org/10.1200/jco.2016.69.4638. Allen NE, Key TJ, Dossus L, Rinaldi S, Cust A, Lukanova A, et al. Endogenous sex hormones and endometrial cancer risk in women in the European Prospective Investigation into Cancer and Nutrition (EPIC). Endocr Relat Cancer 2008;15:485-97. https://doi.org/10.1677/erc-07-0064. Crudele L, Piccinin E, Moschetta A. Visceral Adiposity and Cancer: Role in Pathogenesis and Prognosis. Nutrients 2021;13. https://doi.org/10.3390/nu13062101. Shaw E, Farris M, McNeil J, Friedenreich C. Obesity and Endometrial Cancer. Recent Results Cancer Res 2016;208:107-36. https://doi.org/10.1007/978-3-319-42542-9_7. Abdol Manap N, Ng BK, Phon SE, Abdul Karim AK, Lim PS, Fadhil M. Endometrial Cancer in Pre-Menopausal Women and Younger: Risk Factors and Outcome. Int J Environ Res Public Health 2022;19. https://doi.org/10.3390/ijerph19159059. Burleigh A, Talhouk A, Gilks CB, McAlpine JN. Clinical and pathological characterization of endometrial cancer in young women: identification of a cohort without classical risk factors. Gynecol Oncol 2015;138:141-6. https://doi.org/10.1016/j.ygyno.2015.02.028. Miller KD, Fidler-Benaoudia M, Keegan TH, Hipp HS, Jemal A, Siegel RL. Cancer statistics for adolescents and young adults, 2020. CA Cancer J Clin 2020;70:443-59. https://doi.org/10.3322/caac.21637. Kaaks R, Lukanova A, Kurzer MS. Obesity, endogenous hormones, and endometrial cancer risk: a synthetic review. Cancer Epidemiol Biomarkers Prev 2002;11:1531-43. Amant F, Mirza MR, Koskas M, Creutzberg CL. Cancer of the corpus uteri. International Journal of Gynecology & Obstetrics 2018;143:37-50. https://doi.org/https://doi.org/10.1002/ijgo.12612. Additional Declarations There is NO conflict of interest to disclose Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2025 Read the published version in International Journal of Obesity → Version 1 posted Editorial decision: revise 16 Dec, 2024 Review # 2 received at journal 08 Dec, 2024 Reviewer # 2 agreed at journal 18 Nov, 2024 Review # 1 received at journal 01 Sep, 2024 Reviewer # 1 agreed at journal 28 Aug, 2024 Reviewers invited by journal 23 Aug, 2024 Submission checks completed at journal 12 Aug, 2024 First submitted to journal 10 Aug, 2024 Unknown event 09 Aug, 2024 Editor assigned by journal 08 Aug, 2024 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board 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-4881494","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":344406406,"identity":"596e016e-5fdf-48ea-a995-1f0f6904787a","order_by":0,"name":"MINKYUNG LEE","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDACZgb2Hx94bORI0sIgOUMmzZg0i6R5bA4nNhCtXLed94AxT87h9O3sZwwfVzDYyekS0mx2mC8hcc6Z9NydPTnGhmcYko3NDhDUwmNw4G2Pde6GA2lpkg0MBxK3EaHFsIH3H3O6wfln6T+J1WLMyMPjnGBwI/kYI7FazBhn8KQZbrjx+LBkgwExfjl/xowBGJXyBucTGz82VNjJEdSCBgxIUz4KRsEoGAWjAAcAAMvHQJnZUQK/AAAAAElFTkSuQmCC","orcid":"","institution":"Myongji Hospital, Hanyang University College of Medicine","correspondingAuthor":true,"prefix":"","firstName":"MINKYUNG","middleName":"","lastName":"LEE","suffix":""},{"id":344406407,"identity":"8f468567-792d-4dcf-8ba0-c121b1312faa","order_by":1,"name":"Jung Heo","email":"","orcid":"","institution":"Myongji Hospital, Hanyang University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jung","middleName":"","lastName":"Heo","suffix":""},{"id":344406408,"identity":"b5211490-a76d-47a7-ab13-b9d78a23a889","order_by":2,"name":"Jiyeon Ahn","email":"","orcid":"","institution":"Myongji Hospital, Hanyang University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiyeon","middleName":"","lastName":"Ahn","suffix":""},{"id":344406409,"identity":"9bc37411-8587-41dd-8fff-55182ae3b583","order_by":3,"name":"Kyung Do Han","email":"","orcid":"https://orcid.org/0000-0002-6096-1263","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Kyung","middleName":"Do","lastName":"Han","suffix":""},{"id":344406410,"identity":"cafab8cb-f237-4954-a7c1-f67fc02e2cdc","order_by":4,"name":"Yeon Jee Lee","email":"","orcid":"","institution":"Myongji Hospital, Hanyang University College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yeon","middleName":"Jee","lastName":"Lee","suffix":""},{"id":344406411,"identity":"e9262a4a-36d3-4234-b567-c2cf5380a6f4","order_by":5,"name":"Yong Sang Song","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"Sang","lastName":"Song","suffix":""},{"id":344406412,"identity":"bb33c054-76c2-4f49-9f4f-3b3646a0fa46","order_by":6,"name":"Seo-Young Sohn","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Seo-Young","middleName":"","lastName":"Sohn","suffix":""},{"id":344406413,"identity":"9c1b72be-4c8e-4d79-878a-a6b303f8d1f7","order_by":7,"name":"Jae-Hyuk Lee","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jae-Hyuk","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2024-08-08 13:45:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4881494/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4881494/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41366-025-01862-x","type":"published","date":"2025-08-22T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66761396,"identity":"5b4d099c-3de9-491c-ae95-2a4ec7745c43","added_by":"auto","created_at":"2024-10-16 08:52:37","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":78110,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"IJOAObYECFigure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4881494/v1/6301c021ee06dc308c79edcc.jpg"},{"id":66761398,"identity":"c248b795-a109-4be6-8e68-727d4da2cd59","added_by":"auto","created_at":"2024-10-16 08:52:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1455877,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"IJOAObYECFigure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4881494/v1/0b1041f3d650c35e14d6fe76.jpg"},{"id":89716846,"identity":"8d1f3e05-3f52-440a-aa37-0f60b68c2108","added_by":"auto","created_at":"2025-08-23 07:06:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2452896,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4881494/v1/a4eff0fe-6a3f-4ab6-9cbf-701e52f87e44.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Cumulative abdominal obesity exposure and risk of endometrial cancer in young women: a population-based cohort study","fulltext":[{"header":"What’s new? ","content":"\u003cp\u003eAbdominal obesity is currently being investigated as an indicator of adiposity and cancer risk, and its prevalence is increasing in young women. In this population-based longitudinal cohort study, we observed a progressive increase in the risk of endometrial cancer with cumulative abdominal obesity exposure in young women aged 20\u0026ndash;39 years. In addition, abdominal obesity showed a stronger association with the risk of endometrial cancer than did general obesity, as cumulative exposure increased. These findings underscore the importance of identifying abdominal obesity as a potential risk factor for endometrial cancer in young women.\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003eAbdominal obesity, which is characterized by the accumulation of visceral fat, is currently being investigated as an indicator of adiposity and cancer risk [1]. Although the precise mechanism is not fully understood, obesity-related abnormalities such as impaired glucose tolerance, insulin resistance, and systemic inflammation are expected to contribute to cancer development [2]. Apart from body mass index (BMI), other anthropometric measurements, such as waist-to-hip ratio or waist circumference (WC), are associated with an increased risk of endometrial cancer [3]. However, previous studies have indicated that most endometrial cancers in patients younger than 40 years are associated with general obesity [4]. The role of abdominal obesity in the risk of endometrial cancer, particularly in young women, requires comprehensive exploration.\u003c/p\u003e \u003cp\u003eEndometrial cancer, a gynecological malignancy arising from the inner lining of the uterus, is the most common gynecologic malignancy in developed countries, and its incidence has steadily increased in recent decades [5,6]. The majority of cases occur predominately in women who are postmenopausal; 2‒14% of cases develop in women aged 40 years and younger [7]. The incidence of endometrial cancer continues to increase sharply in younger generations [8]. This demographic shift highlights the need to identify the risk factors that contribute to endometrial cancer development among young women. The faster rise in endometrial cancer occurring before the age of 40 years mirrors similar observations in other cancers, pointing to a possible link with the rising obesity epidemic in younger generations [9].\u003c/p\u003e \u003cp\u003eThis population-based nationwide cohort study of young Korean women aimed to assess the risk of endometrial cancer associated with cumulative exposure to abdominal obesity. This study provides valuable insights into the role of abdominal obesity in the development of endometrial cancer in young women, with implications for preventive strategies and public health interventions aimed at reducing the burden of this disease in this vulnerable population.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003e In this large-scale epidemiological study of a Korean cohort, we used the Korean National Health Information Database (NHID), which combines information obtained from the National Health Insurance Service (NHIS) and general health examinations provided to all Korean adults [10]. The NHIS is the only insurer managing the health insurance system in Korea and enrolls approximately 97% of the Korean population. The general health screening program involves annual or biennial health examinations for the entire population of Korean adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years and for regional household members and dependents of people aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years. The NHID includes claims data, health screening information, detailed lifestyle questionnaires, laboratory results, and anthropometric measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eFrom the NHID, we identified 471,376 women aged 20\u0026ndash;39 years at baseline who underwent at least one health examination between 2009 and 2012 and had three additional consecutive annual health examinations. The index date was defined as the date of the fourth (latest) health examination. Among them, we excluded 20,804 participants with missing data and 3,549 who had been diagnosed with cancer before the index date. We set a 1-year lag period and excluded 1,232 patients diagnosed with uterine corpus cancer to minimize the risk of reverse causality. Consequently, the final study population consisted of 445,791 women, divided into four groups according to their cumulative exposure to abdominal obesity (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was approved by the Institutional Review Board of the Hanyang University of Korea (No. HYU-2023-010). Informed consent was waived because anonymous and de-identified information was used for the analysis. This study was conducted in compliance with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCumulative abdominal obesity exposure\u003c/h2\u003e \u003cp\u003eThe WC, weight, and height of the participants were measured in a standardized manner by trained nurses during each examination. We defined abdominal obesity as a WC\u0026thinsp;\u0026ge;\u0026thinsp;85 cm for Korean women according to the WC cut-off point in the Asian-specific population [11]. WC was measured by trained examiners at the midpoint between the inferior border of the lower rib and the iliac crest on the mid-axillary line after exhalation. General obesity was defined as a BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e according to the Asia-Pacific criteria of the World Health Organization Guidelines [12]. BMI was calculated as weight (kg) divided by height (m) squared.\u003c/p\u003e \u003cp\u003eTo estimate cumulative exposure to abdominal obesity, we assessed the frequency of abdominal obesity (WC\u0026thinsp;\u0026ge;\u0026thinsp;85 cm) across four consecutive annual health examinations over 4 years and counted it as abdominal obesity exposure. In this classification, 0 indicated no abdominal obesity and 1\u0026ndash;4 indicated the frequency of abdominal obesity. Additionally, the number of individuals with obesity ranged from 0 to 4, indicating the frequency of obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e) over the four health examinations. The participants were categorized into five groups based on their abdominal obesity scores: 0, 1, 2, 3, and 4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eOutcome and follow-up\u003c/h2\u003e \u003cp\u003eThe primary outcome of this study was newly diagnosed cases of endometrial cancer. For this study, we created a customized database by merging the NHIS Medical Check-up database, which contains the 2009 NHIS health examination and cancer screening questionnaire results, and the NHIS claims database. Patients diagnosed with endometrial cancer were identified using the International Classification of Diseases, 10th Revision (ICD-10) code C54\u0026ndash;55. The study population was monitored from baseline to the date of diagnosis, death, or until the end of the study period (December 31, 2020), whichever occurred first. The median follow-up duration was 7.12 (5.59\u003cb\u003e\u0026ndash;7\u003c/b\u003e.49) years.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements and definitions of variables\u003c/h2\u003e \u003cp\u003eComorbidities were defined using a combination of ICD-10-CM codes and self-reported medication histories. Metabolic syndrome was defined as a combination of abdominal obesity, impaired fasting glucose, high triglyceride levels, low high-density lipoprotein (HDL) cholesterol levels, and elevated blood pressure (BP), according to the revised National Cholesterol Education Program Adult Treatment Panel III criteria [13]. Hypertension was defined as taking antihypertensive medications for at least one claim per year under ICD-10 codes I10\u0026ndash;11, with systolic BP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic BP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg. Dyslipidemia was defined as having at least one claim per year under ICD-10 code E78 for lipid-lowering agents or total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;240 mg/dL. Diabetes mellitus was defined as a fasting blood glucose level of \u0026ge;\u0026thinsp;126 mg/dL or the presence of one or more claims per year for antihyperglycemic medications with ICD-10 code E10\u0026ndash;14. Polycystic ovarian syndrome (PCOS) was defined as the presence of at least one claim per year under ICD-10 code E28.0\u0026ndash;28.9 in this study. Parity was defined as childbirth using the diagnosis and procedure codes for pregnancy. Smoking status was classified as current or non-current. Heavy alcohol consumption was defined as weekly alcohol consumption of \u0026gt;\u0026thinsp;28 standard drinks (210 g alcohol). Regular physical activity was defined as moderate-to-high-intensity activity\u0026thinsp;\u0026ge;\u0026thinsp;3 times/week. Income levels were defined based on health insurance premiums and categorized into quartiles, with the first quartile representing the low-income group. BP was measured during regular medical checkups using standardized methods. Blood samples were collected after overnight fasting, and the parameters measured included fasting plasma glucose (FPG), total cholesterol, triglycerides, HDL cholesterol, and low-density lipoprotein (LDL) cholesterol (calculated using the Friedewald formula). The hospitals where these health examinations were performed were certified by the NHIS and subjected to regular quality control.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics of the study population, divided by the number of abdominal obesity exposures, are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or proportions (%). Geometric means are used for heavily skewed distributions. The disease-free probability of endometrial cancer was calculated using Kaplan\u0026ndash;Meier curves, and the differences in the numbers of abdominal obesity exposures and general obesity exposures were analyzed using a log-rank test. The incidence rate per 1,000 person-years was estimated as the number of newly diagnosed cases of endometrial cancer during the follow-up period. Survival analyses were performed using Cox proportional hazards regression to estimate the HRs and 95% CIs for incident endometrial cancer. Adjusted models were used to adjust for potential confounding variables. Model 1 was crude. Model 2 was adjusted for age. Model 3 was adjusted for age, hypertension, dyslipidemia, diabetes, PCOS, parity, smoking status, alcohol consumption, physical activity, and low-income status. All statistical tests were two-sided at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.05 statistical significance level. These estimations were performed using the Statistical Analysis System (SAS) statistical software package (version 9.4; SAS Institute Inc., Cary, NC, US). The data analysis was performed between February 2023 and November 2023.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of the study population\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the study population, stratified by the number of abdominal obesity exposures during the four health examinations, are represented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Among the total study population of 445,791 young women (mean [SD] age, 34.08 [4.98] years), 386,323 (86.7%) had no abdominal obesity. A total of 31,467 (7.1%) participants had a one-time occurrence of abdominal obesity, 11,090 (2.5%) had two occurrences, 7,631 (1.7%) had three occurrences, and 9,280 (2.1%) had four consecutive occurrences of abdominal obesity. The mean WC for participants with exposure numbers of 0\u0026ndash;4 were 69.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92, 80.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.67, 85.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95, 89.35\u0026thinsp;\u0026plusmn;\u0026thinsp;7, and 96.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16 cm, respectively. Additionally, there was a trend toward a higher prevalence of metabolic syndrome, hypertension, dyslipidemia, diabetes, PCOS, current smokers, heavy alcohol drinkers, and participants with low-income levels with increased exposure numbers. The parity decreased as the number of exposures increased. As the number of exposures increased, BMI, BP, LDL-cholesterol, triglyceride, and FPG levels showed a gradual increase, whereas HDL cholesterol levels exhibited a decrease.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of Young Women Stratified by Cumulative Exposure to Abdominal Obesity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eExposure number for abdominal obesity (waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;85 cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.55\u0026thinsp;\u0026plusmn;\u0026thinsp;4.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.84\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32\u0026thinsp;\u0026plusmn;\u0026thinsp;4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference, cm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.01\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.88\u0026thinsp;\u0026plusmn;\u0026thinsp;8.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.77\u0026thinsp;\u0026plusmn;\u0026thinsp;6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e89.35\u0026thinsp;\u0026plusmn;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e96.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index, kg/m\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.68\u0026thinsp;\u0026plusmn;\u0026thinsp;2.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.37\u0026thinsp;\u0026plusmn;\u0026thinsp;3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.12\u0026thinsp;\u0026plusmn;\u0026thinsp;3.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.07\u0026thinsp;\u0026plusmn;\u0026thinsp;3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.38\u0026thinsp;\u0026plusmn;\u0026thinsp;4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbdominal obesity, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11940 (37.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6911 (62.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6100 (79.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9280 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20603 (5.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13074 (41.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8393 (75.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6979 (91.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9149 (98.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic syndrome, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3622 (0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2745 (8.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2326 (20.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2341 (30.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4589 (49.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4942 (1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e980 (3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e686 (6.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e763 (10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1691 (18.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDyslipidemia, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13656 (3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3862 (12.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1128 (10.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e893 (11.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1426 (15.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2245 (0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e466 (1.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e369 (3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e419 (5.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1033 (11.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolycystic ovarian syndrome, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3705 (0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e329 (1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100 (1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e158 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29399 (7.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7877 (25.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1733 (15.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e806 (10.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e628 (6.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Smoker, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14566 (3.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1403 (4.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e772 (6.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e632 (8.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e978 (10.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy alcohol drinker, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8492 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e755 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e389 (3.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e274 (3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e362 (3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegular exercise, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53603 (13.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4240 (13.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1768 (15.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1322 (17.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1519 (16.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-income level, No. (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33108 (8.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3334 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1533 (13.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1158 (15.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1545 (16.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113.48\u0026thinsp;\u0026plusmn;\u0026thinsp;11.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.49\u0026thinsp;\u0026plusmn;\u0026thinsp;12.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120.15\u0026thinsp;\u0026plusmn;\u0026thinsp;12.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e124.5\u0026thinsp;\u0026plusmn;\u0026thinsp;13.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure, mmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.56\u0026thinsp;\u0026plusmn;\u0026thinsp;7.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.39\u0026thinsp;\u0026plusmn;\u0026thinsp;8.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.08\u0026thinsp;\u0026plusmn;\u0026thinsp;8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.91\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.81\u0026thinsp;\u0026plusmn;\u0026thinsp;10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178.65\u0026thinsp;\u0026plusmn;\u0026thinsp;30.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e192.06\u0026thinsp;\u0026plusmn;\u0026thinsp;40.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e191.77\u0026thinsp;\u0026plusmn;\u0026thinsp;35.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e193.8\u0026thinsp;\u0026plusmn;\u0026thinsp;35.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198.94\u0026thinsp;\u0026plusmn;\u0026thinsp;36.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-Cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e65.23\u0026thinsp;\u0026plusmn;\u0026thinsp;18.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62\u0026thinsp;\u0026plusmn;\u0026thinsp;18.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.45\u0026thinsp;\u0026plusmn;\u0026thinsp;14.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.02\u0026thinsp;\u0026plusmn;\u0026thinsp;14.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-Cholesterol, mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98.18\u0026thinsp;\u0026plusmn;\u0026thinsp;27.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108.4\u0026thinsp;\u0026plusmn;\u0026thinsp;32.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.52\u0026thinsp;\u0026plusmn;\u0026thinsp;33.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114.19\u0026thinsp;\u0026plusmn;\u0026thinsp;32.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e118.82\u0026thinsp;\u0026plusmn;\u0026thinsp;33.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglyceride level, mg/dL\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69.14 (69.04\u003cb\u003e\u0026ndash;\u003c/b\u003e69.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92.92 (92.33\u003cb\u003e\u0026ndash;\u003c/b\u003e93.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.08 (98.09\u003cb\u003e\u0026ndash;\u003c/b\u003e100.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.54 (105.27\u003cb\u003e\u0026ndash;\u003c/b\u003e107.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120.72 (119.45\u003cb\u003e\u0026ndash;\u003c/b\u003e122.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting plasma glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.67\u0026thinsp;\u0026plusmn;\u0026thinsp;10.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.99\u0026thinsp;\u0026plusmn;\u0026thinsp;21.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.86\u0026thinsp;\u0026plusmn;\u0026thinsp;23.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102.56\u0026thinsp;\u0026plusmn;\u0026thinsp;33.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian follow-up duration, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.13 (5.6\u003cb\u003e\u0026ndash;\u003c/b\u003e7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.13 (5.64\u003cb\u003e\u0026ndash;\u003c/b\u003e7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.07 (5.52\u003cb\u003e\u0026ndash;\u003c/b\u003e7.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.03 (5.43\u003cb\u003e\u0026ndash;\u003c/b\u003e7.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.01 (5.28\u003cb\u003e\u0026ndash;\u003c/b\u003e7.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eData are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables and as proportions (%) for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003eGeometric mean (95% confidence interval [CI])\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRisk of endometrial cancer based on exposure numbers to abdominal obesity in young women\u003c/h2\u003e \u003cp\u003eDuring a median follow-up period of 7.12 years, 302 participants were newly diagnosed with endometrial cancer. In Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (A), the cumulative incidence of endometrial cancer differed significantly according to the number of abdominal obesity exposures (log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The incidence and HRs of endometrial cancer progressively increased with exposure to abdominal obesity (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The incidence rates of endometrial cancer were 0.08048, 0.12086, 0.20876, 0.38767, and 0.66182 per 1,000 person-years for participants with 0\u0026ndash;4 exposures to abdominal obesity, respectively. After adjusting for age, hypertension, dyslipidemia, diabetes, PCOS, parity, smoking status, alcohol consumption, physical activity, and low income, the multivariable-adjusted HRs for incident endometrial cancer were 1.480 (95% CI, 0.970\u0026ndash;2.258), 2.361 (95% CI, 1.391\u0026ndash;4.008), 4.114 (95% CI, 2.546\u0026ndash;6.647), and 6.215 (95% CI, 4.250\u0026ndash;9.088) for participants with numbers of 1\u0026ndash;4, respectively, compared to those with an exposure number of 0 (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable-adjusted Hazard Ratios for Endometrial Cancer According to Abdominal Obesity Exposure in Young Women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber at risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncidence case\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFollow-up duration (person-years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncidence rate\u003c/p\u003e \u003cp\u003e(per 1,000 person-years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e386323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2534678.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e206854.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.12086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.500 (0.990, 2.273)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.417 (0.935, 2.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.480 (0.970, 2.258)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71851.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.615 (1.548, 4.417)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.417 (1.430, 4.085)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.361 (1.391, 4.008)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49010.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.889 (3.055, 7.823)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.482 (2.799, 7.178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.114 (2.546, 6.647)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58928.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.66182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.398 (5.962, 11.829)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.581 (5.375, 10.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.215 (4.250, 9.088)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1 was crude. Model 2 is adjusted for age. Model 3 was adjusted for age, hypertension, dyslipidemia, diabetes, polycystic ovarian syndrome, parity, smoking status, alcohol consumption, physical activity, and income level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRisk of endometrial cancer based on exposure numbers to obesity in young women\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (B) depicts a significant difference in the cumulative incidence of endometrial cancer according to the number of obesity exposures (log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e represents the association between the number of exposures to obesity and the risk of endometrial cancer. The incidence rates of endometrial cancer were 0.07289, 0.15727, 0.15363, 0.18126, and 0.38188 per 1,000 person-years for participants with 0\u0026ndash;4 obesity exposures. The multivariable-adjusted HRs for incident endometrial cancer were 2.136 (95% CI, 1.354\u0026ndash;3.371), 2.006 (95% CI, 1.116\u0026ndash;3.605), 2.269 (95% CI, 1.313\u0026ndash;3.919), and 4.182 (95% CI, 3.133\u0026ndash;5.582) for participants with numbers of 1\u0026ndash;4, respectively, compared to those with an exposure number of 0 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable-adjusted Hazard Ratios for Endometrial Cancer According to Obesity Exposure in Young Women\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber at risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncidence case\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFollow-up duration (person-years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIncidence rate\u003c/p\u003e \u003cp\u003e(per 1,000 person-years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e369789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2428196.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1 (Ref.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e133525.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.163 (1.376, 3.400)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.071 (1.317, 3.256)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.136 (1.354, 3.371)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78110.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.124 (1.183, 3.810)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.010 (1.120, 3.608)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.006 (1.116, 3.605)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77237.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.508 (1.455, 4.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.340 (1.357, 4.034)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.269 (1.313, 3.919)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204253.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.38188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.305 (4.064, 6.925)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.811 (3.679, 6.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.182 (3.133, 5.582)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eModel 1 was crude. Model 2 is adjusted for age. Model 3 was adjusted for age, hypertension, dyslipidemia, diabetes, polycystic ovarian syndrome, parity, smoking status, alcohol consumption, physical activity, and income level.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this population-based longitudinal study, we observed a significant association between cumulative abdominal obesity exposures and the risk of endometrial cancer in young women. The analysis revealed a dose-response relationship, in which the incidence rates of endometrial cancer progressively increased with higher abdominal obesity exposures in women aged\u0026thinsp;\u0026lt;\u0026thinsp;40 years. Furthermore, the risk of endometrial cancer based on the number of abdominal obesity exposure cases was generally higher than that based on the number of individuals with obesity. This difference became more pronounced as the number of exposures increased. These findings suggest that abdominal obesity may be a potential risk factor for the development of endometrial cancer in young women, emphasizing the need for targeted preventive interventions in at-risk populations.\u003c/p\u003e \u003cp\u003eEndometrial cancer in women aged\u0026thinsp;\u0026lt;\u0026thinsp;40 is strongly associated with obesity [14,15]. Many studies have consistently demonstrated a positive correlation between elevated BMI and the risk of developing endometrial cancer [16]. However, the BMI, a crude parameter of body size, does not account for body fat composition [17]. Several studies have reported that body fat distribution confers an additional risk of endometrial cancer [18]. A previous study showed that upper body and visceral fat distributions are more closely associated with endometrial cancer risk than overall adiposity [19]. In addition, a recent epidemiological study reported that WC was more strongly associated with endometrial cancer in younger women, independent of BMI [20]. Consistent with previous research, our study indicated the cumulative impact of prolonged abdominal obesity exposure on the risk of endometrial cancer, particularly among young women, which appeared to be higher than that associated with prolonged general obesity exposure. These findings provide additional insights into the role of adiposity in the pathogenesis of endometrial cancer.\u003c/p\u003e \u003cp\u003eAbdominal obesity is a strong predictor of the risk of all cancers, including endometrial cancer [21]. This condition may affect the pathogenesis of endometrial cancer more than that using BMI through various mechanisms, including alterations in hormonal profiles, chronic inflammation, and insulin resistance [22]. Abdominal obesity, often accompanied by metabolic disturbances such as insulin resistance and dyslipidemia, can promote a pro-inflammatory state and alter hormone levels, particularly estrogen, thereby increasing the risk of endometrial cancer [23]. Moreover, the dysregulated secretion of adipokines and cytokines by visceral fat in individuals with abdominal obesity may contribute to inflammation, angiogenesis, cell growth, and the proliferation and progression of endometrial carcinogenesis [24]. Furthermore, lifestyle factors associated with abdominal obesity exposure, such as a high-calorie diet and sedentary behavior, may exacerbate the risk of endometrial cancer by promoting obesity-related metabolic abnormalities and chronic inflammation. Our study showed that various health conditions and lifestyle factors, such as metabolic syndrome, hypertension, dyslipidemia, diabetes, PCOS, current smoking, heavy alcohol consumption, and low-income levels, were more prevalent among young women who had higher cumulative abdominal obesity exposure. We observed the multifaceted nature of persistent abdominal obesity exposure as a risk factor for endometrial cancer, reflecting its interplay with metabolic dysfunction and unhealthy lifestyle behaviors. Further elucidation of these mechanisms could provide insights into early detection and management strategies.\u003c/p\u003e \u003cp\u003eThe relationship between abdominal obesity and endometrial cancer is primarily attributed to hormonal and metabolic mechanisms, particularly in young adults [25]. Most young women who develop endometrial cancer have obesity, and the rate of obesity is higher among younger women than among women who are postmenopausal [26]. Prolonged exposure to unopposed estrogen, which is linked to obesity, has been implicated in the development of endometrial cancer in young women [27]. This suggests a possible link to the rising obesity epidemic in younger generations, reflecting similar trends observed in other cancers [28]. The results of the current study underscore the importance of risk stratification and preventive interventions for endometrial cancer in young women with prolonged abdominal obesity exposure. Although the incidence of endometrial cancer is lower in younger women than those who are postmenopausal, the increasing trend of endometrial cancer among the younger generation is expected to continue owing to economic development and lifestyle transitions [29]. Further etiological studies focusing on modifiable risk factors in early life are required to elucidate the causes of these emerging trends.\u003c/p\u003e \u003cp\u003eDespite the strengths of our study, including its population-based longitudinal design and identification of a dose-response relationship, several limitations should be considered. First, as an observational study, we examined the association between abdominal obesity and endometrial cancer but could not definitively establish reverse causality or completely explain the effects of unmeasured confounding factors such as estrogen exposure, family history, and genetic predisposition. Second, abdominal obesity was diagnosed based on a single annual health examination. However, this method is commonly used in other epidemiological studies. We addressed this limitation by counting the frequency of abdominal obesity across four consecutive health examinations. Third, we investigated uterine corpus cancer identified through the ICD-10 code as a single entity and did not further differentiate histopathologically; however, endometrial cancer accounts for approximately 90% of uterine cancers [30]. Finally, although the focus on young women is a strength, the findings may not be generalizable to populations with different demographic, ethnic, or socioeconomic characteristics, limiting the applicability of the results to diverse groups. Future prospective studies are needed to validate our findings and assess the potential effect of abdominal obesity on different subtypes of uterine cancer.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eCumulative abdominal obesity exposure in young women is associated with a progressively increased risk of endometrial cancer. In addition, abdominal obesity exposure showed a stronger association with the risk of endometrial cancer than did general obesity exposure, as cumulative exposure increased. These findings underscore the importance of identifying abdominal obesity as a potential risk factor for endometrial cancer in young women. Future research should investigate the underlying mechanisms and the potential impact of abdominal obesity exposure on different subtypes of uterine cancer to enhance risk stratification and develop effective preventive strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by\u0026nbsp;the Technology Innovation Program (20008924), funded by the Ministry of Trade, Industry \u0026amp; Energy (MOTIE, Korea) and the\u0026nbsp;National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (2022R1F1A061069).\u0026nbsp;The funders had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.K.L. and K.D.H. contributed to the study design, analysis, and data interpretation. M.K.L. drafted and edited the manuscript. K.D.H. performed the statistical\u0026nbsp;analysis of the data.\u0026nbsp;M.K.L. and Y.S.S. supervised and revised the manuscript. All the authors have approved the final manuscript. M.K.L. and K.D.H. were the guarantors of this study and responsible for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests in relation interests in in relation to the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProfessor Han had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. The authors are restricted from sharing the data underlying this study because the Korean National Health Insurance Service (NHIS) owns the data. Researchers can request access to the NHIS website (https://nhiss.nhis.or.kr). The details of this process and a provisional guide are available at http://nhiss.nhis.or.kr/bd/ab/bdaba000eng.do.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee KR, Seo MH, Do Han K, Jung J, Hwang IC, on behalf of the Taskforce Team of the Obesity Fact Sheet of the Korean Society for the Study of O. Waist circumference and risk of 23 site-specific cancers: a population-based cohort study of Korean adults. British Journal of Cancer 2018;119:1018-27. https://doi.org/10.1038/s41416-018-0214-7.\u003c/li\u003e\n\u003cli\u003eRamos-Nino ME. The role of chronic inflammation in obesity-associated cancers. ISRN Oncol 2013;2013:697521. https://doi.org/10.1155/2013/697521.\u003c/li\u003e\n\u003cli\u003eAune D, Navarro Rosenblatt DA, Chan DSM, Vingeliene S, Abar L, Vieira AR, et al. Anthropometric factors and endometrial cancer risk: a systematic review and dose\u0026ndash;response meta-analysis of prospective studies. Annals of Oncology 2015;26:1635-48. https://doi.org/https://doi.org/10.1093/annonc/mdv142.\u003c/li\u003e\n\u003cli\u003eGarg K, Soslow RA. Endometrial carcinoma in women aged 40 years and younger. Arch Pathol Lab Med 2014;138:335-42. https://doi.org/10.5858/arpa.2012-0654-RA.\u003c/li\u003e\n\u003cli\u003eKoskas M, Amant F, Mirza MR, Creutzberg CL. Cancer of the corpus uteri: 2021 update. Int J Gynaecol Obstet 2021;155 Suppl 1:45-60. https://doi.org/10.1002/ijgo.13866.\u003c/li\u003e\n\u003cli\u003eZhang S, Gong TT, Liu FH, Jiang YT, Sun H, Ma XX, et al. Global, Regional, and National Burden of Endometrial Cancer, 1990-2017: Results From the Global Burden of Disease Study, 2017. Front Oncol 2019;9:1440. https://doi.org/10.3389/fonc.2019.01440.\u003c/li\u003e\n\u003cli\u003eFelix AS, Brinton LA. Cancer Progress and Priorities: Uterine Cancer. Cancer Epidemiol Biomarkers Prev 2018;27:985-94. https://doi.org/10.1158/1055-9965.Epi-18-0264.\u003c/li\u003e\n\u003cli\u003eLiu L, Habeshian TS, Zhang J, Peeri NC, Du M, De Vivo I, et al. Differential trends in rising endometrial cancer incidence by age, race, and ethnicity. JNCI Cancer Spectrum 2023;7:pkad001. https://doi.org/10.1093/jncics/pkad001.\u003c/li\u003e\n\u003cli\u003eSung H, Siegel RL, Rosenberg PS, Jemal A. Emerging cancer trends among young adults in the USA: analysis of a population-based cancer registry. Lancet Public Health 2019;4:e137-e47. https://doi.org/10.1016/s2468-2667(18)30267-6.\u003c/li\u003e\n\u003cli\u003eCho SW, Kim JH, Choi HS, Ahn HY, Kim MK, Rhee EJ. Big Data Research in the Field of Endocrine Diseases Using the Korean National Health Information Database. Endocrinol Metab (Seoul) 2023;38:10-24. https://doi.org/10.3803/EnM.2023.102.\u003c/li\u003e\n\u003cli\u003eLee SY, Park HS, Kim DJ, Han JH, Kim SM, Cho GJ, et al. Appropriate waist circumference cutoff points for central obesity in Korean adults. Diabetes Res Clin Pract 2007;75:72-80. https://doi.org/10.1016/j.diabres.2006.04.013.\u003c/li\u003e\n\u003cli\u003eHaam JH, Kim BT, Kim EM, Kwon H, Kang JH, Park JH, et al. Diagnosis of Obesity: 2022 Update of Clinical Practice Guidelines for Obesity by the Korean Society for the Study of Obesity. J Obes Metab Syndr 2023;32:121-9. https://doi.org/10.7570/jomes23031.\u003c/li\u003e\n\u003cli\u003eGrundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005;112:2735-52. https://doi.org/10.1161/circulationaha.105.169404.\u003c/li\u003e\n\u003cli\u003eGallup DG, Stock RJ. Adenocarcinoma of the endometrium in women 40 years of age or younger. Obstet Gynecol 1984;64:417-20.\u003c/li\u003e\n\u003cli\u003eSoliman PT, Oh JC, Schmeler KM, Sun CC, Slomovitz BM, Gershenson DM, et al. Risk factors for young premenopausal women with endometrial cancer. Obstet Gynecol 2005;105:575-80. https://doi.org/10.1097/01.AOG.0000154151.14516.f7.\u003c/li\u003e\n\u003cli\u003eJenabi E, Poorolajal J. The effect of body mass index on endometrial cancer: a meta-analysis. Public Health 2015;129:872-80. https://doi.org/10.1016/j.puhe.2015.04.017.\u003c/li\u003e\n\u003cli\u003eLauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. Body Fatness and Cancer--Viewpoint of the IARC Working Group. N Engl J Med 2016;375:794-8. https://doi.org/10.1056/NEJMsr1606602.\u003c/li\u003e\n\u003cli\u003eFriedenreich C, Cust A, Lahmann PH, Steindorf K, Boutron-Ruault MC, Clavel-Chapelon F, et al. Anthropometric factors and risk of endometrial cancer: the European prospective investigation into cancer and nutrition. Cancer Causes Control 2007;18:399-413. https://doi.org/10.1007/s10552-006-0113-8.\u003c/li\u003e\n\u003cli\u003eIemura A, Douchi T, Yamamoto S, Yoshimitsu N, Nagata Y. Body fat distribution as a risk factor of endometrial cancer. J Obstet Gynaecol Res 2000;26:421-5. https://doi.org/10.1111/j.1447-0756.2000.tb01352.x.\u003c/li\u003e\n\u003cli\u003eXu WH, Matthews CE, Xiang YB, Zheng W, Ruan ZX, Cheng JR, et al. Effect of Adiposity and Fat Distribution on Endometrial Cancer Risk in Shanghai Women. American Journal of Epidemiology 2005;161:939-47. https://doi.org/10.1093/aje/kwi127.\u003c/li\u003e\n\u003cli\u003eBarberio AM, Alareeki A, Viner B, Pader J, Vena JE, Arora P, et al. Central body fatness is a stronger predictor of cancer risk than overall body size. Nat Commun 2019;10:383. https://doi.org/10.1038/s41467-018-08159-w.\u003c/li\u003e\n\u003cli\u003eOnstad MA, Schmandt RE, Lu KH. Addressing the Role of Obesity in Endometrial Cancer Risk, Prevention, and Treatment. J Clin Oncol 2016;34:4225-30. https://doi.org/10.1200/jco.2016.69.4638.\u003c/li\u003e\n\u003cli\u003eAllen NE, Key TJ, Dossus L, Rinaldi S, Cust A, Lukanova A, et al. Endogenous sex hormones and endometrial cancer risk in women in the European Prospective Investigation into Cancer and Nutrition (EPIC). Endocr Relat Cancer 2008;15:485-97. https://doi.org/10.1677/erc-07-0064.\u003c/li\u003e\n\u003cli\u003eCrudele L, Piccinin E, Moschetta A. Visceral Adiposity and Cancer: Role in Pathogenesis and Prognosis. Nutrients 2021;13. https://doi.org/10.3390/nu13062101.\u003c/li\u003e\n\u003cli\u003eShaw E, Farris M, McNeil J, Friedenreich C. Obesity and Endometrial Cancer. Recent Results Cancer Res 2016;208:107-36. https://doi.org/10.1007/978-3-319-42542-9_7.\u003c/li\u003e\n\u003cli\u003eAbdol Manap N, Ng BK, Phon SE, Abdul Karim AK, Lim PS, Fadhil M. Endometrial Cancer in Pre-Menopausal Women and Younger: Risk Factors and Outcome. Int J Environ Res Public Health 2022;19. https://doi.org/10.3390/ijerph19159059.\u003c/li\u003e\n\u003cli\u003eBurleigh A, Talhouk A, Gilks CB, McAlpine JN. Clinical and pathological characterization of endometrial cancer in young women: identification of a cohort without classical risk factors. Gynecol Oncol 2015;138:141-6. https://doi.org/10.1016/j.ygyno.2015.02.028.\u003c/li\u003e\n\u003cli\u003eMiller KD, Fidler-Benaoudia M, Keegan TH, Hipp HS, Jemal A, Siegel RL. Cancer statistics for adolescents and young adults, 2020. CA Cancer J Clin 2020;70:443-59. https://doi.org/10.3322/caac.21637.\u003c/li\u003e\n\u003cli\u003eKaaks R, Lukanova A, Kurzer MS. Obesity, endogenous hormones, and endometrial cancer risk: a synthetic review. Cancer Epidemiol Biomarkers Prev 2002;11:1531-43.\u003c/li\u003e\n\u003cli\u003eAmant F, Mirza MR, Koskas M, Creutzberg CL. Cancer of the corpus uteri. International Journal of Gynecology \u0026amp; Obstetrics 2018;143:37-50. https://doi.org/https://doi.org/10.1002/ijgo.12612.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4881494/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4881494/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eAbdominal obesity is currently being investigated as an indicator of adiposity and cancer risk, and its prevalence is increasing in young women. This study aimed to examine whether cumulative abdominal obesity exposure in young women was associated with the development of endometrial cancer.\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eWe used data from the South Korean National Health Insurance Service for women aged 20\u0026ndash;39 years who had completed four consecutive annual health examinations between 2009 and 2015 and had no history of cancer at baseline. Participants were categorized into five groups based on the number of abdominal obesity exposures (waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;85 cm). Exposure numbers ranged from 0 to 4, indicating the frequency of abdominal obesity across the four health examinations over 4 years. The primary outcome was newly diagnosed endometrial cancer during a follow-up period of 7.12 years.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eAmong the 445,791 young women (mean [SD] age 30.82 [4.55] years), 302 (mean [SD], 32.79 [4.53] years) developed endometrial cancer. The cumulative incidence of endometrial cancer differed significantly according to the number of abdominal obesity exposures (log-rank test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). The incidence of endometrial cancer has progressively increased with abdominal obesity exposure. The multivariable-adjusted HRs for incident endometrial cancer were 1.480 (95% CI, 0.970\u0026ndash;2.258), 2.361 (95% CI, 1.391\u0026ndash;4.008), 4.114 (95% CI, 2.546\u0026ndash;6.647), and 6.215 (95% CI, 4.250\u0026ndash;9.088) for participants with exposure numbers of 1\u0026ndash;4, respectively, compared with those with an exposure number of 0.\u003c/p\u003e\u003ch2\u003eCONCLUSION\u003c/h2\u003e \u003cp\u003eIn this population-based nationwide cohort study of young women, we observed a progressive increase in the risk of endometrial cancer with cumulative abdominal obesity exposure.\u003c/p\u003e","manuscriptTitle":"Cumulative abdominal obesity exposure and risk of endometrial cancer in young women: a population-based cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-16 08:52:32","doi":"10.21203/rs.3.rs-4881494/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2024-12-16T10:28:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-12-08T10:25:47+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-11-18T11:05:28+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-09-01T06:31:27+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-08-29T00:55:10+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-08-24T02:14:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-12T12:06:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Obesity","date":"2024-08-10T07:03:09+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2024-08-09T11:01:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-08T13:40:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-obesity","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ijo","sideBox":"Learn more about [International Journal of Obesity](http://www.nature.com/ijo/)","snPcode":"41366","submissionUrl":"https://mts-ijo.nature.com/cgi-bin/main.plex","title":"International Journal of Obesity","twitterHandle":"@intjobesity","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"20f7067b-7895-41d9-ab5e-c3de6ea7711b","owner":[],"postedDate":"October 16th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":36509510,"name":"Health sciences/Risk factors"},{"id":36509511,"name":"Health sciences/Diseases/Cancer"},{"id":36509512,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity"}],"tags":[],"updatedAt":"2025-08-23T07:06:33+00:00","versionOfRecord":{"articleIdentity":"rs-4881494","link":"https://doi.org/10.1038/s41366-025-01862-x","journal":{"identity":"international-journal-of-obesity","isVorOnly":false,"title":"International Journal of Obesity"},"publishedOn":"2025-08-22 04:00:00","publishedOnDateReadable":"August 22nd, 2025"},"versionCreatedAt":"2024-10-16 08:52:32","video":"","vorDoi":"10.1038/s41366-025-01862-x","vorDoiUrl":"https://doi.org/10.1038/s41366-025-01862-x","workflowStages":[]},"version":"v1","identity":"rs-4881494","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4881494","identity":"rs-4881494","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}