Steatotic liver disease and the risk of fertility-related gynecological disorders in reproductive-aged women: a nationwide cohort study

Steatotic liver disease and the risk of fertility-related gynecological disorders in reproductive-aged women: a nationwide cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Steatotic liver disease and the risk of fertility-related gynecological disorders in reproductive-aged women: a nationwide cohort study Jungmin Lee, Eun Seok Kang, Seogsong Jeong, Hwamin Lee This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9434896/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Steatotic liver disease (SLD) is a prevalent metabolic disorder associated with various systemic complications. Emerging evidence suggests a link between SLD and women reproductive health, including infertility and gynecological disorders. We aimed to investigate the association between SLD subtypes and the risk of infertility and related gynecological disorders among reproductive-aged women in Korea. This retrospective, population-based cohort study was conducted using data from the Korean National Health Insurance Service. Women of reproductive age were categorized based on the presence or absence of SLD based on the fatty liver index, with SLD further subdivided into metabolic dysfunction-associated, metabolic alcohol-related, and alcohol-related liver disease. Cause-specific hazard ratios for infertility and gynecological disorders were estimated using the Cox's proportional hazard model, adjusting for potential confounders. Among 342,816 participants, the hazard ratios (95% confidence interval) for infertility were estimated at 1.10 (1.04–1.17) for metabolic dysfunction-associated steatotic liver disease, 1.31 (1.17–1.47) for metabolic alcohol-related liver disease, and 1.32 (1.07–1.62) for alcohol-related liver disease. The risks of polycystic ovary syndrome, adenomyosis, and premature ovarian insufficiency were also significantly elevated in patients with SLD subtypes. A clear dose–response trend was observed, with higher alcohol consumption and elevated fatty liver index scores linearly associated with increased disease risk, demonstrating the independent effects of hepatic steatosis and metabolic dysregulation on reproductive-related gynecological disorders. This study illustrates a significant association between SLD and fertility-related gynecological disorders, underscoring the importance of early identification and management in women with SLD who are planning pregnancy. Health sciences/Diseases Health sciences/Endocrinology Health sciences/Gastroenterology Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors steatotic liver disease infertility polycystic ovarian syndrome adenomyosis premature ovarian insufficiency alcohol Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Infertility is defined as the inability to conceive after one year or more of regular, unprotected sexual intercourse. 1 According to the World Health Organization (WHO), infertility affected approximately 80 million individuals worldwide as of 2020, impacting 10–15% of couples globally. 2 Its prevalence has increased substantially from 1990 to 2023, leading to profound consequences for individual well-being as well as considerable economic burdens on healthcare systems. 3 , 4 Previous studies have implicated a range of gynecologic disorders has been implicated in women infertility, including polycystic ovary syndrome (PCOS), endometriosis, adenomyosis, premature ovarian insufficiency (POI), and endometrial hyperplasia. 5 , 6 While primarily regarded as gynecologic diseases, many of these conditions are now recognized to involve systemic metabolic dysregulation, thereby linking reproductive impairment to broader endocrine and inflammatory processes. 7 – 9 These conditions negatively affect fertility by disrupting fertilization, implantation, and pregnancy maintenance, primarily due to significant disturbances in endocrine regulation and the local reproductive environment. Metabolic dysfunction-associated steatotic liver disease (MASLD), previously classified under the umbrella of nonalcoholic fatty liver disease (NAFLD), represents the hepatic manifestation of metabolic syndrome and is one of the most prevalent chronic liver diseases worldwide. MASLD is defined by hepatic steatosis in the presence of at least one cardiometabolic risk factor, such as obesity, hypertension, hyperglycemia, or dyslipidemia. Under the updated classification of steatotic liver disease (SLD), additional subtypes include metabolic-associated alcohol-related liver disease (MetALD), defined as MASLD with moderate alcohol intake,and alcohol-associated liver disease (ALD), both of which feature hepatic steatosis but differ primarily in their degree of alcohol consumption. Recent studies have highlighted a significant association between SLD and various gynecological disorders contributing to women infertility. Notably, PCOS represents a metabolic-reproductive disorder characterized by hyperandrogenism, insulin resistance, and chronic inflammation, all of which also play a key role in the development of SLD. 10 Even gynecological disorders traditionally not classified as metabolic, such as endometriosis and adenomyosis, are now increasingly recognized as involving systemic inflammation and estrogen dysregulation. 7 , 8 These overlapping mechanisms suggest a potential biological link between hepatic and gynecological dysfunction in the context of infertility. However, the precise impact of SLD on gynecological disorders and women infertility, as well as the underlying pathophysiological mechanisms, remains incompletely understood and requires further investigation. In this study, we aimed to investigate the association between SLD subtypes and women infertility. The primary outcome was the incidence of women infertility, with secondary outcomes including the incidence of various gynecological disorders including PCOS, endometriosis, adenomyosis, POI, and endometrial hyperplasia. Materials and Methods Study population This retrospective study included women of reproductive age (18 ~ 40 years) who participated in the National Health screening program between 2013 and 2014, identified through the Korean National Health Insurance Service (NHIS) database. The NHIS is a mandatory health insurance system that covers approximately 97% of the Korean population and includes information on diagnostic codes, laboratory tests, prescriptions, self-reported questionnaires, anthropometric measurements, and blood tests. 11 The NHIS has been widely used in several well-established epidemiological studies, and the validity of this database has been described in detail in previous research. 12 , 13 Among 399,115 participants, we excluded those who died before the start of follow-up (n = 53), those with a history of gynecological disorders prior to follow-up (n = 36,255), and those with missing covariate data (n = 7). In addition, individuals with underlying conditions such as hepatitis virus infection (n = 410), liver cirrhosis (n = 9), autoimmune hepatitis (n = 7), and toxic hepatitis (n = 170) were excluded. Furthermore, we excluded participants with endocrine disorders that may independently affect reproductive function, including hyperprolactinemia (n = 784), thyrotoxicosis (n = 7,275), and hypothyroidism (n = 11,329) (Fig. 1 ). This study was approved by the Institutional Review Board (IRB) of Korea University Guro Hospital (IRB No.: 2024GR0375). This study was conducted in accordance with the Declaration of Helsinki, and the requirement for informed consent was waived by the IRB due to the retrospective nature of the study using anonymized data. Definition of outcomes The main outcome of this study was the incidence of gynecological disorders, which was identified using the International Classification of Diseases, 10th Revision (ICD-10) codes recorded in the Korean NHIS database. The primary outcome of interest was infertility, defined by ICD-10 codes N97.0 ~ N97.3 and N97.8 ~ N97.9. 14 Secondary outcomes included PCOS (E28.2), endometriosis (N80.1 ~ N80.9), adenomyosis (N80.0), POI (E28.3), and endometrial hyperplasia (N85.0 ~ N85.1). 15–17 Definition of steatotic liver disease The fatty liver index (FLI) is a non-invasive marker used to predict hepatic fat accumulation. It has demonstrated relatively high diagnostic accuracy for detecting fatty liver, with an area under the curve of 0.85. 18 In this study, SLD was defined as a FLI of 30 or higher, based on a previous study conducted in Korean adults that identified this threshold as the optimal cutoff with both sensitivity and specificity of 71%. 19 The formula for calculating the FLI is as follows. $$\:\text{F}\text{L}\text{I}=\frac{1}{\left(1+\text{exp}\left(-\text{x}\right)\right)}\times\:100,\:$$ $$\:\text{x}=0.953\times\:{\text{log}}_{\text{e}}\left(\text{s}\text{e}\text{r}\text{u}\text{m}\:\text{t}\text{r}\text{i}\text{g}\text{l}\text{y}\text{c}\text{e}\text{r}\text{i}\text{d}\text{e}\text{s}\right)+0.139\times\:\left(\text{B}\text{M}\text{I}\right)+0.718\times\:{\text{log}}_{\text{e}}\left(\gamma\:-\text{G}\text{T}\right)+0.053\times\:\left(\text{w}\text{a}\text{i}\text{s}\text{t}\:\text{c}\text{i}\text{r}\text{c}\text{u}\text{m}\text{f}\text{e}\text{r}\text{e}\text{n}\text{c}\text{e}\right)-15.745.$$ MASLD was defined as the presence of hepatic steatosis in individuals with moderate or lower alcohol consumption, along with at least one of cardiometabolic risk factors (CMRFs). These CMRFs included: i) body mass index (BMI) ≥ 23kg/m 2 or waist circumference ≥ 90 cm. 20 ii) fasting blood glucose ≥ 100mg/dL or diagnosis of type 2 diabetes, or use of antidiabetic medications. iii) Blood pressure ≥ 130/85mmHg or use of antihypertensive medications. iv) Triglyceride ≥ 150mg/dL or use of lipid-lowering agents. v) High-density lipoprotein cholesterol (HDL-c) ≤ 40mg/dL. 21 MetALD refers to MASLD accompanied by an increased alcohol consumption to a moderate level, while ALD was defined as further increased alcohol intake beyond MetALD. Specifically, moderate or severe alcohol consumption was classified as ALD in individuals without cardiometabolic risk factors (CMRFs), whereas severe-level alcohol consumption was required for ALD classification in those with CMRFs. 22 Cryptogenic SLD was defined as SLD in individuals without CMRFs and with alcohol consumption at or below the light drinking level, who did not meet the criteria for other SLD subtypes, indicating the absence of identifiable metabolic factors or known etiologies. Alcohol consumption was assessed based on self-reported questionnaires. Individuals who reported no alcohol intake were classified as having none. Light drinking was defined as ≤ 30g/day for men and ≤ 20g/day for women. Moderate drinking was defined as > 30g/day to ≤ 60g/day for men and > 20g/day to ≤ 50g/day for women. Finally, severe drinking was defined as > 60g/day for men and > 50g/day for women. 22 Statistical analysis The incidence rates of gynecological disorders were calculated per 1,000 person-years by dividing the number of events by the total person-years of follow-up according to subcategories of SLD. The proportional hazard assumption was tested by including time-dependent covariates. To evaluate the association between SLD subcategories and the risk of gynecological disorders, a multivariable Cox's proportional hazard regression model was constructed. The model was adjusted for age, income level, Charlson Comorbidity Index (CCI), BMI, cigarette smoking, moderate-to-vigorous physical activity (MVPA), and gravidity. Multicollinearity among covariates included in the model was assessed using variance inflation factor. Hazard ratios (HR) and 95% confidence intervals (95% CIs) were estimated, and a restricted cubic splines model with 4 knots was applied to assess potential non-linear associations. To account for the potential impact of death as a competing risk for the development of gynecological disorders, a multivariable Fine and Gray’s subdistribution hazard model was applied to estimate subdistribution hazard ratios (SHRs). 23 The covariates used for adjustment in this model were the same as those included in the Cox's proportional hazard regression model. To evaluate whether the observed associations were consistent across various subgroups, stratified analyses were conducted by age (18–34, ≥ 35), household income (lower half, upper half), cigarette smoking (never, past or current), CCI (0, ≥ 1), MVPA (No, Yes) and the number of pregnancies (0, ≥ 1). P for interactions were assessed using likelihood ratio tests. For sensitivity analyses, gynecological disorders that occurred within the 1 to 3 years of follow-up were excluded to minimize potential immortal time bias due to delayed exposure classification. All statistical analyses were conducted using SAS Enterprise Guide (version 7.1, SAS Institute, Cary, NC, USA), and statistical significance was defined as a two-sided P value < 0.05. Results Baseline characteristics Among a total of 342,816 women of reproductive age, participants were classified into the following groups: non-SLD (n = 301,668), MASLD (n = 37,003), ALD (n = 825), and cryptogenic SLD (n = 76). Except for the cryptogenic SLD group, which had a relatively small number of participants, the MASLD group had the highest mean age (33.9 years), as well as the highest levels of BMI, waist circumference, total cholesterol, and fasting serum glucose. In contrast, HDL-c levels were lowest in the MASLD group. The proportion of past or current smokers increased progressively across the subcategories of SLD with higher levels of alcohol consumption (Table 1 ). Table 1 Descriptive characteristics of participants by SLD subtype in the National Health Insurance Service cohort. Characteristic Non-SLD (n = 301,668) MASLD (n = 37,003) MetALD (n = 3,244) ALD (n = 825) Cryptogenic SLD (n = 76) Age, years, mean (SD) 32.7 (4.3) 33.9 (4.2) 33.0 (4.4) 32.0 (4.5) 34.4 (3.9) Household income a , n (%) 1st quartile (lowest) 67,261 (22.3) 5,596 (15.1) 422 (13.0) 99 (12.0) 14 (18.4) 2nd quartile 128,441 (42.6) 13,090 (35.4) 1,093 (33.7) 267 (32.4) 19 (25.0) 3rd quartile 80,940 (26.8) 12,600 (34.1) 1,261 (38.9) 320 (38.8) 28 (36.8) 4th quartile (highest) 25,026 (8.3) 5,717 (15.5) 468 (14.4) 139 (16.9) 15 (19.7) BMI, kg/m 2 , mean (SD) 19.9 (1.6) 29.3 (4.0) 28.4 (4.2) 28.2 (4.5) 21.9 (0.9) WC, cm, mean (SD) 67.8 (5.3) 90.4 (26.3) 87.9 (9.4) 88.2 (9.8) 76.4 (4.4) SBP, mmHg, mean (SD) 107.7 (9.3) 121.1 (13.7) 122.8 (13.4) 123.0 (13.3) 111.7 (9.0) DBP, mmHg, mean (SD) 67.8 (7.2) 76.5 (10.0) 78.2 (10.0) 78.3 (10.2) 70.4 (7.3) FSG, mg/dL, mean (SD) 85.9 (7.3) 99.4 (29.0) 99.0 (24.8) 98.0 (23.8) 89.4 (5.8) TC, mg/dL, mean (SD) 179.2 (27.7) 206.2 (41.5) 201.0 (35.9) 198.8 (33.4) 201.5 (37.8) HDL-c, mg/dL, mean (SD) 68.6 (15.9) 54.0 (14.8) 59.4 (14.7) 62.6 (16.3) 71.3 (14.7) TG, mg/dL, mean (SD) 66.0 (24.2) 171.5 (112.1) 180.4 (142.5) 182.9 (142.8) 110.0 (27.8) ALT, IU/L, mean (SD) 13.8 (10.9) 31.1 (36.6) 31.2 (34.8) 32.7 (37.2) 57.1 (59.0) AST, IU/L, mean (SD) 18.9 (10.9) 25.8 (25.4) 27.9 (24.2) 32.1 (59.9) 42.1 (36.1) γ-GT, IU/L, mean (SD) 15.1 (9.4) 36.4 (39.7) 57.6 (71.3) 68.7 (90.3) 208.7 (165.5) Alcohol consumption, n (%) None 156,244 (51.8) 21,466 (58.0) 0 (0) 0 (0) 36 (47.4) b Mild 130,787 (43.4) 15,537 (42.0) 0 (0) 0 (0) 40 (52.6) c Moderate 12,549 (4.2) 0 (0) 3,244 (100) 17 (2.1) 0 (0) d Severe 2,088 (0.7) 0 (0) 0 (0) 808 (97.9) 0 (0) Cigarette smoking, n (%) Never 284,445 (94.3) 33,147 (89.6) 2,008 (61.9) 422 (51.2) 68 (89.5) Past 8,201 (2.7) 1,639 (4.4) 388 (12.0) 115 (13.9) 3 (4.0) Current 9,022 (3.0) 2,217 (6.0) 848 (26.1) 288 (34.9) 5 (6.6) MVPA, n (%) No 168,724 (55.9) 20,406 (55.2) 1,637 (50.5) 484 (58.7) 44 (57.9) 1–2 times/week 103,045 (34.2) 13,092 (35.4) 1,268 (39.1) 266 (32.2) 25 (32.9) 3–4 times/week 23,858 (7.9) 2,812 (7.6) 282 (8.7) 60 (7.3) 4 (5.3) ≥5 times/week 6,041 (2.0) 693 (1.9) 57 (1.8) 15 (1.8) 3 (4.0) CCI, n (%) 0 185,653 (61.5) 20,425 (55.2) 1,859 (57.3) 476 (57.7) 44 (57.9) 1 94,994 (31.5) 12,526 (33.9) 1,087 (33.5) 265 (32.1) 22 (29.0) ≥2 21,021 (7.0) 4,052 (11.0) 298 (9.2) 84 (10.2) 10 (13.2) Continuous data are presented as mean (standard deviation) and median (interquartile range) if normally distributed and not normally distributed, respectively. Categorical data are expressed as the number (%). a Proxy for socioeconomic status based on the insurance premium of the National Health Insurance Service. b 60g/day for men and > 50g/day for women. Acronyms: SLD, steatotic liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic alcohol-related liver disease; ALD, alcohol-related liver disease; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FSG, fasting serum glucose; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; γ-GT, γ-glutamyl transpeptidase; MVPA, moderate-to-vigorous physical activity; CCI, Charlson comorbidity index. The risk of gynecological disorders During a total of 2,253,306 person-years of follow-up, 41,832 events were observed. The absolute risks of gynecological disorders per 1,000 person-years were 19.5 for non-SLD, 11.2 for MASLD, 15.8 for MetALD, 17.3 for ALD. Compared with the non-SLD group, the adjusted HR (95% CIs) were 1.10 (1.04–1.17) for MASLD, 1.31 (1.17–1.47) for MetALD, and 1.32 (1.07–1.62) for ALD (Fig. 2 ). Cryptogenic SLD was excluded from the analysis due to an insufficient number of events, and detailed information is provided in Supplementary Table 1 . These trends showed a dose-relationship with increasing levels of alcohol consumption and increased linearly with higher FLI values (Fig. 3 ). For specific outcomes such as adenomyosis (aHR, 1.68; 95% CIs ,1.27–2.21; P < 0.001) and endometrial hyperplasia (aHR, 1.62; 95% CIs, 1.03–2.56; P = 0.039), the risk was highest among individuals with ALD compared to the non-SLD group, like the pattern observed for infertility (Fig. 4 ). Among other gynecologic conditions, increased risks of PCOS (aHR, 2.28; 95% CIs, 1.92–2.71; P < 0.001) and POI (aHR, 1.83; 95% CIs, 1.16–2.88; P = 0.010) were observed particularly in participants with MetALD. These associations remained consistent even after accounting for death as a competing risk ( Supplementary Table 2 ), and all covariates used in the regression models demonstrated low levels of multicollinearity (VIF < 5 for all variables) ( Supplementary Table 3 ). Subgroup analysis and sensitivity analysis In the subgroup analysis, significant interactions were observed with household income, cigarette smoking, and the number of pregnancies. Among participants without comorbidities or those who were physically inactive, the risk increased by 42% when the degree of hepatic steatosis and alcohol consumption reached the ALD level ( Supplementary Table 4 ). Based on statistically significant findings, past or current smokers were particularly associated with an increased risk of infertility in the MetALD group. In most subgroups, the risk of infertility increased across SLD subcategories with higher levels of alcohol consumption, showing consistently elevated hazard ratios compared to the non-SLD group. In the sensitivity analysis, the results remained consistent with the main findings even after excluding cases of gynecological disorders that occurred within 1 to 3 years of follow-up ( Supplementary Table 5 ). The risk of gynecological disorders after adjustment for alcohol consumption After additional adjustment for alcohol consumption, the risk of infertility remained increased across all SLD subtypes compared to the non-SLD group. The hazard ratios were 1.10 (95% CI: 1.03–1.17) for MASLD, 1.38 (95% CI: 1.22–1.56) for MetALD, and 1.64 (95% CI: 1.29–2.08) for ALD. ( Supplementary Table 6 ). Discussion SLD affects approximately 30% of the global population and has emerged as a major global public health concern. 24 Its association with metabolic syndrome has been well-documented, and mounting evidence suggests a link with various metabolic-associated gynecological conditions, including infertility, PCOS, POI, and endometrial hyperplasia. 25 – 33 However, the specific impact of SLD on gynecological disorders, particularly in the context of alcohol consumption and the underlying pathophysiological mechanisms, remains to be explored. In this nationwide cohort study, we demonstrated that SLD is significantly associated with reduced fertility in women, with the risk rising proportionally to FLI and alcohol intake. Moreover, SLD was associated with an increased prevalence of gynecological disorders, including PCOS, adenomyosis, POI, and endometrial hyperplasia. These associations persisted after adjusting for alcohol consumption in the MASLD group, suggesting an independent effect of SLD on infertility. Numerous studies have underscored the close relationship between hepatic metabolic dysfunction and gynecologic disorders. Consistent with previous findings, our study demonstrated that patients with all subtypes of SLD are at increased risk for developing PCOS. Vassilatou et al. reported a higher prevalence of PCOS among premenopausal women with NAFLD compared to those without NAFLD. 34 In addition, Xu et al. proposed a bidirectional relationship between MASLD and PCOS, suggesting that each condition may contribute to the onset of the other. 35 These associations are likely based on shared pathophysiological mechanisms, such as insulin resistance, chronic inflammation, and hormonal dysregulation. 36 Adenomyosis, characterized by ectopic infiltration of endometrial tissue into the myometrium, was also found to be more prevalent among patients with all SLD subtypes. Although limited data are available, it has been proposed that systemic inflammation, a hallmark of SLD, may play a critical role in the pathogenesis of adenomyosis by amplifying immune responses and promoting a pro-inflammatory microenvironment. 37 Furthermore, the incidence of POI was elevated in patients with MASLD and MetALD. While estrogen depletion associated with POI and menopause is a recognized risk factor for the development of SLD and metabolic syndrome, the influence of SLD on the pathogenesis of POI remains unclear. 38 However, emerging evidence suggests that cardiometabolic dysfunction may adversely affect the hypothalamic–pituitary–ovarian (HPO) axis, thereby impairing ovarian reserve and disrupting sex hormone synthesis. 39 We propose three potential mechanisms through which SLD may adversely impact women's reproductive health, each of which is closely associated with either sex hormone imbalance or chronic inflammation. First, SLD is frequently accompanied by insulin resistance, which in turn disrupts endocrine function by promoting excess production of estrogen and androgens. These hormonal imbalances have been implicated in menstrual irregularities and anovulatory cycles, both of which contribute to infertility. 40 Moreover, prior studies have established that elevated levels of sex hormones are key etiological factors in the development of PCOS and endometrial hyperplasia. 41 Second, the liver and reproductive system are closely interconnected through a bidirectional relationship. The liver plays a pivotal role in the regulation of reproductive function by modulating the metabolism and synthesis of sex hormone-binding globulin (SHBG), a protein essential for the transport and bioavailability of sex hormones. 42 Bourebaba et al. reported that patients with SLD exhibit significantly decreased serum SHBG levels, accompanied by elevated concentrations of total estrogens and androgens, which may directly impair fertility. 43 Lastly, SLD may compromise endometrial receptivity and early placental vascular development through inflammatory and metabolic pathways, resulting in implantation failure and early pregnancy loss. 44 However, the precise mechanisms remain to be elucidated, and further investigations are required to clarify the pathophysiological links between SLD and reproductive-related gynecological disorders in women. We demonstrated a positive correlation between the quantity of alcohol consumption and the incidence of infertility, which aligns with findings from earlier research. Several studies have reported that individuals with higher alcohol intake exhibit significantly increased rates of infertility compared to non-drinkers. 45 , 46 Although the precise underlying mechanisms remain incompletely understood, it has been proposed that alcohol consumption may stimulate estrogen secretion, resulting in suppression of follicle-stimulating hormone (FSH) and consequently disrupting follicular development and ovulation. 47 In addition, evidence indicates a direct correlation between alcohol and impaired ovum maturation, implantation, and embryonic development. 48 lack of physical activity was associated with an increased risk of infertility, which may reflect the negative impact of metabolic dysregulation and insulin resistance on reproductive function. This study has several strengths that contribute to its overall robustness, including a large sample size, the use of a nationally representative and reliable data source (NHIS), and consistent disease definitions based on the ICD. These factors collectively support the reliability and generalizability of the findings. The major clinical implication of this study is the recommendation of preconception counseling for women with SLD, aiming to control modifiable factors contributing to infertility and improve the probability of conception. There are a few limitations to be considered. First, it included only Korean women, which may limit the generalizability of the findings to other ethnicities and populations. Second, hepatic steatosis was not assessed by histology or radiologic imaging but was instead estimated using the FLI. However, numerous previous studies have validated the use of FLI as a surrogate marker for hepatic steatosis in large-scale epidemiologic research, given its adequate diagnostic accuracy. 49 – 52 Third, due to the nature of the NHIS health screening data, information regarding prior surgical history was unavailable. Considering that endometriosis frequently requires surgical diagnosis or management, this limitation may have introduced a prevalence bias. Nevertheless, other gynecologic disorders included in the analysis typically do not require surgical intervention for diagnosis, suggesting that the impact of this limitation may be less significant for those conditions. Conclusions In conclusion, patients with SLD exhibit a higher incidence of gynecological disorders, including PCOS, adenomyosis, POI, and endometrial hyperplasia, as well as infertility. The risk of infertility remained evident after adjusting for alcohol consumption, emphasizing the independent effects of SLD on reproductive health in women. These findings establish a foundation for future research into the underlying mechanisms by which SLD contributes not only to infertility but also to a spectrum of gynecological conditions. Furthermore, the results have important clinical ramifications, underscoring the necessity for early screening and behavioral modification in reproductive-aged women with SLD who are contemplating conception. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board of Korea University Guro Hospital (IRB No.: 2024GR0375). The requirement for informed consent was waived due to the use of de-identified data. Consent for publication Not applicable, as no individual or identifiable personal data are included in this study. Additional Information Completing Interests All authors declare no competing interests. Funding This work was supported by the National Research Foundation of Korea (NRF)(RS-2025-00523629). Author Contribution Jungmin Lee and Eun Seok Kang conceived and designed the study, performed the data analysis, interpreted the data, and drafted the manuscript. Seogsong Jeong and Hwamin Lee contributed to the study design and interpretation of the data and critically revised the manuscript for important intellectual content. Seogsong Jeong and Hwamin Lee also supervised the study, administered the project, and acquired funding. All authors reviewed and approved the final version of the manuscript. Acknowledgement This study is supported by the National Research Foundation of Korea (NRF)(RS-2025-00523629). Data Availability The data that support the findings of this study are available from the Korean National Health Insurance Service (NHIS), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the Big Data Steering Department of the National Health Insurance Service in Seoul, Republic of Korea (NHIS-2025-01-1-032) upon reasonable request and with permission. References Vander Borght, M. & Wyns, C. Fertility and infertility: Definition and epidemiology. Clin. Biochem. 62 , 2–10. 10.1016/j.clinbiochem.2018.03.012 (2018). Hazlina, N. H. N., Norhayati, M. N., Bahari, I. S. & Arif, N. A. N. M. Worldwide prevalence, risk factors and psychological impact of infertility among women: a systematic review and meta-analysis. BMJ Open. 12 (3), e057132. https://doi.org/10.1136/bmjopen-2021-057132 (2022). Feng, J., Wu, Q., Liang, Y., Liang, Y. & Bin, Q. 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H., Rajaram, R. & Lim, L. L. Jeyakantha Ratnasingam, Shireene Ratna Vethakkan. Metabolic Dysfunction-Associated Steatotic Liver Disease: A State-of-the-Art Review. J. Obes. metabolic syndrome . 32 (3), 197–213. https://doi.org/10.7570/jomes23052 (2023). Lee, Y. W. & Yarrington, C. D. Obstetric Outcomes in Women with Nonalcoholic Fatty Liver Disease. Metab. Syndr. Relat. Disord. 15 (8), 387–392. https://doi.org/10.1089/met.2017.0058 (2017). Zhao, J., Liang, A. & Li, Z. Association between nonalcoholic fatty liver disease and infertility in reproductive-aged females. Published online Oct. 1 https://doi.org/10.21203/rs.3.rs-4963920/v1 (2024). Konstantinos Arvanitakis, Chatzikalil, E. et al. Metabolic Dysfunction-Associated Steatotic Liver Disease and Polycystic Ovary Syndrome: A Complex Interplay. J. Clin. Med. 13 (14), 4243–4243. https://doi.org/10.3390/jcm13144243 (2024). Papadimitriou, K. et al. Hypogonadism and nonalcoholic fatty liver disease. Endocrine 86 (1), 28–47. https://doi.org/10.1007/s12020-024-03878-1 (2024). Park, J. H., Hong, J. Y., Han, K., Kang, W. & Shen, J. J. Increased Risk of Early-Onset Endometrial Cancer in Women Aged 20–39 Years with Non-Alcoholic Fatty Liver Disease: A Nationwide Cohort Study. Cancers 17 (8), 1322–1322. https://doi.org/10.3390/cancers17081322 (2025). Wei, J. et al. Relationship between the Metabolic Associated Fatty Liver Disease and Endometrial Thickness in Postmenopausal Women: A Cross-sectional Study in China. Int. J. Med. Sci. 18 (14), 3082–3089. https://doi.org/10.7150/ijms.60780 (2021). Vassilatou, E. et al. Increased prevalence of polycystic ovary syndrome in premenopausal women with nonalcoholic fatty liver disease. Eur. J. Endocrinol. 173 (6), 739–747. https://doi.org/10.1530/eje-15-0567 (2015). Xu, Q., Zhang, J., Lu, Y. & Wu, L. Association of metabolic-dysfunction associated steatotic liver disease with polycystic ovary syndrome. iScience 27 (2), 108783–108783. https://doi.org/10.1016/j.isci.2024.108783 (2024). Asfari, M. M. et al. Association of non-alcoholic fatty liver disease and polycystic ovarian syndrome. BMJ Open. Gastroenterol. 7 (1), e000352. https://doi.org/10.1136/bmjgast-2019-000352 (2020). Bourdon, M. et al. Immunological changes associated with adenomyosis: a systematic review. Hum. Reprod. Update . 27 (1), 108–129. https://doi.org/10.1093/humupd/dmaa038 (2020). Robeva, R. et al. The interplay between metabolic dysregulations and non-alcoholic fatty liver disease in women after menopause. Maturitas 151 , 22–30. https://doi.org/10.1016/j.maturitas.2021.06.012 (2021). Goldsammler, M., Merhi, Z. & Buyuk, E. Role of hormonal and inflammatory alterations in obesity-related reproductive dysfunction at the level of the hypothalamic-pituitary-ovarian axis. Reproductive Biology Endocrinol. 16 (1). https://doi.org/10.1186/s12958-018-0366-6 (2018). Xu, Y. & Qiao, J. Association of Insulin Resistance and Elevated Androgen Levels with Polycystic Ovarian Syndrome (PCOS): A Review of Literature. Abdulhay E, ed. Journal of Healthcare Engineering . ;2022(1):1–13. (2022). https://doi.org/10.1155/2022/9240569 Ring, K. L., Mills, A. M. & Modesitt, S. C. Endometrial Hyperplasia. Obstet. Gynecol. 140 (6). 10.1097/AOG.0000000000004989 (2022). Grossmann, M., Wierman, M. E., Angus, P. & Handelsman, D. J. Reproductive Endocrinology of Nonalcoholic Fatty Liver Disease. Endocr. Rev. 40 (2), 417–446. https://doi.org/10.1210/er.2018-00158 (2018). Nabila Bourebaba, Ngo, T., Agnieszka Śmieszek, Bourebaba, L. & Krzysztof, M. Sex hormone binding globulin as a potential drug candidate for liver-related metabolic disorders treatment. Biomed. Pharmacother. 153 , 113261–113261. https://doi.org/10.1016/j.biopha.2022.113261 (2022). Karachaliou, G. S. & Suzuki, A. Metabolic dysfunction–associated steatotic liver disease: Emerging risk factors for adverse pregnancy outcomes. Clin. Liver Disease . 23 (1). https://doi.org/10.1097/cld.0000000000000121 (2024). Eggert, J., Theobald, H. & Engfeldt, P. Effects of alcohol consumption on female fertility during an 18-year period. Fertil. Steril. 81 (2), 379–383. https://doi.org/10.1016/j.fertnstert.2003.06.018 (2004). Moridi, A. et al. Etiology and Risk Factors Associated With Infertility. Int. J. Women’s Health Reprod. Sci. 7 (3), 346–353. https://doi.org/10.15296/ijwhr.2019.57 (2019). Gavaler, J. S., Thiel, D. H. V., Lester, R. & Ethanol A Gonadal Toxin in the Mature Rat of Both Sexes. Alcoholism: Clin. Experimental Res. 4 (3), 271–276. https://doi.org/10.1111/j.1530-0277.1980.tb04813.x (1980). McKenzie, P. P., McClaran, J. D., Caudle, M. R., Fukuda, A. & Wimalasena, J. Alcohol Inhibits Epidermal Growth Factor-Stimulated Progesterone Secretion from Human Granulosa Cells. Alcohol: Clin. Experimental Res. 19 (6), 1382–1388. https://doi.org/10.1111/j.1530-0277.1995.tb00996.x (1995). Xu, Z. et al. Blood biomarkers for the diagnosis of hepatic steatosis in metabolic dysfunction-associated fatty liver disease. J. Hepatol. 73 (5), 1264–1265. https://doi.org/10.1016/j.jhep.2020.06.003 (2020). Han, E. et al. Mortality in metabolic dysfunction-associated steatotic liver disease: A nationwide population-based Cohort Study. Metabolism 152 , 155789. 10.1016/j.metabol.2024.155789 (2024). Khang, A. R., Lee, H. W., Yi, D., Kang, Y. H. & Son, S. M. The fatty liver index, a simple and useful predictor of metabolic syndrome: analysis of the Korea National Health and Nutrition Examination Survey 2010–2011. Diabetes Metabolic Syndrome Obesity: Targets Therapy . 12 , 181–190. https://doi.org/10.2147/DMSO.S189544 (2019). Liu, Y., Liu, S., Huang, J., Zhu, Y. & Lin, S. Validation of five hepatic steatosis algorithms in metabolic-associated fatty liver disease: A population based study. J. Gastroenterol. Hepatol. 37 (5), 938–945. https://doi.org/10.1111/jgh.15799 (2022). Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 21 May, 2026 Reviews received at journal 20 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers invited by journal 12 May, 2026 Editor assigned by journal 12 May, 2026 Editor invited by journal 08 May, 2026 Submission checks completed at journal 30 Apr, 2026 First submitted to journal 30 Apr, 2026 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-9434896","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":643110401,"identity":"27701678-36f5-44d2-9d77-78f9334e02a3","order_by":0,"name":"Jungmin Lee","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Jungmin","middleName":"","lastName":"Lee","suffix":""},{"id":643110402,"identity":"d0adb181-e1cb-4cdd-8863-31ba7dd03b0c","order_by":1,"name":"Eun Seok Kang","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Eun","middleName":"Seok","lastName":"Kang","suffix":""},{"id":643110403,"identity":"567c476c-9c13-450e-96d5-4cc02f9f0cd7","order_by":2,"name":"Seogsong Jeong","email":"data:image/png;base64,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","orcid":"","institution":"Korea University","correspondingAuthor":true,"prefix":"","firstName":"Seogsong","middleName":"","lastName":"Jeong","suffix":""},{"id":643110404,"identity":"f23637fe-39c2-4c97-9dda-dcbd58553e2d","order_by":3,"name":"Hwamin Lee","email":"","orcid":"","institution":"Korea University","correspondingAuthor":false,"prefix":"","firstName":"Hwamin","middleName":"","lastName":"Lee","suffix":""}],"badges":[],"createdAt":"2026-04-16 07:53:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9434896/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9434896/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109718221,"identity":"659542ca-9ee2-4135-acfd-61e65e0fbbdd","added_by":"auto","created_at":"2026-05-21 14:10:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":503865,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram for inclusion of study participants.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStudy participants were derived from the National Health Insurance Service Health Screening Cohort after excluding participants with death, history of infertility, missing information for other covariates including alcohol consumption, and underlying other liver disease. All participants were followed from January 1, 2015, until the occurrence of infertility, death, or January 31, 2022, whichever came first.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9434896/v1/6465380a5602f59d39b37962.jpg"},{"id":109718222,"identity":"7845a21c-1db4-40d3-911e-af8abbf84bd6","added_by":"auto","created_at":"2026-05-21 14:10:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":295589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of steatotic liver disease subcategories with the risk of incident infertility.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCause-specific hazard ratios (95% CIs) were calculated using the Cox’s proportional hazard model after adjustment for age, household income, Charlson comorbidity index, body mass index, cigarette smoking, moderate-to-vigorous physical activity, and gravidity.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e#\u003c/sup\u003eThe incidence was calculated as a percentage (number of events divided by the number of participants) over the approximately 7-year follow-up period.\u003c/p\u003e\n\u003cp\u003eAcronyms: PY, person-year; AR, absolute risk; aHR, adjusted hazard ratio; CI, confidence interval; SLD, steatotic liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic alcohol-related liver disease; ALD, alcohol-related liver disease.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9434896/v1/6cd0aac6946fe5a405dc24d5.jpg"},{"id":109718203,"identity":"d3e42479-889f-4366-b07f-dd920f1453be","added_by":"auto","created_at":"2026-05-21 14:10:52","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":162445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic splines for the association of fatty liver index with the risk of incident infertility.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAdjusted hazard ratios (95% CIs) were calculated using the Cox’s proportional hazard model after adjustments for age, household income, Charlson comorbidity index, body mass index, cigarette smoking, moderate-to-vigorous physical activity, and gravidity.\u003c/p\u003e\n\u003cp\u003eAcronyms: CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9434896/v1/b98510b2f4ba94c40c0ba6f1.jpg"},{"id":109718204,"identity":"bad02baa-991a-40b2-82df-75c8450f9217","added_by":"auto","created_at":"2026-05-21 14:10:52","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":569795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of steatotic liver disease subtypes with the risk of incident gynecologic disorders.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCause-specific hazard ratios (95% CIs) were calculated using the Cox’s proportional hazard model after adjustment for age, household income, Charlson comorbidity index, body mass index, cigarette smoking, moderate-to-vigorous physical activity, and gravidity.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e#\u003c/sup\u003eThe incidence was calculated as a percentage (number of events divided by the number of participants) over the approximately 7-year follow-up period.\u003c/p\u003e\n\u003cp\u003eAcronyms: PY, person-year; aHR, adjusted hazard ratio; CI, confidence interval; SLD, steatotic liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic alcohol-related liver disease; ALD, alcohol-related liver disease.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9434896/v1/a31fb13c578f8ce668eba8de.jpg"},{"id":109800008,"identity":"d2f9a73a-1049-45ad-8a48-ced25d51be2a","added_by":"auto","created_at":"2026-05-22 15:35:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1895498,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9434896/v1/6e8de305-5dcb-43b9-9c02-f1357812ff3e.pdf"},{"id":109718202,"identity":"0d2d59f7-afd9-488b-b8e6-49bd8431546d","added_by":"auto","created_at":"2026-05-21 14:10:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23355,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-9434896/v1/af4fedf7c44568eaec7c5a16.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Steatotic liver disease and the risk of fertility-related gynecological disorders in reproductive-aged women: a nationwide cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfertility is defined as the inability to conceive after one year or more of regular, unprotected sexual intercourse.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e According to the World Health Organization (WHO), infertility affected approximately 80\u0026nbsp;million individuals worldwide as of 2020, impacting 10\u0026ndash;15% of couples globally.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Its prevalence has increased substantially from 1990 to 2023, leading to profound consequences for individual well-being as well as considerable economic burdens on healthcare systems.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious studies have implicated a range of gynecologic disorders has been implicated in women infertility, including polycystic ovary syndrome (PCOS), endometriosis, adenomyosis, premature ovarian insufficiency (POI), and endometrial hyperplasia.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e While primarily regarded as gynecologic diseases, many of these conditions are now recognized to involve systemic metabolic dysregulation, thereby linking reproductive impairment to broader endocrine and inflammatory processes.\u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e These conditions negatively affect fertility by disrupting fertilization, implantation, and pregnancy maintenance, primarily due to significant disturbances in endocrine regulation and the local reproductive environment.\u003c/p\u003e \u003cp\u003eMetabolic dysfunction-associated steatotic liver disease (MASLD), previously classified under the umbrella of nonalcoholic fatty liver disease (NAFLD), represents the hepatic manifestation of metabolic syndrome and is one of the most prevalent chronic liver diseases worldwide. MASLD is defined by hepatic steatosis in the presence of at least one cardiometabolic risk factor, such as obesity, hypertension, hyperglycemia, or dyslipidemia. Under the updated classification of steatotic liver disease (SLD), additional subtypes include metabolic-associated alcohol-related liver disease (MetALD), defined as MASLD with moderate alcohol intake,and alcohol-associated liver disease (ALD), both of which feature hepatic steatosis but differ primarily in their degree of alcohol consumption.\u003c/p\u003e \u003cp\u003eRecent studies have highlighted a significant association between SLD and various gynecological disorders contributing to women infertility. Notably, PCOS represents a metabolic-reproductive disorder characterized by hyperandrogenism, insulin resistance, and chronic inflammation, all of which also play a key role in the development of SLD.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Even gynecological disorders traditionally not classified as metabolic, such as endometriosis and adenomyosis, are now increasingly recognized as involving systemic inflammation and estrogen dysregulation.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e These overlapping mechanisms suggest a potential biological link between hepatic and gynecological dysfunction in the context of infertility. However, the precise impact of SLD on gynecological disorders and women infertility, as well as the underlying pathophysiological mechanisms, remains incompletely understood and requires further investigation.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to investigate the association between SLD subtypes and women infertility. The primary outcome was the incidence of women infertility, with secondary outcomes including the incidence of various gynecological disorders including PCOS, endometriosis, adenomyosis, POI, and endometrial hyperplasia.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eThis retrospective study included women of reproductive age (18\u0026thinsp;~\u0026thinsp;40 years) who participated in the National Health screening program between 2013 and 2014, identified through the Korean National Health Insurance Service (NHIS) database. The NHIS is a mandatory health insurance system that covers approximately 97% of the Korean population and includes information on diagnostic codes, laboratory tests, prescriptions, self-reported questionnaires, anthropometric measurements, and blood tests.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e The NHIS has been widely used in several well-established epidemiological studies, and the validity of this database has been described in detail in previous research.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAmong 399,115 participants, we excluded those who died before the start of follow-up (n\u0026thinsp;=\u0026thinsp;53), those with a history of gynecological disorders prior to follow-up (n\u0026thinsp;=\u0026thinsp;36,255), and those with missing covariate data (n\u0026thinsp;=\u0026thinsp;7). In addition, individuals with underlying conditions such as hepatitis virus infection (n\u0026thinsp;=\u0026thinsp;410), liver cirrhosis (n\u0026thinsp;=\u0026thinsp;9), autoimmune hepatitis (n\u0026thinsp;=\u0026thinsp;7), and toxic hepatitis (n\u0026thinsp;=\u0026thinsp;170) were excluded. Furthermore, we excluded participants with endocrine disorders that may independently affect reproductive function, including hyperprolactinemia (n\u0026thinsp;=\u0026thinsp;784), thyrotoxicosis (n\u0026thinsp;=\u0026thinsp;7,275), and hypothyroidism (n\u0026thinsp;=\u0026thinsp;11,329) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was approved by the Institutional Review Board (IRB) of Korea University Guro Hospital (IRB No.: 2024GR0375). This study was conducted in accordance with the Declaration of Helsinki, and the requirement for informed consent was waived by the IRB due to the retrospective nature of the study using anonymized data.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDefinition of outcomes\u003c/h3\u003e\n\u003cp\u003eThe main outcome of this study was the incidence of gynecological disorders, which was identified using the International Classification of Diseases, 10th Revision (ICD-10) codes recorded in the Korean NHIS database. The primary outcome of interest was infertility, defined by ICD-10 codes N97.0\u0026thinsp;~\u0026thinsp;N97.3 and N97.8\u0026thinsp;~\u0026thinsp;N97.9.\u003csup\u003e14\u003c/sup\u003e Secondary outcomes included PCOS (E28.2), endometriosis (N80.1\u0026thinsp;~\u0026thinsp;N80.9), adenomyosis (N80.0), POI (E28.3), and endometrial hyperplasia (N85.0\u0026thinsp;~\u0026thinsp;N85.1).\u003csup\u003e15\u0026ndash;17\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eDefinition of steatotic liver disease\u003c/h3\u003e\n\u003cp\u003eThe fatty liver index (FLI) is a non-invasive marker used to predict hepatic fat accumulation. It has demonstrated relatively high diagnostic accuracy for detecting fatty liver, with an area under the curve of 0.85.\u003csup\u003e18\u003c/sup\u003e In this study, SLD was defined as a FLI of 30 or higher, based on a previous study conducted in Korean adults that identified this threshold as the optimal cutoff with both sensitivity and specificity of 71%.\u003csup\u003e19\u003c/sup\u003e The formula for calculating the FLI is as follows.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{F}\\text{L}\\text{I}=\\frac{1}{\\left(1+\\text{exp}\\left(-\\text{x}\\right)\\right)}\\times\\:100,\\:$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{x}=0.953\\times\\:{\\text{log}}_{\\text{e}}\\left(\\text{s}\\text{e}\\text{r}\\text{u}\\text{m}\\:\\text{t}\\text{r}\\text{i}\\text{g}\\text{l}\\text{y}\\text{c}\\text{e}\\text{r}\\text{i}\\text{d}\\text{e}\\text{s}\\right)+0.139\\times\\:\\left(\\text{B}\\text{M}\\text{I}\\right)+0.718\\times\\:{\\text{log}}_{\\text{e}}\\left(\\gamma\\:-\\text{G}\\text{T}\\right)+0.053\\times\\:\\left(\\text{w}\\text{a}\\text{i}\\text{s}\\text{t}\\:\\text{c}\\text{i}\\text{r}\\text{c}\\text{u}\\text{m}\\text{f}\\text{e}\\text{r}\\text{e}\\text{n}\\text{c}\\text{e}\\right)-15.745.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eMASLD was defined as the presence of hepatic steatosis in individuals with moderate or lower alcohol consumption, along with at least one of cardiometabolic risk factors (CMRFs). These CMRFs included: i) body mass index (BMI) \u0026ge; 23kg/m\u003csup\u003e2\u003c/sup\u003e or waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm.\u003csup\u003e20\u003c/sup\u003e ii) fasting blood glucose\u0026thinsp;\u0026ge;\u0026thinsp;100mg/dL or diagnosis of type 2 diabetes, or use of antidiabetic medications. iii) Blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130/85mmHg or use of antihypertensive medications. iv) Triglyceride\u0026thinsp;\u0026ge;\u0026thinsp;150mg/dL or use of lipid-lowering agents. v) High-density lipoprotein cholesterol (HDL-c) \u0026le; 40mg/dL.\u003csup\u003e21\u003c/sup\u003e MetALD refers to MASLD accompanied by an increased alcohol consumption to a moderate level, while ALD was defined as further increased alcohol intake beyond MetALD. Specifically, moderate or severe alcohol consumption was classified as ALD in individuals without cardiometabolic risk factors (CMRFs), whereas severe-level alcohol consumption was required for ALD classification in those with CMRFs.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Cryptogenic SLD was defined as SLD in individuals without CMRFs and with alcohol consumption at or below the light drinking level, who did not meet the criteria for other SLD subtypes, indicating the absence of identifiable metabolic factors or known etiologies.\u003c/p\u003e \u003cp\u003eAlcohol consumption was assessed based on self-reported questionnaires. Individuals who reported no alcohol intake were classified as having none. Light drinking was defined as \u0026le;\u0026thinsp;30g/day for men and \u0026le;\u0026thinsp;20g/day for women. Moderate drinking was defined as \u0026gt;\u0026thinsp;30g/day to \u0026le;\u0026thinsp;60g/day for men and \u0026gt;\u0026thinsp;20g/day to \u0026le;\u0026thinsp;50g/day for women. Finally, severe drinking was defined as \u0026gt;\u0026thinsp;60g/day for men and \u0026gt;\u0026thinsp;50g/day for women.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe incidence rates of gynecological disorders were calculated per 1,000 person-years by dividing the number of events by the total person-years of follow-up according to subcategories of SLD. The proportional hazard assumption was tested by including time-dependent covariates.\u003c/p\u003e \u003cp\u003eTo evaluate the association between SLD subcategories and the risk of gynecological disorders, a multivariable Cox's proportional hazard regression model was constructed. The model was adjusted for age, income level, Charlson Comorbidity Index (CCI), BMI, cigarette smoking, moderate-to-vigorous physical activity (MVPA), and gravidity. Multicollinearity among covariates included in the model was assessed using variance inflation factor. Hazard ratios (HR) and 95% confidence intervals (95% CIs) were estimated, and a restricted cubic splines model with 4 knots was applied to assess potential non-linear associations. To account for the potential impact of death as a competing risk for the development of gynecological disorders, a multivariable Fine and Gray\u0026rsquo;s subdistribution hazard model was applied to estimate subdistribution hazard ratios (SHRs).\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e The covariates used for adjustment in this model were the same as those included in the Cox's proportional hazard regression model.\u003c/p\u003e \u003cp\u003eTo evaluate whether the observed associations were consistent across various subgroups, stratified analyses were conducted by age (18\u0026ndash;34, \u0026ge;\u0026thinsp;35), household income (lower half, upper half), cigarette smoking (never, past or current), CCI (0, \u0026ge;\u0026thinsp;1), MVPA (No, Yes) and the number of pregnancies (0, \u0026ge;\u0026thinsp;1). \u003cem\u003eP\u003c/em\u003e for interactions were assessed using likelihood ratio tests.\u003c/p\u003e \u003cp\u003eFor sensitivity analyses, gynecological disorders that occurred within the 1 to 3 years of follow-up were excluded to minimize potential immortal time bias due to delayed exposure classification. All statistical analyses were conducted using SAS Enterprise Guide (version 7.1, SAS Institute, Cary, NC, USA), and statistical significance was defined as a two-sided \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics\u003c/h2\u003e \u003cp\u003eAmong a total of 342,816 women of reproductive age, participants were classified into the following groups: non-SLD (n\u0026thinsp;=\u0026thinsp;301,668), MASLD (n\u0026thinsp;=\u0026thinsp;37,003), ALD (n\u0026thinsp;=\u0026thinsp;825), and cryptogenic SLD (n\u0026thinsp;=\u0026thinsp;76). Except for the cryptogenic SLD group, which had a relatively small number of participants, the MASLD group had the highest mean age (33.9 years), as well as the highest levels of BMI, waist circumference, total cholesterol, and fasting serum glucose. In contrast, HDL-c levels were lowest in the MASLD group. The proportion of past or current smokers increased progressively across the subcategories of SLD with higher levels of alcohol consumption (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eDescriptive characteristics of participants by SLD subtype in the National Health Insurance Service cohort.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-SLD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;301,668)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMASLD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;37,003)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMetALD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;3,244)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eALD\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;825)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCryptogenic SLD (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32.7 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.9 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.0 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.0 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e34.4 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold income\u003csup\u003ea\u003c/sup\u003e, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1st quartile (lowest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67,261 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,596 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e422 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14 (18.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2nd quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128,441 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,090 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,093 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e267 (32.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19 (25.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3rd quartile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80,940 (26.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,600 (34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,261 (38.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e320 (38.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28 (36.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4th quartile (highest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25,026 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,717 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e468 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 (19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.9 (1.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.3 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.4 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.2 (4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21.9 (0.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWC, cm, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.8 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.4 (26.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.9 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88.2 (9.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.4 (4.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP, mmHg, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107.7 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e121.1 (13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122.8 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e123.0 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111.7 (9.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP, mmHg, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e67.8 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.5 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.2 (10.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.3 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70.4 (7.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFSG, mg/dL, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.9 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.4 (29.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99.0 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e98.0 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.4 (5.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC, mg/dL, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e179.2 (27.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e206.2 (41.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e201.0 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e198.8 (33.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e201.5 (37.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-c, mg/dL, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.6 (15.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.0 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.4 (14.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e62.6 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e71.3 (14.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG, mg/dL, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.0 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171.5 (112.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180.4 (142.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e182.9 (142.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e110.0 (27.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eALT, IU/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.8 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.1 (36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.2 (34.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.7 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.1 (59.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAST, IU/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.9 (10.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.8 (25.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.9 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.1 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42.1 (36.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ-GT, IU/L, mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.1 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.4 (39.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.6 (71.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.7 (90.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e208.7 (165.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e156,244 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21,466 (58.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e36 (47.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130,787 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15,537 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40 (52.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12,549 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,244 (100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003ed\u003c/sup\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,088 (0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e808 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCigarette smoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e284,445 (94.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33,147 (89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,008 (61.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e422 (51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e68 (89.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8,201 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,639 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e388 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e115 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,022 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,217 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e848 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e288 (34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5 (6.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVPA, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168,724 (55.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,406 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,637 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e484 (58.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 (57.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103,045 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13,092 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,268 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e266 (32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25 (32.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23,858 (7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,812 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60 (7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (5.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;5 times/week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,041 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e693 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3 (4.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\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\u003e185,653 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20,425 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,859 (57.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e476 (57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44 (57.9)\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\u003e94,994 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,526 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,087 (33.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e265 (32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (29.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21,021 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,052 (11.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e298 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (13.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eContinuous data are presented as mean (standard deviation) and median (interquartile range) if normally distributed and not normally distributed, respectively.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eCategorical data are expressed as the number (%).\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eProxy for socioeconomic status based on the insurance premium of the National Health Insurance Service.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e\u0026lt;30g/day for men and 20g/day for women.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ec\u003c/sup\u003e30-60g/day for men and 20-50g/day for women.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ed\u003c/sup\u003e\u0026gt;60g/day for men and \u0026gt;\u0026thinsp;50g/day for women.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAcronyms: SLD, steatotic liver disease; MASLD, metabolic dysfunction-associated steatotic liver disease; MetALD, metabolic alcohol-related liver disease; ALD, alcohol-related liver disease; BMI, body mass index; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; FSG, fasting serum glucose; TC, total cholesterol; HDL-c, high-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; γ-GT, γ-glutamyl transpeptidase; MVPA, moderate-to-vigorous physical activity; CCI, Charlson comorbidity index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe risk of gynecological disorders\u003c/h3\u003e\n\u003cp\u003eDuring a total of 2,253,306 person-years of follow-up, 41,832 events were observed. The absolute risks of gynecological disorders per 1,000 person-years were 19.5 for non-SLD, 11.2 for MASLD, 15.8 for MetALD, 17.3 for ALD. Compared with the non-SLD group, the adjusted HR (95% CIs) were 1.10 (1.04\u0026ndash;1.17) for MASLD, 1.31 (1.17\u0026ndash;1.47) for MetALD, and 1.32 (1.07\u0026ndash;1.62) for ALD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Cryptogenic SLD was excluded from the analysis due to an insufficient number of events, and detailed information is provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese trends showed a dose-relationship with increasing levels of alcohol consumption and increased linearly with higher FLI values (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). For specific outcomes such as adenomyosis (aHR, 1.68; 95% CIs ,1.27\u0026ndash;2.21; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and endometrial hyperplasia (aHR, 1.62; 95% CIs, 1.03\u0026ndash;2.56; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.039), the risk was highest among individuals with ALD compared to the non-SLD group, like the pattern observed for infertility (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Among other gynecologic conditions, increased risks of PCOS (aHR, 2.28; 95% CIs, 1.92\u0026ndash;2.71; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and POI (aHR, 1.83; 95% CIs, 1.16\u0026ndash;2.88; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010) were observed particularly in participants with MetALD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese associations remained consistent even after accounting for death as a competing risk (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e), and all covariates used in the regression models demonstrated low levels of multicollinearity (VIF\u0026thinsp;\u0026lt;\u0026thinsp;5 for all variables) (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eSubgroup analysis and sensitivity analysis\u003c/h3\u003e\n\u003cp\u003eIn the subgroup analysis, significant interactions were observed with household income, cigarette smoking, and the number of pregnancies. Among participants without comorbidities or those who were physically inactive, the risk increased by 42% when the degree of hepatic steatosis and alcohol consumption reached the ALD level (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eBased on statistically significant findings, past or current smokers were particularly associated with an increased risk of infertility in the MetALD group. In most subgroups, the risk of infertility increased across SLD subcategories with higher levels of alcohol consumption, showing consistently elevated hazard ratios compared to the non-SLD group.\u003c/p\u003e \u003cp\u003eIn the sensitivity analysis, the results remained consistent with the main findings even after excluding cases of gynecological disorders that occurred within 1 to 3 years of follow-up (\u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe risk of gynecological disorders after adjustment for alcohol consumption\u003c/h2\u003e \u003cp\u003eAfter additional adjustment for alcohol consumption, the risk of infertility remained increased across all SLD subtypes compared to the non-SLD group. The hazard ratios were 1.10 (95% CI: 1.03\u0026ndash;1.17) for MASLD, 1.38 (95% CI: 1.22\u0026ndash;1.56) for MetALD, and 1.64 (95% CI: 1.29\u0026ndash;2.08) for ALD. (\u003cb\u003eSupplementary Table\u0026nbsp;6\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSLD affects approximately 30% of the global population and has emerged as a major global public health concern.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e Its association with metabolic syndrome has been well-documented, and mounting evidence suggests a link with various metabolic-associated gynecological conditions, including infertility, PCOS, POI, and endometrial hyperplasia.\u003csup\u003e\u003cspan additionalcitationids=\"CR26 CR27 CR28 CR29 CR30 CR31 CR32\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e However, the specific impact of SLD on gynecological disorders, particularly in the context of alcohol consumption and the underlying pathophysiological mechanisms, remains to be explored. In this nationwide cohort study, we demonstrated that SLD is significantly associated with reduced fertility in women, with the risk rising proportionally to FLI and alcohol intake. Moreover, SLD was associated with an increased prevalence of gynecological disorders, including PCOS, adenomyosis, POI, and endometrial hyperplasia. These associations persisted after adjusting for alcohol consumption in the MASLD group, suggesting an independent effect of SLD on infertility.\u003c/p\u003e \u003cp\u003eNumerous studies have underscored the close relationship between hepatic metabolic dysfunction and gynecologic disorders. Consistent with previous findings, our study demonstrated that patients with all subtypes of SLD are at increased risk for developing PCOS. Vassilatou et al. reported a higher prevalence of PCOS among premenopausal women with NAFLD compared to those without NAFLD.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e In addition, Xu et al. proposed a bidirectional relationship between MASLD and PCOS, suggesting that each condition may contribute to the onset of the other.\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e These associations are likely based on shared pathophysiological mechanisms, such as insulin resistance, chronic inflammation, and hormonal dysregulation.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAdenomyosis, characterized by ectopic infiltration of endometrial tissue into the myometrium, was also found to be more prevalent among patients with all SLD subtypes. Although limited data are available, it has been proposed that systemic inflammation, a hallmark of SLD, may play a critical role in the pathogenesis of adenomyosis by amplifying immune responses and promoting a pro-inflammatory microenvironment.\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eFurthermore, the incidence of POI was elevated in patients with MASLD and MetALD. While estrogen depletion associated with POI and menopause is a recognized risk factor for the development of SLD and metabolic syndrome, the influence of SLD on the pathogenesis of POI remains unclear.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e However, emerging evidence suggests that cardiometabolic dysfunction may adversely affect the hypothalamic\u0026ndash;pituitary\u0026ndash;ovarian (HPO) axis, thereby impairing ovarian reserve and disrupting sex hormone synthesis.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWe propose three potential mechanisms through which SLD may adversely impact women's reproductive health, each of which is closely associated with either sex hormone imbalance or chronic inflammation. First, SLD is frequently accompanied by insulin resistance, which in turn disrupts endocrine function by promoting excess production of estrogen and androgens. These hormonal imbalances have been implicated in menstrual irregularities and anovulatory cycles, both of which contribute to infertility.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Moreover, prior studies have established that elevated levels of sex hormones are key etiological factors in the development of PCOS and endometrial hyperplasia.\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eSecond, the liver and reproductive system are closely interconnected through a bidirectional relationship. The liver plays a pivotal role in the regulation of reproductive function by modulating the metabolism and synthesis of sex hormone-binding globulin (SHBG), a protein essential for the transport and bioavailability of sex hormones.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Bourebaba et al. reported that patients with SLD exhibit significantly decreased serum SHBG levels, accompanied by elevated concentrations of total estrogens and androgens, which may directly impair fertility.\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eLastly, SLD may compromise endometrial receptivity and early placental vascular development through inflammatory and metabolic pathways, resulting in implantation failure and early pregnancy loss.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e However, the precise mechanisms remain to be elucidated, and further investigations are required to clarify the pathophysiological links between SLD and reproductive-related gynecological disorders in women.\u003c/p\u003e \u003cp\u003eWe demonstrated a positive correlation between the quantity of alcohol consumption and the incidence of infertility, which aligns with findings from earlier research. Several studies have reported that individuals with higher alcohol intake exhibit significantly increased rates of infertility compared to non-drinkers.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e Although the precise underlying mechanisms remain incompletely understood, it has been proposed that alcohol consumption may stimulate estrogen secretion, resulting in suppression of follicle-stimulating hormone (FSH) and consequently disrupting follicular development and ovulation.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e In addition, evidence indicates a direct correlation between alcohol and impaired ovum maturation, implantation, and embryonic development.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e lack of physical activity was associated with an increased risk of infertility, which may reflect the negative impact of metabolic dysregulation and insulin resistance on reproductive function.\u003c/p\u003e \u003cp\u003eThis study has several strengths that contribute to its overall robustness, including a large sample size, the use of a nationally representative and reliable data source (NHIS), and consistent disease definitions based on the ICD. These factors collectively support the reliability and generalizability of the findings. The major clinical implication of this study is the recommendation of preconception counseling for women with SLD, aiming to control modifiable factors contributing to infertility and improve the probability of conception.\u003c/p\u003e \u003cp\u003eThere are a few limitations to be considered. First, it included only Korean women, which may limit the generalizability of the findings to other ethnicities and populations. Second, hepatic steatosis was not assessed by histology or radiologic imaging but was instead estimated using the FLI. However, numerous previous studies have validated the use of FLI as a surrogate marker for hepatic steatosis in large-scale epidemiologic research, given its adequate diagnostic accuracy.\u003csup\u003e\u003cspan additionalcitationids=\"CR50 CR51\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e Third, due to the nature of the NHIS health screening data, information regarding prior surgical history was unavailable. Considering that endometriosis frequently requires surgical diagnosis or management, this limitation may have introduced a prevalence bias. Nevertheless, other gynecologic disorders included in the analysis typically do not require surgical intervention for diagnosis, suggesting that the impact of this limitation may be less significant for those conditions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, patients with SLD exhibit a higher incidence of gynecological disorders, including PCOS, adenomyosis, POI, and endometrial hyperplasia, as well as infertility. The risk of infertility remained evident after adjusting for alcohol consumption, emphasizing the independent effects of SLD on reproductive health in women. These findings establish a foundation for future research into the underlying mechanisms by which SLD contributes not only to infertility but also to a spectrum of gynecological conditions. Furthermore, the results have important clinical ramifications, underscoring the necessity for early screening and behavioral modification in reproductive-aged women with SLD who are contemplating conception.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003eThis study was approved by the Institutional Review Board of Korea University Guro Hospital (IRB No.: 2024GR0375). The requirement for informed consent was waived due to the use of de-identified data.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable, as no individual or identifiable personal data are included in this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003e \u003cb\u003eAdditional Information\u003c/b\u003e \u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCompleting Interests\u003c/strong\u003e \u003cp\u003eAll authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Research Foundation of Korea (NRF)(RS-2025-00523629).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJungmin Lee and Eun Seok Kang conceived and designed the study, performed the data analysis, interpreted the data, and drafted the manuscript. Seogsong Jeong and Hwamin Lee contributed to the study design and interpretation of the data and critically revised the manuscript for important intellectual content. Seogsong Jeong and Hwamin Lee also supervised the study, administered the project, and acquired funding. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study is supported by the National Research Foundation of Korea (NRF)(RS-2025-00523629).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the Korean National Health Insurance Service (NHIS), but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the Big Data Steering Department of the National Health Insurance Service in Seoul, Republic of Korea (NHIS-2025-01-1-032) upon reasonable request and with permission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVander Borght, M. \u0026amp; Wyns, C. Fertility and infertility: Definition and epidemiology. \u003cem\u003eClin. Biochem.\u003c/em\u003e \u003cb\u003e62\u003c/b\u003e, 2\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.clinbiochem.2018.03.012\u003c/span\u003e\u003cspan address=\"10.1016/j.clinbiochem.2018.03.012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHazlina, N. H. N., Norhayati, M. N., Bahari, I. S. \u0026amp; Arif, N. A. N. M. 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Hepatol.\u003c/em\u003e \u003cb\u003e37\u003c/b\u003e (5), 938\u0026ndash;945. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/jgh.15799\u003c/span\u003e\u003cspan address=\"10.1111/jgh.15799\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"steatotic liver disease, infertility, polycystic ovarian syndrome, adenomyosis, premature ovarian insufficiency, alcohol","lastPublishedDoi":"10.21203/rs.3.rs-9434896/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9434896/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSteatotic liver disease (SLD) is a prevalent metabolic disorder associated with various systemic complications. Emerging evidence suggests a link between SLD and women reproductive health, including infertility and gynecological disorders. We aimed to investigate the association between SLD subtypes and the risk of infertility and related gynecological disorders among reproductive-aged women in Korea.\u003c/p\u003e \u003cp\u003eThis retrospective, population-based cohort study was conducted using data from the Korean National Health Insurance Service. Women of reproductive age were categorized based on the presence or absence of SLD based on the fatty liver index, with SLD further subdivided into metabolic dysfunction-associated, metabolic alcohol-related, and alcohol-related liver disease. Cause-specific hazard ratios for infertility and gynecological disorders were estimated using the Cox's proportional hazard model, adjusting for potential confounders.\u003c/p\u003e \u003cp\u003eAmong 342,816 participants, the hazard ratios (95% confidence interval) for infertility were estimated at 1.10 (1.04\u0026ndash;1.17) for metabolic dysfunction-associated steatotic liver disease, 1.31 (1.17\u0026ndash;1.47) for metabolic alcohol-related liver disease, and 1.32 (1.07\u0026ndash;1.62) for alcohol-related liver disease. The risks of polycystic ovary syndrome, adenomyosis, and premature ovarian insufficiency were also significantly elevated in patients with SLD subtypes. A clear dose\u0026ndash;response trend was observed, with higher alcohol consumption and elevated fatty liver index scores linearly associated with increased disease risk, demonstrating the independent effects of hepatic steatosis and metabolic dysregulation on reproductive-related gynecological disorders.\u003c/p\u003e \u003cp\u003eThis study illustrates a significant association between SLD and fertility-related gynecological disorders, underscoring the importance of early identification and management in women with SLD who are planning pregnancy.\u003c/p\u003e","manuscriptTitle":"Steatotic liver disease and the risk of fertility-related gynecological disorders in reproductive-aged women: a nationwide cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-21 14:10:44","doi":"10.21203/rs.3.rs-9434896/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"204218641649548435801146827123295739096","date":"2026-05-21T14:06:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-20T08:50:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280684315391477555718477457606679525355","date":"2026-05-12T19:54:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-12T19:08:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-12T18:51:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-08T06:37:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T08:00:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-30T07:17:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b52ee2cb-8ebb-4507-94af-b0b3956f2376","owner":[],"postedDate":"May 21st, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"204218641649548435801146827123295739096","date":"2026-05-21T14:06:47+00:00","index":50,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-20T08:50:55+00:00","index":46,"fulltext":""},{"type":"reviewerAgreed","content":"280684315391477555718477457606679525355","date":"2026-05-12T19:54:24+00:00","index":43,"fulltext":""},{"type":"reviewersInvited","content":"7","date":"2026-05-12T19:08:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-12T18:51:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-08T06:37:44+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":68357807,"name":"Health sciences/Diseases"},{"id":68357808,"name":"Health sciences/Endocrinology"},{"id":68357809,"name":"Health sciences/Gastroenterology"},{"id":68357810,"name":"Health sciences/Health care"},{"id":68357811,"name":"Health sciences/Medical research"},{"id":68357812,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-21T14:10:44+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-21 14:10:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9434896","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9434896","identity":"rs-9434896","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}