Pre-pregnancy Obesity Phenotypes and the Risk of Oligohydramnios: A Large Population-Based Cohort

Pre-pregnancy Obesity Phenotypes and the Risk of Oligohydramnios: A Large Population-Based Cohort | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 October 2025 V1 Latest version Share on Pre-pregnancy Obesity Phenotypes and the Risk of Oligohydramnios: A Large Population-Based Cohort Authors : Kyung Eun Lee 0000-0003-3888-5417 , Ye Bin Park , In Yang Park , Jaeeun Shin 0000-0002-8326-8541 [email protected] , and Kyungdo Han Authors Info & Affiliations https://doi.org/10.22541/au.176165291.14306563/v1 183 views 163 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Objective To evaluate the association between pre-pregnancy maternal obesity phenotypes—general, abdominal and combined—and the risk of oligohydramnios. Design Nationwide population-based cohort study. Setting South Korean National Health Insurance Service database. Population Women who underwent national health screening within two years before conception (2009–2017) and delivered between 2010 and 2018 (n=571,330). Methods Maternal obesity phenotypes were defined by pre-pregnancy body mass index (general obesity) and waist circumference (abdominal obesity). Oligohydramnios was identified using ICD-10 codes. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using multivariable regression models controlling for maternal and pregnancy factors. Main outcome measures Risk of oligohydramnios according to pre-pregnancy maternal obesity phenotypes. Results Oligohydramnios occurred in 1.13% of pregnancies (n=6,484). Women with general obesity (BMI≥25kg/m2) had a higher risk compared with those with normal BMI (aOR 1.40, 95% CI 1.21–1.61). Abdominal obesity (WC ≥85cm) also increased risk (aOR 1.21, 95% CI 1.10–1.33). The combined phenotype conferred the highest risk (aOR 1.24, 95% CI 1.12–1.37), while either phenotype alone was not significant after adjustment. Conclusion Coexisting general and abdominal obesity before pregnancy independently increase the risk of oligohydramnios. Phenotype-based assessment may improve preconception risk stratification and guide targeted antenatal surveillance. (Original article) Pre-pregnancy Obesity Phenotypes and the Risk of Oligohydramnios: A Large Population-Based Cohort Kyung Eun Lee 1 , Ye Bin Park 2 , In Yang Park 3 , Jae Eun Shin 1* , Kyung Do Han 2* 1 Department of Obstetrics and Gynecology, College of Medicine, Bucheon St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea 2 Department of Statistics and Actuarial Science, Soongsil University, Seoul, Republic of Korea 3 Department of Obstetrics and Gynecology, College of Medicine, Seoul St. Mary’s Hospital, The Catholic University of Korea, Seoul, Republic of Korea * Correspondence Kyung Do Han, Ph.D Department of Statistics and Actuarial Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul, 06978, Republic of Korea Tel.: +82-2-2258-7226, Fax: +82-2-532-6537, E-mail: [email protected] Jae Eun Shin, MD, Ph. D Department of Obstetrics and Gynecology, College of Medicine, Bucheon St. Mary’s Hospital, The Catholic University of Korea, 327, Sosa-ro, Wonmi-gu, Bucheon-si, Gyeonggi-do 14647, Republic of Korea Tel.: 82-32-340-2262, Fax: 82-32-340-2663, E-mail: [email protected] * Jae Eun Shin and Kyung Do Han contributed equally as the correspondence of this work. Running title : Maternal Obesity Phenotypes and Oligohydramnios Word count for text: 1780 Number of tables and figures: 2 tables, 1 figure Objective To evaluate the association between pre-pregnancy maternal obesity phenotypes—general, abdominal and combined—and the risk of oligohydramnios. Design Nationwide population-based cohort study. Setting South Korean National Health Insurance Service database. Population Women who underwent national health screening within two years before conception (2009–2017) and delivered between 2010 and 2018 (n=571,330). Methods Maternal obesity phenotypes were defined by pre-pregnancy body mass index (general obesity) and waist circumference (abdominal obesity). Oligohydramnios was identified using ICD-10 codes. Adjusted odds ratios (aORs) and 95% confidence intervals (CIs) were estimated using multivariable regression models controlling for maternal and pregnancy factors. Main outcome measures Risk of oligohydramnios according to pre-pregnancy maternal obesity phenotypes. Results Oligohydramnios occurred in 1.13% of pregnancies (n=6,484). Women with general obesity (BMI≥25kg/m2) had a higher risk compared with those with normal BMI (aOR 1.40, 95% CI 1.21–1.61). Abdominal obesity (WC ≥85cm) also increased risk (aOR 1.21, 95% CI 1.10–1.33). The combined phenotype conferred the highest risk (aOR 1.24, 95% CI 1.12–1.37), while either phenotype alone was not significant after adjustment. Conclusion Coexisting general and abdominal obesity before pregnancy independently increase the risk of oligohydramnios. Phenotype-based assessment may improve preconception risk stratification and guide targeted antenatal surveillance. Keywords amniotic fluid; body mass index; obesity, abdominal; oligohydramnios; pregnancy complications 1 Introduction The prevalence of obesity has been worldwide and represents a major public health concern with substantial implications for reproductive outcomes. In South Korea, the overall obesity rate rose from 30.2% in 2012 to 38.4% in 2021, with the steepest rise among women of reproductive age 1 . Among women in their 30s, the prevalence of class III obesity (Body Mass Index (BMI) ≥ 30kg/m 2 ) increased more than threefold during this period 2 . Similar trends are observed in other high-income countries. In the United States, approximately one in four women enter pregnancy with obesity 3,4 . Globally, more than 20% of women begin pregnancy with obesity and nearly half are either overweight or obese 5-7 . Pre-pregnancy obesity is associated with a higher risk of gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), fetal macrosomia, and increased cesarean delivery 8-12 . Whether obesity also affects the volume of amniotic fluid is less clear. Oligohydramnios, defined as an amniotic fluid index (AFI) < 5 cm or a maximum vertical pocket (MVP) < 2 cm, occurs in 1–5% of pregnancies and is linked to fetal growth restriction, abnormal fetal heart rate patterns, and increased rates of operative delivery 13-17 . Previous studies examining maternal obesity and oligohydramnios have produced conflicting results. Many were limited by retrospective single-center designs, use of BMI measured during pregnancy rather than before conception, and lack of attention to obesity phenotype 13,18 . Most assessed obesity solely by BMI, overlooking the metabolic and vascular differences related to fat distribution. Central adiposity, measured by waist circumference (WC), is more strongly associated with adverse metabolic and obstetric outcomes than BMI alone 19-23 . We conducted a nationwide population-based cohort study to examine the association between maternal pre-pregnancy maternal obesity and oligohydramnios, evaluating both general obesity and abdominal obesity individually and in combination. We hypothesized that combined phenotype of general and abdominal obesity would be associated with the greatest risk of oligohydramnios. 2 Methods 2.1 Data Resource Data were obtained from the National Health Insurance Service (NHIS) of South Korea, which provides medical coverage for approximately 97% of the Korean population and contains comprehensive healthcare utilization records. The NHIS database includes insurance claims data, demographic characteristics, and results from the National Health Screening Examination (NHSE), a standardized biennial program collecting anthropometric, biochemical, and lifestyle data. Obstetric outcomes were identified from delivery-related claims data. All datasets were anonymized before analysis, and the study protocol was approved by the Institutional Review Board (IRB No. HC25ZISI0045). 2.2 Study Population A total of 2,315,423 deliveries between January 2010 and December 2018 were initially identified. Among these, 617,494 women who had undergone NHSE between 280 days and 2 years before delivery (2009–2017). Women with incomplete demographic or clinical information, or pregnancies complicated by fetal anomalies using the ICD-10 codes were excluded. After applying these criteria, 571,330 women were included in the final analysis. 2.3 Covariates Maternal characteristics included maternal age (categorized as (never, ex-smoker, and current smoker), alcohol consumption (non-drinker, mild, heavy), as assessed by self-report. Reproductive and gynecological histories was identified using the ICD-10 codes and included nulliparity, multifetal pregnancy, history of abortion, uterine myoma, adenomyosis, endometriosis, and polycystic ovarian syndrome (PCOS). Medical comorbidities included pre-existing diabetes mellitus, hypertension, dyslipidemia, chronic kidney disease, and systemic lupus erythematosus (SLE)/antiphospholipid syndrome (APS). Pregnancy complications included GDM and HDP. Continuous variables, including blood pressure, fasting glucose, lipid profile (total cholesterol, HDL-C, LDL-C, and triglycerides), were also analyzed. All comorbidities were defined using claims data. 2.4 Assessment of Pre-pregnancy Obesity Pre-pregnancy BMI was calculated as weight in kilograms divided by height in meters squared (kg/m²). Women were stratified into five categories: underweight (<18.5 kg/m²), normal (18.5–22.9), overweight (23.0–24.9), class I obese (25.0–29.9), and class II or higher obese (≥30.0). In accordance with the Korean Society for the Study of Obesity (KSSO) and World Health Organization (WHO) Asia-Pacific guidelines, BMI ≥25.0 was considered general obesity. WC was classified into six groups: <75 cm, 75–79.9, 80–84.9, 85–89.9, 90–94.9, and ≥95. Abdominal obesity was defined as WC ≥85 cm based on KSSO criteria, and in regression analyses, the 80–84.9 cm group served as the reference category. 2.5 Outcome: Oligohydramnios Oligohydramnios is clinically defined as an MVP <2 cm or an AFI diagnostic codes in the NHIS claims database, which are generally assigned following ultrasound confirmation. Pregnancies with at least one diagnosis of oligohydramnios during gestation were classified as cases. 2.6 Statistical Analysis Baseline characteristics were summarized using descriptive statistics. Continuous variables were presented as means ± standard deviations and compared using ANOVA, while categorical variables were expressed as counts and percentages and compared using the chi-square test. Multivariable logistic regression was applied to estimate the association between pre-pregnancy obesity and oligohydramnios. Model 1 was unadjusted; Model 2 was adjusted for maternal age, smoking status, alcohol consumption, nulliparity, multifetal pregnancy, GDM, HDP, and SLE/APS; and Model 3 was additionally adjusted for gynecologic comorbidities (uterine myoma, adenomyosis, endometriosis, PCOS, and history of abortion). Odds ratios (ORs) and 95% confidence intervals (CIs) were reported. A two-sided p -value <0.05 was considered significant. All statistical analyses were conducted using SAS software, version 9.4 (SAS Institute Inc., Cary, NC, USA). 3 Results This study included 571,330 women, of whom 6,484 (1.13%) were diagnosed with oligohydramnios. Baseline characteristics of the study population are summarized in Table 1. Compared with women with normal amniotic fluid volume, those with oligohydramnios were younger and more frequently nulliparous. They also reported higher rates of past and current smoking and alcohol consumption. Mean BMI, WC, and blood pressure were significantly higher in the oligohydramnios group. Endometriosis and PCOS were also more prevalent in this group. Table 2 presents the association between obesity indices and oligohydramnios risk. Women with BMI ≥ 30 had a 39.8% higher adjusted risk (aOR 1.398, 95% CI 1.21–1.614 in Model 3) than women in the normal BMI category. Additionally, a significantly higher risk was observed in general obesity, defined as BMI ≥ 25 kg/m 2 (aOR 1.127, 95% CI 1.046, 1.213 in Model 3). For WC, the crude risk of oligohydramnios was highest in women with WC ≥ 95 cm, but the association lost statistical significance after adjustment. In contrast, women with WC < 75 cm showed a significantly lower risk than the reference group. When abdominal obesity was analyzed as a binary variable (WC ≥ 85 cm vs. < 85 cm), women with abdominal obesity had a 20.8% increased adjusted risk (aOR 1.208, 95% CI 1.101–1.325 in Model 3). Figure 1 illustrates the combined impact of BMI- and WC-defined obesity phenotypes. Using women without general or abdominal obesity as the reference group, the coexistence of both phenotypes was associated with the highest risk (aOR 1.24, 95% CI 1.12–1.374 in Model 3). Neither general obesity alone nor abdominal obesity alone showed a significant association with oligohydramnios after adjustment. 4 Discussion The coexistence of pre-pregnancy general and abdominal obesity was associated the greatest risk of oligohydramnios. In this large, nationwide population-based cohort study involving more than 570,000 pregnancies, both higher BMI and greater WC were independently associated with an increased risk, and their combination demonstrated a synergetic effect. These findings clarify prior inconsistencies regarding the relationship between maternal obesity and amniotic fluid abnormalities. Bautista-Castaño et al. reported an increased risk of abnormal amniotic fluid volume among obese women, potentially medicated through impaired placental perfusion and altered fetal growth trajectories 7 . In contrast, other large-scale investigations failed to demonstrate a significant association between BMI and oligohydramnios 13,18,24 . Our findings help reconcile these inconsistencies by incorporating both BMI and WC, accounting for the heterogeneity of obesity phenotypes overlooked in earlier studies 19-23 . Although some crude associations, such as those observed in the highest WC category, attenuated following multivariable adjustment, both general and abdominal obesity remained independently associated with a significantly increased risk of oligohydramnios. These results indicate that the observed associations are not merely confounded by maternal comorbidities but reflect the distinct contributions of obesity phenotypes to oligohydramnios risk. Pre-pregnancy obesity induces chronic low-grade inflammation, insulin resistance, and oxidative stress 25-30 , which impair placental angiogenesis and function 31-34 . Placental dysfunction reduces fetal urine output—the primary determinant of amniotic fluid volume 27,31,35,36 . Obesity-related endothelial dysfunction and altered maternal hemodynamics could disrupt maternal-fetal fluid exchange 37,38 , while hormonal dysregulation, including changes in the renin–angiotensin–aldosterone system and vasopressin systems, further contribute to fluid imbalance 39-41 . This study has some limitations. Its retrospective cohort design using claims data introduces potential selection and information bias. Although oligohydramnios was identified using ICD-10 codes confirmed by ultrasound confirmation, some degree of misclassification cannot be excluded. Despite adjustment for multiple confounders, residual confounding by unmeasured factors such as maternal hydration status, placental pathology, or gestational weight gain may persist. Furthermore, although pregnancies with major anomalies were excluded using ICD-10 codes, some anomalies diagnosed postnatally may not have been captured. Lastly, lifestyle factors such as smoking, alcohol consumption, and physical activity were self-reported and subject to recall bias. Nevertheless, this study also has notable strengths. The use of a nationwide, population-based cohort minimizes selection bias and enhances generalizability. The very large sample size provides robust statistical power to detect associations with a relatively uncommon outcome. Moreover, the use of standardized national health screening data ensures reliable pre-pregnancy measurements of both BMI and WC, allowing for precise characterization of maternal obesity phenotypes. By examining general and abdominal obesity both independently and in combination, our study adds novel epidemiological evidence that may inform clinical practice and future guidelines. In conclusion, the coexistence of pre-pregnancy general and abdominal obesity was independently associated with increased risk of oligohydramnios, whereas either phenotype alone is not. These findings clarify inconsistencies in previous literature and highlight the importance of incorporating both BMI and WC when evaluating maternal obesity in perinatal risk assessment. Conflict Of Interest Statement None declared. Author Contributions Conceptualization, KE Lee and JE Shin; Data curation, YB Park and KD Han; Formal analysis, KD Han; Funding acquisition, KE Lee; Investigation, KE Lee; Methodology, YB Park; Project administration, JE Shin and KD Han; Resources, YB Park; Software, YB Park and KD Han; Supervision, JE Shin and IY Park; Validation, KE Lee and YB Park; Visualization, KE Lee; Writing – original draft, KE Lee; Writing – review & editing, JE Shin and IY Park. All authors have read and agreed to the published version of the manuscript. Funding Statement This research was supported by a grant from the Institute of Clinical Medicine Research of Bucheon St. Mary’s Hospital (Grant No. BCMC25BD07). Acknowledgements None. Data Availability Statement None. Ethics Approval All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This study was granted ethical approval by the institutional Review Board of Catholic University of Korea (HC25ZISI0045). References 1. S. M. Jeong, Jung, J. H., Yang, Y. S., Kim, W., Cho, I. Y., Lee, Y. B., et al. ”2023 Obesity Fact Sheet: Prevalence of Obesity and Abdominal Obesity in Adults, Adolescents, and Children in Korea from 2012 to 2021.” Journal of obesity & metabolic syndrome 33 (2024): 27-35. 2. B. Y. Kim, Kang, S. M., Kang, J. H., Kang, S. Y., Kim, K. K., Kim, K. B., et al. ”2020 Korean Society for the Study of Obesity Guidelines for the Management of Obesity in Korea.” Journal of obesity & metabolic syndrome 30 (2021): 81-92. 3. S. D. Emmerich, Fryar, C. D., Stierman, B., Ogden, C. L. ”Obesity and Severe Obesity Prevalence in Adults: United States, August 2021-August 2023.” NCHS Data Brief (2024), https://doi.org/10.15620/cdc/159281. 4. American College of Obestricians & Gynecologists. ”Obesity in Pregnancy: ACOG Practice Bulletin, Number 230.” Obstetrics and Gynecology 137 (2021): e128-e144. 5. L. Ferris, de Vries, B., Sweeting, A. ”Management of obesity in pregnancy.” Australian and New Zealand Journal of Obstetrics and Gynaecology 62 (2022): 623-625. 6. L. Kent, McGirr, M., Eastwood, K. A. ”Global trends in prevalence of maternal overweight and obesity: A systematic review and meta-analysis of routinely collected data retrospective cohorts.” International journal of population data science 9 (2024): 2401. 7. I. Bautista-Castano, Henriquez-Sanchez, P., Aleman-Perez, N., Garcia-Salvador, J. J., Gonzalez-Quesada, A., Garcia-Hernandez, J. A., et al. ”Maternal obesity in early pregnancy and risk of adverse outcomes.” PloS One 8 (2013): e80410. 8. H. Vats, Saxena, R., Sachdeva, M. P., Walia, G. K., Gupta, V. ”Impact of maternal pre-pregnancy body mass index on maternal, fetal and neonatal adverse outcomes in the worldwide populations: A systematic review and meta-analysis.” Obesity Research & Clinical Practice 15 (2021): 536-545. 9. X. J. He, Dai, R. X., Hu, C. L. ”Maternal prepregnancy overweight and obesity and the risk of preeclampsia: A meta-analysis of cohort studies.” Obesity Research & Clinical Practice 14 (2020): 27-33. 10. S. C. Langley-Evans, Pearce, J., Ellis, S. ”Overweight, obesity and excessive weight gain in pregnancy as risk factors for adverse pregnancy outcomes: A narrative review.” Journal of Human Nutrition and Dietetics 35 (2022): 250-264. 11. M. R. Torloni, Betran, A. P., Horta, B. L., Nakamura, M. U., Atallah, A. N., Moron, A. F., et al. ”Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis.” Obesity Reviews 10 (2009): 194-203. 12. T. E. O’Brien, Ray, J. G., Chan, W. S. ”Maternal body mass index and the risk of preeclampsia: a systematic overview.” Epidemiology 14 (2003): 368-374. 13. X. Yan, Bentley, B., Schmidt-Swartz, J., Dinglas, C., El Kady, D., Rosner, J. ”Is Obesity a Risk Factor for Disorders of Amniotic Fluid?” Journal of Ultrasound in Medicine 43 (2024): 1535-1542. 14. American College of Obestricians & Gynecologists. ”Indications for Outpatient Antenatal Fetal Surveillance: ACOG Committee Opinion, Number 828.” Obstetrics and Gynecology 137 (2021): e177-e197. 15. U. M. Reddy, Abuhamad, A. Z., Levine, D., Saade, G. R., Fetal Imaging Workshop Invited, P. ”Fetal imaging: executive summary of a joint Eunice Kennedy Shriver National Institute of Child Health and Human Development, Society for Maternal-Fetal Medicine, American Institute of Ultrasound in Medicine, American College of Obstetricians and Gynecologists, American College of Radiology, Society for Pediatric Radiology, and Society of Radiologists in Ultrasound Fetal Imaging Workshop.” Journal of Ultrasound in Medicine 33 (2014): 745-757. 16. M. A. Shiferaw, Solomon, A., Getachew, S., Gudu, W. ”Maternal and perinatal outcomes of oligohydramnios in late term and post term pregnancies at public hospitals in Ethiopia: a cross-sectional study.” BMC Women’s Health 24 (2024): 113. 17. H. Teka, Gidey, H., Gebrehiwot, T. G., Yemane, A., Abraha, H. E., Ebrahim, M. M., et al. ”Perinatal outcome of oligohydramnios in academic hospitals in a low resource setting.” African Journal of Reproductive Health 27 (2023): 54-64. 18. M. J. Blitz, Rochelson, B., Stork, L. B., Augustine, S., Greenberg, M., Sison, C. P., et al. ”Maternal Body Mass Index and Amniotic Fluid Index in Late Gestation.” Journal of Ultrasound in Medicine 37 (2018): 561-568. 19. E. Lindberger, Wikstrom, A. K., Bergman, E., Eurenius, K., Mulic-Lutvica, A., Sundstrom Poromaa, I., et al. ”Association of maternal central adiposity measured by ultrasound in early mid pregnancy with infant birth size.” Scientific Reports 10 (2020): 19702. 20. A. Suresh, Liu, A., Poulton, A., Quinton, A., Amer, Z., Mongelli, M., et al. ”Comparison of maternal abdominal subcutaneous fat thickness and body mass index as markers for pregnancy outcomes: A stratified cohort study.” Australian and New Zealand Journal of Obstetrics and Gynaecology 52 (2012): 420-426. 21. A. Engin. ”The Definition and Prevalence of Obesity and Metabolic Syndrome: Correlative Clinical Evaluation Based on Phenotypes.” Advances in Experimental Medicine and Biology 1460 (2024): 1-25. 22. V. Eley, Sekar, R., Chin, A., Donovan, T., Krepska, A., Lawrence, M., et al. ”Increased maternal abdominal subcutaneous fat thickness and body mass index are associated with increased cesarean delivery: A prospective cohort study.” Acta Obstetricia et Gynecologica Scandinavica 98 (2019): 196-204. 23. N. C. Prodan, Schmidt, M., Hoopmann, M., Abele, H., Kagan, K. O. ”Obesity in prenatal medicine: a game changer?” Archives of Gynecology and Obstetrics 309 (2024): 961-974. 24. V. L. Deshmukh, Jadhav, M., Yelikar, K. ”Impact of HIGH BMI on Pregnancy: Maternal and Foetal Outcome.” Journal of Obstetrics and Gynaecology of India 66 (2016): 192-197. 25. P. Pantham, Aye, I. L., Powell, T. L. ”Inflammation in maternal obesity and gestational diabetes mellitus.” Placenta 36 (2015): 709-715. 26. E. Musa, Salazar-Petres, E., Arowolo, A., Levitt, N., Matjila, M., Sferruzzi-Perri, A. N. ”Obesity and gestational diabetes independently and collectively induce specific effects on placental structure, inflammation and endocrine function in a cohort of South African women.” Journal of Physiology 601 (2023): 1287-1306. 27. J. G. Wallace, Bellissimo, C. J., Yeo, E., Fei Xia, Y., Petrik, J. J., Surette, M. G., et al. ”Obesity during pregnancy results in maternal intestinal inflammation, placental hypoxia, and alters fetal glucose metabolism at mid-gestation.” Scientific Reports 9 (2019): 17621. 28. D. E. Lackey, Olefsky, J. M. ”Regulation of metabolism by the innate immune system.” Nature Reviews: Endocrinology 12 (2016): 15-28. 29. F. Zatterale, Longo, M., Naderi, J., Raciti, G. A., Desiderio, A., Miele, C., et al. ”Chronic Adipose Tissue Inflammation Linking Obesity to Insulin Resistance and Type 2 Diabetes.” Frontiers in Physiology 10 (2019): 1607. 30. A. C. Richardson, Carpenter, M. W. ”Inflammatory mediators in gestational diabetes mellitus.” Obstetrics and Gynecology Clinics of North America 34 (2007): 213-224, viii. 31. F. Louwen, Kreis, N. N., Ritter, A., Yuan, J. ”Maternal obesity and placental function: impaired maternal-fetal axis.” Archives of Gynecology and Obstetrics 309 (2024): 2279-2288. 32. C. X. W. Zhang, Candia, A. A., Sferruzzi-Perri, A. N. ”Placental inflammation, oxidative stress, and fetal outcomes in maternal obesity.” Trends in Endocrinology and Metabolism 35 (2024): 638-647. 33. A. C. Kelly, Powell, T. L., Jansson, T. ”Placental function in maternal obesity.” Clinical Science (London, England: 1979) 134 (2020): 961-984. 34. C. Brombach, Tong, W., Giussani, D. A. ”Maternal obesity: new placental paradigms unfolded.” Trends in Molecular Medicine 28 (2022): 823-835. 35. R. Gagnon, Harding, R., Brace, R. A. ”Amniotic fluid and fetal urinary responses to severe placental insufficiency in sheep.” American Journal of Obstetrics and Gynecology 186 (2002): 1076-1084. 36. H. J. Seol, Kim, H. Y., Cho, G. J., Oh, M. J. ”Hourly fetal urine production rate in isolated oligohydramnios at term.” PloS One 16 (2021): e0250659. 37. D. Vinayagam, Gutierrez, J., Binder, J., Mantovani, E., Thilaganathan, B., Khalil, A. ”Impaired maternal hemodynamics in morbidly obese women: a case-control study.” Ultrasound in Obstetrics and Gynecology 50 (2017): 761-765. 38. D. Patel, Avesani, M., Johnson, M. R., Di Salvo, G., Savvidou, M. D. ”Maternal cardiovascular adaptation to pregnancy in obese pregnant women.” Acta Obstetricia et Gynecologica Scandinavica 103 (2024): 907-916. 39. T. Bynarowicz, Jenkins, S. M., Shanks, A. L. Oligohydramnios. StatPearls. Treasure Island (FL): StatPearls Publishing; 2025. 40. E. F. Brown, Fronius, M., Brown, C. H. ”Vasopressin regulation of maternal body fluid balance in pregnancy and lactation: A role for TRPV channels?” Molecular and Cellular Endocrinology 558 (2022): 111764. 41. I. Koukoulas, Risvanis, J., Douglas-Denton, R., Burrell, L. M., Moritz, K. M., Wintour, E. M. ”Vasopressin receptor expression in the placenta.” Biology of Reproduction 69 (2003): 679-686. Figure legend FIGURE 1 Adjusted odds ratios for oligohydramnios according to the combination of general and abdominal obesity. Forest plot showing adjusted odds ratios (aORs) and 95% confidence intervals (CI) for oligohydramnios across four categories of maternal obesity: both general and abdominal obesity, general obesity only, abdominal obesity only, and neither. The reference group comprised women with neither general nor abdominal obesity. General obesity was defined as a body mass index (BMI) ≥ 25 kg/m 2 and abdominal obesity as a waist circumference (WC) ≥ 85 cm, according to the Asia-Pacific and Korean Society for the Study of Obesity (KSSO) criteria. Models are described in the Methods section. aORs, adjusted odds ratios; BMI, body mass index; CI, confidence intervals; KSSO, Korean Society for the Study of Obesity; WC, waist cirvumference Supplementary Material File (tables.docx) Download 29.64 KB Information & Authors Information Version history V1 Version 1 28 October 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords diabetes in pregnancy health services research maternal medicine medical disorders in pregnancy Authors Affiliations Kyung Eun Lee 0000-0003-3888-5417 The Catholic University of Korea Bucheon St Mary's Hospital View all articles by this author Ye Bin Park Soongsil University Department of Statistics and Actuarial Science View all articles by this author In Yang Park The Catholic University of Korea Seoul St Mary's Hospital View all articles by this author Jaeeun Shin 0000-0002-8326-8541 [email protected] The Catholic University of Korea Bucheon St Mary's Hospital View all articles by this author Kyungdo Han Soongsil University Department of Statistics and Actuarial Science View all articles by this author Metrics & Citations Metrics Article Usage 183 views 163 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Kyung Eun Lee, Ye Bin Park, In Yang Park, et al. 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