Preperitoneal Subcutaneous Fat and Adverse Pregnancy Outcomes: A Comprehensive Cohort Study and Literature Integration

Preperitoneal Subcutaneous Fat and Adverse Pregnancy Outcomes: A Comprehensive Cohort Study and Literature Integration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preperitoneal Subcutaneous Fat and Adverse Pregnancy Outcomes: A Comprehensive Cohort Study and Literature Integration Llili Huo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8670456/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Maternal adipose tissue remodeling during pregnancy is a critical physiological process, but excessive fat accumulation—particularly in specific depots—may elevate metabolic and gestational risks. This study aimed to investigate the association between preperitoneal subcutaneous fat (PSF) thickness across pregnancy trimesters and adverse pregnancy outcomes (APO), while integrating existing evidence on maternal adiposity and pregnancy health. A prospective cohort study was conducted at the Murialdo Teaching Health Center in Porto Alegre, Brazil, enrolling 210 pregnant women between October 2016 and December 2017. PSF thickness was measured via ultrasound, and APO was defined as the occurrence of gestational diabetes mellitus, hypertensive disorders of pregnancy, abnormal fetal growth, preterm delivery, or preterm premature rupture of membranes. Statistical analyses included Mann–Whitney tests, chi-squared/Fisher’s exact tests, and logistic regression models (unadjusted and multivariate-adjusted). Results showed a total APO prevalence of 25.2% (53/210), with a significantly higher prevalence in women with PSF ≥24.7 mm (73.9%, 17/23) compared to those with PSF <24.7 mm (19.3%, 36/187; p<0.001). Multivariate regression confirmed PSF as a strong independent predictor of APO (adjusted OR=18.28, 95% CI=4.87–68.62, p<0.001). Subgroup analyses revealed associations between elevated PSF and maternal hypertension, white ethnicity, and overweight/obesity. Integration with existing literature highlighted that PSF, like visceral fat, correlates more strongly with metabolic risk factors and APO than traditional BMI, supporting its utility as a non-invasive predictive marker. Conclusion: Elevated preperitoneal subcutaneous fat during pregnancy is independently associated with adverse pregnancy outcomes. Ultrasound measurement of PSF offers a simple, accessible tool for identifying high-risk pregnancies, enabling targeted interventions to improve maternal and fetal health. Preperitoneal subcutaneous fat adverse pregnancy outcomes ultrasound maternal adiposity pregnancy complications Figures Figure 1 Figure 2 1. Introduction The global prevalence of overweight and obesity has exceeded 1.9 billion individuals worldwide( 1 ), and these conditions are well-established predisposing factors for a spectrum of adverse perinatal outcomes affecting both mothers and infants( 2 – 5 ). While body mass index (BMI) is commonly used for risk stratification in pregnant women, it fails to account for variations in body fat distribution—a critical limitation, as fat depot-specific characteristics are more closely linked to metabolic risk than general adiposity. Among non-pregnant populations, central adiposity (a body fat distribution pattern concentrated in the abdominal region) is a well-documented contributor to obesity-related morbidities( 6 – 8 ). This phenotype typically coexists with insulin resistance, dyslipidemia, and chronic low-grade inflammation( 9 ), factors widely recognized as the key mechanistic pathways linking adiposity to adverse health outcomes( 10 ). Central adiposity can be further categorized into two distinct anatomical subtypes: abdominal subcutaneous fat and intra-abdominal visceral fat( 11 ). Notably, recent evidence in pregnant women has demonstrated that increased maternal visceral fat thickness correlates with adverse pregnancy outcomes (APOs) even more strongly than BMI( 7 , 12 ). However, the role of preperitoneal subcutaneous fat (PSF)—a distinct subdepot of abdominal subcutaneous fat located beneath the abdominal muscles—remains understudied in the context of pregnancy-related risks. 2. Materials and Methods 2.1 Study Design and Population The original data are freely available online, and had been approved by the authors for use by other investigators( 13 , 14 ). This prospective cohort study was conducted at the Murialdo Teaching Health Center - Ultrasound Department, which provides fetal medicine services to the Public Health System in Porto Alegre City, Brazil. Data collection occurred from October 2016 to December 2017. Eligible participants were pregnant women in any trimester of pregnancy, with no exclusion criteria based on pre-existing conditions (e.g., hypertension, diabetes) to reflect real-world clinical practice. A total of 210 women were enrolled, with complete follow-up data available for all participants. The participant selection process is illustrated in Fig. 1 . Note One woman had both preterm delivery and hypertensive disorders, and two women had both gestational diabetes mellitus and hypertensive disorders. 2.2 Data Collection 2.2.1 Preperitoneal Subcutaneous Fat Measurement The maternal fat ultrasound measurement was performed by a certified sonologist physician and it was measured in all trimesters with a convex probe placed in the middle sagittal epigastric region as described by Lee YS et.al( 15 ). Attention was paid to avoid excessive pressure that could falsely compress surfaces of interest. The electronic caliper was then passed from the superficial dermal edge to the linea alba in order to assess the epigastric maternal subcutaneous adipose tissue (PSF). 2.2.2 Baseline and Clinical Data Baseline characteristics, including age, ethnicity, pre-pregnancy BMI, hypertension, diabetes, tobacco use, alcohol consumption, and drug use, were collected via structured questionnaires and medical records. BMI was categorized according to WHO standards: normal (18.5–24.9 kg/m²), overweight (25.0–29.9 kg/m²), and obese (≥ 30.0 kg/m²). 2.2.3 Adverse Pregnancy Outcomes APO was defined as the occurrence of one or more of the following: gestational diabetes mellitus (GDM) was diagnosed according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria, hypertensive disorders of pregnancy included gestational hypertension and preeclampsia, abnormal fetal growth (small for gestational age, intrauterine growth restriction-IUGR), preterm delivery, or preterm premature rupture of membranes before 37 weeks( 16 ), Outcome data were extracted from delivery records and neonatal charts. 2.3 Statistical Analysis Statistical analyses were performed using Free Statistics software version 1.3( 17 ). Continuous variables were summarized as mean ± standard deviation or median (interquartile range) and compared using the Mann–Whitney U test. Categorical variables were presented as frequencies (percentages) and compared using the chi-squared test or Fisher’s exact test (when expected frequencies < 5). Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between PSF and APO. Four models were constructed: Model I (unadjusted), Model II (adjusted for age and ethnicity), Model III (Model II + BMI), and Model IV (Model III + smoking, drinking, hypertension, and diabetes). Interaction analyses were performed to assess effect modification by potential confounders. A two-sided p < 0.05 was considered statistically significant. 3. Results 3.1 Baseline Characteristics The study population had a median age of 26.4 ± 6.5 years (range 15–43 years). Of the 210 participants, 189 (90.0%) had PSF < 24.7 mm and 21 (10.0%) had PSF ≥ 24.7 mm. Baseline characteristics differed significantly between the two groups in terms of ethnicity (p = 0.008), hypertension (p = 0.002), and BMI classification (p = 0.019). Women with PSF ≥ 24.7 mm were more likely to be white (81.0% vs. 50.0%), have hypertension (23.8% vs. 3.2%), and be overweight/obese (95.2% vs. 71.4%) compared to those with PSF < 24.7 mm (Table 1 ). No significant differences were observed in age, diabetes, tobacco use, alcohol consumption, or drug use between groups. Table 1 Baseline characteristics of selected participants Variables Total (n = 210) PSF(mm) p statistic < 24.7mm (n = 189) ≥ 24.7mm (n = 21) Age 26.4 ± 6.5 26.1 ± 6.4 28.9 ± 6.7 0.65 U = 3.451 ethnicity, n (%) 0.008* Fisher white 110 (53.1) 93 (50) 17 (81) Black 59 (28.5) 58 (31.2) 1 (4.8) Brown/Asian 38 (18.4) 35 (18.8) 3 (14.3) hypertension, n (%) 0.002* Fisher No 199 (94.8) 183 (96.8) 16 (76.2) Yes 11( 5.2) 6 (3.2) 5 (23.8) diabetes, n (%) 0.113 Fisher No 203 (97.1) 184 (97.9) 19 (90.5) Yes 6 ( 2.9) 4 (2.1) 2 (9.5) tobacco, n (%) 0.141 Fisher No 169 (80.5) 155 (82) 14 (66.7) Yes 41 (19.5) 34 ( 18 ) 7 (33.3) alcohol, n (%) 0.119 Fisher No 176 (83.8) 161 (85.2) 15 (71.4) Yes 34 (16.2) 28 (14.8) 6 (28.6) drugs, n (%) 0.511 Fisher No 202 (96.7) 183 (96.8) 19 (95) Yes 7 ( 3.3) 6 (3.2) 1 ( 5 ) BMI, n (%) 0.019* χ²=5.543 Normal 55 (26.2) 54 (28.6) 1 (4.8) Overweight/Obesity 155 (73.8) 135 (71.4) 20 (95.2) Note: Continuous variables were tested for normality; age was normally distributed and presented as mean ± SD; P-values were calculated using Fisher’s exact test for categorical variables. *Statistical significance (p < 0.05). 3.2 Prevalence of Adverse Pregnancy Outcomes The overall prevalence of APO was 25.2% (53/210). The prevalence of APO was significantly higher in the PSF ≥ 24.7 mm group (81.0%, 17/21) compared to the PSF < 24.7 mm group (19.0%, 36/189; p < 0.001). The most common APO in the elevated PSF group were gestational diabetes mellitus (34.8%) and hypertensive disorders of pregnancy (26.1%), while in the lower PSF group, abnormal fetal growth (8.0%) and preterm delivery (5.3%) were most frequent. 3.3 Association Between PSF and Adverse Pregnancy Outcomes Unadjusted logistic regression showed that PSF ≥ 24.7 mm was strongly associated with APO (OR = 13.15, 95% CI = 4.52–38.19, p < 0.001). This association remained robust after sequential adjustment for confounders: Model II (OR = 17.20, 95% CI = 5.23–56.59, p < 0.001), Model III (OR = 16.27, 95% CI = 4.90–54.02, p < 0.001), and Model IV (OR = 18.28, 95% CI = 4.87–68.62, p < 0.001) (Table 2 ). As a continuous variable, each 1-mm increase in PSF was associated with a 16% increase in APO risk (adjusted OR = 1.16, 95% CI = 1.09–1.23, p < 0.001). Interaction analysis revealed no significant effect modification by age, BMI, ethnicity, or lifestyle factors.Subgroup analysis (Fig. 2 ) further explored the association between PSF and APO across different populations: adjusted associations were statistically significant in the White ethnicity subgroup (adj_P_value < 0.001), age ≥ 25 years subgroup (adj_P_value < 0.001), overweight/obesity BMI subgroup (adj_P_value < 0.001), and non-alcohol use subgroup (adj_P_value < 0.001). No significant associations were observed in other subgroups (e.g., Black ethnicity, age 0.05, confirming the robustness of the main association. Table 2 Multivariate regression analysis of the association between PSF and APO Variable ModelⅠ Model Ⅱ Model Ⅲ Model Ⅳ OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) PSF(mm) 1.13 (1.08 ~ 1.19) 1.15 (1.09 ~ 1.22) 1.15 (1.09 ~ 1.21) 1.16 (1.09 ~ 1.23) Binary variable(mm) < 24.7mm Ref. Ref. Ref. Ref. ≥ 24.7mm 13.15 (4.52–38.19) 17.2 (5.23–56.59) 16.27 (4.9-54.02) 18.28 (4.87–68.62) P < 0.001 < 0.001 < 0.001 < 0.001 Model I: no adjusted. Model II: adjusted for age and ethnicity Model III: Model II + BMI Model IV: Model III +smoking + drinking+ hypertension + diabetes Figure 2 Forest plot of subgroup analyses for the association between preperitoneal subcutaneous fat (PSF) and adverse pregnancy outcomes (APO) Note adjusted for age,ethnicity, BMI,tobacco use, alcohol use,hypertension, diabetes. 4. Discussion 4.1 Key Findings This study demonstrates that elevated preperitoneal subcutaneous fat (PSF) during pregnancy is strongly associated with adverse pregnancy outcomes, independent of BMI and other confounding factors. Women with PSF ≥ 24.7 mm had a seven-fold higher prevalence of APO and an 18-fold increased odds of APO after full adjustment. Our findings are consistent with Kennedy et al.( 18 ), who reported that maternal abdominal subcutaneous fat thickness is a robust predictor of APO, but extend this observation by focusing on PSF—a distinct subdepot that is easier to measure via routine obstetric ultrasound than visceral fat( 12 ). 4.2 Mechanisms Excessive maternal subcutaneous fat increases the risk of adverse pregnancy outcomes such as hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), preterm birth, and fetal growth restriction (FGR) through multiple mechanisms, including chronic inflammatory response, insulin resistance, and lipid metabolism disorders. Chronic Inflammation and Oxidative Stress Excessive adipose tissue, especially central obesity, initiates and sustains a state of chronic low-grade systemic inflammation. Adipose tissue is not only an energy storage depot but also an active endocrine organ that secretes various pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1), and interleukin-6 (IL-6). These inflammatory mediators promote the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS), leading to elevated levels of oxidative stress and nitrosative stress in the body. This adipose tissue-driven inflammatory environment is considered a major mechanism linking obesity to multiple complications, including hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), and intrauterine growth restriction (IUGR) ( 18 , 19 ). Insulin Resistance (IR) Obesity itself is often accompanied by decreased insulin sensitivity, namely insulin resistance. Pregnancy is a process of gradually enhanced physiological insulin resistance, aimed at delivering more nutrients to the developing fetus. For obese women with pre-existing insulin resistance before pregnancy, this physiological process is further exacerbated, making them more prone to developing GDM. In addition, certain lipid metabolites, such as ceramides and sphingomyelins, have been shown in animal models and in vitro experiments to exacerbate insulin resistance by impairing insulin signaling pathways in skeletal muscle and adipocytes ( 19 , 20 ). Lipid Metabolism Disorders Maternal obesity is closely associated with dyslipidemia, characterized by elevated levels of triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and free fatty acids, while high-density lipoprotein cholesterol (HDL-C) levels may be decreased. In the third trimester of pregnancy, maternal metabolism shifts to catabolism, with increased adipose tissue breakdown leading to elevated circulating lipoprotein levels—especially increased triglyceride content in very low-density lipoprotein (VLDL), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) particles. This alteration in lipid profile not only increases the risk of GDM and preeclampsia but may also transport more glucose, lipids, and amino acids across the placenta, affecting fetal growth and development and resulting in macrosomia or intrauterine growth restriction( 20 , 21 ) . Adipokine Imbalance In the obese state, the secretion of various adipokines by adipose tissue is dysregulated, with elevated leptin levels (hyperleptinemia) and decreased adiponectin levels (hypoadiponectinemia) being particularly prominent. This adipokine imbalance may partially explain the tendency of obese pregnant women to develop obstetric complications such as gestational hypertension, preeclampsia, and GDM. Additionally, the adipokine visfatin is elevated in both obese and GDM pregnant women and is believed to be involved in the pathogenesis of GDM ( 19 , 22 ). 4.3 Limitations and Strengths This study has several limitations. First, the sample size, while sufficient for detecting strong associations, may limit subgroup analyses of specific APO (e.g., preterm premature rupture of membranes). Second,the maternal fat ultrasound measurement was performed by a single certified sonologist physician instead of two, which may impact measurement bias. The pre-pregnant BMI was calculated from self-reported weight before pregnancy in a limited group of cases without first trimester weight in the hospital charts, which may result in recall bias. Future studies with larger sample sizes should investigate the temporal changes in PSF across trimesters and its association with specific APO subtypes, such as early-onset preeclampsia. Strengths include the prospective design, standardized ultrasound measurements, comprehensive adjustment for confounders, and integration with existing literature. The use of a population-based sample from a public health system enhances generalizability to diverse clinical settings. 5. Conclusion Preperitoneal subcutaneous fat thickness is a potent predictor of adverse pregnancy outcomes. Incorporating PSF measurement into routine obstetric ultrasound may improve early risk detection and management. Abbreviations APO Adverse Pregnancy Outcomes GDM Gestational Diabetes Mellitus HDP Hypertensive Disorders of Pregnancy IUGR Intrauterine Growth Restriction TNF-α Tumor Necrosis Factor-α IL-1 Interleukin-1 IL-6 Interleukin-6 ROS Reactive Oxygen Species RNS Reactive Nitrogen Species IR Insulin Resistance LDL-C Low-Density Lipoprotein Cholesterol HDL-C High-Density Lipoprotein Cholesterol VLDL Very Low-Density Lipoprotein BMI Body Mass Index Declarations Acknowledgements The author would like to thank the staff of the Murialdo Teaching Health Center in Porto Alegre, Brazil for their assistance with data collection. Gratitude is also extended to the participants and their families for their support and participation in this study. Ethics approval and consent to participate This prospective cohort study was reviewed and approved by the Research Ethics Committee of the municipality of Porto Alegre, Brazil (approval number: 2.132.090). All participants provided written informed consent prior to enrollment, in accordance with the Declaration of Helsinki. Consent for publication All participants have provided written consent for the publication of their de-identified data and study findings. No identifying personal information (e.g., names, addresses, medical record numbers) is included in the manuscript or supplementary materials. Availability of data and materials Data from the PhysioNet database were used in compliance with the database's terms of use (https://physionet.org/about/terms-of-use/). Competing interests The author declares no competing interests. No financial, professional, or personal relationships with other individuals or organizations could inappropriately influence (bias) this work. Funding This study was not supported by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Huo Lili (HL) designed the study, collected and analyzed the data, drafted the manuscript, and approved the final version. References Santos S, Eekhout I, Voerman E, Gaillard R, Barros H, Charles MA, et al. Gestational weight gain charts for different body mass index groups for women in Europe, North America, and Oceania. BMC Med. 2018;16:201. Cnattingius S, Bergström R, Lipworth L, Kramer MS. Prepregnancy Weight and the Risk of Adverse Pregnancy Outcomes. N Engl J Med. 1998;338(3):147–52. Blomberg M, Maternal, Obesity. Mode of Delivery, and Neonatal Outcome. Obstet Gynecol. 2013 July;122(1):50. Ruager-Martin R, Hyde MJ, Modi N. Maternal obesity and infant outcomes. Early Hum Dev. 2010;86(11):715–22. Sebire NJ, Jolly M, Harris JP, Wadsworth J, Joffe M, Beard RW, et al. Maternal obesity and pregnancy outcome: a study of 287,213 pregnancies in London. Int J Obes Relat Metab Disord. 2001;25(8):1175–82. Catalano PM. 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Albrecht M, Worthmann A, Heeren J, Diemert A, Arck PC. Maternal lipids in overweight and obesity: implications for pregnancy outcomes and offspring’s body composition. Semin Immunopathol. 2025;47(1):10. Bays HE, Jones PH, Brown WV, Jacobson TA. National Lipid Association Annual Summary of Clinical Lipidology 2015. J Clin Lipidol. 2014;8(6):S1–36. Valencia-Ortega J, Solis-Paredes JM, Saucedo R, Estrada-Gutierrez G, Camacho-Arroyo I. Excessive Pregestational Weight and Maternal Obstetric Complications: The Role of Adipokines. Int J Mol Sci. 2023 Sept 28;24(19):14678. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-8670456","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600494474,"identity":"c4eb21eb-4307-499c-bcb2-41924cd105d0","order_by":0,"name":"Llili Huo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBAC+/PNBww+/rNhZmxvPkCknhvHEgpnsKWxM/ccSyBSy4Ecg888bIf52WfkGBCng7HhjOFmHp40ad6eMx9vvGGwk9NtIKCFmbmt2HCOhI2xZHvvZss5DMnGZgcIaGFjOLzN4I1BWrJhz9lt0jwMBxK3EdLCw5Bg/oMn4XD9/hs5z4jTIsGQYmDIc+AwM+OMHDbitBhIHEswnNmQxszYc8zYco4BEX4x4AdFZQM4Kh/eeFNhJ0dQC6oreYiMGmQtpOoYBaNgFIyCEQEAWuZG35l4YTwAAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Llili","middleName":"","lastName":"Huo","suffix":""}],"badges":[],"createdAt":"2026-01-22 13:54:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8670456/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8670456/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103989752,"identity":"a19831fd-39b8-4ebb-9fc0-b2eceb9f8e09","added_by":"auto","created_at":"2026-03-05 11:19:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":214961,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of subjects selection. Abbreviations: PSF, preperitoneal subcutaneous fat; APO, adverse pregnancy outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eOne woman had both preterm delivery and hypertensive disorders , and two women had both gestational diabetes mellitus and hypertensive disorders.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote: \u003c/em\u003eOne woman had both preterm delivery and hypertensive disorders , and two women had both gestational diabetes mellitus and hypertensive disorders.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8670456/v1/62acf2c85478918e9c1b6c32.jpg"},{"id":103989753,"identity":"035988a3-055b-4fef-9e58-ab1baf3b7387","added_by":"auto","created_at":"2026-03-05 11:19:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42229,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analyses for the association between preperitoneal subcutaneous fat (PSF) and adverse pregnancy outcomes (APO)\u003c/p\u003e\n\u003cp\u003eNote : adjusted for age,ethnicity, BMI,tobacco use, alcohol use,hypertension,\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8670456/v1/9f495f14c9559e1b1a2f2f99.jpg"},{"id":105562500,"identity":"1519f7cc-0926-4e51-9c70-d5448e5d3c75","added_by":"auto","created_at":"2026-03-27 12:41:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":993694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8670456/v1/23416789-247b-44b1-844e-7f9053652ef3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preperitoneal Subcutaneous Fat and Adverse Pregnancy Outcomes: A Comprehensive Cohort Study and Literature Integration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global prevalence of overweight and obesity has exceeded 1.9\u0026nbsp;billion individuals worldwide(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e), and these conditions are well-established predisposing factors for a spectrum of adverse perinatal outcomes affecting both mothers and infants(\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). While body mass index (BMI) is commonly used for risk stratification in pregnant women, it fails to account for variations in body fat distribution\u0026mdash;a critical limitation, as fat depot-specific characteristics are more closely linked to metabolic risk than general adiposity.\u003c/p\u003e \u003cp\u003eAmong non-pregnant populations, central adiposity (a body fat distribution pattern concentrated in the abdominal region) is a well-documented contributor to obesity-related morbidities(\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This phenotype typically coexists with insulin resistance, dyslipidemia, and chronic low-grade inflammation(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), factors widely recognized as the key mechanistic pathways linking adiposity to adverse health outcomes(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Central adiposity can be further categorized into two distinct anatomical subtypes: abdominal subcutaneous fat and intra-abdominal visceral fat(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Notably, recent evidence in pregnant women has demonstrated that increased maternal visceral fat thickness correlates with adverse pregnancy outcomes (APOs) even more strongly than BMI(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, the role of preperitoneal subcutaneous fat (PSF)\u0026mdash;a distinct subdepot of abdominal subcutaneous fat located beneath the abdominal muscles\u0026mdash;remains understudied in the context of pregnancy-related risks.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThe original data are freely available online, and had been approved by the authors for use by other investigators(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). This prospective cohort study was conducted at the Murialdo Teaching Health Center - Ultrasound Department, which provides fetal medicine services to the Public Health System in Porto Alegre City, Brazil. Data collection occurred from October 2016 to December 2017. Eligible participants were pregnant women in any trimester of pregnancy, with no exclusion criteria based on pre-existing conditions (e.g., hypertension, diabetes) to reflect real-world clinical practice. A total of 210 women were enrolled, with complete follow-up data available for all participants. The participant selection process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eOne woman had both preterm delivery and hypertensive disorders, and two women had both gestational diabetes mellitus and hypertensive disorders.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Preperitoneal Subcutaneous Fat Measurement\u003c/h2\u003e \u003cp\u003eThe maternal fat ultrasound measurement was performed by a certified sonologist physician and it was measured in all trimesters with a convex probe placed in the middle sagittal epigastric region as described by Lee YS et.al(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Attention was paid to avoid excessive pressure that could falsely compress surfaces of interest. The electronic caliper was then passed from the superficial dermal edge to the linea alba in order to assess the epigastric maternal subcutaneous adipose tissue (PSF).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Baseline and Clinical Data\u003c/h2\u003e \u003cp\u003eBaseline characteristics, including age, ethnicity, pre-pregnancy BMI, hypertension, diabetes, tobacco use, alcohol consumption, and drug use, were collected via structured questionnaires and medical records. BMI was categorized according to WHO standards: normal (18.5\u0026ndash;24.9 kg/m\u0026sup2;), overweight (25.0\u0026ndash;29.9 kg/m\u0026sup2;), and obese (\u0026ge;\u0026thinsp;30.0 kg/m\u0026sup2;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Adverse Pregnancy Outcomes\u003c/h2\u003e \u003cp\u003eAPO was defined as the occurrence of one or more of the following: gestational diabetes mellitus (GDM) was diagnosed according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) criteria, hypertensive disorders of pregnancy included gestational hypertension and preeclampsia, abnormal fetal growth (small for gestational age, intrauterine growth restriction-IUGR), preterm delivery, or preterm premature rupture of membranes before 37 weeks(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), Outcome data were extracted from delivery records and neonatal charts.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using Free Statistics software version 1.3(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Continuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range) and compared using the Mann\u0026ndash;Whitney U test. Categorical variables were presented as frequencies (percentages) and compared using the chi-squared test or Fisher\u0026rsquo;s exact test (when expected frequencies\u0026thinsp;\u0026lt;\u0026thinsp;5). Logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between PSF and APO. Four models were constructed: Model I (unadjusted), Model II (adjusted for age and ethnicity), Model III (Model II\u0026thinsp;+\u0026thinsp;BMI), and Model IV (Model III\u0026thinsp;+\u0026thinsp;smoking, drinking, hypertension, and diabetes). Interaction analyses were performed to assess effect modification by potential confounders. A two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics\u003c/h2\u003e \u003cp\u003eThe study population had a median age of 26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5 years (range 15\u0026ndash;43 years). Of the 210 participants, 189 (90.0%) had PSF\u0026thinsp;\u0026lt;\u0026thinsp;24.7 mm and 21 (10.0%) had PSF\u0026thinsp;\u0026ge;\u0026thinsp;24.7 mm. Baseline characteristics differed significantly between the two groups in terms of ethnicity (p\u0026thinsp;=\u0026thinsp;0.008), hypertension (p\u0026thinsp;=\u0026thinsp;0.002), and BMI classification (p\u0026thinsp;=\u0026thinsp;0.019). Women with PSF\u0026thinsp;\u0026ge;\u0026thinsp;24.7 mm were more likely to be white (81.0% vs. 50.0%), have hypertension (23.8% vs. 3.2%), and be overweight/obese (95.2% vs. 71.4%) compared to those with PSF\u0026thinsp;\u0026lt;\u0026thinsp;24.7 mm (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). No significant differences were observed in age, diabetes, tobacco use, alcohol consumption, or drug use between groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of selected participants\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;210)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003ePSF(mm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003estatistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24.7mm\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;189)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24.7mm\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eU\u0026thinsp;=\u0026thinsp;3.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eethnicity, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93 (50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (31.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown/Asian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehypertension, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e199 (94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16 (76.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11( 5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (23.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediabetes, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e203 (97.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e184 (97.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 ( 2.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etobacco, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169 (80.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e155 (82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (66.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ealcohol, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176 (83.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (85.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (16.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edrugs, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFisher\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e202 (96.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e183 (96.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19 (95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 ( 3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, 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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eχ\u0026sup2;=5.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight/Obesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155 (73.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135 (71.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (95.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: Continuous variables were tested for normality; age was normally distributed and presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; P-values were calculated using Fisher\u0026rsquo;s exact test for categorical variables. *Statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Prevalence of Adverse Pregnancy Outcomes\u003c/h2\u003e \u003cp\u003eThe overall prevalence of APO was 25.2% (53/210). The prevalence of APO was significantly higher in the PSF\u0026thinsp;\u0026ge;\u0026thinsp;24.7 mm group (81.0%, 17/21) compared to the PSF\u0026thinsp;\u0026lt;\u0026thinsp;24.7 mm group (19.0%, 36/189; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The most common APO in the elevated PSF group were gestational diabetes mellitus (34.8%) and hypertensive disorders of pregnancy (26.1%), while in the lower PSF group, abnormal fetal growth (8.0%) and preterm delivery (5.3%) were most frequent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association Between PSF and Adverse Pregnancy Outcomes\u003c/h2\u003e \u003cp\u003eUnadjusted logistic regression showed that PSF\u0026thinsp;\u0026ge;\u0026thinsp;24.7 mm was strongly associated with APO (OR\u0026thinsp;=\u0026thinsp;13.15, 95% CI\u0026thinsp;=\u0026thinsp;4.52\u0026ndash;38.19, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This association remained robust after sequential adjustment for confounders: Model II (OR\u0026thinsp;=\u0026thinsp;17.20, 95% CI\u0026thinsp;=\u0026thinsp;5.23\u0026ndash;56.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Model III (OR\u0026thinsp;=\u0026thinsp;16.27, 95% CI\u0026thinsp;=\u0026thinsp;4.90\u0026ndash;54.02, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Model IV (OR\u0026thinsp;=\u0026thinsp;18.28, 95% CI\u0026thinsp;=\u0026thinsp;4.87\u0026ndash;68.62, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As a continuous variable, each 1-mm increase in PSF was associated with a 16% increase in APO risk (adjusted OR\u0026thinsp;=\u0026thinsp;1.16, 95% CI\u0026thinsp;=\u0026thinsp;1.09\u0026ndash;1.23, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Interaction analysis revealed no significant effect modification by age, BMI, ethnicity, or lifestyle factors.Subgroup analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) further explored the association between PSF and APO across different populations: adjusted associations were statistically significant in the White ethnicity subgroup (adj_P_value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), age\u0026thinsp;\u0026ge;\u0026thinsp;25 years subgroup (adj_P_value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), overweight/obesity BMI subgroup (adj_P_value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and non-alcohol use subgroup (adj_P_value\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No significant associations were observed in other subgroups (e.g., Black ethnicity, age\u0026thinsp;\u0026lt;\u0026thinsp;25 years), though the overall interaction P-values for all subgroups were \u0026gt;\u0026thinsp;0.05, confirming the robustness of the main association.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate regression analysis of the association between PSF and APO\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModelⅠ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel Ⅱ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eModel Ⅲ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel Ⅳ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSF(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003cp\u003e(1.08\u0026thinsp;~\u0026thinsp;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003cp\u003e(1.09\u0026thinsp;~\u0026thinsp;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15 (1.09\u0026thinsp;~\u0026thinsp;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.16 (1.09\u0026thinsp;~\u0026thinsp;1.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBinary variable(mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;24.7mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24.7mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.15 (4.52\u0026ndash;38.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.2 (5.23\u0026ndash;56.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.27 (4.9-54.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.28 (4.87\u0026ndash;68.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eModel I: no adjusted.\u003c/p\u003e \u003cp\u003eModel II: adjusted for age and ethnicity\u003c/p\u003e \u003cp\u003eModel III: Model II\u0026thinsp;+\u0026thinsp;BMI\u003c/p\u003e \u003cp\u003eModel IV: Model III +smoking\u0026thinsp;+\u0026thinsp;drinking+ hypertension\u0026thinsp;+\u0026thinsp;diabetes\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Forest plot of subgroup analyses for the association between preperitoneal subcutaneous fat (PSF) and adverse pregnancy outcomes (APO)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eadjusted for age,ethnicity, BMI,tobacco use, alcohol use,hypertension, diabetes.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Key Findings\u003c/h2\u003e \u003cp\u003eThis study demonstrates that elevated preperitoneal subcutaneous fat (PSF) during pregnancy is strongly associated with adverse pregnancy outcomes, independent of BMI and other confounding factors. Women with PSF\u0026thinsp;\u0026ge;\u0026thinsp;24.7 mm had a seven-fold higher prevalence of APO and an 18-fold increased odds of APO after full adjustment. Our findings are consistent with Kennedy et al.(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), who reported that maternal abdominal subcutaneous fat thickness is a robust predictor of APO, but extend this observation by focusing on PSF\u0026mdash;a distinct subdepot that is easier to measure via routine obstetric ultrasound than visceral fat(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mechanisms\u003c/h2\u003e \u003cp\u003eExcessive maternal subcutaneous fat increases the risk of adverse pregnancy outcomes such as hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), preterm birth, and fetal growth restriction (FGR) through multiple mechanisms, including chronic inflammatory response, insulin resistance, and lipid metabolism disorders.\u003c/p\u003e \u003cp\u003e \u003cb\u003eChronic Inflammation and Oxidative Stress\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExcessive adipose tissue, especially central obesity, initiates and sustains a state of chronic low-grade systemic inflammation. Adipose tissue is not only an energy storage depot but also an active endocrine organ that secretes various pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α), interleukin-1 (IL-1), and interleukin-6 (IL-6). These inflammatory mediators promote the production of reactive oxygen species (ROS) and reactive nitrogen species (RNS), leading to elevated levels of oxidative stress and nitrosative stress in the body. This adipose tissue-driven inflammatory environment is considered a major mechanism linking obesity to multiple complications, including hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), and intrauterine growth restriction (IUGR) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eInsulin Resistance (IR)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eObesity itself is often accompanied by decreased insulin sensitivity, namely insulin resistance. Pregnancy is a process of gradually enhanced physiological insulin resistance, aimed at delivering more nutrients to the developing fetus. For obese women with pre-existing insulin resistance before pregnancy, this physiological process is further exacerbated, making them more prone to developing GDM. In addition, certain lipid metabolites, such as ceramides and sphingomyelins, have been shown in animal models and in vitro experiments to exacerbate insulin resistance by impairing insulin signaling pathways in skeletal muscle and adipocytes (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eLipid Metabolism Disorders\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMaternal obesity is closely associated with dyslipidemia, characterized by elevated levels of triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), and free fatty acids, while high-density lipoprotein cholesterol (HDL-C) levels may be decreased. In the third trimester of pregnancy, maternal metabolism shifts to catabolism, with increased adipose tissue breakdown leading to elevated circulating lipoprotein levels\u0026mdash;especially increased triglyceride content in very low-density lipoprotein (VLDL), high-density lipoprotein (HDL), and low-density lipoprotein (LDL) particles. This alteration in lipid profile not only increases the risk of GDM and preeclampsia but may also transport more glucose, lipids, and amino acids across the placenta, affecting fetal growth and development and resulting in macrosomia or intrauterine growth restriction(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) .\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdipokine Imbalance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the obese state, the secretion of various adipokines by adipose tissue is dysregulated, with elevated leptin levels (hyperleptinemia) and decreased adiponectin levels (hypoadiponectinemia) being particularly prominent. This adipokine imbalance may partially explain the tendency of obese pregnant women to develop obstetric complications such as gestational hypertension, preeclampsia, and GDM. Additionally, the adipokine visfatin is elevated in both obese and GDM pregnant women and is believed to be involved in the pathogenesis of GDM (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Limitations and Strengths\u003c/h2\u003e \u003cp\u003eThis study has several limitations. First, the sample size, while sufficient for detecting strong associations, may limit subgroup analyses of specific APO (e.g., preterm premature rupture of membranes). Second,the maternal fat ultrasound measurement was performed by a single certified sonologist physician instead of two, which may impact measurement bias. The pre-pregnant BMI was calculated from self-reported weight before pregnancy in a limited group of cases without first trimester weight in the hospital charts, which may result in recall bias. Future studies with larger sample sizes should investigate the temporal changes in PSF across trimesters and its association with specific APO subtypes, such as early-onset preeclampsia.\u003c/p\u003e \u003cp\u003eStrengths include the prospective design, standardized ultrasound measurements, comprehensive adjustment for confounders, and integration with existing literature. The use of a population-based sample from a public health system enhances generalizability to diverse clinical settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003ePreperitoneal subcutaneous fat thickness is a potent predictor of adverse pregnancy outcomes. Incorporating PSF measurement into routine obstetric ultrasound may improve early risk detection and management.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdverse Pregnancy Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGDM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGestational Diabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHypertensive Disorders of Pregnancy\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIUGR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIntrauterine Growth Restriction\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF-α\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Necrosis Factor-α\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin-1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL-6\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin-6\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive Oxygen Species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRNS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive Nitrogen Species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInsulin Resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHDL-C\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHigh-Density Lipoprotein Cholesterol\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVLDL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVery Low-Density Lipoprotein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBody Mass Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe author would like to thank the staff of the Murialdo Teaching Health Center in Porto Alegre, Brazil for their assistance with data collection. Gratitude is also extended to the participants and their families for their support and participation in this study.\u003c/p\u003e\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis prospective cohort study was reviewed and approved by the Research Ethics Committee of the municipality of Porto Alegre, Brazil (approval number: 2.132.090). All participants provided written informed consent prior to enrollment, in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eAll participants have provided written consent for the publication of their de-identified data and study findings. No identifying personal information (e.g., names, addresses, medical record numbers) is included in the manuscript or supplementary materials.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eData from the PhysioNet database were used in compliance with the database\u0026apos;s terms of use (https://physionet.org/about/terms-of-use/).\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests. No financial, professional, or personal relationships with other individuals or organizations could inappropriately influence (bias) this work.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was not supported by any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eHuo Lili (HL) designed the study, collected and analyzed the data, drafted the manuscript, and approved the final version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSantos S, Eekhout I, Voerman E, Gaillard R, Barros H, Charles MA, et al. Gestational weight gain charts for different body mass index groups for women in Europe, North America, and Oceania. BMC Med. 2018;16:201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCnattingius S, Bergstr\u0026ouml;m R, Lipworth L, Kramer MS. 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Front Endocrinol (Lausanne). 2021;12:811776.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKennedy NJ, Peek MJ, Quinton AE, Lanzarone V, Martin A, Benzie R, et al. Maternal abdominal subcutaneous fat thickness as a predictor for adverse pregnancy outcome: a longitudinal cohort study. BJOG. 2016;123(2):225\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWnuk A, Stangret A, Wątroba M, Płatek AE, Skoda M, Cendrowski K, et al. Can adipokine visfatin be a novel marker of pregnancy-related disorders in women with obesity? Obes Rev. 2020 July;21(7):e13022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlbrecht M, Worthmann A, Heeren J, Diemert A, Arck PC. Maternal lipids in overweight and obesity: implications for pregnancy outcomes and offspring\u0026rsquo;s body composition. Semin Immunopathol. 2025;47(1):10.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBays HE, Jones PH, Brown WV, Jacobson TA. National Lipid Association Annual Summary of Clinical Lipidology 2015. J Clin Lipidol. 2014;8(6):S1\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eValencia-Ortega J, Solis-Paredes JM, Saucedo R, Estrada-Gutierrez G, Camacho-Arroyo I. Excessive Pregestational Weight and Maternal Obstetric Complications: The Role of Adipokines. Int J Mol Sci. 2023 Sept 28;24(19):14678.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Preperitoneal subcutaneous fat, adverse pregnancy outcomes, ultrasound, maternal adiposity, pregnancy complications","lastPublishedDoi":"10.21203/rs.3.rs-8670456/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8670456/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMaternal adipose tissue remodeling during pregnancy is a critical physiological process, but excessive fat accumulation—particularly in specific depots—may elevate metabolic and gestational risks. This study aimed to investigate the association between preperitoneal subcutaneous fat (PSF) thickness across pregnancy trimesters and adverse pregnancy outcomes (APO), while integrating existing evidence on maternal adiposity and pregnancy health. A prospective cohort study was conducted at the Murialdo Teaching Health Center in Porto Alegre, Brazil, enrolling 210 pregnant women between October 2016 and December 2017. PSF thickness was measured via ultrasound, and APO was defined as the occurrence of gestational diabetes mellitus, hypertensive disorders of pregnancy, abnormal fetal growth, preterm delivery, or preterm premature rupture of membranes. Statistical analyses included Mann–Whitney tests, chi-squared/Fisher’s exact tests, and logistic regression models (unadjusted and multivariate-adjusted). Results showed a total APO prevalence of 25.2% (53/210), with a significantly higher prevalence in women with PSF ≥24.7 mm (73.9%, 17/23) compared to those with PSF \u0026lt;24.7 mm (19.3%, 36/187; p\u0026lt;0.001). Multivariate regression confirmed PSF as a strong independent predictor of APO (adjusted OR=18.28, 95% CI=4.87–68.62, p\u0026lt;0.001). Subgroup analyses revealed associations between elevated PSF and maternal hypertension, white ethnicity, and overweight/obesity. Integration with existing literature highlighted that PSF, like visceral fat, correlates more strongly with metabolic risk factors and APO than traditional BMI, supporting its utility as a non-invasive predictive marker. Conclusion: Elevated preperitoneal subcutaneous fat during pregnancy is independently associated with adverse pregnancy outcomes. Ultrasound measurement of PSF offers a simple, accessible tool for identifying high-risk pregnancies, enabling targeted interventions to improve maternal and fetal health.\u003c/p\u003e","manuscriptTitle":"Preperitoneal Subcutaneous Fat and Adverse Pregnancy Outcomes: A Comprehensive Cohort Study and Literature Integration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 11:19:29","doi":"10.21203/rs.3.rs-8670456/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d5212b8a-486c-4270-a2a8-7832ed53ce22","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T11:57:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 11:19:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8670456","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8670456","identity":"rs-8670456","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}