The Relationship Between Maternal Anthropometric Indices And Prediction Of Preeclampsia: A Prospective Cohort Study

The Relationship Between Maternal Anthropometric Indices And Prediction Of Preeclampsia: A Prospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Relationship Between Maternal Anthropometric Indices And Prediction Of Preeclampsia: A Prospective Cohort Study Burcu Bozkurt Özdal, Atakan Tanaçan, Elif Nihan Tekin, Esra Karataş, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6839495/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 Background Preeclampsia is a significant pregnancy complication with challenges in early detection. Given the association between obesity and increased preeclampsia risk, anthropometric indices may serve as useful tools for early prediction. Objective To investigate the performance of anthropometric indices defining obesity in predicting preeclampsia Materials and Methods The study included all pregnant women presenting to the Obstetrics and Gynecology Department of Ankara City Hospital during the first trimester between January and December 2024 irrespective of their body composition. Gestational age was determined based on the last menstrual period or crown-rump length measured during the first trimester. The clinical and demographic data of all participants were recorded, including waist circumference, hip circumference, height, weight, and blood pressure measurements. These data were used to calculate body mass index (BMI), body rounding index (BRI), body adiposity index (BAI), and waist-to-height ratio. Results The preeclampsia-positive group had significantly higher weight, hip circumference, waist circumference, BMI, BAI, BRI, and waist-to-height ratio values compared to the preeclampsia-negative group (p<0.005). The receiver operating characteristic analysis revealed the following optimal cut-off values for predicting preeclampsia: waist-to-height ratio: 0.49 (69% sensitivity, 60% specificity; area under the curve [AUC]=0.659; p=0.038); BMI: 25.9 (75% sensitivity, 58% specificity; AUC=0.729; p=0.003); BRI: 2.92 (69% sensitivity, 59% specificity; AUC=0.652; p=0.049); and BAI: 28.5 (75% sensitivity, 56% specificity; AUC=0.702; p=0.009). Conclusion : BMI, BRI, waist-to-height ratio, and BAI are effective indices for predicting preeclampsia. Health sciences/Diseases/Cardiovascular diseases/Hypertension Health sciences/Risk factors body mass index body rounding index body adiposity index waist-to-height ratio Figures Figure 1 1. Introduction Preeclampsia is been defined hypertension (systolic blood pressure > 140 mmHg, diastolic blood pressure > 90 mmHg) typically after the 20th week of gestation or during the postpartum period in a previously normotensive patient, accompanied by proteinuria or, in the absence of proteinuria, the presence of hypertension and end-organ damage [ 1 ]. According to a systematic review, preeclampsia occurs in 4.6% of pregnancies worldwide [ 2 ]. Early epidemiological studies have highlighted the critical role of the placenta in the pathogenesis of preeclampsia. Research has revealed that placental tissue is necessary for the development of the disease, although the fetus itself is not required [ 3 ]. The development of preeclampsia involves genetic, immunological, maternal, and environmental factors [ 4 ]. Among maternal factors, obesity has been identified as a significant contributor. Studies have established a link between increasing body mass index (BMI) and the risk of preeclampsia [ 5 ]. Preeclampsia (PE) is a leading cause of maternal morbidity and mortality [ 6 ]. Effective predictive models for preeclampsia are essential to achieve better patient outcomes. The literature has introduced novel anthropometric indices for defining obesity. The aim of this study was to examine the performance of these anthropometric indices in predicting preeclampsia and neonatal intensive care unit (NICU) admissions. 2. Material and Method 2.1.Study Population This prospective, single-center study was conducted at a tertiary hospital. Patients diagnosed with pregnancy and presenting to the Ankara City Hospital Department of Obstetrics and Gynecology in their first trimester between January 2024 and December 2024 were included in the study irrespective of their body composition. Informed consent forms were obtained from all participants, and the study was approved by the Ethics Committee of Ankara City Hospital (approval number: TABED 2-24-236). All stages of the study adhered to the principles of the Declaration of Helsinki. Gestational age is based on the first day of the last menstrual period or first trimester fetal measurements. The clinical and demographic information of all included patients was recorded. Waist circumference and hip circumference were measured with a tape measure, and height and weight were documented. Blood pressure measurements were also taken. Using the recorded data, BMI, body rounding index (BRI), body adiposity index (BAI), and the waist circumference-to-height ratio were calculated. The formula we use to calculate BMI is BMI = weight/height², using metric units [ 7 ]. BAI and BRI were measured using an automated calculator developed by Bergman et al., which accounts for ethnicity and age ( https://webfce.com/bri-calculator/ ) [ 8 ]. The patients were followed up from the 20th week of gestation until the 10th postpartum day for the development of preeclampsia. Preeclampsia was diagnosed by hypertension (systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg) combined with either proteinuria (≥ 300 mg in a 24-hour urine collection) or end-organ dysfunction in the absence of proteinuria. The exclusion criteria were multiple pregnancies, organ transplantation, immunodeficiency, pre-existing hypertension, diabetes mellitus, or incomplete data. 2.2.Statistical Analysis Sample size estimation was performed using G*Power software (version 3.1; Franz Foul, University of Kiel, Kiel, Germany). Based on a significance level of 0.05 (two-tailed), 95% power, and a large effect size (0.80), the required sample size was calculated to be 70 participants. Statistical analyses were conducted using SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). The Kolmogorov-Smirnov and Shapiro-Wilk tests were applied to assess the normality of the data distribution. The Mann-Whitney U test was used for comparisons of non-normally distributed variables, and descriptive analyses included medians with minimum and maximum values for such data. The chi-square test was used for categorical variables. Receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off values of BMI, BAI, BRI, and the waist circumference-to-height ratio for predicting preeclampsia. 3. Results A total of 187 patients were initially included in the study. Forty-four patients were excluded due to follow-up at other healthcare facilities, resulting in a final sample of 143 patients. During follow-up, 16 patients developed preeclampsia, while 127 did not. The patients were categorized into two groups: those with preeclampsia and those without preeclampsia. Clinical and demographic data of the preeclampsia-positive and preeclampsia-negative groups, including gestational age at presentation, blood pressure readings, height, weight, waist circumference, hip circumference, BMI, BRI, and BAI indices, are presented in Table 1 . Both groups had similar values for age, gravidity, parity, systolic and diastolic blood pressure at admission, and height. However, the preeclampsia-positive group had significantly higher weight, hip circumference, waist circumference, BMI, BAI, BRI, and waist-to-height ratio values compared to the preeclampsia-negative group (p < 0.05). Table 1 Clinical demographic data, biochemistry values, ​​and anthropometric measurements of the patients Variables Preeclampsia-positive n = 16 Preeclampsia-negative n = 127 P-value Age (years) 28.5 (21–33) 28 (19–40) 0.883 Gestational age at presentation (weeks) 10.0 (3.0–14) 11 (6.0–14) 0.045 Gravida 2.0 (1.0–7.0) 2.0 (1.0–6.0) 0.895 Parity 0.0 (0.0–2.0) 1.0 (0.0–3.0) 0.250 Systolic blood pressure at presentation (mmHg) 110 (100–122) 110 (90–122) 0.209 Diastolic blood pressure at presentation (mmHg) 60 (60–80) 60 (60–80) 0.803 Height (kg) 164.5 (154–175) 161 (150–173) 0.187 Weight (cm) 77 (63–117) 65 (43–108) 0.000 Waist circumference (cm) 92.5 (58–108) 82 (71–119) 0.002 Hip circumference (cm) 104.5 (87–140) 96 (66–131) 0.000 Waist-to-height ratio 0.50 (0.44–0.72) 0.47 (0.35–0.68) 0.038 BAI 31.9 (23.8–48) 27.9 (14.6–48.6) 0.009 BMI 28.8 (23.1–43) 25.2 (16.2–42.1) 0.003 BRI 3.2 (52.2–6.7) 2.8 (0.9–7.4) 0.048 Table 2 Neonatal outcomes of the patients Variables Preeclampsia-positive n = 16 Preeclampsia-negative n = 127 P value NICU admission 4 (25%) 10 (7.8%) 0.038 Birth weight (grams) 2890 (1,500–4,600) 3170 (1,620–4,470) 0.300 Gestational age at delivery (weeks) 36 (33–40) 39 (31–41) 0.005 First-minute Apgar score 7 (6–9) 8 (0–9) 0.268 Fifth-minute Apgar score 9 (8–10) 9 (0–10) 0.265 NICU: neonatal intensive care unit The ROC analysis revealed that the optimal cut-off value for the waist-to-height ratio in predicting preeclampsia was 0.49, with 69% sensitivity and 60% specificity (area under the curve [AUC] = 0.659; p = 0.038). For BMI, the optimal cut-off value was 25.9, with 75% sensitivity and 58% specificity (AUC = 0.729; p = 0.003) (Fig. 1 ). The optimal cut-off value for BRI was 2.92, with 69% sensitivity and 59% specificity (AUC = 0.652; p = 0.049). The optimal cut-off value for BAI was 28.5, with 75% sensitivity and 56% specificity (AUC = 0.702; p = 0.009) (Fig. 1 ). 4. Discussion This study examined anthropometric indices defining obesity as a maternal factor in the etiology of preeclampsia. Each index, including waist-to-height ratio, BMI, BRI, and BAI, was found to have higher values in patients with preeclampsia. Cut-off values for each body fat index were determined.It was observed that patients with pre-eclampsia had high rates of NICU hospitalization. However, no significant relationship was found between body fat indices and neonatal outcomes. Proangiogenic factor (PIGF), anti-angiogenic factor (sflt-1), pregnancy-associated plasma protein A (PAPP-A), maternal age, maternal height and weight are among the many parameters used to predict preeclampsia. The Fetal Medicine Society (FMF) has developed a calculator that combines these parameters and is used in the evaluation of preeclampsia in the first trimester[ 9 ]. The advantage of body adipose indices over other parameters is that they can predict and prevent preeclampsia even before pregnancy occurs. Globally, obesity is becoming one of the most prevalent conditions with significant public health impacts [ 10 ]. Weight gain and obesity are primary risk factors for hypertension [ 11 ]. Increased weight elevates blood pressure by increasing cardiac output, whereas weight loss reduces blood pressure [ 12 ]. Obesity also activates the renin-angiotensin-aldosterone system, leading to hypertension [ 13 ]. Various methods are available for assessing body fat. While dual-energy X-ray, computed tomography, and magnetic resonance imaging, they are costly and time-consuming, making them unsuitable for routine clinical use [ 14 ]. In clinical practice, BMI is the most commonly used method to estimate body fat [ 15 , 16 ]. However, BMI does not differentiate between fat and lean tissue or assess fat distribution. Therefore, alternative indices have been developed, such as BRI, waist-to-height ratio, and BAI [ 17 ]. New adiposity indices have been compared for predicting hypertension, diabetes, and cardiometabolic syndrome, which are risks associated with obesity. Nevertheless, the superiority of these indices over one another remains unclear [ 18 , 19 ]. Furthermore, anthropometric measurements have been shown to vary in predictive power depending on sex and ethnicity [ 20 ]. These indices have demonstrated utility in predicting the progression from prediabetes to diabetes[ 21 ] and have an inverse relationship with osteoporosis [ 22 ]. Obesity contributes to the susceptibility to preeclampsia by inducing chronic inflammation and endothelial dysfunction, making it a factor in the etiology of the disease [ 23 , 24 ]. The preventable nature of obesity as a risk factor for preeclampsia and the ability of novel body fat indices to predict the condition formed the foundation of this study. The only obstetric study on these indices examined BRI as an independent variable for predicting fetal macrosomia [ 25 ]. Unfortunately, the lack of studies in the literature on newly created anthropometric indices in the prediction of preeclampsia during pregnancy has prevented the comparison of our study with other studies. The fact that obesity, known to be a risk factor for pre-eclampsia, is preventable and the ability of new body fat indices to predict the condition formed the basis of this study. At the same time, it is advantageous that obesity can be corrected with lifestyle changes such as diet and exercise before pregnancy. This study has several limitations. Firstly, it was conducted as a single-center investigation, which may restrict the generalizability of the findings to the broader population. Results from multi-center studies conducted in diverse geographic locations could provide additional insights and enhance the validity of the conclusions drawn. Secondly, the sample size of 143 participants may limit the statistical power of the study. A larger sample size would be beneficial for obtaining more robust and reliable results. Additionally, the exclusion criteria, which involved omitting participants with pre-existing health conditions (such as hypertension, diabetes, and immunodeficiency), may have resulted in a focus on a specific patient population. This could limit the applicability of the findings to other demographic groups.. In conclusion , BMI, BRI, waist-to-height ratio, and BAI are useful indices for predicting preeclampsia, with BMI and BAI having the best performance, followed by BRI and waist-to-height ratio. Declarations Availability of data and materials The data supporting this study is available through the corresponding author upon reasonable request. Author contribution: BBÖ: Design the method to achieve results,Data collecting and processing,Literature scan,Article writing AT: Hypothesis of the research,Organizing the execution of the work,Monitor its progress and take responsibility,Article writing NET: Data collecting and processing EK: Data collecting and processing HK: Analysis-Comment,Critical examination ŞB: Analysis-Comment,Critical examination ÖK: : Organizing the execution of the work, DŞ : Article writing,Critical examination,Critical examination Ethics approval and consent to participate All participants signed informed written consent before being enrolled in the study. Human Ethics The study was reviewed and approved by the ethics committee of Ankara City Hospital ethics committee (TABED-1-24-408). All procedures were performed according to the Declaration of Helsinki. Competing interests The authors have no relevant financial or non-financial interests to disclose. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript, and this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Gestational Hypertension and Preeclampsia: ACOG Practice Bulletin, Number 222. Obstet Gynecol. 2020;135(6):e237-e260. doi: 10.1097/AOG.0000000000003891 . PMID: 32443079. Abalos E, Cuesta C, Grosso AL, Chou D, Say L. Global and regional estimates of preeclampsia and eclampsia: a systematic review. Eur J Obstet Gynecol Reprod Biol. 2013;170(1):1–7. doi: 10.1016/j.ejogrb.2013.05.005 . Epub 2013 Jun 7. PMID: 23746796. 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12:45:52","extension":"xml","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77219,"visible":true,"origin":"","legend":"","description":"","filename":"2025EJCN06210structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-6839495/v1/6c7d942414617ccb60663f57.xml"},{"id":91989984,"identity":"8fba7198-c23d-4e5e-93a1-6c94384d4d31","added_by":"auto","created_at":"2025-09-23 12:37:52","extension":"html","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92679,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-6839495/v1/2168c45b69b1743ccb4badc5.html"},{"id":91989980,"identity":"ec6ec2c6-f511-42bb-8630-ff1cf5a1a492","added_by":"auto","created_at":"2025-09-23 12:37:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2068434,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves of anthropometric indices in predicting preeclampsia\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6839495/v1/fe698cb2ec5467d48ca4de91.jpg"},{"id":94729081,"identity":"be92e8bc-701a-4014-8a27-e3ac92af9eeb","added_by":"auto","created_at":"2025-10-30 07:04:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2653421,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6839495/v1/c144f70d-d7bc-4304-8ff9-800c6b3e8dbe.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"The Relationship Between Maternal Anthropometric Indices And Prediction Of Preeclampsia: A Prospective Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePreeclampsia is been defined hypertension (systolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;140 mmHg, diastolic blood pressure\u0026thinsp;\u0026gt;\u0026thinsp;90 mmHg) typically after the 20th week of gestation or during the postpartum period in a previously normotensive patient, accompanied by proteinuria or, in the absence of proteinuria, the presence of hypertension and end-organ damage [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to a systematic review, preeclampsia occurs in 4.6% of pregnancies worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly epidemiological studies have highlighted the critical role of the placenta in the pathogenesis of preeclampsia. Research has revealed that placental tissue is necessary for the development of the disease, although the fetus itself is not required [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The development of preeclampsia involves genetic, immunological, maternal, and environmental factors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Among maternal factors, obesity has been identified as a significant contributor. Studies have established a link between increasing body mass index (BMI) and the risk of preeclampsia [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePreeclampsia (PE) is a leading cause of maternal morbidity and mortality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Effective predictive models for preeclampsia are essential to achieve better patient outcomes. The literature has introduced novel anthropometric indices for defining obesity. The aim of this study was to examine the performance of these anthropometric indices in predicting preeclampsia and neonatal intensive care unit (NICU) admissions.\u003c/p\u003e"},{"header":"2. Material and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.Study Population\u003c/h2\u003e \u003cp\u003eThis prospective, single-center study was conducted at a tertiary hospital. Patients diagnosed with pregnancy and presenting to the Ankara City Hospital Department of Obstetrics and Gynecology in their first trimester between January 2024 and December 2024 were included in the study irrespective of their body composition. Informed consent forms were obtained from all participants, and the study was approved by the Ethics Committee of Ankara City Hospital (approval number: TABED 2-24-236). All stages of the study adhered to the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eGestational age is based on the first day of the last menstrual period or first trimester fetal measurements. The clinical and demographic information of all included patients was recorded. Waist circumference and hip circumference were measured with a tape measure, and height and weight were documented. Blood pressure measurements were also taken. Using the recorded data, BMI, body rounding index (BRI), body adiposity index (BAI), and the waist circumference-to-height ratio were calculated. The formula we use to calculate BMI is BMI\u0026thinsp;=\u0026thinsp;weight/height\u0026sup2;, using metric units [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. BAI and BRI were measured using an automated calculator developed by Bergman et al., which accounts for ethnicity and age (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://webfce.com/bri-calculator/\u003c/span\u003e\u003cspan address=\"https://webfce.com/bri-calculator/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe patients were followed up from the 20th week of gestation until the 10th postpartum day for the development of preeclampsia. Preeclampsia was diagnosed by hypertension (systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg) combined with either proteinuria (\u0026ge;\u0026thinsp;300 mg in a 24-hour urine collection) or end-organ dysfunction in the absence of proteinuria.\u003c/p\u003e \u003cp\u003eThe exclusion criteria were multiple pregnancies, organ transplantation, immunodeficiency, pre-existing hypertension, diabetes mellitus, or incomplete data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2.Statistical Analysis\u003c/h2\u003e \u003cp\u003eSample size estimation was performed using G*Power software (version 3.1; Franz Foul, University of Kiel, Kiel, Germany). Based on a significance level of 0.05 (two-tailed), 95% power, and a large effect size (0.80), the required sample size was calculated to be 70 participants.\u003c/p\u003e \u003cp\u003eStatistical analyses were conducted using SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). The Kolmogorov-Smirnov and Shapiro-Wilk tests were applied to assess the normality of the data distribution. The Mann-Whitney U test was used for comparisons of non-normally distributed variables, and descriptive analyses included medians with minimum and maximum values for such data. The chi-square test was used for categorical variables. Receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off values of BMI, BAI, BRI, and the waist circumference-to-height ratio for predicting preeclampsia.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 187 patients were initially included in the study. Forty-four patients were excluded due to follow-up at other healthcare facilities, resulting in a final sample of 143 patients. During follow-up, 16 patients developed preeclampsia, while 127 did not. The patients were categorized into two groups: those with preeclampsia and those without preeclampsia.\u003c/p\u003e \u003cp\u003eClinical and demographic data of the preeclampsia-positive and preeclampsia-negative groups, including gestational age at presentation, blood pressure readings, height, weight, waist circumference, hip circumference, BMI, BRI, and BAI indices, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Both groups had similar values for age, gravidity, parity, systolic and diastolic blood pressure at admission, and height. However, the preeclampsia-positive group had significantly higher weight, hip circumference, waist circumference, BMI, BAI, BRI, and waist-to-height ratio values compared to the preeclampsia-negative group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003eClinical demographic data, biochemistry values, ​​and anthropometric measurements of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreeclampsia-positive\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreeclampsia-negative\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;127\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.5 (21\u0026ndash;33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (19\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at presentation (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (3.0\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (6.0\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.0\u0026ndash;7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0 (1.0\u0026ndash;6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.895\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0 (0.0\u0026ndash;2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.0\u0026ndash;3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure at presentation (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110 (100\u0026ndash;122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110 (90\u0026ndash;122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure at presentation (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 (60\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60 (60\u0026ndash;80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeight (kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e164.5 (154\u0026ndash;175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e161 (150\u0026ndash;173)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77 (63\u0026ndash;117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65 (43\u0026ndash;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92.5 (58\u0026ndash;108)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (71\u0026ndash;119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHip circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.5 (87\u0026ndash;140)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (66\u0026ndash;131)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist-to-height ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.50 (0.44\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.47 (0.35\u0026ndash;0.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.9 (23.8\u0026ndash;48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.9 (14.6\u0026ndash;48.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.8 (23.1\u0026ndash;43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.2 (16.2\u0026ndash;42.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.2 (52.2\u0026ndash;6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.8 (0.9\u0026ndash;7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\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 \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\u003eNeonatal outcomes of the patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePreeclampsia-positive\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreeclampsia-negative\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;127\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNICU admission\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight (grams)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2890 (1,500\u0026ndash;4,600)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3170 (1,620\u0026ndash;4,470)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational age at delivery (weeks)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36 (33\u0026ndash;40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (31\u0026ndash;41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst-minute Apgar score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (6\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (0\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFifth-minute Apgar score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (8\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (0\u0026ndash;10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNICU: neonatal intensive care unit\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ROC analysis revealed that the optimal cut-off value for the waist-to-height ratio in predicting preeclampsia was 0.49, with 69% sensitivity and 60% specificity (area under the curve [AUC]\u0026thinsp;=\u0026thinsp;0.659; p\u0026thinsp;=\u0026thinsp;0.038). For BMI, the optimal cut-off value was 25.9, with 75% sensitivity and 58% specificity (AUC\u0026thinsp;=\u0026thinsp;0.729; p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The optimal cut-off value for BRI was 2.92, with 69% sensitivity and 59% specificity (AUC\u0026thinsp;=\u0026thinsp;0.652; p\u0026thinsp;=\u0026thinsp;0.049). The optimal cut-off value for BAI was 28.5, with 75% sensitivity and 56% specificity (AUC\u0026thinsp;=\u0026thinsp;0.702; p\u0026thinsp;=\u0026thinsp;0.009) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study examined anthropometric indices defining obesity as a maternal factor in the etiology of preeclampsia. Each index, including waist-to-height ratio, BMI, BRI, and BAI, was found to have higher values in patients with preeclampsia. Cut-off values for each body fat index were determined.It was observed that patients with pre-eclampsia had high rates of NICU hospitalization. However, no significant relationship was found between body fat indices and neonatal outcomes.\u003c/p\u003e \u003cp\u003eProangiogenic factor (PIGF), anti-angiogenic factor (sflt-1), pregnancy-associated plasma protein A (PAPP-A), maternal age, maternal height and weight are among the many parameters used to predict preeclampsia. The Fetal Medicine Society (FMF) has developed a calculator that combines these parameters and is used in the evaluation of preeclampsia in the first trimester[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe advantage of body adipose indices over other parameters is that they can predict and prevent preeclampsia even before pregnancy occurs.\u003c/p\u003e \u003cp\u003eGlobally, obesity is becoming one of the most prevalent conditions with significant public health impacts [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Weight gain and obesity are primary risk factors for hypertension [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Increased weight elevates blood pressure by increasing cardiac output, whereas weight loss reduces blood pressure [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Obesity also activates the renin-angiotensin-aldosterone system, leading to hypertension [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVarious methods are available for assessing body fat. While dual-energy X-ray, computed tomography, and magnetic resonance imaging, they are costly and time-consuming, making them unsuitable for routine clinical use [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In clinical practice, BMI is the most commonly used method to estimate body fat [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, BMI does not differentiate between fat and lean tissue or assess fat distribution. Therefore, alternative indices have been developed, such as BRI, waist-to-height ratio, and BAI [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. New adiposity indices have been compared for predicting hypertension, diabetes, and cardiometabolic syndrome, which are risks associated with obesity. Nevertheless, the superiority of these indices over one another remains unclear [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Furthermore, anthropometric measurements have been shown to vary in predictive power depending on sex and ethnicity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These indices have demonstrated utility in predicting the progression from prediabetes to diabetes[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and have an inverse relationship with osteoporosis [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObesity contributes to the susceptibility to preeclampsia by inducing chronic inflammation and endothelial dysfunction, making it a factor in the etiology of the disease [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The preventable nature of obesity as a risk factor for preeclampsia and the ability of novel body fat indices to predict the condition formed the foundation of this study. The only obstetric study on these indices examined BRI as an independent variable for predicting fetal macrosomia [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Unfortunately, the lack of studies in the literature on newly created anthropometric indices in the prediction of preeclampsia during pregnancy has prevented the comparison of our study with other studies.\u003c/p\u003e \u003cp\u003eThe fact that obesity, known to be a risk factor for pre-eclampsia, is preventable and the ability of new body fat indices to predict the condition formed the basis of this study. At the same time, it is advantageous that obesity can be corrected with lifestyle changes such as diet and exercise before pregnancy.\u003c/p\u003e \u003cp\u003eThis study has several limitations. Firstly, it was conducted as a single-center investigation, which may restrict the generalizability of the findings to the broader population. Results from multi-center studies conducted in diverse geographic locations could provide additional insights and enhance the validity of the conclusions drawn.\u003c/p\u003e \u003cp\u003eSecondly, the sample size of 143 participants may limit the statistical power of the study. A larger sample size would be beneficial for obtaining more robust and reliable results. Additionally, the exclusion criteria, which involved omitting participants with pre-existing health conditions (such as hypertension, diabetes, and immunodeficiency), may have resulted in a focus on a specific patient population. This could limit the applicability of the findings to other demographic groups..\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn conclusion\u003c/b\u003e, BMI, BRI, waist-to-height ratio, and BAI are useful indices for predicting preeclampsia, with BMI and BAI having the best performance, followed by BRI and waist-to-height ratio.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting this study is available through the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBB\u0026Ouml;:\u003c/strong\u003e Design the method to achieve results,Data collecting and processing,Literature scan,Article writing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAT:\u003c/strong\u003e Hypothesis of the research,Organizing the execution of the work,Monitor its progress and take responsibility,Article writing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNET:\u003c/strong\u003eData collecting and processing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEK:\u003c/strong\u003e Data collecting and processing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHK:\u003c/strong\u003e Analysis-Comment,Critical examination\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eŞB:\u0026nbsp;\u003c/strong\u003eAnalysis-Comment,Critical examination\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026Ouml;K:\u003csup\u003e:\u003c/sup\u003e\u003c/strong\u003e Organizing the execution of the work,\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDŞ\u003c/strong\u003e: Article writing,Critical examination,Critical examination\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants signed informed written consent before being enrolled in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman Ethics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by the ethics committee of Ankara City Hospital ethics committee (TABED-1-24-408). All procedures were performed according to the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript, and this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGestational Hypertension and Preeclampsia: ACOG Practice Bulletin, Number 222. 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PMID: 34162292.\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":"body mass index, body rounding index, body adiposity index, waist-to-height ratio","lastPublishedDoi":"10.21203/rs.3.rs-6839495/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6839495/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePreeclampsia is a significant pregnancy complication with challenges in early detection. Given the association between obesity and increased preeclampsia risk, anthropometric indices may serve as useful tools for early prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo investigate the performance of anthropometric indices defining obesity in predicting preeclampsia\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods \u003c/strong\u003eThe study included all pregnant women presenting to the Obstetrics and Gynecology Department of Ankara City Hospital during the first trimester between January and December 2024 irrespective of their body composition. Gestational age was determined based on the last menstrual period or crown-rump length measured during the first trimester. The clinical and demographic data of all participants were recorded, including waist circumference, hip circumference, height, weight, and blood pressure measurements. These data were used to calculate body mass index (BMI), body rounding index (BRI), body adiposity index (BAI), and waist-to-height ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe preeclampsia-positive group had significantly higher weight, hip circumference, waist circumference, BMI, BAI, BRI, and waist-to-height ratio values compared to the preeclampsia-negative group (p\u0026lt;0.005). The receiver operating characteristic analysis revealed the following optimal cut-off values for predicting preeclampsia: waist-to-height ratio: 0.49 (69% sensitivity, 60% specificity; area under the curve [AUC]=0.659; p=0.038); BMI: 25.9 (75% sensitivity, 58% specificity; AUC=0.729; p=0.003); BRI: 2.92 (69% sensitivity, 59% specificity; AUC=0.652; p=0.049); and BAI: 28.5 (75% sensitivity, 56% specificity; AUC=0.702; p=0.009).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: BMI, BRI, waist-to-height ratio, and BAI are effective indices for predicting preeclampsia.\u003c/p\u003e","manuscriptTitle":"The Relationship Between Maternal Anthropometric Indices And Prediction Of Preeclampsia: A Prospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 12:37:48","doi":"10.21203/rs.3.rs-6839495/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":"eafc2e28-25ed-46cd-b205-cfc85cae9395","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":54720134,"name":"Health sciences/Diseases/Cardiovascular diseases/Hypertension"},{"id":54720135,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-10-29T16:32:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 12:37:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6839495","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6839495","identity":"rs-6839495","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}