Pre-Conception Dyslipidemia and risk for preeclampsia in women undergoing IVF-ET

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The retrospective cohort study consisted of 2994 women who conceived by IVF-ET and delivered live neonates. The study population was divided into two components: a training set for the prediction model development (2288 women) and a test set for validation (706 women). Multivariable logistic regression was used for the development and validation of predictive model for the risk of PE. Among the 2288 women in the training set, 266 women (11.6%) developed PE. Multiple logistic regression analysis identified independent predictors for PE: triglyceride (TG) [adjusted odds ratio (aOR) 1.284; 95% confidence interval (CI) 1.113–1.489, P < 0.001]; pre-pregnancy BMI; pre- chronic hypertension; twin pregnancy; protocol of IVF. These independent predictors for PE were used to form a risk prediction model, and the area under the receiver-operator characteristic (ROC) curve (AUC) in the training and the test set was 0.77 and 0.71, respectively. In conclusion, higher TG levels before pregnancy were independently associated with the risk for PE in women pregnant by IVF-ET. Health sciences/Diseases Health sciences/Risk factors in vitro fertilization and embryo transfer preeclampsia dyslipidemia prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Preeclampsia (PE) affects 2–8% of pregnancies and is the leading cause of maternal and fetal mortality and morbidity globally 1-3 . It also increases women’s long-term risk of cardiovascular diseases 4,5 . Studies have shown that PE and cardiovascular diseases have many common risk factors, such as obesity, history of hypertension, diabetes and dyslipidemia 6-8 . The relationship between dyslipidemia and PE has drawn much attention in recent years 9-12 . In normal pregnancies, there is a physiological increase in serum lipid concentration, which ensures the development of the fetus 13 . However, an abnormal increase in serum lipid levels during pregnancy has been associated with the development of PE 9-12 . In contrast to dyslipidemia during gestation, the relationship between pre-gestational dyslipidemia and PE has not been studied extensively. Nowadays, 8-12% childbearing age women are affected by infertility worldwide 14 , and more than 15% of couples suffer from infertility in China 15 .The use of in vitro fertilization and embryo transfer (IVF-ET) has risen steadily in the worldwide 16,17 . While IVF-ET benefits many couples, it has been established that IVF–ET is associated with adverse pregnancy outcomes, including PE 18,19 . Women pregnant by IVF-ET are older and more likely to have chronic disease (i.e. hypertension, diabetes and dyslipidemia) than women who have spontaneously conceived 20,21 . It has been reported that dyslipidemia was highly prevalent in women undergoing IVF-ET 20,21 . Although significant evidence links dyslipidemia and PE 9-12 , studies focusing on women undergoing IVF-ET are still lack. Thus, the purpose of this study was to investigate the relationship between dyslipidemia prior to conception and the risk of PE in women pregnant by IVF-ET. We also aim to establish and validate a prediction model for the risk of PE based on dyslipidemia, maternal characteristics and IVF-ET related variables in this specific group of women. Method Study population This was a retrospective cohort study conducted at Peking University Third Hospital, a leading tertiary university hospital with an excellence reproductive medical center in China. Women conceived by IVF-ET and delivered live neonates between 1 January 2017 and 31 December 2022 in this hospital were included. Fasting lipid profile tests were routinely conducted before IVF-ET. Exclusion criteria were: no record of lipid measurements, the time duration between lipid measurement and conception exceeding 12 months, histories of taking lipid-lowering agents and histories of PE. For women with more than one birth during the study period, only data from the first pregnancy were analyzed. The study population was divided into two components: a training set for the prediction model development (women delivered between 1 January 2017 and 31 December 2020) and a test set for validation (women delivered between 1 January 2021 and 31 December 2022). This study was approved by the ethics review board of Peking University Third Hospital, and was performed in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the ethics review board of Peking University Third Hospital waived the need of obtaining informed consent. Date collection Data were collected from the hospital’s electronic medical records, including age, pre-pregnancy body mass index (BMI), previous history of diabetes mellitus and autoimmune diseases, parity, number of fetus, gestational diabetes mellitus, time at delivery. Blood pressure (BP) was routinely measured at the first visit to the reproductive medical center in the seated position after resting for at least 5 minutes using an Omron automated sphygmomanometer. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded. IVF-ET related information were collected from the electronic medical records of the reproductive medical center, including basal follicle-stimulating hormone (FSH), Luteinizing Hormone (LH)，estradiol (E2) , etiology of infertility, protocol of IVF, type of embryo transfer and stage of transferred embryo. Biochemical analyses were routinely performed in a fasting state at the first visit to the reproductive medical center using an automatic biochemical analyzer. The total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C) and glucose levels were collected. In our hospital, the criteria for diagnosing PE adhere to the guidelines by the American College of Obstetricians and Gynecologists 22 : a systolic BP of 140 mmHg or more or a diastolic BP of 90 mmHg or more, on two occasions at least 4 hours apart after 20 weeks’ gestation with proteinuria, or with severe features: thrombocytopenia, renal insufficiency, impaired liver function, pulmonary edema, new-onset headache unresponsive to medication, or visual symptoms. Statistical analyses The Shapiro-Wilk test was utilized to evaluate the normality of data distribution. Continuous variables of normal distribution were expressed as mean ± standard deviation (SD), and the comparison between two groups was conducted using the independent sample t test. Categorical data were reported as counts (percentages) and the comparison between two groups was conducted using Chi-square test. Independent predictors for PE were identified by multiple logistic regression analysis (backward stepwise), including maternal age and variables with a value of P < 0.10 by univariate analysis. A prediction model of PE was developed using the multivariable logistic regression. Regression coefficients were used to generate a nomogram. The prediction model was assessed by examining discrimination and calibration in the development cohorts (2017–2020 population) and the validation cohorts (2021-2022 population). The discrimination was assessed by the area under the receiver-operator characteristic (ROC) curve (AUC) and its 95% CI. The calibration was constructed to examine the agreement between the predicted probabilities with the observed outcome, which was assessed by the Hosmer-Lemeshow goodness-of-fit test and calibration plots. The calibration plot was calculated by the 1000 repetitions Bootstrap resampling. Development and reporting of the prediction model followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Statistical tests were done with R software (version 4.3.2) and SPSS (version 25.0). Statistical significance was set at two-sided P values less than 0.05. Results Population Characteristics There were 118279 women undergoing IVF-ET between 1 January 2017 and 31 December 2022 in our hospital. Among them, 2994 women were included in this study (Figure 1). The study population was divided into a training set and a test set. The training set included 2288 women delivered between 1 January 2017 and 31 December 2020, and the test set included 706 women delivered between 1 January 2021 and 31 December 2022. Screening for independent predictors of preeclampsia in women pregnant by IVF-ET Among the 2288 women in the training set, 266 women (11.6%) developed PE. Women with PE had higher pre-pregnancy BMI, pre-pregnancy systolic BP, pre-pregnancy diastolic BP compared to women without PE. Twin pregnancy and previous history of chronic hypertension and diabetes mellitus were more common in the PE group. Women with PE delivered at earlier gestational weeks. More women in the PE group underwent IVF-ET due to anovulation and by hormone replacement therapy (HRT) cycles than those in the non- PE group. The pre-pregnancy TC, TG and LDL-C levels were significantly higher and the HDL-C levels were significantly lower in the PE group than in the non- PE group. There were no significant differences between the two groups in terms of maternal age, histories of parturition, histories of autoimmune diseases, glucose level, basal FSH, LH, and E2, type of embryo transfer and stage of transferred embryo (Table 1). Multiple logistic regression analysis included maternal age and 12 variables with a value of P < 0.10 by univariate analysis: maternal age, pre-pregnancy BMI, pre-pregnancy systolic BP, pre-pregnancy diastolic BP, twin pregnancy, previous history of chronic hypertension and diabetes mellitus, TC, TG, LDL-C, HDL-C, etiology of infertility, and protocol of IVF. Independent predictors for PE identified by multiple logistic regression analysis were: TG [adjusted odds ratio (aOR) 1.284; 95% confidence interval (CI) 1.113-1.489, P <0.001]; pre-pregnancy BMI (aOR 1.108; 95% CI 1.059-1.159, P <0.001); pre-chronic hypertension (aOR 6.015; 95% CI 3.542-10.188, P <0.001); twin pregnancy (aOR 4.289; 95% CI 3.211-5.755, P <0.001); protocol of IVF (aOR for HRT cycles 2.158; 95% CI 1.435-3.277, P < 0.001)(Table 2). Establishment and Internal validation of the prediction model The independent predictors for PE identified by multiple logistic regression analysis were used to establish the logistic regression equation and form a risk prediction model. The prediction probability of this model was plotted on the ROC (AUC=0.77, 95%CI 0.73-0.80, Figure 2). We then drew a nomogram to identify the risk of developing PE in women pregnant by IVF-ET (Figure 3). The validation of the prediction model showed that it had good discriminative ability and calibration (AUC =0.71, 95%CI 0.65-0.77，Figure 2). The calibration curve shows a good consistency between the observed probabilities and the predicted probabilities (Figure 4). Discussion This cohort study examined the association between pre-conception dyslipidemia and the risk for PE in women pregnant by IVF-ET and delivered live neonates. The result showed that the increase in TG levels measured before pregnancy was independently associated with the risk for PE. Furthermore, we developed a model to predict the development of PE in women undergoing IVF-ET. The predictors in this model that had significant effect on the risk of PE were: TG, BMI, chronic hypertension, twin pregnancy and HRT cycles. During normal pregnancy, there is a physiological change in lipid metabolism because of the effects of estrogen, progesterone and lactogen 13 . From the 12th week of pregnancy, serum levels of TC, TG, HDL-C and LDL-C gradually increase, especially in the second and third trimesters 13 . By late pregnancy, the four lipid components increase by 45%, 150%, 35% and 35% respectively 23 . Previous studies indicated a relationship between TG and PE 7, 9-12, 23-25 . A meta-analysis by Spracklen et al 24 showed that PE was associated with elevated TG levels during all trimesters of pregnancy, and the differences in TG levels between women with and without PE were substantially more remarkable during the third trimester than in the first/second trimesters. The Amsterdam Born Children and Their Development (ABCD) cohort study observed 4008 women and showed that maternal TG concentrations in early pregnancy (12-14 gestational weeks) were linearly associated with the risk of pregnancy-induced hypertension, PE, preterm birth and large for gestational age (LGA) 10 . Several studies also investigated the relationship between preconception TG and risk of. A community-based Cohort study included 13217 singleton pregnancies without preexisting hypertension, and indicated that elevated TG levels (≥1.7 mmol/L) could predict the risk for PE ( OR 2.4 , 95%CI 1.71-3.30) 7 . A study by Baumfeld et al 25 had similar results, and reported that high TG was independently associated with the composite outcome of gestational diabetes mellitus / or PE with OR of 1.61 (95%CI 1.29-2.01). The relationship between dyslipidemia and the risk of PE in women undergoing IVF-ET has not been reported yet. In the present study, we included women who conceived by IVF-ET and delivered live neonates, and found that TG measured before conception was an independent predictor for the risk of PE. The specific role of TG in the pathogenesis of the PE is still not well established. The possible mechanism is that accumulation of TG in endothelial cells could trigger a decreased production of prostaglandins and nitric oxide and consequently cause endothelial dysfunction 13 . The relationship of preconception TC and HDL-C levels with the risk of PE are still controversial. The meta-analysis by Spracklen et al 24 showed that women with PE had higher levels of TC during all trimesters of pregnancy and lower levels of HDL-C only in the third trimester. In the study by Wiznitzer et al 9 , high TC but not low HDL-C during pregnancy was independently associated with the development of PE. However, Baumfeld et al 25 reported that low HDL-C (≤50mg/dL) before conception was independently associated with the composite outcome of gestational diabetes mellitus / or PE ( OR 1.33, 95%CI 1.09-1.63). A population-based study from China investigated the relationship of TC, TG, HDL-C and LDL-C during pregnancy with pregnancy complications, and indicated that only TG was independently associated with increased risk of pregnancy complications 11 . Previous studies have consistently shown no significant correlation between LDL-C and the occurrence of PE 9,11,24,25 . The previous study showed that although women with PE had significant higher TC, higher LDL-C and lower HDL-C levels before pregnancy, none of the three lipid components were independently associated with the development of PE in multiple logistic regression analysis. It has been established that IVF-ET is associated with adverse pregnancy outcomes 18,19 . In this study, we established and validated a prediction model for the risk of PE in women pregnant by IVF-ET. We found that the model constructed based on the TG, BMI, chronic hypertension, twin pregnancy and protocol of IVF had good predictive power and clinical utility. Clinical Relevance There are currently no recommendations by guidelines to screen for dyslipidemia before ART. Dyslipidemia is common in women undergoing ART. Cirillo et al 21 Investigated 1003 women (median age 40 years) undergoing ART, and found that nearly 60% of them suffered from dyslipidemia. Lipids screening seems to be necessary before beginning infertility treatment. Recognizing dyslipidemia may allow for appropriate intervention (i.e. life style changes and weight management) which could modify lipid profiles and might contribute to improve maternal outcomes 26 . These interventions should be undertaken both before and during the gestation. Furthermore, increased vigilance for early signs of PE might be considered in women with dyslipidemia, especially those with high TG levels before ART. This study had shown that the risks of PE was higher in patients conceiving after HRT cycles than in those conceiving after natural cycles. This was consistent with the results of previous studies 27 ， 28 . Thus, we should consider obstetrical risks when we decide on the endometrium preparation method. Natural cycles and fresh cycles might be better in women with high risk of PE. This study had several limitations. Firstly, we did not included lipoproteins because this was a retrospective study and lipoproteins were not routinely measured before IVF-ET, although previous studies indicated lipoprotein(a) might also be as a predictor of PE 29 . Moreover, our study was a single-center study, which may overestimate the model's performance. Conclusions Higher TG levels before pregnancy were independently associated with the risk of PE in women pregnant by IVF-ET. The model encompassing TG, BMI, chronic hypertension, twin pregnancy and protocol of IVF could predict PE. Declarations Funding This work was supported by the Key clinical projects of the Peking University Third Hospital (to Yang Wang, BYSYZD2021014). Author contributions Shaomin Chen and Yang Wang collected the data and wrote the manuscript; Liyuan Tao and Zhaoyu Wang were responsible for statistical analysis. Yongqing Wang and Yuan Wei helped perform the analysis with constructive discussions; Zhaoping Li contributed significantly to analysis and manuscript preparation; Rong Li designed the study. All authors contributed to the article and approved the submitted version. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data availability statement The datasets used in the current study are available from the corresponding author on reasonable request. References American College of Obstetricians and Gynecolocists. 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Is artificial endometrial preparation more associated with early-onset or late-onset preeclampsia after frozen embryo transfer? J Assist Reprod Genet. 2023 May;40(5):1045-1054. doi: 10.1007/s10815-023-02785-0. Epub 2023 Mar 31. PMID: 37000343; PMCID: PMC10239427. 16117184 Konrad E, Güralp O, Shaalan W, Elzarkaa AA, Moftah R, Alemam D, Malik E, Soliman AA. Correlation of elevated levels of , high-density lipoprotein and low-density lipoprotein with severity of preeclampsia: a prospective longitudinal study. J Obstet Gynaecol. 2020 Jan;40(1):53-58. doi: 10.1080/01443615.2019.1603214. Epub 2019 Jul 13. PMID: 31304822. Tables Table 1. The characteristics of women with and without PE PE (n=266) No PE (n=2022) P Maternal age, years 34.0±3.3 34.4±3.6 0.097 Pre-pregnancy BMI, kg/m 2 23.4±3.3 21.9±2.9 ＜0.001 Pre-pregnancy systolic BP, mmHg 115.3±10.4 113.6±9.3 0.006 Pre-pregnancy diastolic BP, mmHg 71.5±6.8 70.2±6.6 0.004 Primiparous, n(%) 258 (97.0) 1933 (95.6) 0.289 Twin pregnancy, n(%) 147(55.3) 528(26.1) ＜0.001 Chronic hypertension, n(%) 38(14.3) 42(2.1) ＜0.001 Diabetes mellitus, n(%) 16(6.0) 60(3.0) 0.009 Gestational diabetes mellitus 77(29.0) 633(31.3) 0.434 Autoimmune diseases, n(%) 7(2.6) 53(2.6) 0.992 Time at delivery, gestational weeks 36.0±2.6 38.0±2.4 <0.001 Glucose, mmol/L 4.9±0.6 4.9±0.6 0.543 TC, mmol/L 4.5±0.9 4.4±0.8 0.037 TG, mmol/L 1.6±1.4 1.2±0.7 ＜0.001 HDL-C, mmol/L 1.3±0.3 1.4±0.3 0.002 LDL-C, mmol/L 2.7±0.8 2.6±0.7 0.045 Basal FSH ,MIU/mL 6.3±3.1 6.5±2.9 0.441 Basal LH, MIU/mL 4.2±3.3 4.3±3.6 0.939 Basal E2, pmol/L 189.0±252.7 188.3±295.0 0.972 Etiology of infertility 0.027 Tubal factor, n(%) 88(33.1) 624(30.9) Endometriosis, n(%) 14(5.3) 190(9.4) Anovulation, n(%) 71(26.7) 410(20.3) Male factor, n(%) 55(20.7) 496(24.5) Unexplained infertility, n(%) 38(14.3) 302(14.9) Protocol of IVF ＜0.001 Natural cycle, n(%) 45(16.9) 602(29.8) HRT cycle, n(%) 87(32.7) 385(19.0) OI cycle, n(%) 10(3.8) 99(4.9) Fresh cycle , n(%) 124(46.6) 936(46.3) Type of embryo transfer 0.972 Fresh embryo, n(%) 124(46.6) 936(46.3) Frozen-thaw embryo, n(%) 142(53.4) 1086(53.7) Stage of transferred embryo 0.61 Cleavage embryo, n(%) 174(65.4) 1286(63.6) Blastocyst, n(%) 266(34.6) 736(36.4) BMI, body mass index; BP, blood pressure; E2, estradiol; FSH, follicle-stimulating hormone; HDL-C, high density lipoprotein cholesterol; HDP, hypertensive disorders of pregnancy; HRT: hormone replacement therapy; IVF, in vitro fertilization; LDL-C, low density lipoprotein cholesterol; LH, Luteinizing Hormone; OI, ovulation induction; PE, preeclampsia; TC, total cholesterol; TG, triglyceride. Table 2. Multiple logistic regression analysis of variables to identify factors predictive of PE in women conceived by IVF -ET Parameter aOR 95%CI of aOR P TG 1.284 1.113-1.489 ＜0.001 Pre-pregnancy BMI 1.108 1.059-1.159 ＜0.001 Chronic hypertension 6.015 3.542-10.188 ＜0.001 Twin pregnancy 4.289 3.211-5.755 ＜0.001 Protocol of IVF ＜0.001 Natural cycle 1 HRT cycle 2.158 1.435-3.277 ＜0.001 OI cycle 1.131 0.511-2.295 0.746 Fresh cycle 1.090 0.745-1.614 0.667 aOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; HRT: hormone replacement therapy; IVF, in vitro fertilization; OI, ovulation induction; PE, preeclampsia. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 27 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Mar, 2025 Reviews received at journal 18 Mar, 2025 Reviews received at journal 10 Mar, 2025 Reviewers agreed at journal 10 Mar, 2025 Reviewers agreed at journal 04 Mar, 2025 Reviewers invited by journal 18 Jul, 2024 Editor assigned by journal 17 Jul, 2024 Editor invited by journal 21 Jun, 2024 Submission checks completed at journal 18 Jun, 2024 First submitted to journal 05 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4536653","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":322308108,"identity":"3b6c8c73-06ab-4684-95d6-73f5cb069d5a","order_by":0,"name":"Shaomin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYDACCSBmbAASzAwMBz5U2MixsbcfIFoL48EZZ9KM+XjOJBCpBajpMG/b4cR5Eg4GeHXwz24+9vDnDjs53XbeAyAt6W0SDAkMPyq24bbkzrF0Y94zycZmh/kSDs45l57bJt14gLHnzG2cWgwkcsykGduYE7cd5jE48KbMOrdN5kACM2MbPi353yR/ttVDtPCwMaezSSQYENCSwyYB8jVIy0GeNucEglokbqSZSfO2HQf6BagFGMiGbcBAPojPL/wzkp8BHVYtZ3b+jPEHYFTKy7e3H3zwowK3FuzgAInqR8EoGAWjYBSgAQB8R1sjuW0lnQAAAABJRU5ErkJggg==","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":true,"prefix":"","firstName":"Shaomin","middleName":"","lastName":"Chen","suffix":""},{"id":322308110,"identity":"72ac1775-e05a-429c-a1ce-86978daf9d45","order_by":1,"name":"Yang Wang","email":"","orcid":"","institution":"Peking University Third 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"yongqing","middleName":"","lastName":"Wang","suffix":""},{"id":322308115,"identity":"5b25b61d-4530-45ab-a9a3-4c48b2f9990c","order_by":5,"name":"yuan wei","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"yuan","middleName":"","lastName":"wei","suffix":""},{"id":322308116,"identity":"56a68631-b722-43a1-8360-cb07367836c2","order_by":6,"name":"Zhaoping Li","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaoping","middleName":"","lastName":"Li","suffix":""},{"id":322308117,"identity":"7583f27e-76ee-40f1-ac56-e9e7dcaac6c5","order_by":7,"name":"Rong Li","email":"","orcid":"","institution":"Peking University Third Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-06-06 01:44:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4536653/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4536653/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-03513-7","type":"published","date":"2025-05-27T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60529834,"identity":"17ac901e-5b69-4b08-aee6-7368e0cc4c6a","added_by":"auto","created_at":"2024-07-17 20:00:43","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2645937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy flowchart\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIVF-ET, in vitro fertilization and embryo transfer.\u003c/p\u003e","description":"","filename":"figure1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4536653/v1/9f065cc91be093b8e51908a0.jpeg"},{"id":60529836,"identity":"f19f93db-8ba1-4bd4-b451-7955150c6a2e","added_by":"auto","created_at":"2024-07-17 20:00:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":170941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe prediction probability of the prediction model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4536653/v1/0bb07d02983ba4933025dcad.png"},{"id":60529835,"identity":"90e95c47-e63c-4cd4-934a-1810a0ba766c","added_by":"auto","created_at":"2024-07-17 20:00:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting the risk of preeclampsia\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4536653/v1/3dd6e2b0523092ce4eb740c3.png"},{"id":60529833,"identity":"61b40851-eafe-42cb-8954-9a2439598ebb","added_by":"auto","created_at":"2024-07-17 20:00:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":37975,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCalibration curve for predicting the risk of preeclampsia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA Calibration curve of the development set\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB Calibration curve of the validation set\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4536653/v1/f42af3c78e9c7a461e04582a.png"},{"id":83782965,"identity":"7428a8ce-1ffd-4101-802c-49f1b7da4a68","added_by":"auto","created_at":"2025-06-02 16:09:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3990849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4536653/v1/9afc0589-cf53-43e0-afe1-7fe49ea42e85.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pre-Conception Dyslipidemia and risk for preeclampsia in women undergoing IVF-ET","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreeclampsia (PE) affects 2\u0026ndash;8% of pregnancies and is the leading cause of maternal and fetal mortality and morbidity globally\u003csup\u003e1-3\u003c/sup\u003e. It also increases women\u0026rsquo;s long-term risk of cardiovascular diseases\u003csup\u003e4,5\u003c/sup\u003e. Studies have shown that PE and cardiovascular diseases have many common risk factors, such as obesity, history of hypertension, diabetes and dyslipidemia\u003csup\u003e6-8\u003c/sup\u003e. The relationship between dyslipidemia and PE has drawn much attention in recent years\u003csup\u003e9-12\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003eIn normal pregnancies, there is a physiological increase in serum lipid concentration, which ensures the development of the fetus\u003csup\u003e13\u003c/sup\u003e. However, an abnormal increase in serum lipid levels during pregnancy has been associated with the development of PE\u003csup\u003e9-12\u003c/sup\u003e. In contrast to dyslipidemia during gestation, the relationship between pre-gestational dyslipidemia and PE has not been studied extensively. \u003c/p\u003e\n\u003cp\u003eNowadays, 8-12% childbearing age women are affected by infertility worldwide\u003csup\u003e14\u003c/sup\u003e, and more than 15% of couples suffer from infertility in China\u003csup\u003e15\u003c/sup\u003e.The use of in vitro fertilization and embryo transfer (IVF-ET) has risen steadily in the worldwide\u003csup\u003e16,17\u003c/sup\u003e. While IVF-ET benefits many couples, it has been established that IVF\u0026ndash;ET is associated with adverse pregnancy outcomes, including PE\u003csup\u003e18,19\u003c/sup\u003e. Women pregnant by IVF-ET are older and more likely to have chronic disease (i.e. hypertension, diabetes and dyslipidemia) than women who have spontaneously conceived\u003csup\u003e20,21\u003c/sup\u003e. It has been reported that dyslipidemia was highly prevalent in women undergoing IVF-ET\u003csup\u003e20,21\u003c/sup\u003e. Although significant evidence links dyslipidemia and PE\u003csup\u003e9-12\u003c/sup\u003e, studies focusing on women undergoing IVF-ET are still lack. Thus, the purpose of this study was to investigate the relationship between dyslipidemia prior to conception and the risk of PE in women pregnant by IVF-ET. We also aim to establish and validate a prediction model for the risk of PE based on dyslipidemia, maternal characteristics and IVF-ET related variables in this specific group of women.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003e\u003cstrong\u003eStudy population \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis was a retrospective cohort study conducted at Peking University Third Hospital, a leading tertiary university hospital with an excellence reproductive medical center in China. Women conceived by IVF-ET and delivered live neonates between 1 January 2017 and 31 December 2022 in this hospital were included. Fasting lipid profile tests were routinely conducted before IVF-ET. Exclusion criteria were: no record of lipid measurements, the time duration between lipid measurement and conception exceeding 12 months, histories of taking lipid-lowering agents and histories of PE. For women with more than one birth during the study period, only data from the first pregnancy were analyzed. \u003c/p\u003e\n\u003cp\u003eThe study population was divided into two components: a training set for the prediction model development (women delivered between 1 January 2017 and 31 December 2020) and a test set for validation (women delivered between 1 January 2021 and 31 December 2022).\u003c/p\u003e\n\u003cp\u003eThis study was approved by the ethics review board of Peking University Third Hospital, and was performed in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the ethics review board of Peking University Third Hospital waived the need of obtaining informed consent. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDate collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were collected from the hospital\u0026rsquo;s electronic medical records, including age, pre-pregnancy body mass index (BMI), previous history of diabetes mellitus and autoimmune diseases, parity, number of fetus, gestational diabetes mellitus, time at delivery. Blood pressure (BP) was routinely measured at the first visit to the reproductive medical center in the seated position after resting for at least 5 minutes using an Omron automated sphygmomanometer. Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were recorded. \u003c/p\u003e\n\u003cp\u003eIVF-ET related information were collected from the electronic medical records of the reproductive medical center, including basal follicle-stimulating hormone (FSH), Luteinizing Hormone (LH)，estradiol (E2) , etiology of infertility, protocol of IVF, type of embryo transfer and stage of transferred embryo. \u003c/p\u003e\n\u003cp\u003eBiochemical analyses were routinely performed in a fasting state at the first visit to the reproductive medical center using an automatic biochemical analyzer. The total cholesterol (TC), triglyceride (TG), high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C) and glucose levels were collected.\u003c/p\u003e\n\u003cp\u003eIn our hospital, the criteria for diagnosing PE adhere to the guidelines by the American College of Obstetricians and Gynecologists\u003csup\u003e 22\u003c/sup\u003e: a systolic BP of 140 mmHg or more or a diastolic BP of 90 mmHg or more, on two occasions at least 4 hours apart after 20 weeks\u0026rsquo; gestation with proteinuria, or with severe features: thrombocytopenia, renal insufficiency, impaired liver function, pulmonary edema, new-onset headache unresponsive to medication, or visual symptoms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Shapiro-Wilk test was utilized to evaluate the normality of data distribution. Continuous variables of normal distribution were expressed as mean \u0026plusmn; standard deviation (SD), and the comparison between two groups was conducted using the independent sample t test. Categorical data were reported as counts (percentages) and the comparison between two groups was conducted using Chi-square test. Independent predictors for PE were identified by multiple logistic regression analysis (backward stepwise), including maternal age and variables with a value of P \u0026lt; 0.10 by univariate analysis. A prediction model of PE was developed using the multivariable logistic regression. Regression coefficients were used to generate a nomogram. \u003c/p\u003e\n\u003cp\u003eThe prediction model was assessed by examining discrimination and calibration in the development cohorts (2017\u0026ndash;2020 population) and the validation cohorts (2021-2022 population). The discrimination was assessed by the area under the receiver-operator characteristic (ROC) curve (AUC) and its 95% CI. The calibration was constructed to examine the agreement between the predicted probabilities with the observed outcome, which was assessed by the Hosmer-Lemeshow goodness-of-fit test and calibration plots. The calibration plot was calculated by the 1000 repetitions Bootstrap resampling. Development and reporting of the prediction model followed the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. \u003c/p\u003e\n\u003cp\u003eStatistical tests were done with R software (version 4.3.2) and SPSS (version 25.0). Statistical significance was set at two-sided \u003cem\u003eP\u003c/em\u003e values less than 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003ePopulation Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were 118279 women undergoing IVF-ET between 1 January 2017 and 31 December 2022 in our hospital. Among them, 2994 women were included in this study (Figure 1). \u0026nbsp;The study population was divided into a training set and a test set. The training set included 2288 women delivered between 1 January 2017 and 31 December 2020, and the test set included 706 women delivered between 1 January 2021 and 31 December 2022.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScreening for independent predictors of preeclampsia in women pregnant by IVF-ET \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the 2288 women in the training set, 266 women (11.6%) developed PE. Women with PE had higher pre-pregnancy BMI, pre-pregnancy systolic BP, pre-pregnancy diastolic BP compared to women without PE. Twin pregnancy and previous history of chronic hypertension and diabetes mellitus were more common in the PE group. Women with PE delivered at earlier gestational weeks. More women in the PE group underwent IVF-ET due to anovulation and by hormone replacement therapy (HRT) cycles than those in the non- PE group. The pre-pregnancy TC, TG and LDL-C levels were significantly higher and the HDL-C levels were significantly lower in the PE group than in the non- PE group. There were no significant differences between the two groups in terms of maternal age, histories of parturition, histories of autoimmune diseases, glucose level, basal FSH, LH, and E2, type of embryo transfer and stage of transferred embryo (Table 1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultiple logistic regression analysis included maternal age and 12 variables with a value of \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.10 by univariate analysis: maternal age, pre-pregnancy BMI, pre-pregnancy systolic BP, pre-pregnancy diastolic BP, twin pregnancy, previous history of chronic hypertension and diabetes mellitus, TC, TG, LDL-C, HDL-C, etiology of infertility, and protocol of IVF. Independent predictors for PE identified by multiple logistic regression analysis were: TG [adjusted odds ratio (aOR) 1.284; 95% confidence interval (CI) 1.113-1.489, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001]; pre-pregnancy BMI (aOR 1.108; 95% CI 1.059-1.159, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); pre-chronic hypertension (aOR 6.015; 95% CI 3.542-10.188, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); twin pregnancy (aOR 4.289; 95% CI 3.211-5.755, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.001); protocol of IVF (aOR for HRT cycles 2.158; 95% CI 1.435-3.277, \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001)(Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstablishment and Internal validation of the prediction model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe independent predictors for PE identified by multiple logistic regression analysis were used to establish the logistic regression equation and form a risk prediction model. The prediction probability of this model was plotted on the ROC (AUC=0.77, 95%CI 0.73-0.80, Figure 2). We then drew a nomogram to identify the risk of developing \u003cstrong\u003ePE\u003c/strong\u003e in women pregnant by IVF-ET (Figure 3). The validation of the prediction model showed that it had good discriminative ability and calibration (AUC =0.71, 95%CI 0.65-0.77，Figure 2). The calibration curve shows a good consistency between the observed probabilities and the predicted probabilities (Figure 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cohort study examined the association between pre-conception dyslipidemia and the risk for PE in women pregnant by IVF-ET and delivered live neonates. The result showed that the increase in TG levels measured before pregnancy was independently associated with the risk for PE. Furthermore, we developed a model to predict the development of PE in women undergoing IVF-ET. The predictors in this model that had significant effect on the risk of PE were: TG, BMI, chronic hypertension, twin pregnancy and HRT cycles. \u003c/p\u003e\n\u003cp\u003eDuring normal pregnancy, there is a physiological change in lipid metabolism because of the effects of estrogen, progesterone and lactogen\u003csup\u003e13\u003c/sup\u003e. From the 12th week of pregnancy, serum levels of TC, TG, HDL-C and LDL-C gradually increase, especially in the second and third trimesters\u003csup\u003e13\u003c/sup\u003e. By late pregnancy, the four lipid components increase by 45%, 150%, 35% and 35% respectively\u003csup\u003e23\u003c/sup\u003e. Previous studies indicated a relationship between TG and PE\u003csup\u003e7,\u003c/sup\u003e\u003csup\u003e9-12, 23-25\u003c/sup\u003e. A meta-analysis by Spracklen et al\u003csup\u003e24\u003c/sup\u003e showed that PE was associated with elevated TG levels during all trimesters of pregnancy, and the differences in TG levels between women with and without PE were substantially more remarkable during the third trimester than in the first/second trimesters. The Amsterdam Born Children and Their Development (ABCD) cohort study observed 4008 women and showed that maternal TG concentrations in early pregnancy (12-14 gestational weeks) were linearly associated with the risk of pregnancy-induced hypertension, PE, preterm birth and large for gestational age (LGA)\u003csup\u003e10\u003c/sup\u003e. Several studies also investigated the relationship between preconception TG and risk of. A community-based Cohort study included 13217 singleton pregnancies without preexisting hypertension, and indicated that elevated TG levels (\u0026ge;1.7 mmol/L) could predict the risk for PE ( OR 2.4 , 95%CI 1.71-3.30)\u003csup\u003e7\u003c/sup\u003e. A study by Baumfeld et al\u003csup\u003e25 \u003c/sup\u003ehad similar results, and reported that high TG was independently associated with the composite outcome of gestational diabetes mellitus / or PE with OR of 1.61 (95%CI 1.29-2.01). The relationship between dyslipidemia and the risk of PE in women undergoing IVF-ET has not been reported yet. In the present study, we included women who conceived by IVF-ET and delivered live neonates, and found that TG measured before conception was an independent predictor for the risk of PE. The specific role of TG in the pathogenesis of the PE is still not well established. The possible mechanism is that accumulation of TG in endothelial cells could trigger a decreased production of prostaglandins and nitric oxide and consequently cause endothelial dysfunction\u003csup\u003e13\u003c/sup\u003e. \u003c/p\u003e\n\u003cp\u003eThe relationship of preconception TC and HDL-C levels with the risk of PE are still controversial. The meta-analysis by Spracklen et al\u003csup\u003e24\u003c/sup\u003e showed that women with PE had higher levels of TC during all trimesters of pregnancy and lower levels of HDL-C only in the third trimester. In the study by Wiznitzer et al\u003csup\u003e9\u003c/sup\u003e, high TC but not low HDL-C during pregnancy was independently associated with the development of PE. However, Baumfeld et al\u003csup\u003e25\u003c/sup\u003e reported that low HDL-C (\u0026le;50mg/dL) before conception was independently associated with the composite outcome of gestational diabetes mellitus / or PE ( OR 1.33, 95%CI 1.09-1.63). A population-based study from China investigated the relationship of TC, TG, HDL-C and LDL-C during pregnancy with pregnancy complications, and indicated that only TG was independently associated with increased risk of pregnancy complications\u003csup\u003e11\u003c/sup\u003e. Previous studies have consistently shown no significant correlation between LDL-C and the occurrence of PE\u003csup\u003e 9,11,24,25\u003c/sup\u003e. The previous study showed that although women with PE had significant higher TC, higher LDL-C and lower HDL-C levels before pregnancy, none of the three lipid components were independently associated with the development of PE in multiple logistic regression analysis. \u003c/p\u003e\n\u003cp\u003eIt has been established that IVF-ET is associated with adverse pregnancy outcomes\u003csup\u003e18,19\u003c/sup\u003e. In this study, we established and validated a prediction model for the risk of PE in women pregnant by IVF-ET. We found that the model constructed based on the TG, BMI, chronic hypertension, twin pregnancy and protocol of IVF had good predictive power and clinical utility. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Relevance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are currently no recommendations by guidelines to screen for dyslipidemia before ART. Dyslipidemia is common in women undergoing ART. Cirillo et al\u003csup\u003e21\u003c/sup\u003e Investigated 1003 women (median age 40 years) undergoing ART, and found that nearly 60% of them suffered from dyslipidemia. Lipids screening seems to be necessary before beginning infertility treatment. Recognizing dyslipidemia may allow for appropriate intervention (i.e. life style changes and weight management) which could modify lipid profiles and might contribute to improve maternal outcomes\u003csup\u003e26\u003c/sup\u003e. These interventions should be undertaken both before and during the gestation. Furthermore, increased vigilance for early signs of PE might be considered in women with dyslipidemia, especially those with high TG levels before ART.\u003c/p\u003e\n\u003cp\u003eThis study had shown that the risks of PE was higher in patients conceiving after HRT cycles than in those conceiving after natural cycles. This was consistent with the results of previous studies\u003csup\u003e27\u003c/sup\u003e\u003csup\u003e，\u003c/sup\u003e\u003csup\u003e28\u003c/sup\u003e. Thus, we should consider obstetrical risks when we decide on the endometrium preparation method. Natural cycles and fresh cycles might be better in women with high risk of PE. \u003c/p\u003e\n\u003cp\u003eThis study had several limitations. Firstly, we did not included lipoproteins because this was a retrospective study and lipoproteins were not routinely measured before IVF-ET, although previous studies indicated lipoprotein(a) might also be as a predictor of PE\u003csup\u003e29\u003c/sup\u003e. Moreover, our study was a single-center study, which may overestimate the model\u0026apos;s performance.\u003c/p\u003e"},{"header":"Conclusions ","content":"\u003cp\u003eHigher TG levels before pregnancy were independently associated with the risk of PE in women pregnant by IVF-ET. The model encompassing TG, BMI, chronic hypertension, twin pregnancy and protocol of IVF could predict PE.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Key clinical projects of the Peking University Third Hospital (to Yang Wang, BYSYZD2021014).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShaomin Chen and Yang Wang collected the data and wrote the manuscript; Liyuan Tao and Zhaoyu Wang were responsible for statistical analysis. Yongqing Wang and Yuan Wei helped perform the analysis with constructive discussions;\u0026nbsp;Zhaoping Li contributed significantly to analysis and manuscript preparation; Rong Li designed the study. All authors contributed to the article and approved the submitted version.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican College of Obstetricians and Gynecolocists. Gestational hypertension and preeclampsia: ACOG practice bulletin, number 222. Obstet Gynecol. 2020;135:e237\u0026ndash;e260. doi: 10.1097/AOG.0000000000003892\u003c/li\u003e\n\u003cli\u003eJiang L, Tang K, Magee LA, von Dadelszen P, Ekeroma A, Li X, Zhang E, Bhutta ZA. A global view of hypertensive disorders and diabetes mellitus during pregnancy. Nat Rev Endocrinol. 2022 Dec;18(12):760-775. doi: 10.1038/s41574-022-00734-y. Epub 2022 Sep 15. PMID: 36109676; PMCID: PMC9483536.\u003c/li\u003e\n\u003cli\u003eLi F, Qin J, Zhang S, Chen L. Prevalence of hypertensive disorders in pregnancy in China: A systematic review and meta-analysis. Pregnancy Hypertens. 2021 Jun;24:13-21. doi: 10.1016/j.preghy.2021.02.001. Epub 2021 Feb 14. PMID: 33626437.\u003c/li\u003e\n\u003cli\u003eGarovic VD, White WM, Vaughan L, Saiki M, Parashuram S, Garcia-Valencia O, Weissgerber TL, Milic N, Weaver A, Mielke MM. Incidence and Long-Term Outcomes of Hypertensive Disorders of Pregnancy. J Am Coll Cardiol. 2020 May 12;75(18):2323-2334. doi: 10.1016/j.jacc.2020.03.028. PMID: 32381164; PMCID: PMC7213062.\u003c/li\u003e\n\u003cli\u003eTurbeville HR, Sasser JM. Preeclampsia beyond pregnancy: long-term consequences for mother and child. Am J Physiol Renal Physiol. 2020 Jun 1;318(6):F1315-F1326. doi: 10.1152/ajprenal.00071.2020. Epub 2020 Apr 6. PMID: 32249616; PMCID: PMC7311709.\u003c/li\u003e\n\u003cli\u003eUS Preventive Services Task Force; Barry MJ, Nicholson WK, Silverstein M, Cabana MD, Chelmow D, Coker TR, Davis EM, Donahue KE, Ja\u0026eacute;n CR, Li L, Ogedegbe G, Rao G, Ruiz JM, Stevermer J, Tsevat J, Underwood SM, Wong JB. Screening for Hypertensive Disorders of Pregnancy: US Preventive Services Task Force Final Recommendation Statement. JAMA. 2023 Sep 19;330(11):1074-1082. doi: 10.1001/jama.2023.16991. PMID: 37721605.\u003c/li\u003e\n\u003cli\u003eEgeland GM, Klungs\u0026oslash;yr K, \u0026Oslash;yen N, Tell GS, N\u0026aelig;ss \u0026Oslash;, Skj\u0026aelig;rven R. Preconception Cardiovascular Risk Factor Differences Between Gestational Hypertension and Preeclampsia: Cohort Norway Study. Hypertension. 2016 Jun;67(6):1173-80. doi: 10.1161/HYPERTENSIONAHA.116.07099. Epub 2016 Apr 25. PMID: 27113053; PMCID: PMC4861703. \u003c/li\u003e\n\u003cli\u003eRetnakaran R, Shah BR. The adverse cardiovascular risk factor profile of women with pre-eclampsia develops over time in the years before pregnancy. BJOG. 2022 Aug;129(9):1512-1520. doi: 10.1111/1471-0528.17084. Epub 2022 Jan 13. PMID: 34954865.\u003c/li\u003e\n\u003cli\u003eWiznitzer A, Mayer A, Novack V, Sheiner E, Gilutz H, Malhotra A, Novack L. Association of lipid levels during gestation with preeclampsia and gestational diabetes mellitus: a population-based study. Am J Obstet Gynecol. 2009 Nov;201(5):482.e1-8. doi: 10.1016/j.ajog.2009.05.032. Epub 2009 Jul 24. PMID: 19631920; PMCID: PMC5483324\u003c/li\u003e\n\u003cli\u003eVrijkotte TG, Krukziener N, Hutten BA, Vollebregt KC, van Eijsden M, Twickler MB. Maternal lipid profile during early pregnancy and pregnancy complications and outcomes: the ABCD study. J Clin Endocrinol Metab. 2012 Nov;97(11):3917-25. doi: 10.1210/jc.2012-1295. Epub 2012 Aug 29. PMID: 22933545.\u003c/li\u003e\n\u003cli\u003eJin WY, Lin SL, Hou RL, Chen XY, Han T, Jin Y, Tang L, Zhu ZW, Zhao ZY. Associations between maternal lipid profile and pregnancy complications and perinatal outcomes: a population-based study from China. BMC Pregnancy Childbirth. 2016 Mar 21;16:60. doi: 10.1186/s12884-016-0852-9. PMID: 27000102; PMCID: PMC4802610.\u003c/li\u003e\n\u003cli\u003eSerrano NC, Guio-Mahecha E, Quintero-Lesmes DC, Becerra-Bayona S, Paez MC, Beltran M, Herrera VM, Leon LJ, Williams D, Casas JP. Lipid profile, plasma apolipoproteins, and pre-eclampsia risk in the GenPE case-control study. Atherosclerosis. 2018 Sep;276:189-194. doi: 10.1016/j.atherosclerosis.2018.05.051. Epub 2018 Jun 4. PMID: 29914672.\u003c/li\u003e\n\u003cli\u003ePoornima IG, Indaram M, Ross JD, Agarwala A, Wild RA. Hyperlipidemia and risk for preclampsia. J Clin Lipidol. 2022 May-Jun;16(3):253-260. doi: 10.1016/j.jacl.2022.02.005. Epub 2022 Feb 20. PMID: 35260347; PMCID: PMC10320742.\u003c/li\u003e\n\u003cli\u003eVander Borght M, Wyns C. Fertility and infertility: Definition and epidemiology. Clin Biochem. 2018 Dec;62:2-10. doi: 10.1016/j.clinbiochem.2018.03.012. Epub 2018 Mar 16. PMID: 29555319.\u003c/li\u003e\n\u003cli\u003eZhou Z, Zheng D, Wu H, Li R, Xu S, Kang Y, Cao Y, Chen X, Zhu Y, Xu S, Chen ZJ, Mol BW, Qiao J. Epidemiology of infertility in China: a population-based study. BJOG. 2018 Mar;125(4):432-441. doi: 10.1111/1471-0528.14966. Epub 2017 Dec 28. PMID: 29030908.\u003c/li\u003e\n\u003cli\u003eInhorn MC, Patrizio P. Infertility around the globe: new thinking on gender, reproductive technologies and global movements in the 21st century. Hum Reprod Update. 2015 Jul-Aug;21(4):411-26. doi: 10.1093/humupd/dmv016. Epub 2015 Mar 22. PMID: 25801630.\u003c/li\u003e\n\u003cli\u003eDe Geyter C, Calhaz-Jorge C, Kupka MS, Wyns C, Mocanu E, Motrenko T, Scaravelli G, Smeenk J, Vidakovic S, Goossens V; European IVF-monitoring Consortium (EIM) for the European Society of Human Reproduction and Embryology (ESHRE). ART in Europe, 2015: results generated from European registries by ESHRE. Hum Reprod Open. 2020 Feb 24;2020(1):hoz038. doi: 10.1093/hropen/hoz038. Erratum in: Hum Reprod Open. 2020 Sep 22;2020(3):hoaa038. PMID: 32123753; PMCID: PMC7038942.\u003c/li\u003e\n\u003cli\u003eWu P, Sharma GV, Mehta LS, Chew-Graham CA, Lundberg GP, Nerenberg KA, Graham MM, Chappell LC, Kadam UT, Jordan KP, Mamas MA. In-Hospital Complications in Pregnancies Conceived by Assisted Reproductive Technology. J Am Heart Assoc. 2022 Mar;11(5):e022658. doi: 10.1161/JAHA.121.022658. Epub 2022 Feb 22. PMID: 35191320; PMCID: PMC9075081.\u003c/li\u003e\n\u003cli\u003eHeshmatnia F, Jafari M, Bozorgian L, Yadollahi P, Khalajinia Z, Azizi M. Is there a relationship between assisted reproductive technology and maternal outcomes? A systematic review of cohort studies. Int J Reprod Biomed. 2023 Dec 19;21(11):861-880. doi: 10.18502/ijrm.v21i11.14651. PMID: 38292514; PMCID: PMC10823119.\u003c/li\u003e\n\u003cli\u003eCirillo M, Coccia ME, Dimmito A, Fatini C. Preconception period in women and men undergoing Assisted Reproduction: A gender approach for reproductive health. Eur J Obstet Gynecol Reprod Biol. 2022 Aug;275:1-8. doi: 10.1016/j.ejogrb.2022.06.003. Epub 2022 Jun 8. PMID: 35691220.\u003c/li\u003e\n\u003cli\u003eCirillo M, Coccia ME, Fatini C. Lifestyle and Comorbidities: Do We Take Enough Care of Preconception Health in Assisted Reproduction? J Family Reprod Health. 2020 Sep;14(3):150-157. doi: 10.18502/jfrh.v14i3.4667. PMID: 33603806; PMCID: PMC7868650.\u003c/li\u003e\n\u003cli\u003eGestational Hypertension and Preeclampsia: ACOG Practice Bulletin, Number 222. Obstet Gynecol. 2020 Jun;135(6):e237-e260. doi: 10.1097/AOG.0000000000003891. PMID: 32443079.\u003c/li\u003e\n\u003cli\u003eWiznitzer A, Mayer A, Novack V, Sheiner E, Gilutz H, Malhotra A, Novack L. Association of lipid levels during gestation with preeclampsia and gestational diabetes mellitus: a population-based study. Am J Obstet Gynecol. 2009 Nov;201(5):482.e1-8. doi: 10.1016/j.ajog.2009.05.032. Epub 2009 Jul 24. PMID: 19631920; PMCID: PMC5483324.\u003c/li\u003e\n\u003cli\u003eSpracklen CN, Smith CJ, Saftlas AF, Robinson JG, Ryckman KK. Maternal hyperlipidemia and the risk of preeclampsia: a meta-analysis. Am J Epidemiol. 2014 Aug 15;180(4):346-58. doi: 10.1093/aje/kwu145. Epub 2014 Jul 2. PMID: 24989239; PMCID: PMC4565654.\u003c/li\u003e\n\u003cli\u003eBaumfeld Y, Novack L, Wiznitzer A, Sheiner E, Henkin Y, Sherf M, Novack V. Pre-Conception Dyslipidemia Is Associated with Development of Preeclampsia and Gestational Diabetes Mellitus. PLoS One. 2015 Oct 9;10(10):e0139164. doi: 10.1371/journal.pone.0139164. Erratum in: PLoS One. 2015;10(11):e0142462. PMID: 26452270; PMCID: PMC4599807.\u003c/li\u003e\n\u003cli\u003eRaghuraman N, Tuuli MG. Preconception Care as an Opportunity to Optimize Pregnancy Outcomes. JAMA. 2021 Jul 6;326(1):79-80. doi: 10.1001/jama.2020.27244. PMID: 34228078.\u003c/li\u003e\n\u003cli\u003eSaito K, Kuwahara A, Ishikawa T, Morisaki N, Miyado M, Miyado K, Fukami M, Miyasaka N, Ishihara O, Irahara M, Saito H. Endometrial preparation methods for frozen-thawed embryo transfer are associated with altered risks of hypertensive disorders of pregnancy, placenta accreta, and gestational diabetes mellitus. Hum Reprod. 2019 Aug 1;34(8):1567-1575. doi: 10.1093/humrep/dez079. PMID: 31299081.\u003c/li\u003e\n\u003cli\u003eNiu Y, Suo L, Zhao D, Wang Y, Miao R, Zou J, Han X, Chen ZJ, Li Y, Wei D. Is artificial endometrial preparation more associated with early-onset or late-onset preeclampsia after frozen embryo transfer? J Assist Reprod Genet. 2023 May;40(5):1045-1054. doi: 10.1007/s10815-023-02785-0. Epub 2023 Mar 31. PMID: 37000343; PMCID: PMC10239427. 16117184\u003c/li\u003e\n\u003cli\u003eKonrad E, G\u0026uuml;ralp O, Shaalan W, Elzarkaa AA, Moftah R, Alemam D, Malik E, Soliman AA. Correlation of elevated levels of , high-density lipoprotein and low-density lipoprotein with severity of preeclampsia: a prospective longitudinal study. J Obstet Gynaecol. 2020 Jan;40(1):53-58. doi: 10.1080/01443615.2019.1603214. Epub 2019 Jul 13. PMID: 31304822.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. The characteristics of women with and without PE\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"515\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003ePE (n=266)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003eNo PE (n=2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eMaternal age, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e34.0\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e34.4\u0026plusmn;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003ePre-pregnancy BMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e23.4\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e21.9\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003ePre-pregnancy systolic BP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e115.3\u0026plusmn;10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e113.6\u0026plusmn;9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003ePre-pregnancy diastolic BP, mmHg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e71.5\u0026plusmn;6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e70.2\u0026plusmn;6.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003ePrimiparous, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e258 (97.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e1933 (95.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eTwin pregnancy, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e147(55.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e528(26.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eChronic hypertension, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e38(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e42(2.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eDiabetes mellitus, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e16(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e60(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eGestational diabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e77(29.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e633(31.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eAutoimmune diseases, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e7(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e53(2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eTime at delivery, gestational weeks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e36.0\u0026plusmn;2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e38.0\u0026plusmn;2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eGlucose, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e4.9\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e4.9\u0026plusmn;0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.543\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eTC, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e4.5\u0026plusmn;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e4.4\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eTG, mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e1.6\u0026plusmn;1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e1.2\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eHDL-C, mmol/L \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e1.3\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e1.4\u0026plusmn;0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eLDL-C, mmol/L \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e2.7\u0026plusmn;0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e2.6\u0026plusmn;0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\" valign=\"top\"\u003e\n \u003cp\u003eBasal FSH ,MIU/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e6.3\u0026plusmn;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e6.5\u0026plusmn;2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\" valign=\"top\"\u003e\n \u003cp\u003eBasal LH, MIU/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e4.2\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e4.3\u0026plusmn;3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.939\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\" valign=\"top\"\u003e\n \u003cp\u003eBasal E2, pmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e189.0\u0026plusmn;252.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e188.3\u0026plusmn;295.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eEtiology of infertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Tubal factor, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e88(33.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e624(30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eEndometriosis, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e14(5.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e190(9.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eAnovulation, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e71(26.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e410(20.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eMale factor, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e55(20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e496(24.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eUnexplained infertility, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e38(14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e302(14.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eProtocol of IVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eNatural cycle, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e45(16.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e602(29.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eHRT cycle, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e87(32.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e385(19.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003e\u0026nbsp; OI cycle, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e10(3.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e99(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Fresh cycle , n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e124(46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e936(46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eType of embryo transfer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eFresh embryo, n(%) \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e124(46.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e936(46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eFrozen-thaw embryo, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e142(53.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e1086(53.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eStage of transferred embryo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003eCleavage embryo, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e174(65.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e1286(63.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40.38834951456311%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Blastocyst, n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.194174757281555%\"\u003e\n \u003cp\u003e266(34.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.271844660194176%\"\u003e\n \u003cp\u003e736(36.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.145631067961165%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBMI, body mass index; BP, blood pressure;\u0026nbsp;E2, estradiol;\u0026nbsp;FSH,\u0026nbsp;follicle-stimulating hormone;\u0026nbsp;HDL-C, high density lipoprotein cholesterol; HDP,\u0026nbsp;hypertensive disorders of pregnancy;\u0026nbsp;HRT: hormone replacement therapy;\u0026nbsp;IVF, in vitro fertilization; LDL-C, low density lipoprotein cholesterol;\u0026nbsp;LH, Luteinizing Hormone;\u0026nbsp;OI,\u0026nbsp;ovulation induction;\u0026nbsp;PE, preeclampsia;\u0026nbsp;TC,\u0026nbsp;total cholesterol;\u0026nbsp;TG, triglyceride.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMultiple logistic regression\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;analysis of variables to identify factors predictive of PE in women conceived by\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIVF\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;-ET\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"614\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e95%CI of aOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e1.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e1.113-1.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003ePre-pregnancy BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e1.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e1.059-1.159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003eChronic hypertension\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e6.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e3.542-10.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003eTwin pregnancy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e4.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e3.211-5.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003eProtocol of IVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003eNatural cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;HRT cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e2.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e1.435-3.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003eOI cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e1.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e0.511-2.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e0.746\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.40619902120718%\"\u003e\n \u003cp\u003eFresh cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.29200652528548%\"\u003e\n \u003cp\u003e1.090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.62642740619902%\"\u003e\n \u003cp\u003e0.745-1.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.67536704730832%\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eaOR, adjusted odds ratio; BMI, body mass index; CI, confidence interval; HRT: hormone replacement therapy; IVF, in vitro fertilization; OI, ovulation induction; PE, preeclampsia.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"in vitro fertilization and embryo transfer, preeclampsia, dyslipidemia, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-4536653/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4536653/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the relationship between dyslipidemia prior to conception and the risk of preeclampsia (PE) in women pregnant by in vitro fertilization and embryo transfer (IVF-ET). The retrospective cohort study consisted of 2994 women who conceived by IVF-ET and delivered live neonates. The study population was divided into two components: a training set for the prediction model development (2288 women) and a test set for validation (706 women). Multivariable logistic regression was used for the development and validation of predictive model for the risk of PE. Among the 2288 women in the training set, 266 women (11.6%) developed PE. Multiple logistic regression analysis identified independent predictors for PE: triglyceride (TG) [adjusted odds ratio (aOR) 1.284; 95% confidence interval (CI) 1.113\u0026ndash;1.489, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001]; pre-pregnancy BMI; pre- chronic hypertension; twin pregnancy; protocol of IVF. These independent predictors for PE were used to form a risk prediction model, and the area under the receiver-operator characteristic (ROC) curve (AUC) in the training and the test set was 0.77 and 0.71, respectively. In conclusion, higher TG levels before pregnancy were independently associated with the risk for PE in women pregnant by IVF-ET.\u003c/p\u003e","manuscriptTitle":"Pre-Conception Dyslipidemia and risk for preeclampsia in women undergoing IVF-ET","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-17 20:00:39","doi":"10.21203/rs.3.rs-4536653/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-25T06:20:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-18T04:02:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-11T01:32:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153507213319936500699104935466404063512","date":"2025-03-10T05:44:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"94031852515566375762673784328891640839","date":"2025-03-04T23:16:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-18T20:47:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-17T20:25:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-06-21T13:24:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-19T03:27:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-06T01:41:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3dd4e37a-d9cd-47cb-8ecf-8556a6812c71","owner":[],"postedDate":"July 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":34081856,"name":"Health sciences/Diseases"},{"id":34081857,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-06-02T16:03:44+00:00","versionOfRecord":{"articleIdentity":"rs-4536653","link":"https://doi.org/10.1038/s41598-025-03513-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-27 15:57:58","publishedOnDateReadable":"May 27th, 2025"},"versionCreatedAt":"2024-07-17 20:00:39","video":"","vorDoi":"10.1038/s41598-025-03513-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-03513-7","workflowStages":[]},"version":"v1","identity":"rs-4536653","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4536653","identity":"rs-4536653","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}