The prognostic nutritional index is associated with preeclampsia in twin pregnancies

Han et al. BMC Pregnancy and Childbirth (2025) 25:568 https://doi.org/10.1186/s12884-025-07669-y BMC Pregnancy and Childbirth Open Access RESEARCH The prognostic nutritional index is associated with preeclampsia in twin pregnancies Qing Han1,3†, Shuisen Zheng3†, Xiaoling Chen1, Yuting Gao1, Huale Zhang3* and Na Lin1,2* Abstract Objective We aimed to investigate the relationship between the prognostic nutritional index (PNI) during the third trimester and the risk of preeclampsia (PE) in twin pregnancies. Method A total of 2998 twin pregnancies were enrolled in Fujian Maternal and Child Health Hospital from January 2015 to December 2021, including preeclampsia group (n = 421) and control group (n = 2577). The significance of the characteristic variables in predicting PE in twin pregnancies were calculated using the random forest algorithm (Boruta package) and the correlation between PNI and PE in twin pregnancies was examined in three distinct models using multivariable logistic regression corrected for confounders. Receiver operating characteristics (ROC) curves were used to evaluate the ability for PNI to predict PE in twin pregnancies. Results PNI (37.92 ± 3.86 vs. 40.57 ± 3.63, P < 0.001) was significantly lower in the PE group than in the control group. After adjusting for all covariates, the PNI was negatively associated with PE in twin pregnancies (OR = 0.780; 95% CI: 0.753, 0.808). Meanwhile, the higher PNI remained an independent protective factor for PE in twin pregnancies compared to lower PNI (OR, 95% CI: 0.410, 0.438–0.530; 0.144, 0.103–0.201) in sensitivity analysis. ROC curve analysis revealed an area under curve (AUC) of 0.691 for PNI and the cut-off value of PNI was 40.162. Conclusion PNI was negatively correlated with the risk of PE in twin pregnancies, which may help in risk assessment for twin pregnancies. Clinical trial number Not applicable. Keywords Prognostic nutritional index, Preeclampsia, Twin pregnancies, Multivariable logistic regression, The random forest algorithm † Qing Han and Shuisen Zheng contributed equally to this work. *Correspondence: Huale Zhang Na Lin 1 College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China 2 Medical Genetic Diagnosis and Therapy Center, Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China 3 Department of Obstetrics, Fujian Maternity and Child Health Hospital, Fuzhou, China © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creati vecommons.org/licenses/by-nc-nd/4.0/. Han et al. BMC Pregnancy and Childbirth (2025) 25:568 Introduction Preeclampsia (PE), a pregnancy-related hypertensive disorder, affects 2–8% of pregnancies globally and is a leading cause of significant maternal and perinatal morbidity and mortality [1, 2]. The underlying mechanisms contributing to the pathophysiology of preeclampsia remain poorly understood, as it is a complex disease process that originates at the maternal-fetal interface and affects multiple organ systems [3]. It is thought to involve abnormal placental vascular development, characterized by defective deep placentation and inadequate spiral artery. Recent studies have shown that the nutrition and the release of inflammatory factors play an important role in placental endothelial function and oxidative stress [4, 5]. Therefore, the significance of serum inflammatory markers as predictive indicators for preeclampsia has been extensively studied [6, 7]. As pregnant women encounter a wide range of nutritional challenges throughout their pregnancy, there is an increasing number of studies exploring the predictive value of inflammatory-nutritional markers in adverse pregnancy outcomes [8, 9]. Malnutrition, in conjunction with poor health behavior, is one of the most significant factors responsible for preeclampsia among pregnant women [10, 11]. Body mass index (BMI), calculated based on pre-pregnancy weight, is often used to assess nutritional status [12]. Whlie, relying solely on a single nutritional index represents an oversimplified and clinically unreliable evaluation method. The prognostic nutritional index (PNI), which is calculated by using serum albumin (ALB) concentration and peripheral blood lymphocyte count, has been proposed as a marker of immune-nutrition and reflects the chronic inflammation, immune status, and nutrition of the individual [13]. Recently, as an easily accessible and non-invasive biomarker, PNI has attracted more attention and has been extensively used for the clinical evaluation of the prognosis in patients with adverse cardiovascular events and tumor [14, 15]. However, its application in obstetrics has been explored in only a limited number of studies. A recent study found that the PNI is lower in patients with early-onset PE than in normotensive pregnant patients [16]. Besides, Songquan Wei et al. reported that high PNI score at admission was associated with reduced inhospitalization risk of adverse events in patients with PE [17]. Nevertheless, the association between PNI and PE in twin pregnancies remains uncertain. Twin pregnancy rates have increased in the past 30 years, particularly in high-income or middle-income countries, owing to an increased use of assisted reproductive techniques [18]. Twin pregnancies are associated with maternal and fetal adverse outcomes, including severe maternal morbidity (SMM) and neonatal near-miss (NNM) [19]. Besides, the risk of PE in twin pregnancies is 3–4 times higher than in singleton Page 2 of 9 pregnancies [20]. The underlying mechanisms may involve the expanded placental mass, hemodynamic overload, endothelial dysfunction, oxidative stress, and immune dysregulation characteristic of twin pregnancies [21, 22]. The maternal outcomes could also be led by changes in inflammation and nutritional status that are more pronounced than in singleton pregnancies. While there is insufficient evidence regarding the (...truncated)