CRP–Triglyceride–Glucose Index (CTGI) as a Predictor of Pre-eclampsia: A Population-Based Study of Risk Stratification

CRP–Triglyceride–Glucose Index (CTGI) as a Predictor of Pre-eclampsia: A Population-Based Study of Risk Stratification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CRP–Triglyceride–Glucose Index (CTGI) as a Predictor of Pre-eclampsia: A Population-Based Study of Risk Stratification Yuting Liang, Yanqiu Zhang, Yujing Li, Jun Cao, Bin Feng, Jieyu Jin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7233291/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted 9 You are reading this latest preprint version Abstract Background Pre-eclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality worldwide. While metabolic and inflammatory factors are increasingly recognized in its pathogenesis, the clinical utility of composite biomarkers remains underexplored. This study aimed to investigate the association between the C-reactive protein-triglyceride-glucose (CRP-TG-glucose) index (CTGI), a novel marker of metabolic-inflammation stress, and the risk of pre-eclampsia. Methods This retrospective cohort study included 11,916 pregnant women, of whom 486 developed pre-eclampsia. Maternal baseline characteristics were compared between the PE and non-PE groups. Logistic regression analyses were conducted to identify factors associated with PE. The relationship between CTGI and PE risk was further explored using quartile stratification, restricted cubic spline regression, and threshold effect analyses. Subgroup analyses were also performed to assess interaction effects across maternal and obstetric variables. Results Women with PE had significantly higher maternal age, BMI, IVF conception, multifetal pregnancies, and elevated CTGI levels compared to non-PE counterparts (all P < 0.001). Multivariate logistic regression identified CTGI as an independent risk factor for PE (adjusted OR, 1.78; 95% CI, 1.51–2.09; P < 0.001), alongside BMI, maternal age, IVF, and multifetal gestation. A dose–response relationship was observed across CTGI quartiles, with the highest quartile showing a markedly increased PE risk (adjusted OR, 2.06; 95% CI, 1.52–2.81). Restricted cubic spline models and threshold analysis revealed a nonlinear association with a significant inflection point at CTGI = 2.244. Above this threshold, the risk of PE rose sharply (OR, 3.93; 95% CI, 2.09–7.39; P < 0.001). Subgroup analyses demonstrated consistent associations across maternal age, BMI, parity, plurality, and IVF status, without significant interaction. Conclusions Elevated CTGI in early pregnancy is independently and nonlinearly associated with an increased risk of pre-eclampsia, particularly above a critical threshold of 2.244. These findings underscore the potential clinical value of CTGI as an early risk stratification biomarker for PE, enabling timely intervention in high-risk pregnancies. Pre-eclampsia CRP–Triglyceride–Glucose Index (CTGI) Metabolic-inflammation stress Dose-response relationship Risk stratification Figures Figure 1 Figure 2 Background Pre-eclampsia (PE) is a leading cause of maternal and perinatal mortality worldwide, accounting for approximately 14% of maternal deaths and 10–25% of perinatal deaths [ 1 ]. It is strongly associated with increased risks of intrauterine growth restriction, preterm birth, and perinatal mortality, including a fivefold higher risk of fetal death [ 2 , 3 ]. Women who survive PE often face reduced life expectancy and elevated risks of stroke, cardiovascular disease, and diabetes [ 4 , 5 ]. Likewise, infants born to mothers with PE are more likely to experience prematurity, perinatal death, neurodevelopmental disorders, and long-term cardiovascular and metabolic conditions [ 6 ]. Given its severe outcomes and enduring effects on both maternal and offspring health, PE represents a significant global public health burden. Early identification of at-risk individuals is therefore critical for timely intervention and improved prognosis. Various early prediction strategies for PE have been explored. Large-scale clinical trials have demonstrated that combining early pregnancy biomarkers (e.g. placental growth factor [PlGF]) with uterine artery doppler screening can enhance the detection rate of early-onset PE (< 37 weeks) and facilitate the prevention of some cases through low-dose aspirin administration [ 7 , 8 ]. However, these strategies have shown limited effectiveness in predicting term PE [ 7 ]. Existing clinical prediction tools include antenatal blood pressure monitoring, serum PlGF measurement, and uterine artery doppler velocimetry [ 9 ]. Yet, these methods require specialized equipment or costly reagents and have not achieved optimal sensitivity for overall PE prediction. For instance, although mid-gestational PlGF levels may indicate impending PE, the specificity and sensitivity vary considerably in routine screening. Similarly, uterine artery Doppler ultrasound alone typically detects fewer than half of PE cases. These limitations highlight the need for simple, accessible, and sensitive biomarkers to complement current screening methods. Inflammation has been recognized as a fundamental mechanism in the pathogenesis of various diseases, including cardiovascular, renal, autoimmune disorders, and pre-eclampsia [ 10 – 12 ]. Given the association between PE, inflammation, and metabolic dysregulation, the C-reactive protein–triglyceride–glucose index (CTGI, also referred to as CTI) has been proposed as an integrated predictive indicator [ 13 , 14 ]. This index incorporates three routinely available clinical markers—serum C-reactive protein (CRP), fasting plasma glucose (FPG), and serum triglycerides (TG)—to quantify metabolic-inflammatory burden. CTGI has been suggested as a theoretically suitable marker for early maternal risk assessment due to its ability to reflect both inflammation and insulin resistance. Emerging evidence has indicated that CTGI possesses predictive value for cardiovascular and metabolic diseases. For example, a U.S. NHANES-based study found a positive association between CTGI and coronary heart disease risk [ 15 ], while a large-scale Chinese study reported that elevated CTGI levels in hypertensive patients were significantly associated with increased stroke risk [ 16 ]. However, limited research has been conducted on CTGI in pregnant populations, and its value in early pregnancy for predicting PE remains unclear. The objective was to evaluate the association between CTGI levels in early pregnancy and subsequent PE development, assess the role of CTGI as an independent predictive factor, investigate potential nonlinear threshold effects, and determine the stability of this index across different population subgroups. These findings are expected to provide insight into the potential application of CTGI in early PE risk screening and contribute to the improvement of maternal healthcare strategies. Methods Study Population and Design This retrospective cohort study included 11,916 pregnant women who underwent routine prenatal screening in the Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou, China) between 2015 and 2024. Women with incomplete clinical or laboratory data, a history of chronic hypertension, fetal demise, termination of pregnancy, or spontaneous miscarriage were excluded. Participants with non-preeclamptic pregnancies complicated by intrauterine growth restriction or preterm birth were also excluded. The final analytic sample comprised 486 individuals who developed pre-eclampsia and 11,430 who did not. Exposure Measurement: CRP–Triglyceride–Glucose Index (CTGI) Fasting blood samples were collected before 14 weeks of gestation to obtain values for C-reactive protein (CRP), triglycerides, and glucose. The CRP–triglyceride–glucose index (CTGI), a composite marker of systemic inflammation and metabolic stress, was calculated using the formula: CTGI = ln [CRP (mg/L) × triglyceride (mmol/L) × glucose (mmol/L)/2]. Participants were stratified into quartiles based on the distribution of CTGI values. The primary exposure was the CTGI value analyzed both as a continuous variable (including ln-transformed form) and by quartiles. Outcome Definition The primary outcome was the development of pre-eclampsia, defined according to the American College of Obstetricians and Gynecologists (ACOG) criteria as new-onset hypertension (systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg) after 20 weeks of gestation, accompanied by proteinuria (≥ 1 + on urine dipstick on two occasions, ≥ 300 mg per 24-hour urine collection, or urine protein-to-creatinine ratio ≥ 30 mg/mmol). In the absence of proteinuria, pre-eclampsia was diagnosed if hypertension occurred with evidence of maternal organ dysfunction (e.g., thrombocytopenia, renal insufficiency, liver dysfunction, pulmonary edema, or neurologic symptoms). Covariates Demographic and clinical covariates extracted from medical records included maternal age, body mass index (BMI), gravidity, parity, use of in vitro fertilization (IVF), and fetal plurality (singleton vs multifetal gestation). These were included as potential confounders in regression models. Statistical Analyses Descriptive statistics were used to summarize baseline characteristics. Continuous variables were presented as medians with interquartile ranges (IQRs) and compared using the Mann–Whitney U test. Categorical variables were expressed as counts and percentages and compared using the chi-square test. Univariate logistic regression was first used to evaluate the association between individual covariates and the odds of developing pre-eclampsia. Multivariable logistic regression models were then constructed to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for CTGI, adjusting for maternal age, BMI, parity, IVF use, and multifetal gestation. To examine the dose–response relationship, CTGI was analyzed across quartiles in three models: Model 1: unadjusted; Model 2: adjusted for maternal age, BMI, and parity; Model 3: additionally adjusted for IVF and multifetal pregnancy. Restricted cubic spline (RCS) regression was applied to assess nonlinear associations between CTGI and pre-eclampsia risk. The likelihood ratio test was used to compare models with and without spline terms. Two-piecewise linear regression was performed to detect potential threshold effects, with the inflection point determined by a recursive algorithm. Subgroup analyses were conducted to evaluate the robustness of the CTGI–pre-eclampsia association across strata defined by maternal age (< 35 vs ≥ 35 years), obesity status (BMI < 25 vs ≥ 25 kg/m²), parity (nulliparous vs multiparous), plurality (singleton vs multifetal gestation), and conception method (natural vs IVF). Interaction terms were included to assess effect modification, with statistical significance set at P for interaction < .05. All analyses were performed using R software, and a two-sided P < .05 was considered statistically significant. Results Baseline Characteristics of Participants A total of 11,916 pregnant women were included in the analysis, comprising 11,430 without pre-eclampsia and 486 with pre-eclampsia. The median gestational age at delivery was significantly lower in women with pre-eclampsia (37.29 weeks [IQR, 35.29–39.00]) compared with those without pre-eclampsia (39.57 weeks [IQR, 39.00–40.29]; Z = –24.16, P < .001). Maternal age was slightly but significantly higher in the pre-eclampsia group (32.00 years [IQR, 29.00–35.00]) than in the non-pre-eclampsia group (31.00 years [IQR, 28.00–34.00]; Z = –4.14, P < .001) (Table 1). Body mass index (BMI) was significantly higher among women who developed pre-eclampsia (24.03 [IQR, 21.48–26.56]) compared with those who did not (21.79 [IQR, 20.20–23.73]; Z = –12.83, P < .001), indicating a greater prevalence of overweight or obesity in the pre-eclampsia group. Similarly, before the 14th week of gestation—prior to the clinical onset of pre-eclampsia—the CRP-triglyceride-glucose index, a composite marker reflecting metabolic and inflammatory stress, was significantly elevated in the pre-eclampsia group (1.74 [IQR, 1.26–2.24]) compared with the non-pre-eclampsia group (1.35 [IQR, 0.92–1.77]; Z = –11.79, P < .001) (Figure 1). Gravidity did not differ significantly between the two groups (both medians = 2.00; Z = –0.76, P = .450). However, parity was slightly lower in the pre-eclampsia group (Z = –3.56, P < .001), suggesting a higher proportion of nulliparous women among those who developed the condition (Table 1). For categorical variables, the proportion of multifetal pregnancies was substantially higher in women with pre-eclampsia (10.70%) compared to those without pre-eclampsia (1.11%) (χ² = 289.67, P < .001). Additionally, the use of in vitro fertilization (IVF) was more frequent in the pre-eclampsia group (25.10%) than in the non-pre-eclampsia group (8.47%) (χ² = 155.21, P < .001), reflecting a greater reliance on assisted reproductive technology in this population. Logistic Regression Analysis of Factors Associated with pre-eclampsia In univariate analysis, multiple pregnancy (OR, 10.66; 95% CI, 7.62–14.93), in vitro fertilization (IVF) (OR, 3.62; 95% CI, 2.92–4.49), maternal age (OR per year increase, 1.05; 95% CI, 1.03–1.08), body mass index (BMI) (OR per unit increase, 1.23; 95% CI, 1.20–1.26), and the C-reactive protein-triglyceride-glucose (CRP-TG-glucose) index (OR per unit increase, 2.54; 95% CI, 2.21–2.93) were all significantly associated with increased odds of pre-eclampsia. In contrast, parity was associated with a reduced odds (OR, 0.74; 95% CI, 0.61–0.89) (Table 2). In the multivariate model, after adjusting for potential confounders, the CRP-TG-glucose index remained significantly associated with pre-eclampsia (adjusted OR, 1.78; 95% CI, 1.51–2.09; P < .001), indicating it is an independent risk factor. Multiple pregnancy (adjusted OR, 6.56; 95% CI, 4.49–9.59; P < .001), IVF (adjusted OR, 2.04; 95% CI, 1.59–2.62; P < .001), maternal age (adjusted OR per year, 1.05; 95% CI, 1.02–1.08; P < .001), and BMI (adjusted OR per unit, 1.18; 95% CI, 1.14–1.21; P < .001) also remained independently associated with pre-eclampsia. Parity continued to show a protective effect (adjusted OR, 0.56; 95% CI, 0.45–0.70; P < .001) (Table 2). These findings suggest that higher systemic inflammatory and metabolic stress, as indicated by the CRP-TG-glucose index, is significantly associated with the risk of pre-eclampsia, independent of maternal age, BMI, parity, use of IVF, and multifetal gestation. Association Between CRP-Triglyceride-Glucose Index (CTGI) and Risk of pre-eclampsia The association between increasing quartiles of the CRP-triglyceride-glucose index (CTGI) and the odds of pre-eclampsia was evaluated across three logistic regression models of progressive adjustment (Table 3). In the unadjusted model (Model 1), higher CTGI quartiles were significantly associated with increased odds of pre-eclampsia. Compared with the lowest quartile (reference), the odds ratios (ORs) for pre-eclampsia were 1.25 (95% CI, 0.90–1.74) for quartile 2 (P = .181), 1.78 (95% CI, 1.31–2.43) for quartile 3 (P < .001), and 3.68 (95% CI, 2.78–4.87) for quartile 4 (P < .001), demonstrating a dose–response relationship. After adjusting for maternal age, body mass index (BMI), and parity in Model 2, the association remained significant for quartile 3 (OR, 1.51; 95% CI, 1.10–2.07; P = .010) and quartile 4 (OR, 2.48; 95% CI, 1.83–3.36; P < .001). Further adjustment in Model 3 for additional confounders, including multifetal gestation and use of in vitro fertilization (IVF), slightly attenuated the associations. The ORs were 1.11 (95% CI, 0.79–1.55; P = .552) for quartile 2, 1.36 (95% CI, 0.99–1.87; P = .059) for quartile 3, and 2.06 (95% CI, 1.52–2.81; P < .001) for quartile 4. These findings indicate that higher CTGI levels are independently associated with an elevated risk of pre-eclampsia, particularly among individuals in the highest quartile, even after adjustment for multiple maternal and obstetric factors. Threshold Effect of the CRP-Triglyceride-Glucose Index (CTGI) on Risk of pre-eclampsia To explore the potential nonlinear relationship between the CRP-triglyceride-glucose index (CTGI) and the odds of pre-eclampsia, restricted cubic spline (RCS) regression models were fitted. Visual inspection of the RCS curve (Figure 2) demonstrated a nonlinear increasing trend in the risk of pre-eclampsia with rising CTGI levels, particularly above the median reference point. After adjusting for maternal age, BMI, parity, multifetal pregnancy, and IVF status, this positive nonlinear association remained evident. To further characterize the relationship between CTGI and pre-eclampsia risk, a threshold effect analysis was performed using two-piecewise linear regression based on the segmented R package. Evidence of a threshold effect was identified, as indicated by the likelihood ratio test (P = .005), suggesting a nonlinear relationship with a significant inflection point at a CTGI value of 2.244. For CTGI values below 2.244, the association remained positive but was more modest (OR, 1.45; 95% CI, 1.16–1.82; P = .001). For CTGI values equal to or above 2.244, the association was substantially stronger (OR, 3.93; 95% CI, 2.09–7.39; P < .001) (Table 4). These findings suggest a threshold-dependent effect of CTGI on pre-eclampsia risk, with disproportionately higher risk observed beyond a CTGI value of 2.244. This nonlinear pattern underscores the clinical relevance of identifying high-risk individuals based on CTGI stratification. Subgroup analyses The association between the CRP-triglyceride-glucose index (CTGI) and the risk of pre-eclampsia was consistently observed across multiple clinically relevant subgroups (Table 5). In the overall population, higher CTGI was significantly associated with increased odds of pre-eclampsia (OR, 2.54; 95% CI, 2.21–2.93; P < .001). No significant interaction was observed between CTGI and any of the examined subgroups, indicating the association between elevated CTGI and increased risk of pre-eclampsia was consistent across maternal age, obesity status, parity, plurality, and conception method. Discussion Pre-eclampsia is now understood as a multisystem disorder with significant metabolic and inflammatory underpinnings [ 17 ]. In this study, the C-reactive protein–triglyceride–glucose index (CTGI) – a composite marker incorporating systemic inflammation and insulin resistance – has attracted attention as a potential predictor of pre-eclampsia risk. The rationale behind CTGI is grounded in known pathophysiology. Pre-eclampsia is characterized by endothelial dysfunction and abnormal placental perfusion, often preceded by maternal metabolic disturbances and systemic inflammation. Insulin resistance (captured by TyG) and chronic inflammation (captured by CRP) can together drive the cascade of events leading to pre-eclampsia. Insulin resistance (IR) in pregnancy promotes a cluster of pro-pre-eclampsia effects: it is associated with hyperglycemia, dyslipidemia, and overweight – all independent risk factors for pre-eclampsia [ 18 ]. IR can adversely affect the vascular endothelium by promoting oxidative stress and altering nitric oxide bioavailability, exacerbating endothelial dysfunction. At the same time, IR states are accompanied by elevated secretion of inflammatory cytokines and adipokines. In women with insulin resistance, adipose tissue (and even the placenta) releases higher amounts of tumor necrosis factor-α, leptin, and other mediators. This chronic pro-inflammatory milieu contributes to the maternal systemic inflammation observed before overt pre-eclampsia [ 19 , 20 ]. Both mechanisms – endothelial injury and inflammation – converge to impair placental development (e.g. shallow trophoblast invasion, insufficient spiral artery remodeling) and to provoke the clinical manifestations of pre-eclampsia. CTGI is essentially a surrogate for the presence of these two synergistic processes. A high CTGI early in pregnancy may indicate that a patient has significant underlying insulin resistance and an enhanced inflammatory response, creating a fertile soil for the later development of hypertension, proteinuria, and organ dysfunction characteristic of pre-eclampsia. This is consistent with epidemiologic links: women with metabolic syndrome or gestational diabetes (extreme insulin resistance states) have higher rates of pre-eclampsia, and women with elevated inflammatory markers likewise are at increased risk [ 21 , 22 ]. This suggests these factors are not merely consequences of disease but part of its pathogenesis. Compared to any single biomarker, CTGI offers a more comprehensive risk assessment for pre-eclampsia by integrating inflammatory and metabolic status. Another practical advantage of CTGI is its reliance on routine laboratory measurements (fasting glucose, triglycerides, and CRP), which are widely available. Unlike more specialized biomarkers (e.g. PlGF or uterine artery Doppler indices used in formal first-trimester screening), CTGI could be calculated using standard prenatal blood tests, facilitating its incorporation in diverse clinical settings. However, there are important limitations to acknowledge. First, as a composite index, CTGI could be elevated due to non-pre-eclampsia-related factors – for example, an intercurrent infection or inflammatory condition would raise CRP and thus CTGI, potentially yielding false-positive signals. Second, optimal pregnancy-specific cut-offs for CTGI are not yet established; the balance between sensitivity and specificity needs clarification in obstetric cohorts. In summary, CTGI appears to address a gap left by single markers, offering a fuller picture of a patient’s pathophysiological risk profile, but its use should be refined through further research. Conclusions CTGI represents a promising synthesis of inflammatory and metabolic indicators for pre-eclampsia risk assessment. It leverages well-established pathophysiologic links – chronic inflammation (e.g. elevated CRP) and insulin resistance (reflected by abnormal glucose and lipid metabolism) – which together contribute to the development of pre-eclampsia. As research evolves, CTGI could become a valuable component of precision obstetric care, enabling better prediction, prevention, and management of pre-eclampsia through a focus on modifiable inflammatory and metabolic health. Abbreviations PE pre-eclampsia CTGI c-reactive protein-triglyceride-glucose PlGF placental growth factor CRP c-reactive protein FPG fasting plasma glucose TG serum triglycerides ACOG American College of Obstetricians and Gynecologists BMI body mass index IVF in vitro fertilization IQRs interquartile ranges aORs adjusted odds ratios CIs confidence intervals RCS restricted cubic spline IR insulin resistance. Declarations Ethics approval and consent to participate This study protocol was reviewed and approved by the Medical Ethics Committee of the Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou, China). All participants provided broad informed consent. Consent for publication Not applicable Availability of data and materials No datasets were generated or analysed during the current study. Competing interests The authors have no conflicts of interest or competing financial interests to declare. Funding This study was supported by Science Foundation of Jiangsu Province Grant BK20240371, Suzhou Health Talent Program (GSWS2024046, GSWS2021004), Suzhou Key Clinical Technology Research (SKY2023001), the National Natural Science Foundation of China (Grant No. 82001576, No.82001523), the Primary Research & Development Plan of Jiangsu Province (BE2022736), Suzhou Medical College of Soochow University - Qilu Medical Research Fund (24QL200205), and the Jiangsu Province College Students' Innovation and Entrepreneurship Training Program Project (202410285273Y). Authors' contributions Yuting Liang: conceptualization, formal analysis, writing—original draft, supervision, funding acquisition; Yanqiu Zhang: methodology, data curation, formal analysis, writing—review and editing; Yujing Li: investigation, writing—review and editing; Jun Cao: investigation, writing—review and editing; Bin Feng: investigation, writing—review and editing; Jieyu Jin: investigation, writing—review and editing; Sheng Zhang: investigation, writing—review and editing; Qingqin Tang: investigation, writing—review and editing; Longwei Qiao: conceptualization, writing—review and editing, funding acquisition; Zhixing Jin: formal analysis, writing—original draft, supervision, funding acquisition. Acknowledgements The authors express their gratitude to the participants for their invaluable contributions to this study. References Bokuda K, Ichihara A. Preeclampsia up to date-What's going on? Hypertens Res. 2023; 46:1900-7. 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C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study. Diabetol Metab Syndr. 2024;16:277. Countouris ME, Bello NA. Advances in Our Understanding of Cardiovascular Diseases After Preeclampsia. Circ Res. 2025;136:583-93. Li Q, Zhao C, Liu M, Li M, Zhang Y, Yue C. Association between triglyceride-glucose index in early pregnancy and risk of preeclampsia: a multicenter retrospective cohort study. Lipids Health Dis. 2025;24:152. Dunk CE, Bucher M, Zhang J, Hayder H, Geraghty DE, Lye SJ, et al. Human leukocyte antigen HLA-C, HLA-G, HLA-F, and HLA-E placental profiles are altered in early severe preeclampsia and preterm birth with chorioamnionitis. Am J Obstet Gynecol. 2022;227:641 e641-641 e613. Abarca-Castro EA, Talavera-Pena AK, Reyes-Lagos JJ, Becerril-Villanueva E, Perez-Sanchez G, de la Pena FR, et al. Modulation of vagal activity may help reduce neurodevelopmental damage in the offspring of mothers with pre-eclampsia. Front Immunol. 2023;14:1280334. Yang Y, Wu N. Gestational Diabetes Mellitus and Preeclampsia: Correlation and Influencing Factors. Front Cardiovasc Med. 2022;9:831297. Hamadeh R, Mohsen A, Kobeissy F, Karouni A, Akoum H. C-Reactive Protein for Prediction or Early Detection of Pre-Eclampsia: A Systematic Review. Gynecol Obstet Invest. 2021;86:13-26. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files GraphicalabstractCTGI.jpg Tables.docx Cite Share Download PDF Status: Published Journal Publication published 12 Dec, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted Editorial decision: Revision requested 03 Sep, 2025 Reviews received at journal 03 Sep, 2025 Reviewers agreed at journal 02 Sep, 2025 Reviews received at journal 19 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 28 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 28 Jul, 2025 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-7233291","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":493152544,"identity":"53b4d492-7aae-4653-a6b4-e37ca80c10ab","order_by":0,"name":"Yuting Liang","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yuting","middleName":"","lastName":"Liang","suffix":""},{"id":493152545,"identity":"b9abba83-8e26-4e6a-83a9-5957e80d99f9","order_by":1,"name":"Yanqiu Zhang","email":"","orcid":"","institution":"Anhui Medical University, Ministry of Education","correspondingAuthor":false,"prefix":"","firstName":"Yanqiu","middleName":"","lastName":"Zhang","suffix":""},{"id":493152546,"identity":"d4260554-be6d-4b02-9c11-513e810fb98c","order_by":2,"name":"Yujing Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Yujing","middleName":"","lastName":"Li","suffix":""},{"id":493152547,"identity":"ec249ca8-06a2-413d-aed1-29830ec65884","order_by":3,"name":"Jun Cao","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Cao","suffix":""},{"id":493152548,"identity":"b72e4384-5c18-42d6-9ff0-810556d57dd6","order_by":4,"name":"Bin Feng","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Feng","suffix":""},{"id":493152550,"identity":"d33d3eb1-e62a-402c-bde3-48154b8aba87","order_by":5,"name":"Jieyu Jin","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jieyu","middleName":"","lastName":"Jin","suffix":""},{"id":493152552,"identity":"91c9dee2-4348-4db6-bab2-b5b8821b9d96","order_by":6,"name":"Sheng Zhang","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Zhang","suffix":""},{"id":493152553,"identity":"379498a1-998b-4533-bfc1-d2fcbf1b1e55","order_by":7,"name":"Qingqin Tang","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Qingqin","middleName":"","lastName":"Tang","suffix":""},{"id":493152554,"identity":"9e184723-c459-4d1f-940b-1c23f32daf35","order_by":8,"name":"Longwei Qiao","email":"","orcid":"","institution":"The Affiliated Suzhou Hospital of Nanjing Medical University, Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Longwei","middleName":"","lastName":"Qiao","suffix":""},{"id":493152560,"identity":"20ee7d1a-cb41-46a6-bad6-ba7559102ae9","order_by":9,"name":"Zhixing Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACxmYGBmYGAwYeEPNBQsUBsOiBB8RpYW4zeHDmAIjFcCCBgE3MEIq9QfJhG0QLAz4tzO28hz8XFByWMedf2GCQOO+OnL3Y4YdAW+zkdBtwOYwvwXiGQRqP5YyHDQ8Stz0z5pFOMwBqSTY2O4BLC49BMo+BDY/BjYNAW7YdTuyRTgBpOZC4DY+WwzwGEmAtEolzQFrSPxDSYtgMtuV8I1BLA0hLDkFbjJl5gH4xuMHYZpBw7LAxz+2cggMJBrj9Yth/xvgzz5/D9gbnjz9++KPmsBz77PTNHz5U2Mnh1NIAY0kkIIsbYFcOAvJwFj8OQ0fBKBgFo2AUAAB35WLqI+nY5AAAAABJRU5ErkJggg==","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Zhixing","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2025-07-28 11:23:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7233291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7233291/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12944-025-02829-7","type":"published","date":"2025-12-12T15:57:20+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88321323,"identity":"c073cc69-48e0-4f72-be1d-784c22a4215e","added_by":"auto","created_at":"2025-08-05 08:56:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":148694,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation Between First-Trimester CTGI and Risk of Pre-eclampsia.\u003c/p\u003e","description":"","filename":"Figure1.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7233291/v1/94803d265285dfc1593ca14c.jpg"},{"id":88321325,"identity":"381b37a5-1240-462f-87d5-bc211ce9662e","added_by":"auto","created_at":"2025-08-05 08:56:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":230553,"visible":true,"origin":"","legend":"\u003cp\u003eNonlinear Association Between First-Trimester CTGI and Risk of pre-eclampsia Based on Restricted Cubic Spline Regression. (A) shows the unadjusted model, in which a strong nonlinear association was observed (P for overall \u0026lt; .001; P for nonlinearity \u0026lt; .001). (B) displays the fully adjusted model, controlling for maternal age, body mass index (BMI), parity, in vitro fertilization (IVF), and multifetal pregnancy. The nonlinear association persisted after adjustment (P for overall \u0026lt; .001; P for nonlinearity = .001), although attenuated in magnitude.\u003c/p\u003e","description":"","filename":"Figure2.tif.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7233291/v1/cd5480639bcbcc8c889a7335.jpg"},{"id":98243510,"identity":"0617f9e8-7f72-4408-99cf-f7b1bf62fad4","added_by":"auto","created_at":"2025-12-15 16:07:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1034956,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7233291/v1/e9e66bd8-781d-4c16-a153-c92e12d97e8a.pdf"},{"id":88322774,"identity":"3508fcd5-8a10-4b56-b40f-4c7d73647637","added_by":"auto","created_at":"2025-08-05 09:12:40","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1274660,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalabstractCTGI.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7233291/v1/1cd9f4fd4898382f83ce2e7c.jpg"},{"id":88321324,"identity":"977cb77a-bea6-46e8-a3c6-efdc9d33f242","added_by":"auto","created_at":"2025-08-05 08:56:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28287,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7233291/v1/39291b5548f530fd699391ee.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"CRP–Triglyceride–Glucose Index (CTGI) as a Predictor of Pre-eclampsia: A Population-Based Study of Risk Stratification","fulltext":[{"header":"Background","content":"\u003cp\u003ePre-eclampsia (PE) is a leading cause of maternal and perinatal mortality worldwide, accounting for approximately 14% of maternal deaths and 10–25% of perinatal deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is strongly associated with increased risks of intrauterine growth restriction, preterm birth, and perinatal mortality, including a fivefold higher risk of fetal death [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Women who survive PE often face reduced life expectancy and elevated risks of stroke, cardiovascular disease, and diabetes [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Likewise, infants born to mothers with PE are more likely to experience prematurity, perinatal death, neurodevelopmental disorders, and long-term cardiovascular and metabolic conditions [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Given its severe outcomes and enduring effects on both maternal and offspring health, PE represents a significant global public health burden. Early identification of at-risk individuals is therefore critical for timely intervention and improved prognosis.\u003c/p\u003e\u003cp\u003eVarious early prediction strategies for PE have been explored. Large-scale clinical trials have demonstrated that combining early pregnancy biomarkers (e.g. placental growth factor [PlGF]) with uterine artery doppler screening can enhance the detection rate of early-onset PE (\u0026lt; 37 weeks) and facilitate the prevention of some cases through low-dose aspirin administration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, these strategies have shown limited effectiveness in predicting term PE [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Existing clinical prediction tools include antenatal blood pressure monitoring, serum PlGF measurement, and uterine artery doppler velocimetry [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Yet, these methods require specialized equipment or costly reagents and have not achieved optimal sensitivity for overall PE prediction. For instance, although mid-gestational PlGF levels may indicate impending PE, the specificity and sensitivity vary considerably in routine screening. Similarly, uterine artery Doppler ultrasound alone typically detects fewer than half of PE cases. These limitations highlight the need for simple, accessible, and sensitive biomarkers to complement current screening methods.\u003c/p\u003e\u003cp\u003eInflammation has been recognized as a fundamental mechanism in the pathogenesis of various diseases, including cardiovascular, renal, autoimmune disorders, and pre-eclampsia [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e–\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given the association between PE, inflammation, and metabolic dysregulation, the C-reactive protein–triglyceride–glucose index (CTGI, also referred to as CTI) has been proposed as an integrated predictive indicator [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This index incorporates three routinely available clinical markers—serum C-reactive protein (CRP), fasting plasma glucose (FPG), and serum triglycerides (TG)—to quantify metabolic-inflammatory burden. CTGI has been suggested as a theoretically suitable marker for early maternal risk assessment due to its ability to reflect both inflammation and insulin resistance. Emerging evidence has indicated that CTGI possesses predictive value for cardiovascular and metabolic diseases. For example, a U.S. NHANES-based study found a positive association between CTGI and coronary heart disease risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while a large-scale Chinese study reported that elevated CTGI levels in hypertensive patients were significantly associated with increased stroke risk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, limited research has been conducted on CTGI in pregnant populations, and its value in early pregnancy for predicting PE remains unclear.\u003c/p\u003e\u003cp\u003eThe objective was to evaluate the association between CTGI levels in early pregnancy and subsequent PE development, assess the role of CTGI as an independent predictive factor, investigate potential nonlinear threshold effects, and determine the stability of this index across different population subgroups. These findings are expected to provide insight into the potential application of CTGI in early PE risk screening and contribute to the improvement of maternal healthcare strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Population and Design\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis retrospective cohort study included 11,916 pregnant women who underwent routine prenatal screening in the Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou, China) between 2015 and 2024. Women with incomplete clinical or laboratory data, a history of chronic hypertension, fetal demise, termination of pregnancy, or spontaneous miscarriage were excluded. Participants with non-preeclamptic pregnancies complicated by intrauterine growth restriction or preterm birth were also excluded. The final analytic sample comprised 486 individuals who developed pre-eclampsia and 11,430 who did not.\u003c/p\u003e\u003cp\u003e\u003cb\u003eExposure Measurement: CRP–Triglyceride–Glucose Index (CTGI)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFasting blood samples were collected before 14 weeks of gestation to obtain values for C-reactive protein (CRP), triglycerides, and glucose. The CRP–triglyceride–glucose index (CTGI), a composite marker of systemic inflammation and metabolic stress, was calculated using the formula: CTGI = ln [CRP (mg/L) × triglyceride (mmol/L) × glucose (mmol/L)/2]. Participants were stratified into quartiles based on the distribution of CTGI values. The primary exposure was the CTGI value analyzed both as a continuous variable (including ln-transformed form) and by quartiles.\u003c/p\u003e\u003cp\u003e\u003cb\u003eOutcome Definition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe primary outcome was the development of pre-eclampsia, defined according to the American College of Obstetricians and Gynecologists (ACOG) criteria as new-onset hypertension (systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg) after 20 weeks of gestation, accompanied by proteinuria (≥ 1 + on urine dipstick on two occasions, ≥ 300 mg per 24-hour urine collection, or urine protein-to-creatinine ratio ≥ 30 mg/mmol). In the absence of proteinuria, pre-eclampsia was diagnosed if hypertension occurred with evidence of maternal organ dysfunction (e.g., thrombocytopenia, renal insufficiency, liver dysfunction, pulmonary edema, or neurologic symptoms).\u003c/p\u003e\u003cp\u003e\u003cb\u003eCovariates\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDemographic and clinical covariates extracted from medical records included maternal age, body mass index (BMI), gravidity, parity, use of in vitro fertilization (IVF), and fetal plurality (singleton vs multifetal gestation). These were included as potential confounders in regression models.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDescriptive statistics were used to summarize baseline characteristics. Continuous variables were presented as medians with interquartile ranges (IQRs) and compared using the Mann–Whitney U test. Categorical variables were expressed as counts and percentages and compared using the chi-square test.\u003c/p\u003e\u003cp\u003eUnivariate logistic regression was first used to evaluate the association between individual covariates and the odds of developing pre-eclampsia. Multivariable logistic regression models were then constructed to estimate adjusted odds ratios (aORs) and 95% confidence intervals (CIs) for CTGI, adjusting for maternal age, BMI, parity, IVF use, and multifetal gestation.\u003c/p\u003e\u003cp\u003eTo examine the dose–response relationship, CTGI was analyzed across quartiles in three models: Model 1: unadjusted; Model 2: adjusted for maternal age, BMI, and parity; Model 3: additionally adjusted for IVF and multifetal pregnancy. Restricted cubic spline (RCS) regression was applied to assess nonlinear associations between CTGI and pre-eclampsia risk. The likelihood ratio test was used to compare models with and without spline terms. Two-piecewise linear regression was performed to detect potential threshold effects, with the inflection point determined by a recursive algorithm.\u003c/p\u003e\u003cp\u003eSubgroup analyses were conducted to evaluate the robustness of the CTGI–pre-eclampsia association across strata defined by maternal age (\u0026lt; 35 vs ≥ 35 years), obesity status (BMI \u0026lt; 25 vs ≥ 25 kg/m²), parity (nulliparous vs multiparous), plurality (singleton vs multifetal gestation), and conception method (natural vs IVF). Interaction terms were included to assess effect modification, with statistical significance set at P for interaction \u0026lt; .05.\u003c/p\u003e\u003cp\u003eAll analyses were performed using R software, and a two-sided P \u0026lt; .05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Characteristics of Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 11,916 pregnant women were included in the analysis, comprising 11,430 without pre-eclampsia and 486 with pre-eclampsia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe median gestational age at delivery was significantly lower in women with pre-eclampsia (37.29 weeks [IQR, 35.29\u0026ndash;39.00]) compared with those without pre-eclampsia (39.57 weeks [IQR, 39.00\u0026ndash;40.29]; Z = \u0026ndash;24.16, P \u0026lt; .001). Maternal age was slightly but significantly higher in the pre-eclampsia group (32.00 years [IQR, 29.00\u0026ndash;35.00]) than in the non-pre-eclampsia group (31.00 years [IQR, 28.00\u0026ndash;34.00]; Z = \u0026ndash;4.14, P \u0026lt; .001) (Table 1).\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI) was significantly higher among women who developed pre-eclampsia (24.03 [IQR, 21.48\u0026ndash;26.56]) compared with those who did not (21.79 [IQR, 20.20\u0026ndash;23.73]; Z = \u0026ndash;12.83, P \u0026lt; .001), indicating a greater prevalence of overweight or obesity in the pre-eclampsia group. Similarly, before the 14th week of gestation\u0026mdash;prior to the clinical onset of pre-eclampsia\u0026mdash;the CRP-triglyceride-glucose index, a composite marker reflecting metabolic and inflammatory stress, was significantly elevated in the pre-eclampsia group (1.74 [IQR, 1.26\u0026ndash;2.24]) compared with the non-pre-eclampsia group (1.35 [IQR, 0.92\u0026ndash;1.77]; Z = \u0026ndash;11.79, P \u0026lt; .001) (Figure 1). Gravidity did not differ significantly between the two groups (both medians = 2.00; Z = \u0026ndash;0.76, P = .450). However, parity was slightly lower in the pre-eclampsia group (Z = \u0026ndash;3.56, P \u0026lt; .001), suggesting a higher proportion of nulliparous women among those who developed the condition (Table 1).\u003c/p\u003e\n\u003cp\u003eFor categorical variables, the proportion of multifetal pregnancies was substantially higher in women with pre-eclampsia (10.70%) compared to those without pre-eclampsia (1.11%) (\u0026chi;\u0026sup2; = 289.67, P \u0026lt; .001). Additionally, the use of in vitro fertilization (IVF) was more frequent in the pre-eclampsia group (25.10%) than in the non-pre-eclampsia group (8.47%) (\u0026chi;\u0026sup2; = 155.21, P \u0026lt; .001), reflecting a greater reliance on assisted reproductive technology in this population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLogistic Regression Analysis of Factors Associated with pre-eclampsia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn univariate analysis, multiple pregnancy (OR, 10.66; 95% CI, 7.62\u0026ndash;14.93), in vitro fertilization (IVF) (OR, 3.62; 95% CI, 2.92\u0026ndash;4.49), maternal age (OR per year increase, 1.05; 95% CI, 1.03\u0026ndash;1.08), body mass index (BMI) (OR per unit increase, 1.23; 95% CI, 1.20\u0026ndash;1.26), and the C-reactive protein-triglyceride-glucose (CRP-TG-glucose) index (OR per unit increase, 2.54; 95% CI, 2.21\u0026ndash;2.93) were all significantly associated with increased odds of pre-eclampsia. In contrast, parity was associated with a reduced odds (OR, 0.74; 95% CI, 0.61\u0026ndash;0.89) (Table 2).\u003c/p\u003e\n\u003cp\u003eIn the multivariate model, after adjusting for potential confounders, the CRP-TG-glucose index remained significantly associated with pre-eclampsia (adjusted OR, 1.78; 95% CI, 1.51\u0026ndash;2.09; P \u0026lt; .001), indicating it is an independent risk factor. Multiple pregnancy (adjusted OR, 6.56; 95% CI, 4.49\u0026ndash;9.59; P \u0026lt; .001), IVF (adjusted OR, 2.04; 95% CI, 1.59\u0026ndash;2.62; P \u0026lt; .001), maternal age (adjusted OR per year, 1.05; 95% CI, 1.02\u0026ndash;1.08; P \u0026lt; .001), and BMI (adjusted OR per unit, 1.18; 95% CI, 1.14\u0026ndash;1.21; P \u0026lt; .001) also remained independently associated with pre-eclampsia. Parity continued to show a protective effect (adjusted OR, 0.56; 95% CI, 0.45\u0026ndash;0.70; P \u0026lt; .001) (Table 2). These findings suggest that higher systemic inflammatory and metabolic stress, as indicated by the CRP-TG-glucose index, is significantly associated with the risk of pre-eclampsia, independent of maternal age, BMI, parity, use of IVF, and multifetal gestation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between CRP-Triglyceride-Glucose Index (CTGI) and Risk of pre-eclampsia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between increasing quartiles of the CRP-triglyceride-glucose index (CTGI) and the odds of pre-eclampsia was evaluated across three logistic regression models of progressive adjustment (Table 3).\u003c/p\u003e\n\u003cp\u003eIn the unadjusted model (Model 1), higher CTGI quartiles were significantly associated with increased odds of pre-eclampsia. Compared with the lowest quartile (reference), the odds ratios (ORs) for pre-eclampsia were 1.25 (95% CI, 0.90\u0026ndash;1.74) for quartile 2 (P = .181), 1.78 (95% CI, 1.31\u0026ndash;2.43) for quartile 3 (P \u0026lt; .001), and 3.68 (95% CI, 2.78\u0026ndash;4.87) for quartile 4 (P \u0026lt; .001), demonstrating a dose\u0026ndash;response relationship.\u003c/p\u003e\n\u003cp\u003eAfter adjusting for maternal age, body mass index (BMI), and parity in Model 2, the association remained significant for quartile 3 (OR, 1.51; 95% CI, 1.10\u0026ndash;2.07; P = .010) and quartile 4 (OR, 2.48; 95% CI, 1.83\u0026ndash;3.36; P \u0026lt; .001).\u003c/p\u003e\n\u003cp\u003eFurther adjustment in Model 3 for additional confounders, including multifetal gestation and use of in vitro fertilization (IVF), slightly attenuated the associations. The ORs were 1.11 (95% CI, 0.79\u0026ndash;1.55; P = .552) for quartile 2, 1.36 (95% CI, 0.99\u0026ndash;1.87; P = .059) for quartile 3, and 2.06 (95% CI, 1.52\u0026ndash;2.81; P \u0026lt; .001) for quartile 4.\u003c/p\u003e\n\u003cp\u003eThese findings indicate that higher CTGI levels are independently associated with an elevated risk of pre-eclampsia, particularly among individuals in the highest quartile, even after adjustment for multiple maternal and obstetric factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThreshold Effect of the CRP-Triglyceride-Glucose Index (CTGI) on Risk of pre-eclampsia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the potential nonlinear relationship between the CRP-triglyceride-glucose index (CTGI) and the odds of pre-eclampsia, restricted cubic spline (RCS) regression models were fitted. Visual inspection of the RCS curve (Figure 2) demonstrated a nonlinear increasing trend in the risk of pre-eclampsia with rising CTGI levels, particularly above the median reference point. After adjusting for maternal age, BMI, parity, multifetal pregnancy, and IVF status, this positive nonlinear association remained evident.\u003c/p\u003e\n\u003cp\u003eTo further characterize the relationship between CTGI and pre-eclampsia risk, a threshold effect analysis was performed using two-piecewise linear regression based on the segmented R package. Evidence of a threshold effect was identified, as indicated by the likelihood ratio test (P = .005), suggesting a nonlinear relationship with a significant inflection point at a CTGI value of 2.244. For CTGI values below 2.244, the association remained positive but was more modest (OR, 1.45; 95% CI, 1.16\u0026ndash;1.82; P = .001). For CTGI values equal to or above 2.244, the association was substantially stronger (OR, 3.93; 95% CI, 2.09\u0026ndash;7.39; P \u0026lt; .001) (Table 4). These findings suggest a threshold-dependent effect of CTGI on pre-eclampsia risk, with disproportionately higher risk observed beyond a CTGI value of 2.244. This nonlinear pattern underscores the clinical relevance of identifying high-risk individuals based on CTGI stratification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between the CRP-triglyceride-glucose index (CTGI) and the risk of pre-eclampsia was consistently observed across multiple clinically relevant subgroups (Table 5). In the overall population, higher CTGI was significantly associated with increased odds of pre-eclampsia (OR, 2.54; 95% CI, 2.21\u0026ndash;2.93; P \u0026lt; .001). No significant interaction was observed between CTGI and any of the examined subgroups, indicating the association between elevated CTGI and increased risk of pre-eclampsia was consistent across maternal age, obesity status, parity, plurality, and conception method.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003ePre-eclampsia is now understood as a multisystem disorder with significant metabolic and inflammatory underpinnings [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In this study, the C-reactive protein\u0026ndash;triglyceride\u0026ndash;glucose index (CTGI) \u0026ndash; a composite marker incorporating systemic inflammation and insulin resistance \u0026ndash; has attracted attention as a potential predictor of pre-eclampsia risk.\u003c/p\u003e\u003cp\u003eThe rationale behind CTGI is grounded in known pathophysiology. Pre-eclampsia is characterized by endothelial dysfunction and abnormal placental perfusion, often preceded by maternal metabolic disturbances and systemic inflammation. Insulin resistance (captured by TyG) and chronic inflammation (captured by CRP) can together drive the cascade of events leading to pre-eclampsia. Insulin resistance (IR) in pregnancy promotes a cluster of pro-pre-eclampsia effects: it is associated with hyperglycemia, dyslipidemia, and overweight \u0026ndash; all independent risk factors for pre-eclampsia [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. IR can adversely affect the vascular endothelium by promoting oxidative stress and altering nitric oxide bioavailability, exacerbating endothelial dysfunction. At the same time, IR states are accompanied by elevated secretion of inflammatory cytokines and adipokines. In women with insulin resistance, adipose tissue (and even the placenta) releases higher amounts of tumor necrosis factor-α, leptin, and other mediators. This chronic pro-inflammatory milieu contributes to the maternal systemic inflammation observed before overt pre-eclampsia [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Both mechanisms \u0026ndash; endothelial injury and inflammation \u0026ndash; converge to impair placental development (e.g. shallow trophoblast invasion, insufficient spiral artery remodeling) and to provoke the clinical manifestations of pre-eclampsia. CTGI is essentially a surrogate for the presence of these two synergistic processes. A high CTGI early in pregnancy may indicate that a patient has significant underlying insulin resistance and an enhanced inflammatory response, creating a fertile soil for the later development of hypertension, proteinuria, and organ dysfunction characteristic of pre-eclampsia. This is consistent with epidemiologic links: women with metabolic syndrome or gestational diabetes (extreme insulin resistance states) have higher rates of pre-eclampsia, and women with elevated inflammatory markers likewise are at increased risk [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This suggests these factors are not merely consequences of disease but part of its pathogenesis.\u003c/p\u003e\u003cp\u003eCompared to any single biomarker, CTGI offers a more comprehensive risk assessment for pre-eclampsia by integrating inflammatory and metabolic status. Another practical advantage of CTGI is its reliance on routine laboratory measurements (fasting glucose, triglycerides, and CRP), which are widely available. Unlike more specialized biomarkers (e.g. PlGF or uterine artery Doppler indices used in formal first-trimester screening), CTGI could be calculated using standard prenatal blood tests, facilitating its incorporation in diverse clinical settings.\u003c/p\u003e\u003cp\u003eHowever, there are important limitations to acknowledge. First, as a composite index, CTGI could be elevated due to non-pre-eclampsia-related factors \u0026ndash; for example, an intercurrent infection or inflammatory condition would raise CRP and thus CTGI, potentially yielding false-positive signals. Second, optimal pregnancy-specific cut-offs for CTGI are not yet established; the balance between sensitivity and specificity needs clarification in obstetric cohorts. In summary, CTGI appears to address a gap left by single markers, offering a fuller picture of a patient\u0026rsquo;s pathophysiological risk profile, but its use should be refined through further research.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eCTGI represents a promising synthesis of inflammatory and metabolic indicators for pre-eclampsia risk assessment. It leverages well-established pathophysiologic links \u0026ndash; chronic inflammation (e.g. elevated CRP) and insulin resistance (reflected by abnormal glucose and lipid metabolism) \u0026ndash; which together contribute to the development of pre-eclampsia. As research evolves, CTGI could become a valuable component of precision obstetric care, enabling better prediction, prevention, and management of pre-eclampsia through a focus on modifiable inflammatory and metabolic health.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003epre-eclampsia\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCTGI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ec-reactive protein-triglyceride-glucose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePlGF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eplacental growth factor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ec-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFPG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efasting plasma glucose\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eserum triglycerides\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eACOG\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican College of Obstetricians and Gynecologists\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBMI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ebody mass index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIVF\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ein vitro fertilization\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIQRs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einterquartile ranges\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eaORs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eadjusted odds ratios\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCIs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence intervals\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003erestricted cubic spline\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003einsulin resistance.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study protocol was reviewed and approved by the Medical Ethics Committee of the Affiliated Suzhou Hospital of Nanjing Medical University (Suzhou, China). All participants provided broad informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo datasets were generated or analysed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest or competing financial interests to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Science Foundation of Jiangsu Province Grant BK20240371, Suzhou Health Talent Program (GSWS2024046, GSWS2021004), Suzhou Key Clinical Technology Research (SKY2023001), the National Natural Science Foundation of China (Grant No. 82001576, No.82001523), the Primary Research \u0026amp; Development Plan of Jiangsu Province (BE2022736), Suzhou Medical College of Soochow University - Qilu Medical Research Fund (24QL200205), and the Jiangsu Province College Students\u0026apos; Innovation and Entrepreneurship Training Program Project (202410285273Y).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYuting Liang: conceptualization, formal analysis, writing\u0026mdash;original draft, supervision, funding acquisition; Yanqiu Zhang: methodology, data curation, formal analysis, writing\u0026mdash;review and editing; Yujing Li: investigation, writing\u0026mdash;review and editing; Jun Cao: investigation, writing\u0026mdash;review and editing; Bin Feng: investigation, writing\u0026mdash;review and editing; Jieyu Jin: investigation, writing\u0026mdash;review and editing; Sheng Zhang: investigation, writing\u0026mdash;review and editing; Qingqin Tang: investigation, writing\u0026mdash;review and editing; Longwei Qiao: conceptualization, writing\u0026mdash;review and editing, funding acquisition; Zhixing Jin: formal analysis, writing\u0026mdash;original draft, supervision, funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their gratitude to the participants for their invaluable contributions to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBokuda K, Ichihara A. Preeclampsia up to date-What\u0026apos;s going on? Hypertens Res. 2023; 46:1900-7.\u003c/li\u003e\n\u003cli\u003eHubel CA, Powers RW, Snaedal S, Gammill HS, Ness RB, Roberts JM, et al. C-reactive protein is elevated 30 years after eclamptic pregnancy. Hypertension. 2008;51:1499-1505.\u003c/li\u003e\n\u003cli\u003eTong J, Zhang L, Bai J, Zhang C. Exploring the role of FTO in preeclampsia pathogenesis: Insights into m(6)A modification and decidualization. Genes Dis. 2025;12:101504.\u003c/li\u003e\n\u003cli\u003eRamos A, Youssef L, Molina P, Torramade-Moix S, Martinez-Sanchez J, Moreno-Castano AB, et al. Circulating extracellular vesicles and neutrophil extracellular traps contribute to endothelial dysfunction in preeclampsia. Front Immunol. 2024;15:1488127.\u003c/li\u003e\n\u003cli\u003eLi Y, Sang Y, Chang Y, Xu C, Lin Y, Zhang Y, et al. A Galectin-9-Driven CD11c(high) Decidual Macrophage Subset Suppresses Uterine Vascular Remodeling in Preeclampsia. Circulation. 2024;149:1670-88.\u003c/li\u003e\n\u003cli\u003eDimitriadis E, Rolnik DL, Zhou W, Estrada-Gutierrez G, Koga K, Francisco RPV, et al. Pre-eclampsia. Nat Rev Dis Primers. 2023;9:8.\u003c/li\u003e\n\u003cli\u003eTuytten R, Syngelaki A, Thomas G, Panigassi A, Brown LW, Ortea P, et al. First-trimester preterm preeclampsia prediction with metabolite biomarkers: differential prediction according to maternal body mass index. Am J Obstet Gynecol. 2023;229:55 e51-55 e10.\u003c/li\u003e\n\u003cli\u003eCavoretto PI, Farina A, Salmeri N, Syngelaki A, Tan MY, Nicolaides KH. First trimester risk of preeclampsia and rate of spontaneous birth in patients without preeclampsia. Am J Obstet Gynecol. 2024;231:452 e451-452 e457.\u003c/li\u003e\n\u003cli\u003eCuenca-Gomez D, de Paco Matallana C, Rolle V, Valino N, Revello R, Adiego B, et al. Performance of first-trimester combined screening for preterm pre-eclampsia: findings from cohort of 10 110 pregnancies in Spain. Ultrasound Obstet Gynecol. 2023;62:522-30.\u003c/li\u003e\n\u003cli\u003eKim S, Shim S, Kwon J, Ryoo S, Byeon J, Hong J, et al. Alleviation of preeclampsia-like symptoms through PlGF and eNOS regulation by hypoxia- and NF-kappaB-responsive miR-214-3p deletion. Exp Mol Med. 2024;56:1388-1400.\u003c/li\u003e\n\u003cli\u003eLiu C, Li Q, Ma JX, Lu B, Criswell T, Zhang Y. Exosome-mediated renal protection: Halting the progression of fibrosis. Genes Dis. 2024;11:101117.\u003c/li\u003e\n\u003cli\u003eChen P, Yao L, Yuan M, Wang Z, Zhang Q, Jiang Y, et al. Mitochondrial dysfunction: A promising therapeutic target for liver diseases. Genes Dis. 2024;11:101115.\u003c/li\u003e\n\u003cli\u003eHuo G, Tang Y, Liu Z, Cao J, Yao Z, Zhou D. Association between C-reactive protein-triglyceride glucose index and stroke risk in different glycemic status: insights from the China Health and Retirement Longitudinal Study (CHARLS). Cardiovasc Diabetol. 2025;24:142.\u003c/li\u003e\n\u003cli\u003eBernstein IM, Badger GJ, McBride CA. Prepregnancy physiology and subsequent preterm preeclampsia. Am J Obstet Gynecol. 2025;232:314 e311-314 e312.\u003c/li\u003e\n\u003cli\u003eXu M, Zhang L, Xu D, Shi W, Zhang W. Usefulness of C-reactive protein-triglyceride glucose index in detecting prevalent coronary heart disease: findings from the National Health and Nutrition Examination Survey 1999-2018. Front Cardiovasc Med. 2024;11:1485538.\u003c/li\u003e\n\u003cli\u003eTang S, Wang H, Li K, Chen Y, Zheng Q, Meng J, et al. C-reactive protein-triglyceride glucose index predicts stroke incidence in a hypertensive population: a national cohort study. Diabetol Metab Syndr. 2024;16:277.\u003c/li\u003e\n\u003cli\u003eCountouris ME, Bello NA. Advances in Our Understanding of Cardiovascular Diseases After Preeclampsia. Circ Res. 2025;136:583-93.\u003c/li\u003e\n\u003cli\u003eLi Q, Zhao C, Liu M, Li M, Zhang Y, Yue C. Association between triglyceride-glucose index in early pregnancy and risk of preeclampsia: a multicenter retrospective cohort study. Lipids Health Dis. 2025;24:152.\u003c/li\u003e\n\u003cli\u003eDunk CE, Bucher M, Zhang J, Hayder H, Geraghty DE, Lye SJ, et al. Human leukocyte antigen HLA-C, HLA-G, HLA-F, and HLA-E placental profiles are altered in early severe preeclampsia and preterm birth with chorioamnionitis. Am J Obstet Gynecol. 2022;227:641 e641-641 e613.\u003c/li\u003e\n\u003cli\u003eAbarca-Castro EA, Talavera-Pena AK, Reyes-Lagos JJ, Becerril-Villanueva E, Perez-Sanchez G, de la Pena FR, et al. Modulation of vagal activity may help reduce neurodevelopmental damage in the offspring of mothers with pre-eclampsia. Front Immunol. 2023;14:1280334.\u003c/li\u003e\n\u003cli\u003eYang Y, Wu N. Gestational Diabetes Mellitus and Preeclampsia: Correlation and Influencing Factors. Front Cardiovasc Med. 2022;9:831297.\u003c/li\u003e\n\u003cli\u003eHamadeh R, Mohsen A, Kobeissy F, Karouni A, Akoum H. C-Reactive Protein for Prediction or Early Detection of Pre-Eclampsia: A Systematic Review. Gynecol Obstet Invest. 2021;86:13-26.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\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":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Pre-eclampsia, CRP–Triglyceride–Glucose Index (CTGI), Metabolic-inflammation stress, Dose-response relationship, Risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-7233291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7233291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003ePre-eclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality worldwide. While metabolic and inflammatory factors are increasingly recognized in its pathogenesis, the clinical utility of composite biomarkers remains underexplored. This study aimed to investigate the association between the C-reactive protein-triglyceride-glucose (CRP-TG-glucose) index (CTGI), a novel marker of metabolic-inflammation stress, and the risk of pre-eclampsia.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective cohort study included 11,916 pregnant women, of whom 486 developed pre-eclampsia. Maternal baseline characteristics were compared between the PE and non-PE groups. Logistic regression analyses were conducted to identify factors associated with PE. The relationship between CTGI and PE risk was further explored using quartile stratification, restricted cubic spline regression, and threshold effect analyses. Subgroup analyses were also performed to assess interaction effects across maternal and obstetric variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWomen with PE had significantly higher maternal age, BMI, IVF conception, multifetal pregnancies, and elevated CTGI levels compared to non-PE counterparts (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate logistic regression identified CTGI as an independent risk factor for PE (adjusted OR, 1.78; 95% CI, 1.51\u0026ndash;2.09; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), alongside BMI, maternal age, IVF, and multifetal gestation. A dose\u0026ndash;response relationship was observed across CTGI quartiles, with the highest quartile showing a markedly increased PE risk (adjusted OR, 2.06; 95% CI, 1.52\u0026ndash;2.81). Restricted cubic spline models and threshold analysis revealed a nonlinear association with a significant inflection point at CTGI\u0026thinsp;=\u0026thinsp;2.244. Above this threshold, the risk of PE rose sharply (OR, 3.93; 95% CI, 2.09\u0026ndash;7.39; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analyses demonstrated consistent associations across maternal age, BMI, parity, plurality, and IVF status, without significant interaction.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eElevated CTGI in early pregnancy is independently and nonlinearly associated with an increased risk of pre-eclampsia, particularly above a critical threshold of 2.244. These findings underscore the potential clinical value of CTGI as an early risk stratification biomarker for PE, enabling timely intervention in high-risk pregnancies.\u003c/p\u003e","manuscriptTitle":"CRP–Triglyceride–Glucose Index (CTGI) as a Predictor of Pre-eclampsia: A Population-Based Study of Risk Stratification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 08:56:35","doi":"10.21203/rs.3.rs-7233291/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-03T16:04:59+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-03T09:17:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"184953086248263933311982623049209574236","date":"2025-09-02T05:19:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-19T10:18:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287295231557485513096500900279360527702","date":"2025-08-10T05:29:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T00:30:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-28T16:57:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T11:50:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lipids in Health and Disease","date":"2025-07-28T11:12:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f304ed2-4554-4e06-a830-d93d3b6cc031","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:00:59+00:00","versionOfRecord":{"articleIdentity":"rs-7233291","link":"https://doi.org/10.1186/s12944-025-02829-7","journal":{"identity":"lipids-in-health-and-disease","isVorOnly":false,"title":"Lipids in Health and Disease"},"publishedOn":"2025-12-12 15:57:20","publishedOnDateReadable":"December 12th, 2025"},"versionCreatedAt":"2025-08-05 08:56:35","video":"","vorDoi":"10.1186/s12944-025-02829-7","vorDoiUrl":"https://doi.org/10.1186/s12944-025-02829-7","workflowStages":[]},"version":"v1","identity":"rs-7233291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7233291","identity":"rs-7233291","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}