Monocyte-to-HDL Ratio in Early Pregnancy as a Biomarker for Preeclampsia Risk Stratification

Monocyte-to-HDL Ratio in Early Pregnancy as a Biomarker for Preeclampsia 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 Monocyte-to-HDL Ratio in Early Pregnancy as a Biomarker for Preeclampsia Risk Stratification Yi Zhu, Yanqiu Zhang, Longwei Qiao, Xiangyu Dong, Sheng Zhang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8834497/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Background Preeclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality worldwide. Early identification of women at risk is essential for timely preventive interventions, yet current screening approaches have limitations in accessibility and predictive performance. The monocyte-to-high-density lipoprotein cholesterol ratio (MHR), a composite marker of inflammation and oxidative stress, has emerged as a potential biomarker but has not been adequately studied for early pregnancy prediction of PE. Methods This retrospective cohort study ultimately included 11,895 pregnant women in China who underwent routine first-trimester blood testing before 14 weeks’ gestation. The association between ln-transformed MHR (ln[MHR]) and subsequent development of PE was assessed using multivariable logistic regression, restricted cubic spline models, and two-piecewise linear regression, adjusting for maternal age, body mass index (BMI), parity, in vitro fertilization (IVF) conception, and multifetal gestation. Subgroup analyses were performed to evaluate potential effect modification. Results First-trimester ln(MHR) was significantly higher in women who later developed PE (P < 0.001). In multivariable analysis, elevated ln(MHR) was independently associated with increased risk of PE (adjusted OR: 1.80; 95% CI: 1.38–2.36; P < 0.001). A dose-response relationship was observed across ln(MHR) quartiles, with women in the highest quartile having a 67% increased risk compared to the lowest (adjusted OR: 1.67; 95% CI: 1.25–2.23; P < 0.001). Restricted cubic spline analysis indicated a near-linear association, and a two-piecewise model identified a threshold at ln(MHR) = − 0.97 above which risk increased sharply. The association remained consistent across subgroups defined by maternal age, BMI, parity, IVF conception and multifetal gestation (P for interaction > 0.05 for all). Conclusion Elevated first-trimester ln(MHR) was independently associated with subsequent PE, showing dose-dependent and threshold effects. These findings suggest that MHR may serve as a simple, accessible biomarker for early risk stratification of PE in prenatal care. Preeclampsia Monocyte High-density lipoprotein cholesterol Restricted cubic splines Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Preeclampsia (PE) is a life-threatening hypertensive disorder that complicates approximately 2% to 8% of pregnancies and remains a leading global cause of maternal and neonatal morbidity and mortality[ 1 – 3 ]. Affected women face increased lifetime risks of stroke, cardiovascular disease, and diabetes, while their offspring are more likely to experience preterm birth, perinatal death, and long-term metabolic and neurodevelopmental impairment[ 4 – 6 ]. Because PE can progress rapidly in late gestation, early identification of at-risk individuals is essential. Current first-trimester screening algorithms—based on maternal demographic characteristics, uterine artery Doppler indices, and placental biomarkers such as pregnancy-associated plasma protein-A and placental growth factor (PlGF)—have shown utility for identifying women at elevated risk[ 7 , 8 ]. Prophylactic administration of low-dose aspirin before 16 weeks’ gestation can significantly reduce the incidence of early-onset PE in high-risk populations[ 9 , 10 ]. However, these multimodal screening tools require specialized testing infrastructure and perform suboptimally for predicting term PE[ 4 ]. As such, there remains a clinical need for simple, widely available, blood-based biomarkers measurable in the first trimester to improve risk stratification and enable timely preventive interventions. Inflammation and oxidative stress form a mutually reinforcing axis that underpins many disorders[ 11 , 12 ]. In PE, aberrant placentation and intermittent hypoxia-reoxygenation provoke excessive reactive oxygen species and sterile inflammation, which amplify endothelial dysfunction and anti-angiogenic signaling, accelerating disease progression[ 13 , 14 ]. The monocyte-to-high-density lipoprotein (HDL) cholesterol ratio (MHR) has recently emerged as a promising composite marker of systemic inflammation and oxidative stress. Monocytes contribute to vascular dysfunction through secretion of proinflammatory cytokines and promotion of endothelial activation, whereas HDL cholesterol counteracts these effects via anti-inflammatory and antioxidant mechanisms[ 15 , 16 ]. As an integrative index, MHR reflects the balance between proinflammatory activity and anti-inflammatory lipid defense in the circulation, and it has been associated with various chronic conditions, including type 2 diabetes, metabolic syndrome, atherosclerotic cardiovascular disease, and nonalcoholic fatty liver disease[ 17 – 19 ]. Given the central role of inflammation, oxidative stress, and endothelial dysfunction in the pathophysiology of PE[ 13 , 14 , 20 ], MHR represents a biologically plausible candidate biomarker for identifying women at risk. A small-scale, retrospective studies have reported associations between elevated MHR and term PE, suggesting potential diagnostic relevance for the condition[ 21 ]. However, the evidence remains limited and inconsistent. To date, no large-scale study has examined whether MHR measured during early pregnancy independently predicts the later development of PE. Consequently, the clinical utility of MHR as an early screening tool for PE risk remains uncertain. This study aimed to evaluate whether elevated MHR, measured in the first trimester before 14 weeks’ gestation, is independently associated with the subsequent development of PE. Using a large retrospective cohort, we examined both continuous and quartile-based associations, applied multivariable and spline regression models to explore linearity and threshold effects, and performed subgroup analyses across key maternal characteristics. The objective was to determine whether early-pregnancy MHR serves as a broadly applicable, dose-responsive biomarker that could enhance PE risk stratification and support timely preventive interventions. Materials and Methods Study design and population The overall flow chart of this study is shown in Fig. 1 and Figure S1 . This retrospective cohort study initially included 12,000 pregnant individuals who underwent routine first-trimester prenatal screening before 14 weeks of gestation at Suzhou Hospital of Nanjing Medical University in Suzhou, China, between 2015 and 2024. The primary aim of this study was to assess the association between first-trimester MHR and the subsequent development of PE within a general obstetric population. Therefore, participants were excluded if they had missing laboratory or obstetric data; a documented history of chronic hypertension; intrauterine fetal demise, termination of pregnancy, or spontaneous miscarriage; or non-preeclamptic pregnancies complicated by intrauterine growth restriction (IUGR) or preterm birth[ 22 ]. After applying these exclusion criteria, a total of 11,895 women with complete data on MHR and subsequent pregnancy outcomes were retained for final analysis, including 11,410 women who did not develop PE and 485 women who did. Exposure measurement: Monocyte-to-HDL ratio (MHR) At the time of first-trimester screening (< 14 weeks of gestation), demographic and laboratory data were collected. MHR was calculated by dividing the absolute monocyte count (×10⁹/L) by the concentration of HDL cholesterol (mmol/L), both obtained from fasting peripheral blood samples. Participants were stratified into quartiles based on the distribution of ln-transformed MHR (ln[MHR]) values in the study population. Outcome assessment The primary outcome was the development of PE, defined according to the American College of Obstetricians and Gynecologists (ACOG) criteria as new-onset hypertension and proteinuria after 20 weeks of gestation. Hypertension was defined as a systolic blood pressure ≥ 140 mm Hg and/or diastolic blood pressure ≥ 90 mm Hg. Proteinuria was diagnosed by the presence of ≥ 1 + protein on urine dipstick on at least two occasions, 24-hour urinary protein excretion ≥ 300 mg, or a spot urine protein-to-creatinine ratio ≥ 30 mg/mmol. In the absence of proteinuria, PE was diagnosed if hypertension was accompanied by maternal organ dysfunction, including thrombocytopenia, elevated liver enzymes, renal insufficiency, pulmonary edema, or new-onset neurological symptoms. Pregnancy outcomes were extracted from electronic medical records and independently verified by two obstetricians blinded to MHR levels. Covariates and confounders Potential confounding variables—including maternal age, body mass index (BMI), parity, mode of conception (natural vs in vitro fertilization [IVF]), and fetal plurality (singleton vs multifetal gestation)—were extracted from the medical record and included in multivariable models. Statistical analyses Continuous variables were summarized as means (standard deviations) or medians (interquartile ranges [IQR]) and compared using Mann-Whitney U test, as appropriate. Categorical variables were analyzed using the Chi-square test (χ² test). Logistic regression was used to estimate adjusted odds ratios (aORs) and ninety-five percent confidence intervals (95% CIs) for the association between first-trimester ln(MHR) and risk of PE. Three models were constructed: Model 1 (unadjusted), Model 2 (adjusted for maternal age, BMI, and parity), and Model 3 (additionally adjusted for IVF conception and multifetal gestation). To assess potential dose-response relationships, ln(MHR) was analyzed both as a categorical variable (quartiles) and a continuous variable using restricted cubic spline regression. Non-linearity was evaluated using likelihood ratio tests comparing models with and without spline terms. Two-piecewise linear regression was employed to explore potential threshold effects, with the inflection point estimated via a recursive algorithm. The superiority of the threshold model was assessed using likelihood ratio testing. Subgroup analyses were performed across predefined strata, including maternal age (< 35 vs ≥ 35 years), BMI (< 30 vs ≥ 30 kg/m²), parity (nulliparous vs multiparous), IVF conception (yes vs no), and multifetal gestation (yes vs no). Interaction terms were included to test for effect modification, with statistical significance defined as P for interaction < 0.05. All analyses were conducted using R software, and a two-sided P < 0.05 was considered statistically significant. In this cohort study, 6,530 pregnant women underwent first-trimester screening for PE using the Fetal Medicine Foundation (FMF) model; measurements of PlGF and mean arterial pressure (MAP), converted into multiples of the median (MoM), were taken to evaluate the independent and combined predictive value of ln(MHR) for early PE prediction alongside these established markers. Pearson correlation coefficients were computed to assess associations between ln(MHR) and PlGF. 95% CIs and two-sided P values were reported. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative performance of ln(MHR), PlGF, and MAP for early prediction of PE. The area under the curve (AUC) with 95% CIs was calculated for each marker. Comparisons of AUCs were conducted using the DeLong test. Statistical significance was defined as a two-sided P < 0.05. Results Association between first-trimester MHR and subsequent development of PE The baseline characteristics of the study population are summarized in Table 1 . MHR levels measured before the 14th week of gestation—prior to the clinical onset of PE—were significantly higher in women who subsequently developed PE compared to those who did not (Table 1 , Fig. 2 ). The median MHR value was markedly elevated in the future PE group (P < 0.001), suggesting a strong early association between elevated MHR and the later development of PE. Table 1 Baseline maternal and pregnancy characteristics in women with and without PE Variables Total (n = 11895) Non-PE (n = 11410) PE (n = 485) Statistic P Gestational Age at Delivery (weeks), M (Q₁, Q₃) 39.43 (38.86, 40.14) 39.57 (39.00, 40.29) 37.29 (35.29, 39.00) Z = − 24.11 < 0.001 Age (years), M (Q₁, Q₃) 31.00 (28.00, 34.00) 31.00 (28.00, 34.00) 32.00 (29.00, 35.00) Z = − 4.11 < 0.001 BMI (kg/m 2 ), M (Q₁, Q₃) 21.85 (20.20, 23.86) 21.79 (20.20, 23.73) 24.03 (21.48, 26.56) Z = − 12.84 < 0.001 Gravidity, M (Q₁, Q₃) 2.00 (1.00, 2.00) 2.00 (1.00, 2.00) 2.00 (1.00, 2.00) Z = − 0.73 0.465 Parity, M (Q₁, Q₃) 0.00 (0.00, 1.00) 0.00 (0.00, 1.00) 0.00 (0.00, 1.00) Z = − 3.60 < 0.001 IVF conception, n(%) χ² = 155.62 < 0.001 No 10806 (90.84) 10443 (91.52) 363 (74.85) Yes 1089 (9.16) 967 (8.48) 122 (25.15) Multifetal gestation, n(%) χ² = 289.79 < 0.001 No 11716 (98.50) 11283 (98.89) 433 (89.28) Yes 179 (1.50) 127 (1.11) 52 (10.72) MHR, M (Q₁, Q₃) 0.21 (0.17, 0.27) 0.21 (0.17, 0.26) 0.24 (0.19, 0.31) Z = − 7.69 < 0.001 Z: Mann-Whitney test, χ²: Chi-square test M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile To further evaluate the predictive value of first-trimester MHR for PE, a multivariate logistic regression analysis was conducted, adjusting for maternal age, BMI, parity, IVF conception, and multifetal gestation. In multivariate analysis, a higher ln(MHR) before the 14th gestational week was significantly associated with the subsequent development of PE (OR: 1.80; 95% CI: 1.38–2.36; P < 0.001), indicating that elevated early-pregnancy ln(MHR) independently predicts the risk of PE (Table 2 ). Table 2 Independent association of first-trimester ln(MHR) and maternal characteristics with subsequent PE risk Variables β S.E Z P OR (95%CI) Age (years) 0.05 0.01 3.69 < 0.001 1.05 (1.02 ~ 1.08) BMI (kg/m 2 ) 0.19 0.01 13.97 < 0.001 1.21 (1.18 ~ 1.25) Parity −0.37 0.11 −3.25 0.001 0.69 (0.55 ~ 0.86) IVF conception No 1.00 (Reference) Yes 0.78 0.13 6.11 < 0.001 2.18 (1.70 ~ 2.79) Multifetal gestation No 1.00 (Reference) Yes 1.97 0.19 10.32 < 0.001 7.20 (4.95 ~ 10.48) ln(MHR) 0.59 0.14 4.28 < 0.001 1.80 (1.38 ~ 2.36) Other significant risk factors included higher maternal age (OR: 1.05; 95% CI: 1.02–1.08; P < 0.001), increased BMI (OR: 1.21; 95% CI: 1.18–1.25; P < 0.001), low parity (OR: 0.69; 95% CI: 0.55–0.86; P = 0.001), IVF conception (OR: 2.18; 95% CI: 1.70–2.79; P < 0.001), and multifetal gestation (OR: 7.20; 95% CI: 4.95–10.48; P < 0.001) (Table 2 ). Quartile-based stratification of ln(MHR) identifies high-risk pregnancies for PE development Participants were stratified into quartiles based on the ln(MHR): Q1 (n = 2,928; 24.62%), Q2 (n = 3,018; 25.37%), Q3 (n = 2,974; 25.00%), and Q4 (n = 2,975; 25.01%). Significant differences in maternal characteristics were observed across the four ln(MHR) quartiles (P < 0.05 for all). Women in the highest quartile (Q4) had a higher median BMI (22.72 [IQR, 20.89–24.94]) compared to those in Q1 (21.34 [IQR, 19.78–23.08]; P < 0.001). Similarly, parity and maternal age showed statistically significant variation across groups. Importantly, the proportion of women who developed PE increased significantly across the quartiles (Q1: 2.53%, Q2: 3.71%, Q3: 3.63%, Q4: 6.42%; P < 0.001), suggesting a dose-response relationship between early-pregnancy ln(MHR) and subsequent PE risk (Table 3 ). Table 3 Baseline maternal characteristics and PE incidence across quartiles of first-trimester ln(MHR) Variables Total (n = 11895) 1 (n = 2928) 2 (n = 3018) 3 (n = 2974) 4 (n = 2975) Statistic P Age (years), M (Q₁, Q₃) 31.00 (28.00, 34.00) 31.00 (28.00, 34.00) 31.00 (28.00, 34.00) 30.00 (28.00, 34.00) 30.00 (28.00, 34.00) χ² = 41.60# < 0.001 BMI (kg/m 2 ), M (Q₁, Q₃) 21.85 (20.20, 23.86) 21.34 (19.78, 23.08) 21.61 (20.08, 23.50) 22.02 (20.20, 23.88) 22.72 (20.89, 24.94) χ² = 402.42# < 0.001 Parity, M (Q₁, Q₃) 0.00 (0.00, 1.00) 0.00 (0.00, 1.00) 0.00 (0.00, 1.00) 0.00 (0.00, 1.00) 0.00 (0.00, 1.00) χ² = 164.16# < 0.001 IVF conception, n(%) χ² = 22.36 < 0.001 No 10806 (90.84) 2689 (91.84) 2779 (92.08) 2692 (90.52) 2646 (88.94) Yes 1089 (9.16) 239 (8.16) 239 (7.92) 282 (9.48) 329 (11.06) Multifetal gestation, n(%) χ² = 12.94 0.005 No 11716 (98.50) 2899 (99.01) 2973 (98.51) 2932 (98.59) 2912 (97.88) Yes 179 (1.50) 29 (0.99) 45 (1.49) 42 (1.41) 63 (2.12) PE(%) χ² = 62.28 < 0.001 Non-PE 11410 (95.92) 2854 (97.47) 2906 (96.29) 2866 (96.37) 2784 (93.58) PE 485 (4.08) 74 (2.53) 112 (3.71) 108 (3.63) 191 (6.42) #: Kruskal-waills test, χ²: Chi-square test M: Median, Q₁: 1st Quartile, Q₃: 3st Quartile To further investigate the association between first-trimester ln(MHR) levels and the risk of PE, logistic regression models were constructed based on ln(MHR) quartiles. In the unadjusted model (Model 1), women in the highest ln(MHR) quartile (Q4) had a significantly increased risk of developing PE compared to those in the lowest quartile (Q1) (OR: 2.65; 95% CI: 2.01–3.48; P < 0.001), with significant associations also observed for Q2 (OR: 1.49; 95% CI: 1.10–2.00; P = 0.009) and Q3 (OR: 1.45; 95% CI: 1.08–1.96; P = 0.015), indicating a dose-response relationship. After adjusting for maternal age, BMI, and parity (Model 2), the association for Q4 remained statistically significant (OR: 1.77; 95% CI: 1.33–2.35; P < 0.001), whereas associations for Q2 and Q3 were attenuated. In the fully adjusted model (Model 3), which included maternal age, BMI, parity, IVF conception and multifetal gestation, women in the highest quartile still had a significantly elevated risk of PE (OR: 1.67; 95% CI: 1.25–2.23; P < 0.001), suggesting that elevated ln(MHR) in early pregnancy is independently associated with an increased risk of PE, even after controlling for major confounding factors. Although the associations for the second and third quartiles attenuated after full adjustment, the trend analysis demonstrated a significant linear increase in PE risk across ln(MHR) quartiles (P for trend < 0.001 in all models) (Table 4 ). Table 4 Association between ln(MHR) and risk of PE across multivariable models Variables Model 1 Model 2 Model 3 OR (95%CI) P OR (95%CI) P OR (95%CI) P ln(MHR) 3.15 (2.44 ~ 4.07) < 0.001 1.99 (1.52 ~ 2.60) < 0.001 1.80 (1.38 ~ 2.36) < 0.001 ln(MHR) (Quartile) 1 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 2 1.49 (1.10 ~ 2.00) 0.009 1.36 (1.01 ~ 1.84) 0.044 1.32 (0.98 ~ 1.80) 0.072 3 1.45 (1.08 ~ 1.96) 0.015 1.23 (0.91 ~ 1.67) 0.187 1.18 (0.87 ~ 1.61) 0.289 4 2.65 (2.01 ~ 3.48) < 0.001 1.77 (1.33 ~ 2.35) < 0.001 1.67 (1.25 ~ 2.23) < 0.001 P for trend < 0.001 < 0.001 < 0.001 Nonlinear dose-response relationship between first-trimester ln(MHR) and PE risk To explore the dose-response relationship between early-pregnancy ln(MHR) and the risk of PE, a restricted cubic spline regression was performed. In the unadjusted model, a significant non-linear association was observed (P for overall < 0.001; P for nonlinearity = 0.007), suggesting a curved dose-response pattern. The risk of PE increased markedly with higher ln(MHR) values, especially beyond a certain threshold (Fig. 3 A). After adjustment for maternal age, BMI, parity, IVF conception, and multifetal gestation, the association between ln(MHR) and PE risk remained statistically significant (P for overall < 0.001), although the non-linear trend was attenuated (P for nonlinearity = 0.056). These findings indicate that elevated ln(MHR) levels in early pregnancy are independently and positively associated with the subsequent development of PE, with a near-linear increase in odds across most of the ln(MHR) range (Fig. 3 B). Nonlinear and threshold effects of ln(MHR) on the development of PE in first-trimester To further explore the potential threshold effect of ln(MHR) on the risk of PE, two-piecewise linear regression models were applied. In the two-piecewise linear regression model, a potential inflection point was identified at − 0.97. Below this threshold (ln[MHR] < − 0.97), the association between ln(MHR) and PE was modest but significant (effect estimate: 1.46; 95% CI: 1.05–2.05; P = 0.027). Above the threshold (ln[MHR] ≥ − 0.97), the association became markedly stronger (effect estimate: 24.54; 95% CI: 4.88–123.54; P < 0.001). The likelihood ratio test comparing the two models indicated that the two-piecewise model provided a significantly better fit (P = 0.011), suggesting a threshold-dependent relationship between ln(MHR) and the subsequent development of PE (Table 5 ). Table 5 Threshold effect of first-trimester ln(MHR) on risk of PE: Comparison of linear and two-piecewise regression models Outcome OR (95%CI) P Model 1 Fitting model by standard linear regression 1.80 (1.38 ~ 2.36) < 0.001 Model 2 Fitting model by two-piecewise linear regression Inflection point −0.97 < −0.97 1.46 (1.05 ~ 2.05) 0.027 ≥ −0.97 24.54 (4.88 ~ 123.54) < 0.001 P for likelihood test 0.011 Association between first-trimester ln(MHR) and risk of PE across maternal subgroups Subgroup analyses were conducted to evaluate the consistency of the association between ln(MHR) and the risk of PE across clinically relevant strata. The association remained robust in all subgroups, with no significant interactions observed. Among women of advanced maternal age (≥ 35 years), elevated ln(MHR) was significantly associated with increased PE risk (OR: 3.25; 95% CI: 1.98–5.35; P < 0.001), similar to the younger subgroup (< 35 years) (OR: 3.20; 95% CI: 2.37–4.32; P < 0.001; P for interaction = 0.955). Although the association was attenuated in women with obesity (BMI ≥ 30 kg/m²) (OR: 1.37; 95% CI: 0.61–3.08; P = 0.448), the interaction was not statistically significant (P for interaction = 0.077). In both nulliparous (OR: 2.97; 95% CI: 2.20–4.01) and multiparous women (OR: 3.11; 95% CI: 1.86–5.22), ln(MHR) showed a consistent positive association with PE (P < 0.001; P for interaction = 0.879). Similarly, the association remained significant in both women with and without IVF conception (OR: 2.23 vs. 3.20; P for interaction = 0.227), and in those with and without multifetal gestation (OR: 1.63 vs. 3.10; P for interaction = 0.140). These findings suggest that the positive association between early-pregnancy ln(MHR) and the risk of PE is generally consistent across maternal subgroups, without significant effect modification (Table 6 ). Table 6 Subgroup analyses and interaction effects Variables n (%) OR (95%CI) P P for interaction All patients 11895 (100.00) 3.15 (2.44 ~ 4.07) < 0.001 Age 0.955 < 35 9427 (79.11) 3.20 (2.37 ~ 4.32) < 0.001 ≥ 35 2489 (20.89) 3.25 (1.98 ~ 5.35) < 0.001 BMI 0.077 < 30 11695 (98.15) 2.98 (2.27 ~ 3.92) < 0.001 ≥ 30 221 (1.85) 1.37 (0.61 ~ 3.08) 0.448 Parity 0.879 Nulliparous(0) 7873 (66.07) 2.97 (2.20 ~ 4.01) 0) 4043 (33.93) 3.11 (1.86 ~ 5.22) < 0.001 IVF conception 0.227 No 10826 (90.85) 3.20 (2.37 ~ 4.31) < 0.001 Yes 1090 (9.15) 2.23 (1.35 ~ 3.69) 0.002 Multifetal gestation 0.140 No 11737 (98.50) 3.10 (2.36 ~ 4.07) < 0.001 Yes 179 (1.50) 1.63 (0.73 ~ 3.63) 0.235 Comparison of first-trimester ln(MHR), PlGF, and MAP for early prediction of PE Among 6530 pregnant women, PlGF and MAP were measured in the first trimester. To evaluate the diagnostic value of ln(MHR) as an early biomarker for PE, its correlation with the established angiogenic marker PlGF was assessed, and its discriminative performance was compared with that of PlGF and the conventional clinical indicator MAP. The aim of the study was to determine whether MHR, a simple inflammation-based marker derived from routine laboratory testing, could serve as an independent alternative or complementary tool to existing predictors. Correlation analysis demonstrated that ln(MHR) was not associated with PlGF (Pearson correlation coefficient r = 0.011; 95% CI, − 0.014–0.035; P = 0.39), indicating that the two parameters reflect distinct biological processes: systemic inflammation and impaired placental angiogenesis. This low correlation suggests that ln(MHR) may provide nonredundant information when incorporated into predictive models for PE. Regarding diagnostic performance, ROC analysis showed that the AUC for ln(MHR) was 0.613 (95% CI, 0.577–0.649). The AUC for PlGF was 0.603 (95% CI, 0.568–0.639; P = 0.72 by the DeLong test compared with ln[MHR]). Notably, the discriminative performance of both ln(MHR) and PlGF was inferior to that of MAP (AUC, 0.700; 95% CI, 0.666–0.734), with statistically significant differences observed between MAP and PlGF (P = 0.0002) and between MAP and ln(MHR) (P = 0.0007) (Fig. 4 A). When applying optimal cut-off values (ln[MHR], − 1.372; PlGF, 0.859, MAP, 1.079), ln(MHR) achieved a moderate specificity (70.6%) but limited sensitivity (48.9%), whereas PlGF demonstrated higher sensitivity (66.4%) but limited specificity (51.6%). In contrast, MAP yielded a higher specificity of 75.6% but moderate sensitivity (56.7%) (Table S1 ). To explore the combined predictive value of these biomarkers representing distinct biological pathways, we constructed multimarker logistic regression models. The model combining ln(MHR) and PlGF yielded an AUC of 0.643 (95% CI, 0.608–0.677), while the model integrating ln(MHR) and MAP achieved a higher AUC of 0.703 (95% CI, 0.669–0.737), and the model combining PlGF and MAP yielded an AUC of 0.698 (95% CI, 0.663–0.733). Notably, the triple-marker model (ln[MHR], PlGF, and MAP) demonstrated the highest predictive performance, with an AUC of 0.712 (95% CI, 0.678–0.745) (Fig. 4 B–E). Furthermore, a comprehensive model incorporating maternal risk factors (age, BMI, parity, IVF conception, and multifetal gestation) along with these biomarkers achieved an even higher AUC of 0.805 (95% CI, 0.775–0.834) (Fig. 4 F). This progression indicates that ln(MHR) provides incremental predictive value beyond established markers. The superior performance of the integrated model, coupled with the low statistical correlation between ln(MHR) and PlGF, supports the hypothesis that the inflammatory axis captured by MHR complements the angiogenic and hemodynamic pathways in the pathophysiology of PE. Consequently, ln(MHR) represents a scalable and cost-effective biomarker that could enhance multimodal risk prediction strategies, particularly in resource-limited settings. Performance by PE subtype: preterm (< 37 weeks) vs. term (≥ 37 weeks) Given the unique clinical significance and pathophysiology of preterm PE, we compared the biomarker performance between preterm (PE < 37 weeks, n = 87) and term (PE ≥ 37 weeks, n = 181) PE cases. In preterm PE, PlGF demonstrated the highest individual biomarker performance, with an AUC of 0.717 (95% CI, 0.665–0.768), which was significantly higher than that of ln(MHR) (AUC: 0.603, 95% CI, 0.533–0.673) or MAP (AUC: 0.690, 95% CI, 0.625–0.755) (Fig. 5 A). The combination of ln(MHR), PlGF, and MAP achieved an AUC of 0.758 (95% CI, 0.706–0.810) (Fig. 5 B). Additionally, a comprehensive model incorporating maternal risk factors reached an AUC of 0.824 (95% CI, 0.779–0.870) (Fig. 5 C). In term PE, ln(MHR) performed similarly to the overall cohort (AUC: 0.618, 95% CI, 0.577–0.658), while PlGF performed less effectively as a standalone marker (AUC: 0.549, 95% CI, 0.506–0.592). MAP remained the strongest individual predictor for term PE (AUC: 0.705, 95% CI, 0.666–0.744) (Fig. 5 D). The combination of ln(MHR), PlGF, and MAP yielded an AUC of 0.729 (95% CI, 0.692–0.766), while the integrated model incorporating maternal risk factors reached an AUC of 0.809 (95% CI, 0.775–0.842) (Fig. 5 E, F). PlGF appears especially important in predicting preterm PE, potentially reflecting its association with more severe placental dysfunction. In contrast, ln(MHR) and MAP show more consistent contributions across both PE subtypes. This differential performance underscores the heterogeneity of PE and supports the adoption of multimodal prediction strategies that capture both angiogenic and inflammatory pathways. Discussion In this large retrospective cohort, we found that elevated first-trimester ln(MHR) was independently associated with subsequent PE. Higher ln(MHR) quartiles showed a clear dose‐response increase in PE risk, and restricted cubic spline analysis indicated a near-linear rise in risk with increasing ln(MHR). Importantly, a two-piecewise regression suggested a threshold at ln(MHR) ≈ − 0.97 (roughly MHR ≈ 0.38), above which PE risk escalated sharply. These associations persisted across maternal subgroups (age, BMI, parity, IVF conception, multifetal gestation) with no significant interactions, indicating a robust effect of ln(MHR) on PE risk regardless of conventional risk factors. Previous studies have established that MHR levels are elevated in patients with PE, supporting its role as a disease-associated inflammatory biomarker. For instance, a Turkish case-control study reported significantly higher MHR values in both preeclamptic and severe preeclamptic pregnancies, with a multivariable-aORs of approximately 1.09 per unit increase in MHR[ 23 ]. Similarly, late-onset PE has been associated with elevated triglycerides and total cholesterol, and reduced HDL levels—components that jointly contribute to a pro-inflammatory, pro-atherogenic profile well captured by MHR[ 24 , 25 ]. However, these investigations typically assessed MHR at or after the onset of clinical disease, thereby reflecting its diagnostic or concurrent association rather than its predictive capability. Our study adds a novel dimension to this literature by demonstrating that elevated ln(MHR) levels measured before 14 weeks of gestation, well in advance of clinical PE, are independently associated with a significantly increased risk of subsequent disease. This temporal precedence supports the potential predictive value of MHR rather than mere correlation with established PE. Moreover, our analyses incorporated comprehensive multivariable adjustment for known risk factors—including maternal age, BMI, parity, IVF conception, and multifetal gestation—which many prior studies have overlooked or only partially controlled for. This methodological rigor strengthens the inference that MHR in early pregnancy captures a pathophysiological process relevant to PE development, rather than simply reflecting coexistent risk factor burden. The biological plausibility of our findings is supported by the known roles of monocytes and HDL in vascular inflammation and endothelial regulation. Monocytes are key drivers of pro-inflammatory and pro-oxidative responses, and have been implicated in the pathophysiology of hypertensive disorders of pregnancy[ 26 , 27 ]. Castleman et al. reported that women with a history of hypertensive pregnancy exhibited persistently elevated levels of classical monocytes in early subsequent gestation, suggesting a state of sustained innate immune activation and its potential link to cardiovascular vulnerability[ 25 ]. Conversely, HDL particles possess anti-inflammatory, antioxidant, and endothelial-protective properties. Experimental studies have shown that HDL, primarily via apolipoprotein A-I and its role in cholesterol efflux, inhibits monocyte activation, reduces reactive oxygen species, and suppresses adhesion molecule expression on the endothelium[ 28 , 29 ]. A high MHR therefore represents a dual-risk signal: increased pro-inflammatory cellular burden and diminished anti-inflammatory lipid defense. In our cohort, women who later developed PE exhibited both elevated MHR and reduced HDL levels, consistent with the pattern reported by Melekoglu et al., who observed significantly lower HDL and higher MHR values among patients with PE[ 23 , 24 ]. These findings support the hypothesis that subclinical inflammation and lipid dysregulation in early gestation may precede and predispose to the endothelial dysfunction and oxidative stress characteristic of PE. The MHR thus serves not merely as a correlate of inflammation, but as a composite index capturing the imbalance between immune activation and lipid-mediated vascular protection, underscoring its relevance in the early pathogenesis of PE. A potential threshold effect was also revealed by our analysis. Although the spline curve was approximately linear, a cutoff at ln(MHR) = − 0.97 was identified, suggesting that modest elevations in ln(MHR) may confer minimal additional risk until a critical inflection point is reached, beyond which the risk increases substantially. This pattern is consistent with the “second-hit” model of PE, in which an accumulated inflammatory burden exceeds a physiological threshold, triggering disease onset. To our knowledge, no previous studies have specifically modeled nonlinear associations between MHR and PE risk. Nevertheless, our findings are aligned with general dose-response principles observed in biomarker research. Furthermore, no significant effect modification was observed across strata defined by age, BMI, parity, IVF conception, or multifetal pregnancy. These findings suggest that the MHR-PE association is broadly applicable across diverse maternal profiles. In other words, MHR may serve as a general risk marker rather than one limited to conventionally high-risk subgroups. This stands in contrast to predictors such as maternal BMI or PlGF, whose predictive performance may vary by population characteristics. In our cohort, for example, obese and non-obese women exhibited similar increases in PE risk per unit rise in ln(MHR). This consistency reinforces the potential of ln(MHR) as a universally applicable biomarker for early pregnancy risk stratification. Notably, PlGF exhibited superior discrimination for preterm PE (AUC: 0.717) but substantially poorer performance for term PE (AUC: 0.549), likely reflecting its closer association with severe placental dysfunction characteristic of early-onset disease. In contrast, ln(MHR) showed comparable predictive performance across the entire cohort (AUC: 0.613), preterm PE (AUC: 0.603), and term PE (AUC: 0.618), underscoring its role as a stable inflammatory indicator throughout the PE spectrum. A key finding of our study is that the integration of ln(MHR) with established biomarkers (PlGF and MAP) into a multimarker model achieved a higher predictive performance (AUC: 0.712) in the entire cohort. This added value was particularly evident in subtype-specific analyses: for preterm PE, incorporating ln(MHR) into a model already containing PlGF and MAP improved the AUC to 0.758; for term PE, the corresponding improvement reached 0.729. Furthermore, the comprehensive model that additionally included maternal risk factors achieved excellent discrimination for both preterm (AUC: 0.824) and term PE (AUC: 0.809). This demonstrates that ln(MHR) provides incremental value beyond the angiogenic and hemodynamic pathways alone. The low correlation between ln(MHR) and PlGF biologically validates this approach, confirming that they capture distinct aspects of PE pathophysiology. This supports the evolving paradigm that PE arises from the convergence of multiple pathological processes, and that a multiple-marker strategy reflecting these distinct axes—inflammatory, angiogenic, and hemodynamic—is likely superior for risk stratification. Given its accessibility, low cost, and reliance on routine laboratory testing, MHR may represent a scalable early pregnancy biomarker for PE, especially in resource-limited settings or as a component of multimodal risk prediction strategies. This study has several limitations. First, despite performing subtype analyses, the number of preterm PE cases remained modest, which may limit the precision of estimates for this high-risk subgroup. Second, the exclusion of women with chronic hypertension or non‑PE placental complications (IUGR/preterm birth) enhances diagnostic specificity but may limit the generalizability of our findings to higher‑risk populations. Third, as a single‑center cohort, further validation is required in diverse ethnic, geographic, and clinical settings. Fourth, MHR values were derived from fasting samples; their performance under non‑fasting conditions remains to be evaluated. Finally, although multifetal gestations were included, their limited number precluded separate, well‑powered analyses. Therefore, future large‑scale studies are needed to validate and extend these findings across key subgroups—including preterm versus term PE, multifetal gestations, comorbid populations, and varied screening contexts—and to assess model performance in relation to preventive interventions such as aspirin prophylaxis. Conclusions In this retrospective cohort study of 11,895 pregnant women in China, higher first-trimester ln(MHR) was independently associated with an increased risk of PE, even after adjustment for maternal age, BMI, parity, IVF conception, and multifetal gestation. A dose-response and threshold relationship was observed, and the association was consistent across clinical subgroups without significant effect modification. Ln(MHR) provided significant incremental predictive value when combined with PlGF and MAP, supporting its role as a complementary biomarker in a multiple‑marker framework. These findings suggest that MHR may be a useful early-pregnancy biomarker for identifying women at increased risk of PE and could support future risk stratification strategies in prenatal care. Abbreviations Preeclampsia (PE) Monocyte-to-high-density lipoprotein cholesterol ratio (MHR) Body mass index (BMI) In vitro fertilization (IVF) Placental growth factor (PlGF) American College of Obstetricians and Gynecologists (ACOG) Mean arterial pressure (MAP) Declarations Ethics Statement and Consent to Participate This retrospective analysis was conducted using previously collected clinical records that were fully de-identified, and it involved no direct patient interaction or clinical intervention. The study protocol was reviewed and approved by the Institutional Review Board (IRB) of Suzhou Municipal Hospital and the Reproductive Medicine Ethics Committee (No. K-2025-200-K01). In line with Article 39 of the Ethical Review Measures for Biomedical Research Involving Humans (2016, China), the IRB granted a waiver of informed consent. The research was performed in accordance with the Declaration of Helsinki. All processes related to ethical oversight and the consent waiver complied with Article 39 of the above Measures and Article 13 of the Personal Information Protection Law of the People’s Republic of China (2021). All authors have read and approved the final manuscript and agree to its publication. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors state that they conducted the research without any commercial or financial relationships that could be considered a potential conflict of interest. Fundings This study was supported by Science Foundation of Jiangsu Province Grant (BK20240371); Suzhou Health Talent Program (GSWS2024046); Suzhou Key Clinical Technology Research (SKY2023001); the National Natural Science Foundation of China (82001576); Postdoctoral Fellowship Program of CPSF (GZC20251571); Jiangsu Funding Program for Excellent Postdoctoral Talent (2025ZB269); the Joint Project of the Shanghai Yangpu District Science, Technology and Economy Commission and the Health Commission (No. YPM202309); the Primary Research & Development Plan of Jiangsu Province (BE2022736), and the Jiangsu Province College Students' Innovation and Entrepreneurship Training Program Project (202410285273Y). Authors' contributions Y. Liang served as the first corresponding author. Y. Zhu, Y. Zhang, and L. Qiao contributed equally to this work. Y. Liang, L. Qiao, X. Dong, J. Sun, and J. Li conceived and designed the study. Y. Zhu, Y. Zhang, L. Qiao, and X. Dong collected and curated the data. Y. Zhang and J. Li performed statistical analyses. Y. Zhu, Y. Zhang, L. Qiao, X. Dong, S. Zhang, Q. Tang, J. Cao, B. Feng, and J. Jin were involved in patient information verification and data quality control. Y. Zhu, Y. Zhang, L. Qiao, X. Dong, S. Zhang, Q. Tang, J. Cao, B. Feng, J. Jin, J. Sun, J. Li, and Y. Liang drafted, revised, and critically edited the manuscript. Y. Liang supervised the study. All authors reviewed and approved the final version of the manuscript. Acknowledgements The authors express their gratitude to the participants for their invaluable contributions to this study. References Brown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, Hall DR, Warren CE, Adoyi G, Ishaku S. International Society for the Study of Hypertension in P: Hypertensive Disorders of Pregnancy: ISSHP Classification, Diagnosis, and Management Recommendations for International Practice. Hypertension. 2018;72:24–43. 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Measuring the impact of system level strategies on psychotropic medicine use in aged care facilities: A scoping review. Res Social Adm Pharm. 2020;16:746–59. Magistrelli L, Contaldi E, Comi C. The Immune System as a Therapeutic Target for Old and New Drugs in Parkinson's Disease. CNS Neurol Disord Drug Targets. 2023;22:66–70. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 08 May, 2026 Reviews received at journal 14 Mar, 2026 Reviews received at journal 13 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviews received at journal 07 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers invited by journal 04 Mar, 2026 Editor invited by journal 23 Feb, 2026 Editor assigned by journal 22 Feb, 2026 Submission checks completed at journal 20 Feb, 2026 First submitted to journal 20 Feb, 2026 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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21:38:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8834497/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8834497/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104228211,"identity":"2a51b83c-36b9-47c9-a3a8-03930e9b120f","added_by":"auto","created_at":"2026-03-09 11:33:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14887346,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFirst trimester ln(MHR) predicts PE in a dose-dependent nonlinear pattern.\u003c/strong\u003e This retrospective cohort study ultimately included 11,895 pregnant women in China who underwent routine first-trimester blood testing before 14 weeks’ gestation. The exposure was measured as the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) from fasting blood samples collected in early pregnancy, and potential confounders such as maternal age, body mass index (BMI), parity, in vitro fertilization (IVF) conception, and multifetal gestation were also documented. Multivariable logistic regression assessed the independent association between ln(MHR) and PE. Dose-response and threshold effects were examined using quartile trend analysis, restricted cubic splines (RCS), and two-piecewise linear regression. Subgroup analyses tested the association's consistency. The predictive performance of ln(MHR)—alone and combined with PlGF and MAP—was evaluated and compared. Ln(MHR) showed moderate predictive performance, suggesting its potential as an early biomarker for PE risk in prenatal screening.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8834497/v1/dad15355907600eaf54cade1.png"},{"id":104228208,"identity":"e21cc65a-175f-4a36-80aa-b6e9abef4cb7","added_by":"auto","created_at":"2026-03-09 11:33:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":479223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of first-trimester MHR in pregnancies with and without subsequent PE. \u003c/strong\u003eMHR levels were significantly higher in women who subsequently developed PE compared to those who did not.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8834497/v1/2e527550f9430f8137192955.png"},{"id":104228210,"identity":"e2804280-075d-484c-9a2a-5e642b4b2af0","added_by":"auto","created_at":"2026-03-09 11:33:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":863446,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNonlinear association between first-trimester ln(MHR) and risk of PE based on restricted cubic spline regression.\u003c/strong\u003e(A) shows the unadjusted model, which revealed a significant nonlinear association (P for overall \u0026lt; 0.001; P for nonlinearity = 0.007), indicating a curved relationship between ln(MHR) and odds of PE. (B) displays the fully adjusted model, accounting for maternal age, BMI, parity, IVF conception, and multifetal gestation. The association remained significant (P for overall \u0026lt; 0.001), but the nonlinearity was attenuated (P for nonlinearity = 0.056), suggesting a near-linear relationship after covariate adjustment.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8834497/v1/617625dab527ea8f21bffef3.png"},{"id":104228207,"identity":"7e7ddcef-df66-44fd-bdf1-b658d5507dc6","added_by":"auto","created_at":"2026-03-09 11:33:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3404228,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of diagnostic performance of first-trimester biomarkers for early prediction of PE.\u003c/strong\u003e(A) ROC analysis showed that the AUC for ln(MHR) was 0.613, comparable to PlGF (AUC: 0.603; P = 0.72 by the DeLong test), while both performed less well than MAP (AUC: 0.700; P = 0.0007 vs. ln(MHR), P = 0.0002 vs. PlGF). Predictive performance of models (B) combined ln(MHR) and PlGF (AUC: 0.643); (C) combined ln(MHR) and MAP (AUC: 0.703); (D) combined MAP and PlGF (AUC: 0.698); (E) combined ln(MHR), PlGF and MAP (AUC: 0.712); (F) combined maternal risk factors (age, BMI, parity, IVF conception, and multifetal gestation), ln(MHR), PlGF and MAP (AUC: 0.805).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8834497/v1/8c391948eaa070825c1e6037.png"},{"id":104228212,"identity":"d962ff5e-78f5-40c0-9313-d631e29d0bc8","added_by":"auto","created_at":"2026-03-09 11:33:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3598864,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of first-trimester biomarker performance in predicting preterm and term PE.\u003c/strong\u003e ROC curves for the prediction of preterm PE: (A) Individual biomarkers (ln(MHR), AUC: 0.603; PlGF, AUC: 0.717; MAP, AUC: 0.690); (B) A combined model incorporating ln[MHR], PlGF, and MAP (AUC: 0.758); (C) An integrated model that further includes maternal risk factors in addition to ln[MHR], PlGF, and MAP (AUC: 0.824); ROC curves for the prediction of term PE: (D) Individual biomarkers (ln[MHR] (AUC: 0.618), PlGF (AUC: 0.549), and MAP (AUC: 0.705)); (E) A combined model incorporating ln[MHR], PlGF, and MAP (AUC: 0.729); (F) An integrated model that further includes maternal risk factors in addition to ln[MHR], PlGF, and MAP (AUC: 0.809).\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8834497/v1/e49f528a1ef583aa14ea10d3.png"},{"id":104409388,"identity":"98e0212d-4db3-4c8b-aea5-bedf69786792","added_by":"auto","created_at":"2026-03-11 12:44:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":23773521,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8834497/v1/a9ebdde2-6952-4e9e-aab2-e5331b850de3.pdf"},{"id":104405228,"identity":"377c11d6-215b-406e-aac5-7489279384c0","added_by":"auto","created_at":"2026-03-11 12:22:11","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":351320,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8834497/v1/c6cc1c9171f4b15e47920fb4.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Monocyte-to-HDL Ratio in Early Pregnancy as a Biomarker for Preeclampsia Risk Stratification","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreeclampsia (PE) is a life-threatening hypertensive disorder that complicates approximately 2% to 8% of pregnancies and remains a leading global cause of maternal and neonatal morbidity and mortality[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Affected women face increased lifetime risks of stroke, cardiovascular disease, and diabetes, while their offspring are more likely to experience preterm birth, perinatal death, and long-term metabolic and neurodevelopmental impairment[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Because PE can progress rapidly in late gestation, early identification of at-risk individuals is essential. Current first-trimester screening algorithms\u0026mdash;based on maternal demographic characteristics, uterine artery Doppler indices, and placental biomarkers such as pregnancy-associated plasma protein-A and placental growth factor (PlGF)\u0026mdash;have shown utility for identifying women at elevated risk[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Prophylactic administration of low-dose aspirin before 16 weeks\u0026rsquo; gestation can significantly reduce the incidence of early-onset PE in high-risk populations[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, these multimodal screening tools require specialized testing infrastructure and perform suboptimally for predicting term PE[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As such, there remains a clinical need for simple, widely available, blood-based biomarkers measurable in the first trimester to improve risk stratification and enable timely preventive interventions.\u003c/p\u003e \u003cp\u003eInflammation and oxidative stress form a mutually reinforcing axis that underpins many disorders[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In PE, aberrant placentation and intermittent hypoxia-reoxygenation provoke excessive reactive oxygen species and sterile inflammation, which amplify endothelial dysfunction and anti-angiogenic signaling, accelerating disease progression[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The monocyte-to-high-density lipoprotein (HDL) cholesterol ratio (MHR) has recently emerged as a promising composite marker of systemic inflammation and oxidative stress. Monocytes contribute to vascular dysfunction through secretion of proinflammatory cytokines and promotion of endothelial activation, whereas HDL cholesterol counteracts these effects via anti-inflammatory and antioxidant mechanisms[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. As an integrative index, MHR reflects the balance between proinflammatory activity and anti-inflammatory lipid defense in the circulation, and it has been associated with various chronic conditions, including type 2 diabetes, metabolic syndrome, atherosclerotic cardiovascular disease, and nonalcoholic fatty liver disease[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Given the central role of inflammation, oxidative stress, and endothelial dysfunction in the pathophysiology of PE[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], MHR represents a biologically plausible candidate biomarker for identifying women at risk. A small-scale, retrospective studies have reported associations between elevated MHR and term PE, suggesting potential diagnostic relevance for the condition[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, the evidence remains limited and inconsistent. To date, no large-scale study has examined whether MHR measured during early pregnancy independently predicts the later development of PE. Consequently, the clinical utility of MHR as an early screening tool for PE risk remains uncertain.\u003c/p\u003e \u003cp\u003eThis study aimed to evaluate whether elevated MHR, measured in the first trimester before 14 weeks\u0026rsquo; gestation, is independently associated with the subsequent development of PE. Using a large retrospective cohort, we examined both continuous and quartile-based associations, applied multivariable and spline regression models to explore linearity and threshold effects, and performed subgroup analyses across key maternal characteristics. The objective was to determine whether early-pregnancy MHR serves as a broadly applicable, dose-responsive biomarker that could enhance PE risk stratification and support timely preventive interventions.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and population\u003c/h2\u003e \u003cp\u003eThe overall flow chart of this study is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. This retrospective cohort study initially included 12,000 pregnant individuals who underwent routine first-trimester prenatal screening before 14 weeks of gestation at Suzhou Hospital of Nanjing Medical University in Suzhou, China, between 2015 and 2024. The primary aim of this study was to assess the association between first-trimester MHR and the subsequent development of PE within a general obstetric population. Therefore, participants were excluded if they had missing laboratory or obstetric data; a documented history of chronic hypertension; intrauterine fetal demise, termination of pregnancy, or spontaneous miscarriage; or non-preeclamptic pregnancies complicated by intrauterine growth restriction (IUGR) or preterm birth[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. After applying these exclusion criteria, a total of 11,895 women with complete data on MHR and subsequent pregnancy outcomes were retained for final analysis, including 11,410 women who did not develop PE and 485 women who did.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExposure measurement: Monocyte-to-HDL ratio (MHR)\u003c/h3\u003e\n\u003cp\u003eAt the time of first-trimester screening (\u0026lt;\u0026thinsp;14 weeks of gestation), demographic and laboratory data were collected. MHR was calculated by dividing the absolute monocyte count (\u0026times;10⁹/L) by the concentration of HDL cholesterol (mmol/L), both obtained from fasting peripheral blood samples. Participants were stratified into quartiles based on the distribution of ln-transformed MHR (ln[MHR]) values in the study population.\u003c/p\u003e\n\u003ch3\u003eOutcome assessment\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the development of PE, defined according to the American College of Obstetricians and Gynecologists (ACOG) criteria as new-onset hypertension and proteinuria after 20 weeks of gestation. Hypertension was defined as a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mm Hg and/or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mm Hg. Proteinuria was diagnosed by the presence of \u0026ge;\u0026thinsp;1\u0026thinsp;+\u0026thinsp;protein on urine dipstick on at least two occasions, 24-hour urinary protein excretion\u0026thinsp;\u0026ge;\u0026thinsp;300 mg, or a spot urine protein-to-creatinine ratio\u0026thinsp;\u0026ge;\u0026thinsp;30 mg/mmol. In the absence of proteinuria, PE was diagnosed if hypertension was accompanied by maternal organ dysfunction, including thrombocytopenia, elevated liver enzymes, renal insufficiency, pulmonary edema, or new-onset neurological symptoms. Pregnancy outcomes were extracted from electronic medical records and independently verified by two obstetricians blinded to MHR levels.\u003c/p\u003e\n\u003ch3\u003eCovariates and confounders\u003c/h3\u003e\n\u003cp\u003ePotential confounding variables\u0026mdash;including maternal age, body mass index (BMI), parity, mode of conception (natural vs in vitro fertilization [IVF]), and fetal plurality (singleton vs multifetal gestation)\u0026mdash;were extracted from the medical record and included in multivariable models.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eContinuous variables were summarized as means (standard deviations) or medians (interquartile ranges [IQR]) and compared using Mann-Whitney U test, as appropriate. Categorical variables were analyzed using the Chi-square test (χ\u0026sup2; test). Logistic regression was used to estimate adjusted odds ratios (aORs) and ninety-five percent confidence intervals (95% CIs) for the association between first-trimester ln(MHR) and risk of PE. Three models were constructed: Model 1 (unadjusted), Model 2 (adjusted for maternal age, BMI, and parity), and Model 3 (additionally adjusted for IVF conception and multifetal gestation).\u003c/p\u003e \u003cp\u003eTo assess potential dose-response relationships, ln(MHR) was analyzed both as a categorical variable (quartiles) and a continuous variable using restricted cubic spline regression. Non-linearity was evaluated using likelihood ratio tests comparing models with and without spline terms. Two-piecewise linear regression was employed to explore potential threshold effects, with the inflection point estimated via a recursive algorithm. The superiority of the threshold model was assessed using likelihood ratio testing.\u003c/p\u003e \u003cp\u003eSubgroup analyses were performed across predefined strata, including maternal age (\u0026lt;\u0026thinsp;35 vs\u0026thinsp;\u0026ge;\u0026thinsp;35 years), BMI (\u0026lt;\u0026thinsp;30 vs\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;), parity (nulliparous vs multiparous), IVF conception (yes vs no), and multifetal gestation (yes vs no). Interaction terms were included to test for effect modification, with statistical significance defined as P for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were conducted using R software, and a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eIn this cohort study, 6,530 pregnant women underwent first-trimester screening for PE using the Fetal Medicine Foundation (FMF) model; measurements of PlGF and mean arterial pressure (MAP), converted into multiples of the median (MoM), were taken to evaluate the independent and combined predictive value of ln(MHR) for early PE prediction alongside these established markers. Pearson correlation coefficients were computed to assess associations between ln(MHR) and PlGF. 95% CIs and two-sided P values were reported. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discriminative performance of ln(MHR), PlGF, and MAP for early prediction of PE. The area under the curve (AUC) with 95% CIs was calculated for each marker. Comparisons of AUCs were conducted using the DeLong test. Statistical significance was defined as a two-sided P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between first-trimester MHR and subsequent development of PE\u003c/h2\u003e \u003cp\u003eThe baseline characteristics of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. MHR levels measured before the 14th week of gestation\u0026mdash;prior to the clinical onset of PE\u0026mdash;were significantly higher in women who subsequently developed PE compared to those who did not (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The median MHR value was markedly elevated in the future PE group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a strong early association between elevated MHR and the later development of PE.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline maternal and pregnancy characteristics in women with and without PE\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;11895)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PE (n\u0026thinsp;=\u0026thinsp;11410)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePE (n\u0026thinsp;=\u0026thinsp;485)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGestational Age at Delivery (weeks), M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.43 (38.86, 40.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.57 (39.00, 40.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.29 (35.29, 39.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;24.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.00 (28.00, 34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.00 (28.00, 34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.00 (29.00, 35.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;4.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.85 (20.20, 23.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.79 (20.20, 23.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.03 (21.48, 26.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;12.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGravidity, M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.00 (1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.00 (1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 (1.00, 2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.465\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity, M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (0.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF conception, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2; = 155.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10806 (90.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10443 (91.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363 (74.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1089 (9.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e967 (8.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122 (25.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifetal gestation, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u0026sup2; = 289.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11716 (98.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11283 (98.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e433 (89.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127 (1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (10.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMHR, M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.21 (0.17, 0.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.21 (0.17, 0.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24 (0.19, 0.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;7.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eZ: Mann-Whitney test, χ\u0026sup2;: Chi-square test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eM: Median, Q₁: 1st Quartile, Q₃: 3st Quartile\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further evaluate the predictive value of first-trimester MHR for PE, a multivariate logistic regression analysis was conducted, adjusting for maternal age, BMI, parity, IVF conception, and multifetal gestation. In multivariate analysis, a higher ln(MHR) before the 14th gestational week was significantly associated with the subsequent development of PE (OR: 1.80; 95% CI: 1.38\u0026ndash;2.36; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that elevated early-pregnancy ln(MHR) independently predicts the risk of PE (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndependent association of first-trimester ln(MHR) and maternal characteristics with subsequent PE risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05 (1.02\u0026thinsp;~\u0026thinsp;1.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21 (1.18\u0026thinsp;~\u0026thinsp;1.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;3.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.69 (0.55\u0026thinsp;~\u0026thinsp;0.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF conception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.18 (1.70\u0026thinsp;~\u0026thinsp;2.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifetal gestation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.20 (4.95\u0026thinsp;~\u0026thinsp;10.48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(MHR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.80 (1.38\u0026thinsp;~\u0026thinsp;2.36)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOther significant risk factors included higher maternal age (OR: 1.05; 95% CI: 1.02\u0026ndash;1.08; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), increased BMI (OR: 1.21; 95% CI: 1.18\u0026ndash;1.25; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), low parity (OR: 0.69; 95% CI: 0.55\u0026ndash;0.86; P\u0026thinsp;=\u0026thinsp;0.001), IVF conception (OR: 2.18; 95% CI: 1.70\u0026ndash;2.79; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and multifetal gestation (OR: 7.20; 95% CI: 4.95\u0026ndash;10.48; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eQuartile-based stratification of ln(MHR) identifies high-risk pregnancies for PE development\u003c/h3\u003e\n\u003cp\u003eParticipants were stratified into quartiles based on the ln(MHR): Q1 (n\u0026thinsp;=\u0026thinsp;2,928; 24.62%), Q2 (n\u0026thinsp;=\u0026thinsp;3,018; 25.37%), Q3 (n\u0026thinsp;=\u0026thinsp;2,974; 25.00%), and Q4 (n\u0026thinsp;=\u0026thinsp;2,975; 25.01%). Significant differences in maternal characteristics were observed across the four ln(MHR) quartiles (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all). Women in the highest quartile (Q4) had a higher median BMI (22.72 [IQR, 20.89\u0026ndash;24.94]) compared to those in Q1 (21.34 [IQR, 19.78\u0026ndash;23.08]; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, parity and maternal age showed statistically significant variation across groups. Importantly, the proportion of women who developed PE increased significantly across the quartiles (Q1: 2.53%, Q2: 3.71%, Q3: 3.63%, Q4: 6.42%; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting a dose-response relationship between early-pregnancy ln(MHR) and subsequent PE risk (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBaseline maternal characteristics and PE incidence across quartiles of first-trimester ln(MHR)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;11895)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (n\u0026thinsp;=\u0026thinsp;2928)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (n\u0026thinsp;=\u0026thinsp;3018)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (n\u0026thinsp;=\u0026thinsp;2974)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (n\u0026thinsp;=\u0026thinsp;2975)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.00 (28.00, 34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.00 (28.00, 34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.00 (28.00, 34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.00 (28.00, 34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.00 (28.00, 34.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eχ\u0026sup2; = 41.60#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.85 (20.20, 23.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.34 (19.78, 23.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.61 (20.08, 23.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.02 (20.20, 23.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.72 (20.89, 24.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eχ\u0026sup2; = 402.42#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity, M (Q₁, Q₃)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00 (0.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00 (0.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00 (0.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00 (0.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00 (0.00, 1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eχ\u0026sup2; = 164.16#\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF conception, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eχ\u0026sup2; = 22.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10806 (90.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2689 (91.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2779 (92.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2692 (90.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2646 (88.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1089 (9.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e239 (8.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e239 (7.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e282 (9.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e329 (11.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifetal gestation, n(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eχ\u0026sup2; = 12.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11716 (98.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2899 (99.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2973 (98.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2932 (98.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2912 (97.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 (0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45 (1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e63 (2.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eχ\u0026sup2; = 62.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-PE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11410 (95.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2854 (97.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2906 (96.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2866 (96.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2784 (93.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485 (4.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e112 (3.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e108 (3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e191 (6.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e#: Kruskal-waills test, χ\u0026sup2;: Chi-square test\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eM: Median, Q₁: 1st Quartile, Q₃: 3st Quartile\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the association between first-trimester ln(MHR) levels and the risk of PE, logistic regression models were constructed based on ln(MHR) quartiles. In the unadjusted model (Model 1), women in the highest ln(MHR) quartile (Q4) had a significantly increased risk of developing PE compared to those in the lowest quartile (Q1) (OR: 2.65; 95% CI: 2.01\u0026ndash;3.48; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with significant associations also observed for Q2 (OR: 1.49; 95% CI: 1.10\u0026ndash;2.00; P\u0026thinsp;=\u0026thinsp;0.009) and Q3 (OR: 1.45; 95% CI: 1.08\u0026ndash;1.96; P\u0026thinsp;=\u0026thinsp;0.015), indicating a dose-response relationship. After adjusting for maternal age, BMI, and parity (Model 2), the association for Q4 remained statistically significant (OR: 1.77; 95% CI: 1.33\u0026ndash;2.35; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas associations for Q2 and Q3 were attenuated. In the fully adjusted model (Model 3), which included maternal age, BMI, parity, IVF conception and multifetal gestation, women in the highest quartile still had a significantly elevated risk of PE (OR: 1.67; 95% CI: 1.25\u0026ndash;2.23; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that elevated ln(MHR) in early pregnancy is independently associated with an increased risk of PE, even after controlling for major confounding factors. Although the associations for the second and third quartiles attenuated after full adjustment, the trend analysis demonstrated a significant linear increase in PE risk across ln(MHR) quartiles (P for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001 in all models) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between ln(MHR) and risk of PE across multivariable models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(MHR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.15 (2.44\u0026thinsp;~\u0026thinsp;4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99 (1.52\u0026thinsp;~\u0026thinsp;2.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.80 (1.38\u0026thinsp;~\u0026thinsp;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eln(MHR) (Quartile)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49 (1.10\u0026thinsp;~\u0026thinsp;2.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36 (1.01\u0026thinsp;~\u0026thinsp;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32 (0.98\u0026thinsp;~\u0026thinsp;1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45 (1.08\u0026thinsp;~\u0026thinsp;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.23 (0.91\u0026thinsp;~\u0026thinsp;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.18 (0.87\u0026thinsp;~\u0026thinsp;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.65 (2.01\u0026thinsp;~\u0026thinsp;3.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77 (1.33\u0026thinsp;~\u0026thinsp;2.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.67 (1.25\u0026thinsp;~\u0026thinsp;2.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eNonlinear dose-response relationship between first-trimester ln(MHR) and PE risk\u003c/h2\u003e \u003cp\u003eTo explore the dose-response relationship between early-pregnancy ln(MHR) and the risk of PE, a restricted cubic spline regression was performed. In the unadjusted model, a significant non-linear association was observed (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for nonlinearity\u0026thinsp;=\u0026thinsp;0.007), suggesting a curved dose-response pattern. The risk of PE increased markedly with higher ln(MHR) values, especially beyond a certain threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter adjustment for maternal age, BMI, parity, IVF conception, and multifetal gestation, the association between ln(MHR) and PE risk remained statistically significant (P for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001), although the non-linear trend was attenuated (P for nonlinearity\u0026thinsp;=\u0026thinsp;0.056). These findings indicate that elevated ln(MHR) levels in early pregnancy are independently and positively associated with the subsequent development of PE, with a near-linear increase in odds across most of the ln(MHR) range (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eNonlinear and threshold effects of ln(MHR) on the development of PE in first-trimester\u003c/h2\u003e \u003cp\u003eTo further explore the potential threshold effect of ln(MHR) on the risk of PE, two-piecewise linear regression models were applied. In the two-piecewise linear regression model, a potential inflection point was identified at \u0026minus;\u0026thinsp;0.97. Below this threshold (ln[MHR]\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.97), the association between ln(MHR) and PE was modest but significant (effect estimate: 1.46; 95% CI: 1.05\u0026ndash;2.05; P\u0026thinsp;=\u0026thinsp;0.027). Above the threshold (ln[MHR]\u0026thinsp;\u0026ge;\u0026thinsp;\u0026minus;\u0026thinsp;0.97), the association became markedly stronger (effect estimate: 24.54; 95% CI: 4.88\u0026ndash;123.54; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The likelihood ratio test comparing the two models indicated that the two-piecewise model provided a significantly better fit (P\u0026thinsp;=\u0026thinsp;0.011), suggesting a threshold-dependent relationship between ln(MHR) and the subsequent development of PE (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThreshold effect of first-trimester ln(MHR) on risk of PE: Comparison of linear and two-piecewise regression models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 Fitting model by standard linear regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.80 (1.38\u0026thinsp;~\u0026thinsp;2.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 Fitting model by two-piecewise linear regression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflection point\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; \u0026minus;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.46 (1.05\u0026thinsp;~\u0026thinsp;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; \u0026minus;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.54 (4.88\u0026thinsp;~\u0026thinsp;123.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP for likelihood test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eAssociation between first-trimester ln(MHR) and risk of PE across maternal subgroups\u003c/h2\u003e \u003cp\u003eSubgroup analyses were conducted to evaluate the consistency of the association between ln(MHR) and the risk of PE across clinically relevant strata. The association remained robust in all subgroups, with no significant interactions observed. Among women of advanced maternal age (\u0026ge;\u0026thinsp;35 years), elevated ln(MHR) was significantly associated with increased PE risk (OR: 3.25; 95% CI: 1.98\u0026ndash;5.35; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), similar to the younger subgroup (\u0026lt;\u0026thinsp;35 years) (OR: 3.20; 95% CI: 2.37\u0026ndash;4.32; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for interaction\u0026thinsp;=\u0026thinsp;0.955). Although the association was attenuated in women with obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30 kg/m\u0026sup2;) (OR: 1.37; 95% CI: 0.61\u0026ndash;3.08; P\u0026thinsp;=\u0026thinsp;0.448), the interaction was not statistically significant (P for interaction\u0026thinsp;=\u0026thinsp;0.077). In both nulliparous (OR: 2.97; 95% CI: 2.20\u0026ndash;4.01) and multiparous women (OR: 3.11; 95% CI: 1.86\u0026ndash;5.22), ln(MHR) showed a consistent positive association with PE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; P for interaction\u0026thinsp;=\u0026thinsp;0.879). Similarly, the association remained significant in both women with and without IVF conception (OR: 2.23 vs. 3.20; P for interaction\u0026thinsp;=\u0026thinsp;0.227), and in those with and without multifetal gestation (OR: 1.63 vs. 3.10; P for interaction\u0026thinsp;=\u0026thinsp;0.140). These findings suggest that the positive association between early-pregnancy ln(MHR) and the risk of PE is generally consistent across maternal subgroups, without significant effect modification (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSubgroup analyses and interaction effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11895 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.15 (2.44\u0026thinsp;~\u0026thinsp;4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.955\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9427 (79.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.20 (2.37\u0026thinsp;~\u0026thinsp;4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2489 (20.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.25 (1.98\u0026thinsp;~\u0026thinsp;5.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11695 (98.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.98 (2.27\u0026thinsp;~\u0026thinsp;3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e221 (1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.37 (0.61\u0026thinsp;~\u0026thinsp;3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNulliparous(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7873 (66.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.97 (2.20\u0026thinsp;~\u0026thinsp;4.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultiparous(\u0026gt;\u0026thinsp;0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4043 (33.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.11 (1.86\u0026thinsp;~\u0026thinsp;5.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVF conception\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10826 (90.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.20 (2.37\u0026thinsp;~\u0026thinsp;4.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1090 (9.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.23 (1.35\u0026thinsp;~\u0026thinsp;3.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultifetal gestation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11737 (98.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.10 (2.36\u0026thinsp;~\u0026thinsp;4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179 (1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.63 (0.73\u0026thinsp;~\u0026thinsp;3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eComparison of first-trimester ln(MHR), PlGF, and MAP for early prediction of PE\u003c/h2\u003e \u003cp\u003eAmong 6530 pregnant women, PlGF and MAP were measured in the first trimester. To evaluate the diagnostic value of ln(MHR) as an early biomarker for PE, its correlation with the established angiogenic marker PlGF was assessed, and its discriminative performance was compared with that of PlGF and the conventional clinical indicator MAP. The aim of the study was to determine whether MHR, a simple inflammation-based marker derived from routine laboratory testing, could serve as an independent alternative or complementary tool to existing predictors.\u003c/p\u003e \u003cp\u003eCorrelation analysis demonstrated that ln(MHR) was not associated with PlGF (Pearson correlation coefficient r\u0026thinsp;=\u0026thinsp;0.011; 95% CI, \u0026minus;\u0026thinsp;0.014\u0026ndash;0.035; P\u0026thinsp;=\u0026thinsp;0.39), indicating that the two parameters reflect distinct biological processes: systemic inflammation and impaired placental angiogenesis. This low correlation suggests that ln(MHR) may provide nonredundant information when incorporated into predictive models for PE.\u003c/p\u003e \u003cp\u003eRegarding diagnostic performance, ROC analysis showed that the AUC for ln(MHR) was 0.613 (95% CI, 0.577\u0026ndash;0.649). The AUC for PlGF was 0.603 (95% CI, 0.568\u0026ndash;0.639; P\u0026thinsp;=\u0026thinsp;0.72 by the DeLong test compared with ln[MHR]). Notably, the discriminative performance of both ln(MHR) and PlGF was inferior to that of MAP (AUC, 0.700; 95% CI, 0.666\u0026ndash;0.734), with statistically significant differences observed between MAP and PlGF (P\u0026thinsp;=\u0026thinsp;0.0002) and between MAP and ln(MHR) (P\u0026thinsp;=\u0026thinsp;0.0007) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). When applying optimal cut-off values (ln[MHR], \u0026minus;\u0026thinsp;1.372; PlGF, 0.859, MAP, 1.079), ln(MHR) achieved a moderate specificity (70.6%) but limited sensitivity (48.9%), whereas PlGF demonstrated higher sensitivity (66.4%) but limited specificity (51.6%). In contrast, MAP yielded a higher specificity of 75.6% but moderate sensitivity (56.7%) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo explore the combined predictive value of these biomarkers representing distinct biological pathways, we constructed multimarker logistic regression models. The model combining ln(MHR) and PlGF yielded an AUC of 0.643 (95% CI, 0.608\u0026ndash;0.677), while the model integrating ln(MHR) and MAP achieved a higher AUC of 0.703 (95% CI, 0.669\u0026ndash;0.737), and the model combining PlGF and MAP yielded an AUC of 0.698 (95% CI, 0.663\u0026ndash;0.733). Notably, the triple-marker model (ln[MHR], PlGF, and MAP) demonstrated the highest predictive performance, with an AUC of 0.712 (95% CI, 0.678\u0026ndash;0.745) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u0026ndash;E). Furthermore, a comprehensive model incorporating maternal risk factors (age, BMI, parity, IVF conception, and multifetal gestation) along with these biomarkers achieved an even higher AUC of 0.805 (95% CI, 0.775\u0026ndash;0.834) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). This progression indicates that ln(MHR) provides incremental predictive value beyond established markers. The superior performance of the integrated model, coupled with the low statistical correlation between ln(MHR) and PlGF, supports the hypothesis that the inflammatory axis captured by MHR complements the angiogenic and hemodynamic pathways in the pathophysiology of PE. Consequently, ln(MHR) represents a scalable and cost-effective biomarker that could enhance multimodal risk prediction strategies, particularly in resource-limited settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003ePerformance by PE subtype: preterm (\u0026lt;\u0026thinsp;37 weeks) vs. term (\u0026ge;\u0026thinsp;37 weeks)\u003c/h2\u003e \u003cp\u003eGiven the unique clinical significance and pathophysiology of preterm PE, we compared the biomarker performance between preterm (PE\u0026thinsp;\u0026lt;\u0026thinsp;37 weeks, n\u0026thinsp;=\u0026thinsp;87) and term (PE\u0026thinsp;\u0026ge;\u0026thinsp;37 weeks, n\u0026thinsp;=\u0026thinsp;181) PE cases. In preterm PE, PlGF demonstrated the highest individual biomarker performance, with an AUC of 0.717 (95% CI, 0.665\u0026ndash;0.768), which was significantly higher than that of ln(MHR) (AUC: 0.603, 95% CI, 0.533\u0026ndash;0.673) or MAP (AUC: 0.690, 95% CI, 0.625\u0026ndash;0.755) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The combination of ln(MHR), PlGF, and MAP achieved an AUC of 0.758 (95% CI, 0.706\u0026ndash;0.810) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Additionally, a comprehensive model incorporating maternal risk factors reached an AUC of 0.824 (95% CI, 0.779\u0026ndash;0.870) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn term PE, ln(MHR) performed similarly to the overall cohort (AUC: 0.618, 95% CI, 0.577\u0026ndash;0.658), while PlGF performed less effectively as a standalone marker (AUC: 0.549, 95% CI, 0.506\u0026ndash;0.592). MAP remained the strongest individual predictor for term PE (AUC: 0.705, 95% CI, 0.666\u0026ndash;0.744) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The combination of ln(MHR), PlGF, and MAP yielded an AUC of 0.729 (95% CI, 0.692\u0026ndash;0.766), while the integrated model incorporating maternal risk factors reached an AUC of 0.809 (95% CI, 0.775\u0026ndash;0.842) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, F).\u003c/p\u003e \u003cp\u003ePlGF appears especially important in predicting preterm PE, potentially reflecting its association with more severe placental dysfunction. In contrast, ln(MHR) and MAP show more consistent contributions across both PE subtypes. This differential performance underscores the heterogeneity of PE and supports the adoption of multimodal prediction strategies that capture both angiogenic and inflammatory pathways.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large retrospective cohort, we found that elevated first-trimester ln(MHR) was independently associated with subsequent PE. Higher ln(MHR) quartiles showed a clear dose‐response increase in PE risk, and restricted cubic spline analysis indicated a near-linear rise in risk with increasing ln(MHR). Importantly, a two-piecewise regression suggested a threshold at ln(MHR)\u0026thinsp;\u0026asymp;\u0026thinsp;\u0026minus;\u0026thinsp;0.97 (roughly MHR\u0026thinsp;\u0026asymp;\u0026thinsp;0.38), above which PE risk escalated sharply. These associations persisted across maternal subgroups (age, BMI, parity, IVF conception, multifetal gestation) with no significant interactions, indicating a robust effect of ln(MHR) on PE risk regardless of conventional risk factors.\u003c/p\u003e \u003cp\u003ePrevious studies have established that MHR levels are elevated in patients with PE, supporting its role as a disease-associated inflammatory biomarker. For instance, a Turkish case-control study reported significantly higher MHR values in both preeclamptic and severe preeclamptic pregnancies, with a multivariable-aORs of approximately 1.09 per unit increase in MHR[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, late-onset PE has been associated with elevated triglycerides and total cholesterol, and reduced HDL levels\u0026mdash;components that jointly contribute to a pro-inflammatory, pro-atherogenic profile well captured by MHR[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, these investigations typically assessed MHR at or after the onset of clinical disease, thereby reflecting its diagnostic or concurrent association rather than its predictive capability. Our study adds a novel dimension to this literature by demonstrating that elevated ln(MHR) levels measured before 14 weeks of gestation, well in advance of clinical PE, are independently associated with a significantly increased risk of subsequent disease. This temporal precedence supports the potential predictive value of MHR rather than mere correlation with established PE. Moreover, our analyses incorporated comprehensive multivariable adjustment for known risk factors\u0026mdash;including maternal age, BMI, parity, IVF conception, and multifetal gestation\u0026mdash;which many prior studies have overlooked or only partially controlled for. This methodological rigor strengthens the inference that MHR in early pregnancy captures a pathophysiological process relevant to PE development, rather than simply reflecting coexistent risk factor burden.\u003c/p\u003e \u003cp\u003eThe biological plausibility of our findings is supported by the known roles of monocytes and HDL in vascular inflammation and endothelial regulation. Monocytes are key drivers of pro-inflammatory and pro-oxidative responses, and have been implicated in the pathophysiology of hypertensive disorders of pregnancy[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Castleman et al. reported that women with a history of hypertensive pregnancy exhibited persistently elevated levels of classical monocytes in early subsequent gestation, suggesting a state of sustained innate immune activation and its potential link to cardiovascular vulnerability[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Conversely, HDL particles possess anti-inflammatory, antioxidant, and endothelial-protective properties. Experimental studies have shown that HDL, primarily via apolipoprotein A-I and its role in cholesterol efflux, inhibits monocyte activation, reduces reactive oxygen species, and suppresses adhesion molecule expression on the endothelium[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A high MHR therefore represents a dual-risk signal: increased pro-inflammatory cellular burden and diminished anti-inflammatory lipid defense. In our cohort, women who later developed PE exhibited both elevated MHR and reduced HDL levels, consistent with the pattern reported by Melekoglu et al., who observed significantly lower HDL and higher MHR values among patients with PE[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These findings support the hypothesis that subclinical inflammation and lipid dysregulation in early gestation may precede and predispose to the endothelial dysfunction and oxidative stress characteristic of PE. The MHR thus serves not merely as a correlate of inflammation, but as a composite index capturing the imbalance between immune activation and lipid-mediated vascular protection, underscoring its relevance in the early pathogenesis of PE.\u003c/p\u003e \u003cp\u003eA potential threshold effect was also revealed by our analysis. Although the spline curve was approximately linear, a cutoff at ln(MHR)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.97 was identified, suggesting that modest elevations in ln(MHR) may confer minimal additional risk until a critical inflection point is reached, beyond which the risk increases substantially. This pattern is consistent with the \u0026ldquo;second-hit\u0026rdquo; model of PE, in which an accumulated inflammatory burden exceeds a physiological threshold, triggering disease onset. To our knowledge, no previous studies have specifically modeled nonlinear associations between MHR and PE risk. Nevertheless, our findings are aligned with general dose-response principles observed in biomarker research. Furthermore, no significant effect modification was observed across strata defined by age, BMI, parity, IVF conception, or multifetal pregnancy. These findings suggest that the MHR-PE association is broadly applicable across diverse maternal profiles. In other words, MHR may serve as a general risk marker rather than one limited to conventionally high-risk subgroups. This stands in contrast to predictors such as maternal BMI or PlGF, whose predictive performance may vary by population characteristics. In our cohort, for example, obese and non-obese women exhibited similar increases in PE risk per unit rise in ln(MHR). This consistency reinforces the potential of ln(MHR) as a universally applicable biomarker for early pregnancy risk stratification. Notably, PlGF exhibited superior discrimination for preterm PE (AUC: 0.717) but substantially poorer performance for term PE (AUC: 0.549), likely reflecting its closer association with severe placental dysfunction characteristic of early-onset disease. In contrast, ln(MHR) showed comparable predictive performance across the entire cohort (AUC: 0.613), preterm PE (AUC: 0.603), and term PE (AUC: 0.618), underscoring its role as a stable inflammatory indicator throughout the PE spectrum.\u003c/p\u003e \u003cp\u003eA key finding of our study is that the integration of ln(MHR) with established biomarkers (PlGF and MAP) into a multimarker model achieved a higher predictive performance (AUC: 0.712) in the entire cohort. This added value was particularly evident in subtype-specific analyses: for preterm PE, incorporating ln(MHR) into a model already containing PlGF and MAP improved the AUC to 0.758; for term PE, the corresponding improvement reached 0.729. Furthermore, the comprehensive model that additionally included maternal risk factors achieved excellent discrimination for both preterm (AUC: 0.824) and term PE (AUC: 0.809). This demonstrates that ln(MHR) provides incremental value beyond the angiogenic and hemodynamic pathways alone. The low correlation between ln(MHR) and PlGF biologically validates this approach, confirming that they capture distinct aspects of PE pathophysiology. This supports the evolving paradigm that PE arises from the convergence of multiple pathological processes, and that a multiple-marker strategy reflecting these distinct axes\u0026mdash;inflammatory, angiogenic, and hemodynamic\u0026mdash;is likely superior for risk stratification. Given its accessibility, low cost, and reliance on routine laboratory testing, MHR may represent a scalable early pregnancy biomarker for PE, especially in resource-limited settings or as a component of multimodal risk prediction strategies.\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, despite performing subtype analyses, the number of preterm PE cases remained modest, which may limit the precision of estimates for this high-risk subgroup. Second, the exclusion of women with chronic hypertension or non‑PE placental complications (IUGR/preterm birth) enhances diagnostic specificity but may limit the generalizability of our findings to higher‑risk populations. Third, as a single‑center cohort, further validation is required in diverse ethnic, geographic, and clinical settings. Fourth, MHR values were derived from fasting samples; their performance under non‑fasting conditions remains to be evaluated. Finally, although multifetal gestations were included, their limited number precluded separate, well‑powered analyses. Therefore, future large‑scale studies are needed to validate and extend these findings across key subgroups\u0026mdash;including preterm versus term PE, multifetal gestations, comorbid populations, and varied screening contexts\u0026mdash;and to assess model performance in relation to preventive interventions such as aspirin prophylaxis.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this retrospective cohort study of 11,895 pregnant women in China, higher first-trimester ln(MHR) was independently associated with an increased risk of PE, even after adjustment for maternal age, BMI, parity, IVF conception, and multifetal gestation. A dose-response and threshold relationship was observed, and the association was consistent across clinical subgroups without significant effect modification. Ln(MHR) provided significant incremental predictive value when combined with PlGF and MAP, supporting its role as a complementary biomarker in a multiple‑marker framework. These findings suggest that MHR may be a useful early-pregnancy biomarker for identifying women at increased risk of PE and could support future risk stratification strategies in prenatal care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePreeclampsia (PE)\u003c/p\u003e\n\u003cp\u003eMonocyte-to-high-density lipoprotein cholesterol ratio (MHR)\u003c/p\u003e\n\u003cp\u003eBody mass index (BMI)\u003c/p\u003e\n\u003cp\u003eIn vitro fertilization (IVF)\u003c/p\u003e\n\u003cp\u003ePlacental growth factor (PlGF)\u003c/p\u003e\n\u003cp\u003eAmerican College of Obstetricians and Gynecologists (ACOG)\u003c/p\u003e\n\u003cp\u003eMean arterial pressure (MAP)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Statement and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective analysis was conducted using previously collected clinical records that were fully de-identified, and it involved no direct patient interaction or clinical intervention. The study protocol was reviewed and approved by the Institutional Review Board (IRB) of Suzhou Municipal Hospital and the Reproductive Medicine Ethics Committee (No. K-2025-200-K01). In line with Article 39 of the Ethical Review Measures for Biomedical Research Involving Humans (2016, China), the IRB granted a waiver of informed consent. The research was performed in accordance with the Declaration of Helsinki. All processes related to ethical oversight and the consent waiver complied with Article 39 of the above Measures and Article 13 of the Personal Information Protection Law of the People\u0026rsquo;s Republic of China (2021). All authors have read and approved the final manuscript and agree to its publication.\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\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state that they conducted the research without any commercial or financial relationships that could be considered a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFundings\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); Suzhou Key Clinical Technology Research (SKY2023001); the National Natural Science Foundation of China (82001576); Postdoctoral Fellowship Program of CPSF (GZC20251571); Jiangsu Funding Program for Excellent Postdoctoral Talent (2025ZB269); the Joint Project of the Shanghai Yangpu District Science, Technology and Economy Commission and the Health Commission (No. YPM202309); the Primary Research \u0026amp; Development Plan of Jiangsu Province (BE2022736), 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\u003eY. Liang served as the first corresponding author. Y. Zhu, Y. Zhang, and L. Qiao contributed equally to this work. Y. Liang, L. Qiao, X. Dong, J. Sun, and J. Li conceived and designed the study. Y. Zhu, Y. Zhang, L. Qiao, and X. Dong collected and curated the data. Y. Zhang and J. Li performed statistical analyses. Y. Zhu, Y. Zhang, L. Qiao, X. Dong, S. Zhang, Q. Tang, J. Cao, B. Feng, and J. Jin were involved in patient information verification and data quality control. Y. Zhu, Y. Zhang, L. Qiao, X. Dong, S. Zhang, Q. Tang, J. Cao, B. Feng, J. Jin, J. Sun, J. Li, and Y. Liang drafted, revised, and critically edited the manuscript. Y. Liang supervised the study. All authors reviewed and approved the final version of the manuscript.\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\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrown MA, Magee LA, Kenny LC, Karumanchi SA, McCarthy FP, Saito S, Hall DR, Warren CE, Adoyi G, Ishaku S. International Society for the Study of Hypertension in P: Hypertensive Disorders of Pregnancy: ISSHP Classification, Diagnosis, and Management Recommendations for International Practice. Hypertension. 2018;72:24\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdil M, Kolarova TR, Doebley AL, Chen LA, Tobey CL, Galipeau P, Rosen S, Yang M, Colbert B, Patton RD, et al. Preeclampsia risk prediction from prenatal cell-free DNA screening. 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Measuring the impact of system level strategies on psychotropic medicine use in aged care facilities: A scoping review. Res Social Adm Pharm. 2020;16:746\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagistrelli L, Contaldi E, Comi C. The Immune System as a Therapeutic Target for Old and New Drugs in Parkinson's Disease. CNS Neurol Disord Drug Targets. 2023;22:66\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Preeclampsia, Monocyte, High-density lipoprotein cholesterol, Restricted cubic splines","lastPublishedDoi":"10.21203/rs.3.rs-8834497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8834497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePreeclampsia (PE) remains a leading cause of maternal and perinatal morbidity and mortality worldwide. Early identification of women at risk is essential for timely preventive interventions, yet current screening approaches have limitations in accessibility and predictive performance. The monocyte-to-high-density lipoprotein cholesterol ratio (MHR), a composite marker of inflammation and oxidative stress, has emerged as a potential biomarker but has not been adequately studied for early pregnancy prediction of PE.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis retrospective cohort study ultimately included 11,895 pregnant women in China who underwent routine first-trimester blood testing before 14 weeks\u0026rsquo; gestation. The association between ln-transformed MHR (ln[MHR]) and subsequent development of PE was assessed using multivariable logistic regression, restricted cubic spline models, and two-piecewise linear regression, adjusting for maternal age, body mass index (BMI), parity, in vitro fertilization (IVF) conception, and multifetal gestation. Subgroup analyses were performed to evaluate potential effect modification.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFirst-trimester ln(MHR) was significantly higher in women who later developed PE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In multivariable analysis, elevated ln(MHR) was independently associated with increased risk of PE (adjusted OR: 1.80; 95% CI: 1.38\u0026ndash;2.36; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A dose-response relationship was observed across ln(MHR) quartiles, with women in the highest quartile having a 67% increased risk compared to the lowest (adjusted OR: 1.67; 95% CI: 1.25\u0026ndash;2.23; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Restricted cubic spline analysis indicated a near-linear association, and a two-piecewise model identified a threshold at ln(MHR)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.97 above which risk increased sharply. The association remained consistent across subgroups defined by maternal age, BMI, parity, IVF conception and multifetal gestation (P for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eElevated first-trimester ln(MHR) was independently associated with subsequent PE, showing dose-dependent and threshold effects. These findings suggest that MHR may serve as a simple, accessible biomarker for early risk stratification of PE in prenatal care.\u003c/p\u003e","manuscriptTitle":"Monocyte-to-HDL Ratio in Early Pregnancy as a Biomarker for Preeclampsia Risk Stratification","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-09 11:33:28","doi":"10.21203/rs.3.rs-8834497/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-08T14:49:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-14T16:10:16+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-13T08:49:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"292447243092958786880566270718201532900","date":"2026-03-11T00:24:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-07T20:41:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141242817492728481532447980710482543378","date":"2026-03-07T20:20:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"256620477327394634882007122548867025123","date":"2026-03-05T13:04:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-05T03:09:42+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-23T13:14:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-23T04:25:52+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-20T21:01:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pregnancy and Childbirth","date":"2026-02-20T20:56:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pregnancy-and-childbirth","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"prch","sideBox":"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/prch/default.aspx","title":"BMC Pregnancy and Childbirth","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fcd8b693-b92f-472d-8c84-3fdc680b4f46","owner":[],"postedDate":"March 9th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-08T14:49:51+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T16:00:15+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-09 11:33:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8834497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8834497","identity":"rs-8834497","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}