Serum inflammation index, inflammatory biomarkers, and preeclampsia risk: a hospital- based case-control study

Serum inflammation index, inflammatory biomarkers, and preeclampsia risk: a hospital- based case-control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Serum inflammation index, inflammatory biomarkers, and preeclampsia risk: a hospital- based case-control study Shunping Ma, Yacong Bo, Zheng Yuan, Xianlan Zhao, Yuan Cao, Dandan Duan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6587652/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Many studies have suggested that serum inflammatory biomarkers influence preeclampsia (PE) risk in pregnant women. However, few studies have assessed whether serum inflammation index and inflammatory biomarkers are correlated with PE risk. Methods : A 1:1 matched case-control study was conducted to explore the association between the serum inflammation index and inflammatory biomarkers and the risk of PE in pregnant Chinese women. A total of 440 pregnant women with PE and 440 control pregnant women were included in the study. Sociodemographic and lifestyle characteristics information was collected through face-to-face questionnaires. The platelet counts, neutrophil counts and lymphocyte counts in the blood samples were detected using a Coulter HMX Hematology Analyzer, and the inflammation index was calculated. Inflammatory biomarkers were analyzed by ELISA kits. Results : Compared with the lowest quartile, the multivariate-adjusted odds ratios (95% confidence interval (CI)) of the highest quartiles were 0.41 (95% CI: 0.30–0.55, P trend < 0.001) for systemic immune inflammation index (SII), 0.53 (95% CI: 0.39–0.71, P trend < 0.001) for lymphocyte/monocyte ratio (MLR), and 0.63 (95% CI: 0.48–0.83, P trend < 0.01) for neutrophil/lymphocyte ratio (NLR). Additionally, for serum inflammation biomarkers concentrations, the multivariate-adjusted odds ratios (95% CI) were 2.41 (95% CI: 1.22, – 4.76, P trend < 0.05) for C-reactive protein (CRP). Conclusions : SII, MLR and NLR was negatively correlated with PE risk, but serum CRP concentrations were positively correlated with PE risk among pregnant Chinese women. Health sciences/Diseases Health sciences/Health care inflammation index inflammation biomarkers preeclampsia Chinese case-control study Figures Figure 1 Figure 2 1. Introduction Preeclampsia(PE)refers to the occurrence of pregnancy after 20 weeks, with hypertension as the main manifestation, accompanied by proteinuria, or no proteinuria, but liver function damage, renal function damage, thrombocytopenia, pulmonary edema, central nervous system abnormalities or visual impairment of any of the abnormalities(Banerjee et al., 2021). According to statistics, 500,000 fetuses and 70,000 newborns worldwide die of preeclampsia every year, which is one of the main causes of maternal and child health, maternal and perinatal deaths so far(Li et al., 2022). At present, the main method of clinical treatment is to terminate pregnancy by cesarean section. Therefore, it is very important to find effective ways to prevent PE. Inflammation refers to a series of complex biological responses to toxic stimuli in the process of host defense, which is divided into infectious inflammation and non-infectious inflammation(Diakos et al., 2014). Many studies have shown that inflammation plays an important role in the pathogenesis of PE, which is characterized by non-infectious inflammation(Kapci et al., 2024; Michalczyk et al., 2020). At present, there are many studies on inflammation involved in the pathogenesis of PE. Some studies show that Inflammasomes recognize receptors and initiate inflammatory responses, leading to interleukin-1β (IL-1β) and interleukin-18(IL-18) release and apoptosis(Banerjee et al., 2021). Other studies suggest that in PE patients, trophoblast invasion leads to placental ischemia, increases pro-inflammatory CD4 + T cells to break the Th1/Th2 balance, and ischemic placenta produces cytokines such as interleukin-4 (IL-4) and tumor necrosis factor-α (TNF-α)(Amaral et al., 2017; Harmon et al., 2016; Saadaty et al., 2023). What’s more, a retrospective study found that the systemic immune inflammation index (SII)was significantly higher with all-cause and cause-specific mortality in hypertensive individuals(Cao et al., 2023; Cevher Akdulum et al., 2023). Although systemic inflammation has been implicated in the pathogenesis of preeclampsia, whether serum inflammation index (e.g. SII, lymphocyte/monocyte ratio (LMR), neutrophil/lymphocyte ratio (NLR)) and inflammatory markers (TNF-α, C-reactive protein (CRP), IL-4) is associated with the development of preeclampsia is not known. Thus, this case-control study was conducted to explore the associations between serum inflammation index and inflammatory markers of PE risk in Chinese pregnant women. 2. Method 2.1 Study participants This 1:1 matched case-control study was performed in the First Affiliated Hospital of Zhengzhou University, China from March 2016 to June 2019. The study design was as described previously(Cao et al., 2020). Cases were defined as women diagnosed with PE based on China’s ‘Diagnosis and treatment guideline of hypertensive disorders in pregnancy (2015)’("[Diagnosis and treatment guideline of hypertensive disorders in pregnancy (2015)]," 2015). In this guideline, PE is defined as a systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg after 20 weeks of gestation, accompanied by any of the following characteristics: ( 1 ) urinary protein ≥ 0.3 g/24 h, or a urinary protein/creatinine ratio ≥ 0.3, or random urinary protein ≥ (+) (the test method used when urinary protein cannot be quantified); ( 2 ) non-albuminuria but with damage to organs or systems such as the heart, lung, liver, kidney, and other important organs, or abnormal changes in the blood system, digestive system, nervous system, placental-fetal involvement. Pregnant women from the same hospital without hypertension or proteinuria were enrolled as controls and matched with the case group based on age (± 3 years), gestational weeks (± 1 week), and gestational diabetes mellitus (GDM) status. The exclusion criteria for participants were as follows: ( 1 ) refusal to participate in the study; ( 2 ) heart disease, malignant tumor (s), hyperthyroidism, an immune system disease, chronic renal insufficiency, or other chronic diseases; and ( 3 ) mental or cognitive disorders such as schizophrenia or depression. This study was approved by the Ethics Committee of Scientific Research and Clinical Trials of the First Affiliated Hospital of Zhengzhou University (No. Scientific research 2016-LW-34). All participants provided written informed consent before epidemiological data and biological specimens were collected. All procedures were performed according to the Declaration of Helsinki guidelines and regulations. 2.2 Calculation of sample size The sample size of this 1:1-matched case-control study was calculated based on the OR estimated from previous studies (OR = 3.20)(Wolf et al., 2001). A sample size of 134 was calculated based on the above assumptions. With 80% statistical power and 0.05 two-sided significance level, the sample size of each group was estimated to be 134. This study included 440 cases and 440 controls, thereby meeting the sample size requirements. 2.3 Data collection A structured questionnaire was used to collect information about sociodemographic characteristics (age, weeks of gestation, marital status, educational level, and household income). The participants’ height (m), weight (kg), and blood pressure were measured using digital scales, and the body mass index (BMI, kg/m 2 ) was calculated. Gestational age was calculated from the first day of the last menstrual period. 2.4 Serum inflammation index and inflammation markers Platelet, lymphocyte, neutrophil, and monocyte counts and C-reactive protein were derived from pathological examination records. SII was calculated according to the following equation: peripheral platelet count ( × 10 9 / L ) × absolute neutrophil count ( × 10 9 / L ) / absolute lymphocyte count ( × 10 9 / L ).(Chi et al., 2024). The neutrophil/lymphocyte ratio (NLR) and lymphocyte/ monocyte ratio (LMR) were also calculated(Wang et al., 2022). Blood samples were collected on the day of delivery, and the blood collection criteria were the same. Samples were centrifuged at 2,500 rpm at 4°C for 10 min to separate the sera, and serum samples were stored at − 80°C(Cakmak et al., 2017). Inflammatory biomarkers were measured in this study using ELISA kits, including TNF-α and IL -4. 2.5 Statistical analysis Paired t-tests or Wilcoxon signed-rank tests were used to test differences in quantitative variables, and unpaired chi-squared tests were used to identify differences in qualitative variables between cases and controls. According to the distribution among the controls, the serum inflammation index and inflammatory markers were divided into quartiles (Q1–Q4). Conditional logistic regression was used to evaluate the association between serum inflammation index and inflammatory markers and the risk of PE, and the results are expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Tests for trends were performed by using the median of each quartile as a continuous variable in the regression models. Potential confounders were adjusted for in the multivariate models, including age, gestational age, pre-pregnancy BMI, family history of hypertension (yes or no), education level (primary school or less, secondary/high school, college/university or above), physical activity, and gestational diabetes mellitus (GDM). A sensitivity analysis of the relationship between serum inflammation index and inflammatory markers and PE risk was performed by excluding participants with GDM. Potential nonlinear associations of serum inflammation index and inflammatory markers concentrations with PE risk were examined using restricted cubic spline (RCS) analysis. The 20th, 50th, and 80th percentiles were retained as knots. The RCS was calculated using R 4.4.1. All other analyses were performed using SPSS 26.0 (SPSS Inc., Chicago, IL, USA). A two-tailed P value less than 0.05 was considered statistically significant. The missing values in our study were ignored as they were less than 10%. 3. Results 3.1 General characteristics of participants. The demographic characteristics and PE-related factors of 440 cases and controls are described in Table 1 . There were no significant differences identified between PE cases and controls in terms of age, gestational week, GDM, polycystic ovarian syndrome, income, physical activity, or passive smoker. Compared with the control group, PE patients had a higher frequency of a family history of hypertension greater pre-pregnancy BMI ( P < 0.001), and a lower educational level ( P = 0.014) and daily energy intake ( P = 0.001). Table 1 Sociodemographic and lifestyle characteristics and selected PE risk factors of the study population (n = 440 pairs). Cases (n = 440) Controls (n = 440) P a Age (years) b 30.9 ± 5.03 31.0 ± 4.85 0.114 Gestational age (weeks) b 34.2 ± 2.90 34.2 ± 2.67 0.066 Pre-pregnancy BMI (kg/m 2 ) b 23.7 ± 3.89 22.4 ± 3.35 < 0.001 Gestational diabetes mellitus c 59 (13.0) 59 (13.0) 1.000 Polycystic ovarian syndrome c 10 (2.3) 6 (1.4) 0.454 Family history of hypertension c 167 (38.0) 83 (18.9) < 0.001 Education level c 0.014 Junior high school or below 207 (47.0) 164 (37.4) Senior high school 75 (17.0) 83 (18.9) College or above 158 (35.9) 192 (43.7) Income (Yuan/month) c 0.405 ≤ 2,000 61 (13.9) 46 (10.5) 2,001–4,000 216 (49.1) 211 (48.0) 4,001–6,000 78 (17.7) 82 (18.6) > 6,000 59 (13.4) 81 (18.4) Passive smoker c 67 (15.2) 58 (13.2) 0.488 Parity 0.001 0 births 185 (42.0) 135 (30.7) 1 birth 180 (40.9) 211 (48.0) ≥ 2 births 73 (16.6) 93 (21.1) Physical activity (MET-h/day) b 27.0 ± 3.96 26.6 ± 4.48 0.241 Abbreviation: BMI, body mass index; SII, systemic immune inflammation index. a Continuous variables were evaluated using paired t -tests or Wilcoxon rank-sum tests. Categorical variables were evaluated using paired chi-squared tests. b Data are presented as the mean ± standard deviation. c Data are presented as the number (%). d Data are presented as the M (P25, P75). 3.2 Comparison of serum concentrations. The serum concentrations of the inflammation index and markers among participants are shown in Table 2 . Platelet, neutrophil, and monocyte counts were lower in PE patients than in control participants (all P < 0.001). In addition, PE patients reported a greater serum concentration of lymphocyte counts, IL-4 and CRP (all P < 0.001). Lower levels of SII, LMR and NLR were reported in the PE group than in the control group (all P < 0.001). Table 2 Inflammation index and biomarkers among preeclampsia cases and controls. Cases Controls P values a Median IQR Median IQR PLT(10 9 /L) 162.5 (165,248) 204.5 (123, 212) 0.001 Neutrophil(10 9 /L) 6.87 (5.29,8.7) 7.06 (5.56,9.51) 0.001 Lymphocyte(10 9 /L) 1.63 (1.28,2.10) 1.40 (1.10,1.69) 0.001 Monocyte(10 9 /L) 0.51 (0.39,0.67) 0.54 (0.43, 0.70) < 0.001 CRP(µg/L) 11.25 (4.08,37.38) 4.20 (2.11,11.14) 0.001 IL-4(pg/ml) 1.87 (1.32,2.41) 1.76 (0.97,2.15) 0.018 TNF-α(pg/ml) 7.22 (5.31,10.13) 5.74 (5.06,8.11) 0.152 SII 625.85 (407.53,1058.54) 1029.78 (694.73, 1629.20) < 0.001 NLR 4.05 (2.87,5.87) 5.00 (3.66,7.47) < 0.001 MLR 0.30 (0.22,0.40) 0.39 (0.29,0.50) < 0.001 Abbreviation: PLT, platelet count; CRP, C-reactive protein;IL-4, interleulkin-4;TNF-α,Tumor necrosis factor-α;SII, systemic immune inflammation index; LMR, Lymphocyte-to-monocyte ratio; NLR, neutrophil–lymphocyte ratio. a The difference between cases and controls were compared using a paired Wilcoxon signed-rank test. b SII was calculated by peripheral platelet count ( × 10 9 / L ) × absolute neutrophil count ( × 10 9 / L ) / absolute lymphocyte count ( × 10 9 / L ). 3.3 Serum inflammation index and PE risk. Serum concentrations of SII, NLR, MLR, and platelet count were negatively correlated with PE risk (Table 3 ). After adjusting for possible confounders, the OR for PE in the highest quartile relative to the lowest quartile was 0.41 (95% CI: 0.30–0.55, P trend < 0.001) for SII, 0.63 (95% CI: 0.48–0.83, P trend < 0.001) for NLR (Table 3 ), 0.53 (95% CI: 0.39–0.71, P trend < 0.001) for MLR, and 0.44 (95% CI: 0.33–0.59, P trend < 0.001) for platelet count. Serum concentrations of lymphocyte count were positively correlated with PE risk (Table 3 ). After adjusting for possible confounders, the OR for PE in the highest quartile relative to the lowest quartile was 1.62 (95% CI: 1.24–2.13, P trend < 0.002) for lymphocyte count. Sensitivity analysis results are shown in Supplement Table 1 . After excluding 58 participant case-control pairs with GDM, no substantial changes were observed in the relationship between SII, NLR, MLR, platelet count, lymphocyte count and PE risk. Table 3 Odds ratios (95% CIs) for preeclampsia risk according to the serum inflammation index related characteristics in Chinese pregnant women. Q1 Q2 Q3 Q4 P -trend c PLT Median (10 9 /L) 110 165 210 274 - Cases/controls (n) 158/60 113/109 95/125 70/144 - Basic model 1 0.70(0.55,0.89) ** 0.60(0.46,0.77) ** 0.45(0.34,0.60) ** < 0.001 Model 1 a 1 0.72(0.57,0.92) ** 0.62(0.48,0.80) ** 0.46(0.34,0.61) ** < 0.001 Model 2 b 1 0.71(0.56,0.91) ** 0.59(0.45,0.76) ** 0.44(0.33,0.59) ** < 0.001 Neutrophil Median (10 9 /L) 4.50 6.30 7.89 11.01 - Cases/controls (n) 120/98 107/113 117/100 107/127 - Basic model 1 0.88(0.68,1.15) 0.98(0.76,1.26) 0.58(0.58,0.96) 0.186 Model 1 a 1 0.99(0.95,1.02) 0.88(0.68,1.15) 0.94(0.73,1.22) 0.135 Model 2 b 1 1.02(0.99,1.04) 0.85(0.65,1.11) 0.94(0.72,1.23) 0.457 Lymphocyte Median (10 9 /L) 0.98 1.38 1.68 2.29 - Cases/controls (n) 92/146 87/125 114/96 71/142 - Basic model 1 1.06(0.79,1.42) 1.40(1.07,1.85) ** 1.73(1.33,2.24) ** < 0.001 Model 1 a 1 1.07(0.79,1.43) 1.44(1.09,1.89) ** 1.72(1.32,2.25) ** < 0.001 Model 2 b 1 1.08(0.80,1.45) 1.38(1.04,1.82) ** 1.62(1.24,2.13) ** 0.002 Monocyte Median (10 9 /L) 0.31 0.47 0.61 0.53 Cases/controls (n) 123/95 110/114 104/114 98/115 Basic model 1 1.34(0.50,3.59) 1.01(0.14,7.17) 1.34(0.34,5.39) 0.916 Model 1 a 1 1.3(0.48,3.52) 1.0(0.14,7.15) 1.04(0.25,4.32) 0.966 Model 2 b 1 1.42(0.52,3.85) 0.91(0.13,6.56) 0.93(0.22,3.89) 0.92 SII a Median 353.63 651.58 1036.51 2008.31 - Cases/controls (n) 159/59 120/98 92/127 64/154 - Basic model 1 0.76(0.60,0.96) * 0.58(0.45,0.75) *** 0.40(0.30,0.54) *** < 0.001 Model 1 a 1 0.76(0.60,0.97) * 0.59(0.45,0.76) ** 0.40(0.30,0.54) ** < 0.001 Model 2 b 1 0.78(0.61,0.99) * 0.57(0.44,0.73) *** 0.41(0.30,0.55) *** < 0.001 NLR Median 2.44 3.85 5.37 11.78 - Cases/controls (n) 1410/78 118/101 90/128 87/131 - Basic model 1 0.84(0.66,1.07) 0.64(0.49,0.84) ** 0.62(0.48,0.81) *** 0.001 Model 1 a 1 0.84(0.65,1.08) 0.66(0.50,0.86) ** 0.61(0.47,0.80) *** 0.001 Model 2 b 1 0.85(0.66,1.11) 0.69(0.52,0.90) ** 0.63(0.48,0.83) *** 0.003 MLR Median 0.20 0.30 0.40 0.55 - Cases/controls (n) 70/148 90/128 132/90 142/72 - Basic model 1 0.91(0.72,1.15) 0.63(0.48,0.82) *** 0.49(0.37,0.65) *** < 0.001 Model 1 a 1 0.93(0.73,1.18) 0.63(0.48,0.82) *** 0.50(0.38,0.67) *** < 0.001 Model 2 b 1 0.96(0.76,1.23) 0.64(0.49,0.83) *** 0.53(0.39,0.71) *** < 0.001 Abbreviation: OR, odds ratio; PLT, platelet count ;CI, confidence interval; Q,quantiles; SII, systemic immune inflammation index; NLR, Neutrophil -lymphocyte ratio; MLR, Monocyte–lymphocyte ratio. a OR adjusted for age, gestational age, household income, educational level. b Additionally adjusted for pre-pregnancy BMI, parental hypertension history, gestational diabetes mellitus, physical activity. c Performed by entering the median in each quartile as continuous variables in the regression models. * P < 0.05; ** P < 0.01; *** P < 0.001 Multivariable-adjusted RCS analyses revealed a significant non-linear association between the SII, NLR MLR, platelet count, monocyte count and PE risk (Fig. 1 (a, c-g)) ( P overall association < 0.0001). With increasing levels of SII, NLR, MLR, and platelet count, and PE risk initially decreased sharply and then plateaued. However, the risk of preeclampsia increased slowly as the lymphocyte count increased; the risk of preeclampsia initially leveled off as the monocyte count increased and then declined sharply (Fig. 1 (c-d)) . After multivariate adjustment, restriction cubic spline (RCS) analysis showed no significant correlation between neutrophil counts and the risk of PE (Fig. 1 (b)). 3.4 Serum inflammatory markers and PE risk Table 4 shows the ORs and 95% CIs of PE risk stratified by serum CRP, IL-4, and TNF-α concentration quartiles. Significant positive dose-dependent associations were seen for serum CRP concentrations in both univariate and multivariate models. Compared with the lowest quartiles, the adjusted ORs for PE of the highest quartile were 2.41 (95% CI: 1.22–4.76, P trend = 0.015) for serum CRP concentrations. Table 4 Odds ratios (95% CIs) for preeclampsia risk according to the serum inflammatory markers in Chinese pregnant women. Q1 Q2 Q3 Q4 P -trend b CRP Median (mg/L) 1.50 3.60 9.05 42.30 - Cases/controls (n) 13/58 12/58 17/53 32/38 - Basic model 1 0.94(0.43,2.05) 1.33(0.64,2.73) 2.50(1.31,4.76) * 0.004 Model 1 c 1 0.88(0.40,1.93) 1.31(0.63,2.71) 2.26(1.18,4.33) * 0.011 Model 2 d 1 0.94(0.42,2.10) 1.46(0.67,3.14) 2.41(1.22,4.76) * 0.015 IL-4 Median (pg/ml) 0.44 1.47 2.02 2.87 - Cases/controls (n) 28/42 40/30 33/38 44/25 - Basic model 1 0.63(0.39,1.01) 0.90(0.58,1.38) 0.73(0.46,1.15) 0.211 Model 1 c 1 0.60(0.36,1.09) 0.90(0.57,1.40) 0.71(0.45,1.13) 0.199 Model 2 d 1 0.58(0.34,0.97) * 0.83(0.53,1.30) 0.73(0.46,1.17) 0.204 TNF-α Median (pg/ml) 3.99 5.47 7.42 13.58 - Cases/controls (n) 29/37 22/44 42/24 39/28 - Basic model 1 0.76(0.47,1.22) 0.61(0.37,1.01) 1.09(0.71,1.69) 0.085 Model 1 c 1 0.72(0.44,1.19) 0.57(0.33,0.96) * 0.81(0.69,1.66) 0.057 Model 2 d 1 0.69(0.42,1.15) 0.54(0.31,0.92) * 0.72(0.62,1.55) 0.065 Abbreviation: OR, odds ratio; CI, confidence interval; C-reactive protein;IL-4, interleulkin-4; TNF-α,Tumor necrosis factor-α;Q, quantiles. a Performed by entering the median in each quartile as continuous variables in the regression models. b OR adjusted for age, gestational age, household income, educational level. c Additionally adjusted for pre-pregnancy BMI, parental hypertension history, gestational diabetes mellitus, physical activity. * P < 0.05. ** P < 0.01. *** P < 0.001. Multivariable-adjusted RCS analyses revealed a significant linear association between the CRP and PE risk (Fig. 2 (a)) ( P overall association < 0.0001). With increasing levels of CPR and PE risk initially increased sharply and then decreased. 4. Discussion This 1:1 matched case-control study found that SII, MLR, and NLR were negatively correlated with PE risk, but CRP concentrations were positively correlated with PE risk among pregnant Chinese women. Meanwhile, the RCS analysis results showed that the inflammatory index and inflammatory markers (except IL-4) were non-linearly associated with PE risk. Our findings are of great significance for the prediction and evaluation of PE risk. The conclusions of the associations between SII, MLR NLR, and PE risk are not consistent. A study collected blood cell counts from 63 PE patients and 63 healthy pregnant women, and calculated the inflammation index showed that there was no significant difference in SII ( P = 0.083) and NLR ( P = 0.357) between the two groups, but there was a difference in MLR( P = 0.001)(Maziashvili et al., 2023). The study did not match age and gestational age. Another study included patients diagnosed with late-onset preeclampsia or severe between 34 and 40 weeks of gestation and found that NLR, SII, and MLR were higher in the case group than in the control group(Melekoğlu et al., 2022). In contrast, our results showed that SII, NLR, and MLR in the preeclampsia group were significantly lower than those in the control group, and were negatively correlated with PE risk. The reason for the inconsistency between our study and the above research results may be that the gestational weeks of the subjects included in the study are different and the sample size is different. However, our results are consistent with some previous studies. A study collected the hematological indexes of the first pregnant women before delivery showed that the NLR (case vs. controls: 3.52 ± 0.95 vs 4.69 ± 2.29, P = 0.001) and MLR (case vs. controls: 0.32 ± 0.10 vs 0.41 ± 0.20, P < 0.001 ) of the control group were higher than those of the mild PE group and the severe PE group(Cui et al., 2023). Similarly, a Japanese study including 76,853 singletons delivered at 28–41 weeks of gestation found that NLR(OR = 0.49, 95% CI: 0.29–0.82) were negatively correlated with the risk of preterm delivery, and LMR(OR = 1.80,95% CI:1.02–3.19) was positively correlated with the risk of preterm delivery(Morisaki et al., 2021). What’s more, a retrospective and single-center study found that SII was lower (944.23 ± 861.12 vs 1600.37 ± 1486.27, P = 0.018) in the PE group than in the control group, and SII was negatively correlated with PE risk (OR = 0.998,95% CI:0.996–0.999, P = 0.005)(Kapci et al., 2024). Another retrospective study also found that SII was lower in the PE group than in the control group (813.7 ± 394.1vs 1009.8 ± 590.4, P = 0.031)(Cevher Akdulum et al., 2023). The low SII, NLR, and NLR values in the preeclampsia group may be attributed to the decrease in platelet count, neutrophil count, and monocyte count, and the increase in lymphocyte count(Kapci et al., 2024). In our study, the RCS curves suggest reverse J-shaped associations between the SII, NLR and NLR and PE risk. Therefore, further clinical trials are needed to compare the effect of inflammation index on predicting PE in pregnant women. Our results showed that serum concentrations of CRP were positively correlated with PE risk (OR = 2.41, 95% CI: 1.22–4.76, P trend = 0.015), while IL-4 and TNF-α were not correlated with PE risk. This is consistent with the results of some previous studies. Twenty-three studies included in a systematic literature review showed that women with higher levels of CRP may have an increased risk of developing preeclampsia ( 2.30 mg / l ( 95% CI: 1.27–3.34 ) )(Rebelo et al., 2013). A case-control study in Colombia included 145 PE patients and 253 controls with gestational age between 28 and 36 weeks. The results showed that PE women had higher serum CRP concentrations(Herrera et al., 2007). A prospective study of maternal serum C-reactive protein concentrations and risk of preeclampsia found that after adjusting for parity and first-degree family history of chronic hypertension, the OR in the highest tertile was 3.2 (95% CI = 1.5 to 6.7) for serum CRP(Qiu et al., 2004). Serum CRP may be a sensitive indicator of systemic inflammation in PE(Nóbrega et al., 2022). In our results, the RCS curve showed a nonlinear relationship between serum CRP concentration and PE risk ( P -overall < 0.0001 and P -nonlinear < 0.001). What’s more, some studies have suggested that IL-4 and TNF-α are associated with the development of preeclampsia(Aggarwal et al., 2019) and found that the levels of IL-4(5.35 ± 0.95pg/mL vs 2.39 ± 0.71pg/mL, P = 0.019) and TNF-α(381.21 ± 43.28pg/mL vs 73.57 ± 13.37pg/ mL, P < 0.001 ) in PE patients are higher than those in the control group(Kumar et al., 2013). In our study, we found that the level of IL-4 in the PE group was higher than that in the control group ( P = 0.018), and there was no difference in TNF-α between the two groups, but both of them were not related to the risk of PE. The reason for the inconsistency may be that the study included 14–18 weeks of gestation in PE patients, perhaps TNF-α and IL-4 are early markers of PE(Kumar et al., 2013). Some studies also found that there was no significant difference in serum concentrations of TNF-α and IL-4 between the PE group and normal pregnant women, which was consistent with our results(Djurovic et al., 2002; Tangerås et al., 2015; Taylor, Ness, et al., 2016; Taylor, Tang, et al., 2016). In addition, some studies suggest that the serum TNF-α concentration in the PE group is higher than that in the control group at 3 months of pregnancy(Singh et al., 2010; Szarka et al., 2010). However, there was no significant difference in serum TNF-αconcentrations between the PE group and the control group in women who were more than 20 weeks of gestation(Mundim et al., 2016). Recent studies have found that in healthy pregnancy, maternal serum TNF-α concentration increased significantly in the second and third trimesters of pregnancy(Lindsay et al., 2018; Subha et al., 2016), but maternal serum IL-4 concentration seems to remain constant throughout the pregnancy(Chatterjee et al., 2014). PE is a pregnancy-specific hypertension characterized by endothelial dysfunction and systemic inflammation(Pabon et al., 2025). During normal pregnancy, immune adaptation maintains immune surveillance while ensuring tolerance to fetal antigens(Weng et al., 2023). Overactivated platelets release pro-thrombotic factors such as thromboxane A2, which promote vasoconstriction and worsen placental ischemia(Chen et al., 2024). A significant increase in neutrophils leads to the release of reactive oxygen species (ROS), neutrophil extracellular traps (NETs), and pro-inflammatory cytokines (TNF-α, IL-6), further damaging the vascular endothelium and exacerbating vascular dysfunction(Sansores-España et al., 2021). Meanwhile, a marked increase in lymphocytes and monocytes, intensifies inflammation and fetal tissue immune rejection, aggravating placental dysfunction(Lan et al., 2022). Therefore, a lower platelet count and neutrophil count, along with a higher lymphocyte count—reflected in a lower systemic immune-inflammation index (SII)—may exert a protective effect against preeclampsia by reducing vascular dysfunction, immune rejection, and inflammation(Jin et al., 2024). In addition, oxidative stress caused by placental ischemia-reperfusion injury contributes to endothelial dysfunction. Elevated levels of inflammatory biomarkers such as CRP are associated with vascular injury, impaired bioavailability of nitric oxide, and increased vasoconstriction(Oliver et al., 2024; Puri et al., 2024). These changes contribute to hypertension and proteinuria. Serum inflammatory index and CRP may affect the occurrence of PE by regulating inflammation and vascular endothelial injury. In our study, some limitations should be acknowledged. First, it is important to note that, as a case-control study, we cannot ignore the possibility of reverse causality. However, the information on the inflammation index is derived by calculating the relevant indicators from the patient's routine blood test reports, and the data on inflammatory markers are obtained from laboratory tests, which minimizes information bias. Second, we did not analyze other inflammatory markers, but we selected more representative inflammatory markers based on previously published articles(Liu et al., 2023). The acquisition of SIII, NLR, and MLR values is simple and cheap, which is of great significance for predicting the diagnosis and prognosis of PE. Third, although we adjusted for possible confounding variables, potentially unknown factors may have influenced the results. Thirdly, there may have been confounders that affected the relevance of our study, but we performed age and gestational week matching and corrected for which confounders, but given the limitations of case-control studies, we are still unable to make causal inferences. Therefore, large cohort and experimental studies are needed to validate this. 5. Conclusion SII, MLR and NLR was negatively correlated with PE risk, but serum CRP concentrations were positively correlated with PE risk among pregnant Chinese women. Further prospective cohort studies and RCTs are warranted to verify these associations. Abbreviations PE, preeclampsia; BMI, body mass index; SII, systemic immune inflammation index; PLT, platelet count; CRP, C-reactive protein;IL-4, interleulkin-4; TNF-α,Tumor necrosis factor-α; NLR, neutrophil–lymphocyte ratio; LMR, Lymphocyte-to-monocyte ratio; OR, odds ratio; CI, confidence interval; Q, quantiles;IL-1β, interleukin-1β; DBP, diastolic blood pressure; SBP , systolic blood pressure; GDM, gestational diabetes mellitus; RCS ,restricted cubic spline ; ROS , reactive oxygen species, NETs ,neutrophil extracellular traps . Declarations Acknowledgment The authors declare no acknowledgments. Sources of Support Chinese Nutrition Society (CNS) Nutrition Science Foundation- Hyproca Maternal and Infant Nutrition Research Fund (CNS- HPNK2023-43). National Natural Science Foundation of China (Grant No. 81602852). Author Contribution Statement Y.-H.L., X.-L.Z., and Q.-J.L. constructed the study design; Y.C., D.-D.D., W.-F.D., and W.-J.F. performed the investigation; S.-P.M. analysed the data; S.-P.M. drafted the manuscript; Y.-H.L., X.-Y.Z., R.L., P.Q., Y.Z., and Y.-C.B. reviewed the manuscript. All authors read and approved the final manuscript. Author Declarations The authors declare no conflicts of interest. References Aggarwal, R. et al. Association of pro- and anti-inflammatory cytokines in preeclampsia. J. Clin. Lab. Anal. 33 (4), e22834. https://doi.org/10.1002/jcla.22834 (2019). Amaral, L. M., Wallace, K., Owens, M. & LaMarca, B. Pathophysiology and Current Clinical Management of Preeclampsia. Curr. Hypertens. Rep. 19 (8), 61. https://doi.org/10.1007/s11906-017-0757-7 (2017). Banerjee, S. et al. Etiological Value of Sterile Inflammation in Preeclampsia: Is It a Non-Infectious Pregnancy Complication? Front. Cell. Infect. 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First and second trimester immune biomarkers in preeclamptic and normotensive women. Pregnancy Hypertens. 6 (4), 388–393. https://doi.org/10.1016/j.preghy.2016.09.002 (2016). Taylor, B. D. et al. Mid-pregnancy circulating immune biomarkers in women with preeclampsia and normotensive controls. Pregnancy Hypertens. 6 (1), 72–78. https://doi.org/10.1016/j.preghy.2015.11.002 (2016). Wang, X. et al. Development of a Clinically Oriented Model to Predict Antitumor Effects after PD-1/PD-L1 Inhibitor Therapy. J Oncol , 2022 , 9030782. (2022). https://doi.org/10.1155/2022/9030782 Weng, J., Couture, C. & Girard, S. Innate and Adaptive Immune Systems in Physiological and Pathological Pregnancy. Biology (Basel) . 12 (3). https://doi.org/10.3390/biology12030402 (2023). Wolf, M. et al. Obesity and preeclampsia: the potential role of inflammation. Obstet. Gynecol. 98 (5 Pt 1), 757–762. https://doi.org/10.1016/s0029-7844(01)01551-4 (2001). Additional Declarations No competing interests reported. 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09:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6587652/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6587652/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83591138,"identity":"ae1c1485-8bfd-482e-95ec-59c2eaed80ce","added_by":"auto","created_at":"2025-05-29 06:10:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":181270,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable-adjusted ORs (solid lines) and 95% CIs (dashed lines) for PE risk according to serum inflammation index. The model was adjusted for age, gestational age, pre-pregnancy BMI, family history of hypertension, education level, parity, physical activity, and daily energy intake. OR, odds ratio; CI, confidence interval; PE, pre-eclampsia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6587652/v1/1dc70e712e5f3900b768b149.png"},{"id":83591346,"identity":"c26e0500-d12f-4583-91f3-b179a4fd53d5","added_by":"auto","created_at":"2025-05-29 06:18:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":123373,"visible":true,"origin":"","legend":"\u003cp\u003eMultivariable-adjusted ORs (solid lines) and 95% CIs (dashed lines) for PE risk according to serum inflammation markers. The model was adjusted for age, gestational age, pre-pregnancy BMI, family history of hypertension, education level, parity, physical activity, and daily energy intake. OR, odds ratio; CI, confidence interval; PE, pre-eclampsia.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6587652/v1/ca6b06e4bfb70c85a6aea15c.png"},{"id":92247515,"identity":"dac246d9-4749-43fd-ad5a-82df93026ef7","added_by":"auto","created_at":"2025-09-26 09:54:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1466817,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6587652/v1/d0491c08-9f3c-4436-bc59-259f70391ebb.pdf"},{"id":83591347,"identity":"4f0765e5-5965-417b-8d39-6c35c73b9950","added_by":"auto","created_at":"2025-05-29 06:18:08","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":106496,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementTable1and2.doc","url":"https://assets-eu.researchsquare.com/files/rs-6587652/v1/7d9dd319cad94224cc5809db.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Serum inflammation index, inflammatory biomarkers, and preeclampsia risk: a hospital- based case-control study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePreeclampsia(PE)refers to the occurrence of pregnancy after 20 weeks, with hypertension as the main manifestation, accompanied by proteinuria, or no proteinuria, but liver function damage, renal function damage, thrombocytopenia, pulmonary edema, central nervous system abnormalities or visual impairment of any of the abnormalities(Banerjee et al., 2021). According to statistics, 500,000 fetuses and 70,000 newborns worldwide die of preeclampsia every year, which is one of the main causes of maternal and child health, maternal and perinatal deaths so far(Li et al., 2022). At present, the main method of clinical treatment is to terminate pregnancy by cesarean section. Therefore, it is very important to find effective ways to prevent PE.\u003c/p\u003e \u003cp\u003eInflammation refers to a series of complex biological responses to toxic stimuli in the process of host defense, which is divided into infectious inflammation and non-infectious inflammation(Diakos et al., 2014). Many studies have shown that inflammation plays an important role in the pathogenesis of PE, which is characterized by non-infectious inflammation(Kapci et al., 2024; Michalczyk et al., 2020). At present, there are many studies on inflammation involved in the pathogenesis of PE. Some studies show that Inflammasomes recognize receptors and initiate inflammatory responses, leading to interleukin-1β (IL-1β) and interleukin-18(IL-18) release and apoptosis(Banerjee et al., 2021). Other studies suggest that in PE patients, trophoblast invasion leads to placental ischemia, increases pro-inflammatory CD4\u0026thinsp;+\u0026thinsp;T cells to break the Th1/Th2 balance, and ischemic placenta produces cytokines such as interleukin-4 (IL-4) and tumor necrosis factor-α (TNF-α)(Amaral et al., 2017; Harmon et al., 2016; Saadaty et al., 2023). What\u0026rsquo;s more, a retrospective study found that the systemic immune inflammation index (SII)was significantly higher with all-cause and cause-specific mortality in hypertensive individuals(Cao et al., 2023; Cevher Akdulum et al., 2023). Although systemic inflammation has been implicated in the pathogenesis of preeclampsia, whether serum inflammation index (e.g. SII, lymphocyte/monocyte ratio (LMR), neutrophil/lymphocyte ratio (NLR)) and inflammatory markers (TNF-α, C-reactive protein (CRP), IL-4) is associated with the development of preeclampsia is not known. Thus, this case-control study was conducted to explore the associations between serum inflammation index and inflammatory markers of PE risk in Chinese pregnant women.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study participants\u003c/h2\u003e \u003cp\u003eThis 1:1 matched case-control study was performed in the First Affiliated Hospital of Zhengzhou University, China from March 2016 to June 2019. The study design was as described previously(Cao et al., 2020). Cases were defined as women diagnosed with PE based on China\u0026rsquo;s \u0026lsquo;Diagnosis and treatment guideline of hypertensive disorders in pregnancy (2015)\u0026rsquo;(\"[Diagnosis and treatment guideline of hypertensive disorders in pregnancy (2015)],\" 2015). In this guideline, PE is defined as a systolic blood pressure (SBP)\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg or diastolic blood pressure (DBP)\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg after 20 weeks of gestation, accompanied by any of the following characteristics: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) urinary protein\u0026thinsp;\u0026ge;\u0026thinsp;0.3 g/24 h, or a urinary protein/creatinine ratio\u0026thinsp;\u0026ge;\u0026thinsp;0.3, or random urinary protein \u0026ge; (+) (the test method used when urinary protein cannot be quantified); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) non-albuminuria but with damage to organs or systems such as the heart, lung, liver, kidney, and other important organs, or abnormal changes in the blood system, digestive system, nervous system, placental-fetal involvement. Pregnant women from the same hospital without hypertension or proteinuria were enrolled as controls and matched with the case group based on age (\u0026plusmn;\u0026thinsp;3 years), gestational weeks (\u0026plusmn;\u0026thinsp;1 week), and gestational diabetes mellitus (GDM) status. The exclusion criteria for participants were as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) refusal to participate in the study; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) heart disease, malignant tumor (s), hyperthyroidism, an immune system disease, chronic renal insufficiency, or other chronic diseases; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) mental or cognitive disorders such as schizophrenia or depression.\u003c/p\u003e \u003cp\u003e This study was approved by the Ethics Committee of Scientific Research and Clinical Trials of the First Affiliated Hospital of Zhengzhou University (No. Scientific research 2016-LW-34). All participants provided written informed consent before epidemiological data and biological specimens were collected. All procedures were performed according to the Declaration of Helsinki guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Calculation of sample size\u003c/h2\u003e \u003cp\u003eThe sample size of this 1:1-matched case-control study was calculated based on the OR estimated from previous studies (OR\u0026thinsp;=\u0026thinsp;3.20)(Wolf et al., 2001). A sample size of 134 was calculated based on the above assumptions.\u003c/p\u003e \u003cp\u003eWith 80% statistical power and 0.05 two-sided significance level, the sample size of each group was estimated to be 134. This study included 440 cases and 440 controls, thereby meeting the sample size requirements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection\u003c/h2\u003e \u003cp\u003eA structured questionnaire was used to collect information about sociodemographic characteristics (age, weeks of gestation, marital status, educational level, and household income). The participants\u0026rsquo; height (m), weight (kg), and blood pressure were measured using digital scales, and the body mass index (BMI, kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated. Gestational age was calculated from the first day of the last menstrual period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Serum inflammation index and inflammation markers\u003c/h2\u003e \u003cp\u003ePlatelet, lymphocyte, neutrophil, and monocyte counts and C-reactive protein were derived from pathological examination records. SII was calculated according to the following equation: peripheral platelet count ( \u0026times; 10\u003csup\u003e9\u003c/sup\u003e / L ) \u0026times; absolute neutrophil count ( \u0026times; 10\u003csup\u003e9\u003c/sup\u003e / L ) / absolute lymphocyte count ( \u0026times; 10\u003csup\u003e9\u003c/sup\u003e / L ).(Chi et al., 2024). The neutrophil/lymphocyte ratio (NLR) and lymphocyte/ monocyte ratio (LMR) were also calculated(Wang et al., 2022). Blood samples were collected on the day of delivery, and the blood collection criteria were the same. Samples were centrifuged at 2,500 rpm at 4\u0026deg;C for 10 min to separate the sera, and serum samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C(Cakmak et al., 2017). Inflammatory biomarkers were measured in this study using ELISA kits, including TNF-α and IL -4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003ePaired t-tests or Wilcoxon signed-rank tests were used to test differences in quantitative variables, and unpaired chi-squared tests were used to identify differences in qualitative variables between cases and controls. According to the distribution among the controls, the serum inflammation index and inflammatory markers were divided into quartiles (Q1\u0026ndash;Q4). Conditional logistic regression was used to evaluate the association between serum inflammation index and inflammatory markers and the risk of PE, and the results are expressed as odds ratios (ORs) with 95% confidence intervals (CIs). Tests for trends were performed by using the median of each quartile as a continuous variable in the regression models.\u003c/p\u003e \u003cp\u003ePotential confounders were adjusted for in the multivariate models, including age, gestational age, pre-pregnancy BMI, family history of hypertension (yes or no), education level (primary school or less, secondary/high school, college/university or above), physical activity, and gestational diabetes mellitus (GDM). A sensitivity analysis of the relationship between serum inflammation index and inflammatory markers and PE risk was performed by excluding participants with GDM. Potential nonlinear associations of serum inflammation index and inflammatory markers concentrations with PE risk were examined using restricted cubic spline (RCS) analysis. The 20th, 50th, and 80th percentiles were retained as knots. The RCS was calculated using R 4.4.1. All other analyses were performed using SPSS 26.0 (SPSS Inc., Chicago, IL, USA). A two-tailed P value less than 0.05 was considered statistically significant. The missing values in our study were ignored as they were less than 10%.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 General characteristics of participants.\u003c/h2\u003e \u003cp\u003eThe demographic characteristics and PE-related factors of 440 cases and controls are described in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no significant differences identified between PE cases and controls in terms of age, gestational week, GDM, polycystic ovarian syndrome, income, physical activity, or passive smoker. Compared with the control group, PE patients had a higher frequency of a family history of hypertension greater pre-pregnancy BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and a lower educational level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and daily energy intake (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Sociodemographic and lifestyle characteristics and selected PE risk factors of the study population (n\u003cem\u003e\u0026nbsp;\u003c/em\u003e= 440 pairs).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"736\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003eCases (n = 440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003eControls (n = 440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003cem\u003e\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eAge (years)\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e30.9 \u0026plusmn; 5.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e31.0 \u0026plusmn; 4.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eGestational age (weeks)\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e34.2 \u0026plusmn; 2.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e34.2 \u0026plusmn; 2.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003ePre-pregnancy BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e23.7 \u0026plusmn; 3.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e22.4 \u0026plusmn; 3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eGestational diabetes mellitus\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e59 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e59 (13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003ePolycystic ovarian syndrome\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e10 (2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e6 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.454\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eFamily history of hypertension\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e167 (38.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e83 (18.9) \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eEducation level\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eJunior high school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e207 (47.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e164 (37.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eSenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e75 (17.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e83 (18.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e158 (35.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e192 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eIncome (Yuan/month)\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003e\u0026le;\u0026nbsp;2,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e61 (13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e46 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003e2,001\u0026ndash;4,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e216 (49.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e211 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003e4,001\u0026ndash;6,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e78 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e82 (18.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003e\u0026gt; 6,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e59 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e81 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003ePassive smoker\u003csup\u003e\u0026nbsp;c\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e67 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e58 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003eParity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003e0 births\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e185 (42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e135 (30.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003e1 birth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e180 (40.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e211 (48.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003e\u0026ge;\u0026nbsp;2 births\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e73 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e93 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 303px;\"\u003e\n \u003cp\u003ePhysical activity (MET-h/day)\u003csup\u003e\u0026nbsp;b\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 188px;\"\u003e\n \u003cp\u003e27.0 \u0026plusmn; 3.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e26.6 \u0026plusmn; 4.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e0.241\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation: BMI, body mass index; SII, systemic immune inflammation index.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Continuous variables were evaluated using paired \u003cem\u003et\u003c/em\u003e-tests or Wilcoxon rank-sum tests. Categorical variables were evaluated using paired chi-squared tests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eData are presented as the mean \u0026plusmn; standard deviation.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Data are presented as the number (%).\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u0026nbsp;\u003c/sup\u003eData are presented as the\u003csup\u003e\u0026nbsp;\u003c/sup\u003eM (P25, P75).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of serum concentrations.\u003c/h2\u003e \u003cp\u003eThe serum concentrations of the inflammation index and markers among participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Platelet, neutrophil, and monocyte counts were lower in PE patients than in control participants (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, PE patients reported a greater serum concentration of lymphocyte counts, IL-4 and CRP (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lower levels of SII, LMR and NLR were reported in the PE group than in the control group (all \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001).\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\u003eInflammation index and biomarkers among preeclampsia cases and controls.\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eControls\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e values \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIQR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePLT(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(165,248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e204.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(123, 212)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNeutrophil(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.29,8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5.56,9.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLymphocyte(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.28,2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.10,1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMonocyte(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.39,0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.43, 0.70)\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCRP(\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(4.08,37.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(2.11,11.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eIL-4(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.32,2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.97,2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTNF-α(pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(5.31,10.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(5.06,8.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e625.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(407.53,1058.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1029.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(694.73, 1629.20)\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(2.87,5.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(3.66,7.47)\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\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.22,0.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.29,0.50)\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviation: PLT, platelet count; CRP, C-reactive protein;IL-4, interleulkin-4;TNF-α,Tumor necrosis factor-α;SII, systemic immune inflammation index; LMR,\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eLymphocyte-to-monocyte ratio; NLR, neutrophil\u0026ndash;lymphocyte ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003ea\u003c/sup\u003e The difference between cases and controls were compared using a paired Wilcoxon signed-rank test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003e SII was calculated by peripheral platelet count ( \u0026times; 10\u003csup\u003e9\u003c/sup\u003e / L ) \u0026times; absolute neutrophil count ( \u0026times; 10\u003csup\u003e9\u003c/sup\u003e / L ) / absolute lymphocyte count ( \u0026times; 10\u003csup\u003e9\u003c/sup\u003e / L ).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Serum inflammation index and PE risk.\u003c/h2\u003e \u003cp\u003eSerum concentrations of SII, NLR, MLR, and platelet count were negatively correlated with PE risk (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting for possible confounders, the OR for PE in the highest quartile relative to the lowest quartile was 0.41 (95% CI: 0.30\u0026ndash;0.55, \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for SII, 0.63 (95% CI: 0.48\u0026ndash;0.83, \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for NLR (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), 0.53 (95% CI: 0.39\u0026ndash;0.71, \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for MLR, and 0.44 (95% CI: 0.33\u0026ndash;0.59, \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for platelet count. Serum concentrations of lymphocyte count were positively correlated with PE risk (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). After adjusting for possible confounders, the OR for PE in the highest quartile relative to the lowest quartile was 1.62 (95% CI: 1.24\u0026ndash;2.13, \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;\u0026lt;\u0026thinsp;0.002) for lymphocyte count. Sensitivity analysis results are shown in \u003cb\u003eSupplement\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. After excluding 58 participant case-control pairs with GDM, no substantial changes were observed in the relationship between SII, NLR, MLR, platelet count, lymphocyte count and PE risk.\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\u003eOdds ratios (95% CIs) for preeclampsia risk according to the serum inflammation index related characteristics in Chinese pregnant women.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-trend \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003ePLT\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158/60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e113/109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e95/125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e70/144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.70(0.55,0.89) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.60(0.46,0.77) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.45(0.34,0.60) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.72(0.57,0.92) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.62(0.48,0.80) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.46(0.34,0.61) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.71(0.56,0.91) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.59(0.45,0.76) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.44(0.33,0.59) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNeutrophil\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e11.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120/98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e107/113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e117/100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e107/127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.88(0.68,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.98(0.76,1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.58(0.58,0.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.99(0.95,1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.88(0.68,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.94(0.73,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.02(0.99,1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.85(0.65,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.94(0.72,1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLymphocyte\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92/146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e87/125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e114/96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e71/142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.06(0.79,1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.40(1.07,1.85) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.73(1.33,2.24) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.07(0.79,1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.44(1.09,1.89) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.72(1.32,2.25) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.08(0.80,1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.38(1.04,1.82) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.62(1.24,2.13) \u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMonocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123/95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e110/114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e104/114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e98/115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.34(0.50,3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.01(0.14,7.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.34(0.34,5.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.916\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.3(0.48,3.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.0(0.14,7.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.04(0.25,4.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.42(0.52,3.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.91(0.13,6.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.93(0.22,3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSII \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e353.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e651.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1036.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2008.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159/59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e120/98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e92/127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e64/154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.76(0.60,0.96)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.58(0.45,0.75)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.40(0.30,0.54)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.76(0.60,0.97)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.59(0.45,0.76)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.40(0.30,0.54)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.78(0.61,0.99)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.57(0.44,0.73)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.41(0.30,0.55)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e5.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e11.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1410/78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e118/101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e90/128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e87/131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.84(0.66,1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.64(0.49,0.84)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.62(0.48,0.81)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.84(0.65,1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.66(0.50,0.86)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.61(0.47,0.80)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.85(0.66,1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.69(0.52,0.90)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.63(0.48,0.83)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70/148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e90/128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e132/90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e142/72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.91(0.72,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.63(0.48,0.82)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.49(0.37,0.65)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.93(0.73,1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.63(0.48,0.82)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.50(0.38,0.67)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.96(0.76,1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.64(0.49,0.83)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.53(0.39,0.71)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviation: OR, odds ratio; PLT, platelet count ;CI, confidence interval; Q,quantiles; SII, systemic immune inflammation index; NLR, Neutrophil -lymphocyte ratio; MLR, Monocyte\u0026ndash;lymphocyte ratio.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e OR adjusted for age, gestational age, household income, educational level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003e Additionally adjusted for pre-pregnancy BMI, parental hypertension history, gestational diabetes mellitus, physical activity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ec\u003c/sup\u003e Performed by entering the median in each quartile as continuous variables in the regression models.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e*\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e**\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003e***\u003c/sup\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariable-adjusted RCS analyses revealed a significant non-linear association between the SII, NLR MLR, platelet count, monocyte count and PE risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e(a, c-g))\u003c/b\u003e (\u003cem\u003eP\u003c/em\u003e overall association\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). With increasing levels of SII, NLR, MLR, and platelet count, and PE risk initially decreased sharply and then plateaued. However, the risk of preeclampsia increased slowly as the lymphocyte count increased; the risk of preeclampsia initially leveled off as the monocyte count increased and then declined sharply (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e(c-d))\u003c/b\u003e. After multivariate adjustment, restriction cubic spline (RCS) analysis showed no significant correlation between neutrophil counts and the risk of PE (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e(b)).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Serum inflammatory markers and PE risk\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the ORs and 95% CIs of PE risk stratified by serum CRP, IL-4, and TNF-α concentration quartiles. Significant positive dose-dependent associations were seen for serum CRP concentrations in both univariate and multivariate models. Compared with the lowest quartiles, the adjusted ORs for PE of the highest quartile were 2.41 (95% CI: 1.22\u0026ndash;4.76, \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.015) for serum CRP concentrations.\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\u003eOdds ratios (95% CIs) for preeclampsia risk according to the serum inflammatory markers in Chinese pregnant women.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-trend \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian (mg/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e9.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e42.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13/58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e12/58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e17/53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e32/38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.94(0.43,2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.33(0.64,2.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.50(1.31,4.76)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.88(0.40,1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.31(0.63,2.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.26(1.18,4.33) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.94(0.42,2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e1.46(0.67,3.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.41(1.22,4.76)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIL-4\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28/42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e40/30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e33/38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e44/25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.63(0.39,1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.90(0.58,1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.73(0.46,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.60(0.36,1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.90(0.57,1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.71(0.45,1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.58(0.34,0.97)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.83(0.53,1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.73(0.46,1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTNF-α\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMedian (pg/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e5.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e13.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eCases/controls (n)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29/37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e22/44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e42/24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e39/28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBasic model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.76(0.47,1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.61(0.37,1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e1.09(0.71,1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.72(0.44,1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.57(0.33,0.96)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.81(0.69,1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.69(0.42,1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.54(0.31,0.92)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e0.72(0.62,1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003eAbbreviation: OR, odds ratio; CI, confidence interval; C-reactive protein;IL-4, interleulkin-4; TNF-α,Tumor necrosis factor-α;Q, quantiles.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ea\u003c/sup\u003e Performed by entering the median in each quartile as continuous variables in the regression models.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003eb\u003c/sup\u003e OR adjusted for age, gestational age, household income, educational level.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csup\u003ec\u003c/sup\u003e Additionally adjusted for pre-pregnancy BMI, parental hypertension history, gestational diabetes mellitus, physical activity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e**\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMultivariable-adjusted RCS analyses revealed a significant linear association between the CRP and PE risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e(a))\u003c/b\u003e (\u003cem\u003eP\u003c/em\u003e overall association\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). With increasing levels of CPR and PE risk initially increased sharply and then decreased.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis 1:1 matched case-control study found that SII, MLR, and NLR were negatively correlated with PE risk, but CRP concentrations were positively correlated with PE risk among pregnant Chinese women. Meanwhile, the RCS analysis results showed that the inflammatory index and inflammatory markers (except IL-4) were non-linearly associated with PE risk. Our findings are of great significance for the prediction and evaluation of PE risk.\u003c/p\u003e \u003cp\u003eThe conclusions of the associations between SII, MLR NLR, and PE risk are not consistent. A study collected blood cell counts from 63 PE patients and 63 healthy pregnant women, and calculated the inflammation index showed that there was no significant difference in SII (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.083) and NLR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.357) between the two groups, but there was a difference in MLR(\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001)(Maziashvili et al., 2023). The study did not match age and gestational age. Another study included patients diagnosed with late-onset preeclampsia or severe between 34 and 40 weeks of gestation and found that NLR, SII, and MLR were higher in the case group than in the control group(Melekoğlu et al., 2022). In contrast, our results showed that SII, NLR, and MLR in the preeclampsia group were significantly lower than those in the control group, and were negatively correlated with PE risk. The reason for the inconsistency between our study and the above research results may be that the gestational weeks of the subjects included in the study are different and the sample size is different. However, our results are consistent with some previous studies. A study collected the hematological indexes of the first pregnant women before delivery showed that the NLR (case vs. controls: 3.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95 vs 4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and MLR (case vs. controls: 0.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 vs 0.41\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ) of the control group were higher than those of the mild PE group and the severe PE group(Cui et al., 2023). Similarly, a Japanese study including 76,853 singletons delivered at 28\u0026ndash;41 weeks of gestation found that NLR(OR\u0026thinsp;=\u0026thinsp;0.49, 95% CI: 0.29\u0026ndash;0.82) were negatively correlated with the risk of preterm delivery, and LMR(OR\u0026thinsp;=\u0026thinsp;1.80,95% CI:1.02\u0026ndash;3.19) was positively correlated with the risk of preterm delivery(Morisaki et al., 2021). What\u0026rsquo;s more, a retrospective and single-center study found that SII was lower (944.23\u0026thinsp;\u0026plusmn;\u0026thinsp;861.12 vs 1600.37\u0026thinsp;\u0026plusmn;\u0026thinsp;1486.27, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) in the PE group than in the control group, and SII was negatively correlated with PE risk (OR\u0026thinsp;=\u0026thinsp;0.998,95% CI:0.996\u0026ndash;0.999, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005)(Kapci et al., 2024). Another retrospective study also found that SII was lower in the PE group than in the control group (813.7\u0026thinsp;\u0026plusmn;\u0026thinsp;394.1vs 1009.8\u0026thinsp;\u0026plusmn;\u0026thinsp;590.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031)(Cevher Akdulum et al., 2023). The low SII, NLR, and NLR values in the preeclampsia group may be attributed to the decrease in platelet count, neutrophil count, and monocyte count, and the increase in lymphocyte count(Kapci et al., 2024). In our study, the RCS curves suggest reverse J-shaped associations between the SII, NLR and NLR and PE risk. Therefore, further clinical trials are needed to compare the effect of inflammation index on predicting PE in pregnant women.\u003c/p\u003e \u003cp\u003eOur results showed that serum concentrations of CRP were positively correlated with PE risk (OR\u0026thinsp;=\u0026thinsp;2.41, 95% CI: 1.22\u0026ndash;4.76, \u003cem\u003eP\u003c/em\u003e trend\u0026thinsp;=\u0026thinsp;0.015), while IL-4 and TNF-α were not correlated with PE risk. This is consistent with the results of some previous studies. Twenty-three studies included in a systematic literature review showed that women with higher levels of CRP may have an increased risk of developing preeclampsia ( 2.30 mg / l ( 95% CI: 1.27\u0026ndash;3.34 ) )(Rebelo et al., 2013). A case-control study in Colombia included 145 PE patients and 253 controls with gestational age between 28 and 36 weeks. The results showed that PE women had higher serum CRP concentrations(Herrera et al., 2007). A prospective study of maternal serum C-reactive protein concentrations and risk of preeclampsia found that after adjusting for parity and first-degree family history of chronic hypertension, the OR in the highest tertile was 3.2 (95% CI\u0026thinsp;=\u0026thinsp;1.5 to 6.7) for serum CRP(Qiu et al., 2004). Serum CRP may be a sensitive indicator of systemic inflammation in PE(N\u0026oacute;brega et al., 2022). In our results, the RCS curve showed a nonlinear relationship between serum CRP concentration and PE risk (\u003cem\u003eP\u003c/em\u003e-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 and \u003cem\u003eP\u003c/em\u003e-nonlinear\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eWhat\u0026rsquo;s more, some studies have suggested that IL-4 and TNF-α are associated with the development of preeclampsia(Aggarwal et al., 2019) and found that the levels of IL-4(5.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95pg/mL vs 2.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71pg/mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019) and TNF-α(381.21\u0026thinsp;\u0026plusmn;\u0026thinsp;43.28pg/mL vs 73.57\u0026thinsp;\u0026plusmn;\u0026thinsp;13.37pg/ mL, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 ) in PE patients are higher than those in the control group(Kumar et al., 2013). In our study, we found that the level of IL-4 in the PE group was higher than that in the control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), and there was no difference in TNF-α between the two groups, but both of them were not related to the risk of PE. The reason for the inconsistency may be that the study included 14\u0026ndash;18 weeks of gestation in PE patients, perhaps TNF-α and IL-4 are early markers of PE(Kumar et al., 2013). Some studies also found that there was no significant difference in serum concentrations of TNF-α and IL-4 between the PE group and normal pregnant women, which was consistent with our results(Djurovic et al., 2002; Tanger\u0026aring;s et al., 2015; Taylor, Ness, et al., 2016; Taylor, Tang, et al., 2016). In addition, some studies suggest that the serum TNF-α concentration in the PE group is higher than that in the control group at 3 months of pregnancy(Singh et al., 2010; Szarka et al., 2010). However, there was no significant difference in serum TNF-αconcentrations between the PE group and the control group in women who were more than 20 weeks of gestation(Mundim et al., 2016). Recent studies have found that in healthy pregnancy, maternal serum TNF-α concentration increased significantly in the second and third trimesters of pregnancy(Lindsay et al., 2018; Subha et al., 2016), but maternal serum IL-4 concentration seems to remain constant throughout the pregnancy(Chatterjee et al., 2014).\u003c/p\u003e \u003cp\u003ePE is a pregnancy-specific hypertension characterized by endothelial dysfunction and systemic inflammation(Pabon et al., 2025). During normal pregnancy, immune adaptation maintains immune surveillance while ensuring tolerance to fetal antigens(Weng et al., 2023). Overactivated platelets release pro-thrombotic factors such as thromboxane A2, which promote vasoconstriction and worsen placental ischemia(Chen et al., 2024). A significant increase in neutrophils leads to the release of reactive oxygen species (ROS), neutrophil extracellular traps (NETs), and pro-inflammatory cytokines (TNF-α, IL-6), further damaging the vascular endothelium and exacerbating vascular dysfunction(Sansores-Espa\u0026ntilde;a et al., 2021). Meanwhile, a marked increase in lymphocytes and monocytes, intensifies inflammation and fetal tissue immune rejection, aggravating placental dysfunction(Lan et al., 2022). Therefore, a lower platelet count and neutrophil count, along with a higher lymphocyte count\u0026mdash;reflected in a lower systemic immune-inflammation index (SII)\u0026mdash;may exert a protective effect against preeclampsia by reducing vascular dysfunction, immune rejection, and inflammation(Jin et al., 2024). In addition, oxidative stress caused by placental ischemia-reperfusion injury contributes to endothelial dysfunction. Elevated levels of inflammatory biomarkers such as CRP are associated with vascular injury, impaired bioavailability of nitric oxide, and increased vasoconstriction(Oliver et al., 2024; Puri et al., 2024). These changes contribute to hypertension and proteinuria. Serum inflammatory index and CRP may affect the occurrence of PE by regulating inflammation and vascular endothelial injury.\u003c/p\u003e \u003cp\u003eIn our study, some limitations should be acknowledged. First, it is important to note that, as a case-control study, we cannot ignore the possibility of reverse causality. However, the information on the inflammation index is derived by calculating the relevant indicators from the patient's routine blood test reports, and the data on inflammatory markers are obtained from laboratory tests, which minimizes information bias. Second, we did not analyze other inflammatory markers, but we selected more representative inflammatory markers based on previously published articles(Liu et al., 2023). The acquisition of SIII, NLR, and MLR values is simple and cheap, which is of great significance for predicting the diagnosis and prognosis of PE. Third, although we adjusted for possible confounding variables, potentially unknown factors may have influenced the results. Thirdly, there may have been confounders that affected the relevance of our study, but we performed age and gestational week matching and corrected for which confounders, but given the limitations of case-control studies, we are still unable to make causal inferences. Therefore, large cohort and experimental studies are needed to validate this.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eSII, MLR and NLR was negatively correlated with PE risk, but serum CRP concentrations were positively correlated with PE risk among pregnant Chinese women. Further prospective cohort studies and RCTs are warranted to verify these associations.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePE, preeclampsia; BMI, body mass index; SII, systemic immune inflammation index; PLT, platelet count; CRP, C-reactive protein;IL-4, interleulkin-4; TNF-\u0026alpha;,Tumor necrosis factor-\u0026alpha;; NLR, neutrophil\u0026ndash;lymphocyte ratio; LMR, Lymphocyte-to-monocyte ratio; OR, odds ratio; CI, confidence interval; Q, quantiles;IL-1\u0026beta;, interleukin-1\u0026beta;; DBP, diastolic blood pressure; SBP , systolic blood pressure; GDM, gestational diabetes mellitus; RCS ,restricted cubic spline ; ROS , reactive oxygen species, NETs ,neutrophil extracellular traps .\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no acknowledgments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Support\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChinese Nutrition Society (CNS) Nutrition Science Foundation- Hyproca Maternal and Infant Nutrition Research Fund (CNS- HPNK2023-43). National Natural Science Foundation of China (Grant No. 81602852).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eY.-H.L., X.-L.Z., and Q.-J.L. constructed the study design; Y.C., D.-D.D., W.-F.D., and W.-J.F. performed the investigation; S.-P.M. analysed the data; S.-P.M. drafted the manuscript; Y.-H.L., X.-Y.Z., R.L., P.Q., Y.Z., and Y.-C.B. reviewed the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Declarations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAggarwal, R. et al. Association of pro- and anti-inflammatory cytokines in preeclampsia. \u003cem\u003eJ. Clin. Lab. 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Gynecol.\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e (5 Pt 1), 757\u0026ndash;762. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/s0029-7844(01)01551-4\u003c/span\u003e\u003cspan address=\"10.1016/s0029-7844(01)01551-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2001).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"inflammation index, inflammation biomarkers, preeclampsia, Chinese, case-control study","lastPublishedDoi":"10.21203/rs.3.rs-6587652/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6587652/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Many studies have suggested that serum inflammatory biomarkers influence preeclampsia (PE) risk in pregnant women. However, few studies have assessed whether serum inflammation index and inflammatory biomarkers are correlated with PE risk.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A 1:1 matched case-control study was conducted to explore the association between the serum inflammation index and inflammatory biomarkers and the risk of PE in pregnant Chinese women. A total of 440 pregnant women with PE and 440 control pregnant women were included in the study. Sociodemographic and lifestyle characteristics information was collected through face-to-face questionnaires. The platelet counts, neutrophil counts and lymphocyte counts in the blood samples were detected using a Coulter HMX Hematology Analyzer, and the inflammation index was calculated. Inflammatory biomarkers were analyzed by ELISA kits.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Compared with the lowest quartile, the multivariate-adjusted odds ratios (95% confidence interval (CI)) of the highest quartiles were 0.41 (95% CI: 0.30–0.55, \u003cem\u003eP\u003c/em\u003e trend \u0026lt; 0.001) for systemic immune inflammation index (SII), 0.53 (95% CI: 0.39–0.71, \u003cem\u003eP\u003c/em\u003e trend \u0026lt; 0.001) for lymphocyte/monocyte ratio (MLR), and 0.63 (95% CI: 0.48–0.83, \u003cem\u003eP\u003c/em\u003e trend \u0026lt; 0.01) for neutrophil/lymphocyte ratio (NLR). Additionally, for serum inflammation biomarkers concentrations, the multivariate-adjusted odds ratios (95% CI) were 2.41 (95% CI: 1.22, – 4.76, \u003cem\u003eP\u003c/em\u003e trend \u0026lt; 0.05) for C-reactive protein (CRP).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: SII, MLR and NLR was negatively correlated with PE risk, but serum CRP concentrations were positively correlated with PE risk among pregnant Chinese women.\u003c/p\u003e","manuscriptTitle":"Serum inflammation index, inflammatory biomarkers, and preeclampsia risk: a hospital- based case-control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 06:02:04","doi":"10.21203/rs.3.rs-6587652/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ff9a8ad3-aabf-4fa9-bd33-8a10a4b60374","owner":[],"postedDate":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49067964,"name":"Health sciences/Diseases"},{"id":49067965,"name":"Health sciences/Health care"}],"tags":[],"updatedAt":"2025-09-26T09:54:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-29 06:02:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6587652","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6587652","identity":"rs-6587652","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}