{"paper_id":"d1fe5497-6454-4b61-9015-095844bfa688","body_text":"Superiority of neutrophil count over other inflammatory markers in predicting gestational diabetes: A prospective cohort 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Superiority of neutrophil count over other inflammatory markers in predicting gestational diabetes: A prospective cohort study Sima Hashemipour, Seyedeh Sareh kalantarian, Hamidreza Panahi, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3972163/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted 11 You are reading this latest preprint version Abstract Background: To investigate the predictive values of blood cell components and blood cells-derived inflammatory indices for predicting gestational diabetes mellitus (GDM). Methods: This study is part of the Qazvin Maternal and Neonatal Metabolic Study (QMNMS) in Iran (2018-2021. The association of blood cells and blood cell-derived inflammatory indices including neutrophil to lymphocyte ratio (NLR), systemic inflammatory response index (SIRI), systemic immune inflammation index (SII), and aggregate systemic inflammatory response index (AISI) in early pregnancy with early insulin resistance and development of GDM in following months was investigated using univariate and multivariate statistical analysis. Results : The final analysis was performed on 612 participants. GDM developed in 96 participants (15,7%). In univariate analysis, neutrophil quartiles had the highest predictive value for GDM development. Lymphocytes and monocytes quartiles were not associated with GDM development. In the fully adjusted model, neutrophil quartiles remained the strongest predictors of GDM development with relative risks of 3.7, 4.2, and 8.3 for 2 nd , 3 rd , and 4 th neutrophil quartiles compared to the first quartile (P<0.001). Other inflammatory indices including NLR, SIRI, SII, and AISI had no additional yield to predict GDM. Despite the strong associate of neutrophil quartiles with GDM, there was no association between neutrophil counts and early pregnancy insulin resistance assessed by Homeostasis of Model Assessment -Insulin Resistance (HOMA-IR). Conclusion : Neutrophil count is the best predictor of GDM development among blood cell components and blood cell-derived inflammatory indices. The role of neutrophils in GDM development is independent of early pregnancy insulin resistance. Gestational diabetes mellitus Blood cell count Neutrophil HOMA-IR Systemic Blood cell-derived inflammatory indices Introduction Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, occurring in 7–27% of pregnancies ( 1 ). GDM affects the health of both mother and baby in various ways during pregnancy, labor, and thereafter ( 2 ). There are traditional risk factors for GDM, including older maternal age, a positive family history of type 2 diabetes, a high body mass index (BMI), excessive gestational weight gain, and a history of GDM in previous pregnancies ( 3 ). However, GDM occurs in about 5% of pregnant women without any of these known risk factors ( 4 ). Considering the serious complications of GDM, it is valuable to identify other predictors of GDM in early gestational weeks. Regarding the profound immunological changes during pregnancy and the known metabolic consequences of low-grade inflammation ( 5 ), predictive values of hematological parameters including various blood cell counts and some calculated blood-cell-derived inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR) for the prediction of GDM have been investigated in recent years ( 5 – 10 ). Pregnancy is associated with changes in the maternal immune system to adapt the mother to drastic changes in the body’s environment ( 11 ). Neutrophils play an essential role in all stages of reproduction by influencing successful implantation, fetal well-being, and successful delivery. On the other hand, altered neutrophil function is associated with gestational complications such as preeclampsia and GDM ( 12 ). When the mother’s body is exposed to paternal antigens of the fetus, lymphocytes play a critical role in immunomodulation. T helper 1 cells (Th1) induce inflammation, while T helper 2 cells (Th2) have anti-inflammatory effects, and a higher ratio of Th1 to Th2 cells is associated with pregnancy complications such as recurrent abortion, preeclampsia, and GDM ( 13 , 14 ). In addition, over-activation of platelets may play some roles in the pathogenesis of GDM ( 15 ). Data on the relationship between various blood cell counts and GDM are somewhat inconsistent. In some studies, all the parameters of platelets, neutrophil, and lymphocyte counts, and related inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR) are predictors of GDM development ( 8 ), while in other studies, no association between these parameters and GDM has been found ( 16 ). Furthermore, to the best of our knowledge, there are no data on the interaction of first-trimester insulin resistance (as the most important pathogenic factor of GDM) with different blood cell counts in the pathogenesis of GDM development. In recent years, the relationship of some novel blood cell-derived inflammatory indices such as systemic inflammatory response index (SIRI), systemic immune inflammation index (SII), and aggregate systemic inflammatory response index (AISI) with the severity and prognosis of some inflammatory diseases have been reported; however, except for NLR, there are no data on the predictive values of these parameters for GDM ( 17 – 19 ). According to the above considerations, this study was designed to investigate the predictive values of various blood cell counts and some novel blood cell-derived inflammatory indices in early pregnancy to predict GDM development, as well as the role of insulin resistance in the relationship of these parameters with GDM. Materials and methods This study is part of the Qazvin Maternal and Neonatal Metabolic Pregnancy Outcome Study (QMNMS). QMNMS is an observational prospective longitudinal study on Iranian pregnant women in Qazvin province, Iran. Pregnant women who received prenatal care at the obstetrics and gynecology clinic were recruited from September 2018 to May 2020 and from February 2021 to June 2021. The COVID-19 pandemic was the main reason for the transient interruption in the study. Inclusion criteria were age ≥ 18 years and gestational age ≤ 14 weeks based on the date of the last menstrual period or ultrasound. Women with known diabetes before pregnancy or undiagnosed overt diabetes discovered in early pregnancy laboratory assessment were excluded from the study. Sampling was performed using the convenience method. The objectives and details of the study were explained to the participants individually. Participation in the study was voluntary, and all participants signed written informed consent forms. Data were collected at the first antenatal visit at ≤ 14th gestational week, during the 22nd -28th gestational weeks, and during the first 6 weeks postpartum. At the first visit, personal information including demographic characteristics, history of complications in previous pregnancies, and history of chronic diseases were collected using questionnaires designed before the study. Blood samples were taken after 12 hours of fasting, at most one week after the first visit. Blood cell counting was performed and all serum samples were frozen at -80°C. Other laboratory measurements were done on the de-freeze samples after the completion of the study. All participants were screened for GDM using a 75gr oral glucose tolerance test (OGTT) at 24–28 weeks of pregnancy. A normal OGTT was defined as fasting blood sugar (FBS) < 92 mg/dl, 1-h plasma glucose < 180 mg/dl, and 2-h plasma glucose < 153 mg/dl. Having at least one measurement above these values was defined as GDM ( 20 ). Blood cell counting was performed using SYSMEX XS-500i hematology analyzer. FBS and insulin were measured by enzymatic and electrochemiluminescence (ECL) methods, respectively, using the Roche/Hitachi Cobas® 6000 immunoassay system and Roche Kits. The intra-assay and inter-assay CV of the insulin assay were 1.2% and 4.5%, respectively. The HOMA-IR index was calculated as follows ( 21 ): HOMA-IR = Fasting blood sugar (mg/dl) × Insulin (mU/l)/405 Blood cell-derived inflammatory indices of systemic inflammatory response index (SIRI), systemic immune-inflammation index (SII), and aggregate systemic inflammatory response index (AISI) were calculated by (N⤬M)/L, (N⤬P)/L, and (N⤬M⤬P)/L where N, P, M and L represent neutrophil counts, platelet counts, monocyte count, and lymphocyte count, respectively ( 17 – 19 ). Sample size calculation: Considering a prevalence of GDM of 10% in the general population ( 22 ), power 80%, α = 0.05, the lowest relative risk of 1.36 of blood cell components for GDM [relative risk of platelet quartiles in Ye et al. study ( 8 )], and the drop rate of 20%, the sample size calculated at least 560. According to other objectives of the QMNMS primary study, 821 participants recruited in to the study. Statistical analysis The Kolmogorov-Smirnov test was used to check the normality of quantitative data distributions. Quantitative data with normal distribution were presented as mean ± SD and compared between GDM and non-GDM groups using t-test. Quantitative data with non-normal distribution were presented as median (interquartile range) and compared between groups using Mann-Whitney U test and Kruskal-Wallis test. Categorical data were presented as percentage and compared between groups using Chi-square test. Multivariate logistic regression test was performed to investigate the independent association of quartiles of various blood cells and inflammatory indices with the development of GDM. Model 1 was adjusted for parameters with significant difference between GDM and non-GDM groups (age, pre-pregnancy BMI, GDM history in previous pregnancies). Model 2 was adjusted for parameters in mode 1 plus HOMA-IR level. Results Of the 821 pregnant women participated in this study, 209 participants were excluded from the final analysis due to: having a history of diabetes before pregnancy (n = 12), undiagnosed overt diabetes discovered in the first prenatal laboratory tests (n = 6), loss to follow-up (n = 124), and loss of some laboratory data (n = 67). Baseline characteristics of participants who were lost to follow-up were not significantly different from those who completed the study. Finally, 612 participants were evaluated for the association of CBC and inflammatory indices with GDM. GDM developed in 96 participants (15,7%). Baseline characteristics are presented in Table 1 . Age and pre-pregnancy body mass index (BMI) in the GDM group were significantly higher than the non-GDM group (P < 0.001 and P = 0.002, respectively). GDM developed in 46.4% of women with a past medical history of GDM in previous pregnancies compared to 14.9% of the participants without a history of GDM (P < 0.001). WBC count was higher in the GDM group compared to non-GDM one [median (interquartile range): 9.56 (4.10) *10 3 /mm 3 vs. 8.77 (2.90) *10 3 / mm 3 , respectively, P < 0.001]. This difference was attributable to the neutrophil count, because other main component of WBC in terms of Lymphocyte and monocyte counts were not significantly different between GDM and non-GDM groups. Neutrophil count in the GDM group was significantly higher than that in the non-GDM group [median (interquartile range): 6.89 (2.73) *10 3 /mm 3 vs.5.76 (2.52) *10 3 /mm 3 , respectively, P < 0.001]. Platelet count was higher in the GDM group compared to non-GDM one [ median (interquartile range): 249.50 (72.75) *10 3 /mm 3 vs. 235.00 (73.00) *10 3 /mm 3 , respectively, P = 0.006]. Other inflammatory indices in terms of CRP, NLR, SII, SIRI, and AISI were significantly higher in the GDM group compared to the non-GDM group (Tale 1). Table 1 Baseline demographic and laboratory data of the participants categorized by GDM development in the later months. GDM 96 (15.7%) Non-GDM 516 (84.3%) P Age 32.0 (7.0) 29.0 (7.0) < 0.001 Gravidity Parity 0.164 Nulliparous 38 (13.5%) 244 (86.5%) Multiparous 58 (17.5%) 272 (82.5%) History of GDM† < 0.001 Positive 13 (46.4%) 15 (53.6%) Negative 45 (14.9%) 257 (85.1%) BMI before pregnancy 26.12 (5.52) 24.34 (5.17) 0.002 Weight gain†† (Kg) 9.21 ± 4.51 8.89 ± 4.60 0.589 FBS (mg/dl) 93 (13.25) 90 (9.75) < 0.001 Insulin(mU/L) 13.15 (8.02) 9.81 (6.97) 0.001 HOMA-IR 3.02 (2.04) 2.19 (1.65) < 0.001 CRP (mg/dl) 4.65 (5.77) 3.85 (5.15) 0.009 RBC (*10 6 /mm3)) 4.48 (0.55) 4.47 (0.53) 0.732 HCT (%) 38.60 (3.28) 38.60 (3.60) 0.409 WBC (*10 3 /mm 3 ) 9.56 (4.10) 8.77 (2.90) < 0.001 Neutrophils (*10 3 /mm 3 ) 6.89 (2.73) 5.76 (2.52) < 0.001 Lymphocyte (*10 3 /mm 3 ) 2.17 (0.88) 2.13 (0.73) 0.631 Monocyte (*10 3 /mm 3 ) 0.48 (0.23) 0.46 (0.23) 0.499 Platelets (*10 3 /mm 3 ) 249.50 (72.75) 235.00 (73.00) 0.006 NLR 2.97 (1.32) 2.65 (1.26) < 0.001 SIRI (*10 3 /mm 3 ) 1.38 (1.18) 1.15 (0.95) 0.005 SII (*10 3 /mm 3 ) 751.11 (369.79) 618.78 (323.46) < 0.001 AISI (*10 6 mm 3 ) 341.90 (325.73) 277.18 (244.96) 0.001 Parametric data are presented by mean ± SD; non-parametric data are presented by median (interquartile range); GDM: gestational diabetes mellitus. † Based on the data of 420 multiparous participants. †† Weight gain until the 24th -28th gestational week. BMI: body mass index, NLR: neutrophil to lymphocyte ratio, SIRI: systemic inflammatory response index, SII: systemic immune-inflammation index, AISI: aggregate systemic inflammatory response index. The association of HOMA-IR with quartiles of blood cells and inflammatory indices is shown in Table 2 . There was a positive association between higher levels of HOMA-IR and higher lymphocytes, platelets, and CRP quartiles (P < 0.001 for lymphocyte and CRP quartiles, P < 0.004 for platelet quartiles). The number of neutrophils, monocytes and levels of NLR, SII, SIRI, AISI had no significant associated with HOMA-IR level (Table 2 ). Table 2 Association of HOMA-IR with quartiles of blood cells and inflammatory indices. Q1 Q2 Q3 Q4 P Neutrophil 2.09 (1.66) 2.21 (1.49) 2.40 (1.73) 2.49 (1.98) 0.102 Lymphocytes 1.90 (1.18) 2.29 (1.60) 2.35 (1.89) 2.80 (1.78) < 0.001 Monocytes 2.17 (1.53) 2.27 (1.88) 2.60 (2.01) 2.24 (1.66) 0.476 Platelet 2.03 (1.54) 2.15 (1.63) 2.37 (1.84) 2.57 (2.06) 0.004 CRP 1.74 (1.15) 2.29 (1.44) 2.74 (2.12) 2.79 (1.97) < 0.001 NLR 2.21 (1.76) 2.70 (1.87) 2.22 (1.70) 2.12 (1.77) 0.154 SIRI 2.34 (1.64) 2.30 (1.81) 2.34 (1.83) 2.23 (1.74) 0.860 SII 2.15 (1.69) 2.37 (1.85) 2.43 (1.50) 2.19 (1.75) 0.248 AISI 2.06 (1.56) 2.38 (1.83) 2.55 (1.95) 2.21 (1.71) 0.071 HOMA- IR values are presented as median (interquartile range). HOMA-IR levels were compared between quartiles using Kruskal-Wallis test. NLR: neutrophil to lymphocyte ratio, SIRI: systemic inflammatory response index, SII: systemic immune-inflammation index, AISI: aggregate systemic inflammatory response index. Univariate logistic regression results of predictive values of blood cells and inflammatory indices for predicting GDM are presented in Table 3 . Neutrophil quartiles had the highest predictive value for the development of GDM. Neutrophil quartiles 2, 3 and 4 were associated with a 3.1, 3.3, and 5.9 higher risk of GDM occurrence compared to the first quartile (P < 0.001). Lymphocyte and monocytes quartiles were not associated with GDM development. Other parameters including platelets, CRP, NLR, SIRI, SII, and AISI were significant predictors for GDM, but the significance levels and relative risk values were lower than neutrophil quartiles (Table 3 ). Table 3 Univariate logistic regression analysis of quartiles of blood cells and inflammatory indices as predictors of GDM development. RR (CI95%) Q1 Q2 Q3 Q4 P Neutrophil Ref 3.1 (1.3–7.2) 3.3 (1.4–7.6) 5.9 (2.6–13.2) < 0.001 Lymphocytes Ref 1.2 (0.6–2.2) 0.6 (0.3–1.3) 1.3 (0.7–2.4) 0.225 Monocytes Ref 0.9 (0.4–1.7) 1.1 (0.6–2.1) 1.0 (0.5–1.8) 0.896 Platelet Ref 1.6 (0.8–3.3) 1.8 (0.9–3.6) 2.6 (1.3–5.1) 0.028 CRP Ref 2.7 (1.1–6.5) 3.1 (1.3–7.5) 3.6 (1.5–8.5) 0.023 NLR Ref 2.9 (1.3–6.5) 3.7 (1.7–8.3) 3.9 (1.8–8.6) 0.004 SIRI Ref 0.9 (0.4-2.0) 1.7 (0.9–3.3) 2.1 (1.1-4.0) 0.031 SII Ref 1.4 (0.6–3.2) 3.2 (1.5–6.6) 3.8 (1.8–7.8) < 0.001 AISI Ref 1.4 (0.6–2.9) 2.0 (1.0-4.1) 2.8 (1.4–5.5) 0.012 GDM: gestational diabetes mellitus, NLR: neutrophil to lymphocyte ratio, SIRI: systemic inflammatory response index, SII: systemic immune-inflammation index, AISI: aggregate systemic inflammatory response index. The results of multivariate logistic regression on the predictive values of blood cells and inflammatory indices for predicting GDM are presented in Table 4 . After adjusting for pre-pregnancy age, BMI, and GDM history in previous pregnancies (model 1), CRP was no longer a significant predictor of GDM, but other significant predictors of GDM in univariate analysis, remained significant in this adjusted model. After adding HOMA-IR to adjustment model 1 (model 2), the associations of quartiles of platelet, SIRI, and AISI with the occurrence of GDM became non-significant. Neutrophil quartiles remained the strongest predictors of GDM development in the model 2. The highest quartile of neutrophil was associated with 8.3 times higher risk of GDM (95% CI: 3.0-23.4, P < 0.001). The predictive values of NLR and SII had no additional values compared to neutrophil (RR = 3.1 (95%CI: 1.2–2.7) and RR = 3.7 (95% CI:1.5–8.9) for the highest quartile NLR, and SII, respectively) (Table 4 ). Table 4 Multivariate analysis of quartiles of blood cell and inflammatory indices as predictors of GDM. Model 1 Model 2 Q1 Q2 Q3 Q4 P Q1 Q2 Q3 Q4 P Neutrophil Ref 3.9 (1.5–9.6) 3.8 (1.5–9.5) 7.8 (3.2–18.7) < 0.001 Ref 3.7 (1.2–10.8) 4.2 (1.4–12.2) 8.3 (3.0-23.4) < 0.001 Lymphocyte Ref 1.3 (0.6–2.5) 0.7 (0.3–1.5) 1.5 (0.7–2.8) 0.184 Ref 1.3 (0.6-3.0) 1.1 (0.4–2.5) 2.0 (0.9-1.0) 0.214 Monocyte Ref 1.0 (0.5–1.9) 1.3 (0.7–2.6) 1.2 (0.6–2.4) 0.665 Ref 0.7 (0.3–1.6) 1.0 (0.4–2.1) 1.1 (0.5–2.4) 0.763 Platelet Ref 1.5 (0.7–3.2) 1.6 (0.8–3.4) 2.6 (1.3–5.2) 0.040 Ref 1.9 (0.8–4.5) 1.6 (0.7–3.9) 2.9 (1.3–6.6) 0.059 CRP Ref 2.4 (1.0-5.9) 2.7 (1.1–6.6) 2.4 (0.9–6.1) 0.137 Ref 2.4 (1.0-5.9) 2.6 (1.0-6.5) 2.4 (1.0-6.1) 0.153 NLR Ref 3.6 (1.5–8.7) 5.0 (2.1–11.7) 4.6 (1.9–10.8) 0.002 Ref 3.1 (1.2–7.8) 3.4 (1.4–8.5) 3.1 (1.2–7.7) 0.037 SIRI Ref 1.1 (0.5–2.3) 1.9 (0.9-4.0) 2.6 (1.3–5.2) 0.010 Ref 0.8 (0.3–1.9) 1.5 (0.7–3.2) 1.9 (0.9–4.1) 0.129 SII Ref 1.6 (0.6–3.8) 3.1 (1.4–6.8) 4.5 (2.0-9.8) < 0.001 Ref 1.6 (0.6–4.2) 3.0 (1.2–7.3) 3.7 (1.5–8.9) 0.010 AISI Ref 1.5 (0.7–3.3) 2.1 (1.0-4.4) 3.4 (1.6-7.0) 0.004 Ref 1.4 (0.6–3.3) 1.5 (0.7–3.5) 2.6 (1.2–5.8) 0.075 GDM: gestational diabetes mellitus, NLR: neutrophil to lymphocyte ratio, SIRI: systemic inflammatory response index, SII: systemic immune-inflammation index, AISI: aggregate systemic inflammatory response index. Model 1: Adjusted for age, pre-pregnancy BMI, GDM history in previous pregnancies. Model 2: Model 1 plus HOMA-IR Discussion In the present study, neutrophil count, platelet counts, and some inflammatory indices including CRP, NLR, SIRI, SII, and AISI in early pregnancy were associated with the development of GDM in the later months. However, in the fully adjustment model, only neutrophils and some neutrophil -related parameters (NLR and SII) were independent predictors of GDM development. Furthermore, the predictive value of neutrophil count was much higher than of each other blood cell or inflammatory index. The association of different blood cell counts with GDM has been investigated previously. The design of some of these studies is cross-sectional, so the predictive values of these parameters cannot be evaluated ( 23 – 25 ). In most longitudinal studies, higher neutrophil counts in the first trimester are associated with higher risk of GDM development in later months ( 6 – 9 ), and in some studies this association is stronger than other blood cells ( 6 – 8 ). In Sun et al. study, there was a stepwise increased in the occurrence of GDM with each increased tertile of neutrophil count and the highest tertile of neutrophil count was associated with a more than three times higher GDM rate. In addition, the predictive value of neutrophil count outperformed other blood cells including lymphocyte and platelet count ( 6 ). Similarly, in the studies by Kong et al. and Ye et al. the neutrophil count was a better predictor of GDM occurrence compared to other blood cells ( 7 , 8 ). In some studies, no association was found between neutrophil count and GDM. Most of these studies are cross-sectional ( 23 – 24 ) or retrospective cohort using medical records ( 26 ). In a prospective longitudinal study by Hassan et al. in Sudanese women, no association was reported between neutrophil count and GDM, however also in this study, no association was found between well-known risk factors of GDM such as age or BMI and GDM development ( 16 ). In our study, only the 4th quartile of platelet count was significantly associated with GDM, while in the case of neutrophil count, each increment of neutrophil quartiles was associated with a higher risk of GDM development compared to first quartile. Furthermore, after adjusting for significant risk factors of GDM and HOMA-IR, the association between platelet count and GDM became only borderline significant. The results of longitudinal studies on the association of platelet count in early pregnancy and GDM development is inconsistent. In several studies, higher platelet count is predictor of GDM development ( 8 , 27 , 28 ), however, in some of them, this association became non-significant after adjustment for other GDM risk factors ( 8 , 27 ). In other studies, no association was found between platelet count in early pregnancy and GDM development ( 6 , 29 ). Data on the association between lymphocyte count and GDM development is even more conflicting. In the present study, higher lymphocyte count was associated with higher insulin resistance assessed by HOMA-IR, however, lymphocyte count was not predictor of GDM in crude or adjusted models. Similar to our results, in the study by Sun et al. no association was found between lymphocyte count in early pregnancy and GDM development ( 6 ). In some studies, higher lymphocyte count in early pregnancy was associated with a higher risk of GDM development in later months ( 8 , 9 ). In contrast, in retrospective case-control study by Wang et al, lymphocyte count in women with hyperglycemia first time detected during pregnancy (including GDM and diabetes in pregnancy) was significantly lower than in control group ( 10 ). The conflicting data on the relationship between lymphocyte count and GDM can be attributed to different and even opposite functions of various lymphocyte subsets. Th1 and Th2 Subsets have inhibitory and stimulatory effects on inflammation, respectively ( 30 ). Other CD4 + lymphocyte, regulatory T cells (T regs) have immunosuppressive properties and contribute to maternal-fetal immune tolerance and seems to play some roles in maternal insulin resistance ( 30 ). Considering the contradictory effects of different lymphocyte subsets on inflammation, it does not seem that the total lymphocyte count is an appropriate parameter for predicting gestational complications such as preeclampsia and diabetes in which inflammation plays an essential role. In the recent years, some inflammatory indices including NLR, SIRI, SII, and AISI have been found as useful predictors of severity of different inflammatory disease ( 17 – 19 ). Among these parameters, the positive relationship of NLR with GDM development has been reported ( 8 , 10 , 31 ). In the present study, NLR, SIRI, SII, and AISI were associated with GDM in univariate analysis and multivariate analysis adjusted by demographic variables, but after adding HOMA-IR to the model, only SII and NLR remained significant predictors of GDM. However, their predictive values were lower than that of neutrophils. Similarly, In the study by Sun et al. the role of NLR in GDM occurrence was inferior to neutrophil count ( 6 ). The authors concluded that when lymphocyte count is not associated with GDM, the impact of neutrophils on GDM is diluted in NLR in which lymphocyte are in their formula ( 6 ). Other inflammatory indices mentioned above also have lymphocyte in their formula, so because of lack of association of lymphocytes with GDM in the present study, their association is weaker than that of neutrophil. The mechanisms by which neutrophils influence pregnancy complications have been previously investigated. Pregnancy is an inflammatory state in which neutrophils are involved from fertilization to delivery ( 32 ). In addition to phagocytosis, neutrophils produce neutrophil extracellular traps (NETs) by extrusion of DNA in to the extracellular space and trap invaded antigen ( 33 ). During normal pregnancy neutrophils exhibit enhanced NETosis and in situations such as preeclampsia NETosis is exaggerated to levels even higher than sepsis ( 32 ). During a study by Giaglis et al. in normal pregnant women, the researchers found an unusual high NETosis in a serum sample taken from a wrongly supposed healthy pregnant woman ( 34 ). By reassessment of this case, it was revealed that this pregnant woman had GDM and erroneously included into the study. This enhanced NETosis by neutrophils in pregnant women with GDM was supported by an in vitro study by Stoikou et al. ( 35 ). Some of researchers hypothesized that in pregnant women with GDM, hyperglycemia stimulates NETosis by neutrophils ( 12 ). However, the direction of causality of this relationship has not been demonstrated. In the present study and other above mentioned longitudinal studies, neutrophil count was higher in participants who developed GDM later, therefore, the role of hyperglycemia as the cause of neutrophil hyperactivation is somewhat questionable. There is scanty data on the relationship of high neutrophil count with insulin resistance in the early months of pregnancy. In the present study, higher lymphocyte and platelet counts were positively associated with insulin resistance assessed by HOMA-IR. However, lymphocyte count had no association with the occurrence of GDM, and the association of platelet with GDM became non-significant after adding HOMA-IR to adjustment model in multivariate analysis. Surprisingly, neutrophil count had no association with early gestational HOMA-IR level, and the association of neutrophil count with GDM development became even stronger after adjusting for HOMA –IR in early pregnancy. In the study by Sun et al. neutrophil count had positively association with HOMA-IR in GDM patients. However, in this study, HOMA-IR was assessed simultaneously with OGTT in the second trimester ( 6 ). According to the results of our study, it seems that the increase in neutrophil count is one of the primary abnormalities in the etiological hierarchy of GDM development and can induce insulin resistance or pancreatic beta cell dysfunction in the later gestational months. Consistent with our hypothesis, in most studies on the relationship between low-grade inflammation and insulin resistance, low grade inflammation precedes and induces insulin resistance ( 36 , 37 ), however, in a few studies, insulin resistance precedes low-grade inflammation and provokes it or there is a vicious cycle between these two abnormality ( 38 ). Our studies had limitations and strengths. The main limitation was not repeating assessment of the HOMA-IR in the second trimester of pregnancy to find more pathophysiological information about the relationship of neutrophil count and insulin resistance. The strengths of our study were to investigate the predictive values of some novel inflammatory markers as well as assessment of HOMA-IR simultaneously with the measurement of blood cell count and inflammatory markers. Conclusion In conclusion, the present study showed superiority of neutrophil count over other blood cells and blood cell-derived inflammatory indices for prediction GDM. Another important result of our study was causal precedence of increased neutrophil count on the insulin resistance in the pathogenesis of GDM. Designing other studies on the mechanisms involved in the relationship of neutrophil count and insulin resistance and interventional studies on the use anti-inflammatory agents as a preventive strategy for the development of GDM in high risk groups can increase our knowledge about the pathophysiology and prevention of GDM. Declarations Acknowledgements We thank members of the Metabolic Diseases Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin, Iran, for their assistance with this project. Author’s contributions SH, and KE designed the study. SH, MB, SSK, HP, SEK, AG, SMC, and SK wrote the study manuscript. SH, SMC, SK, AG, FL, FM, SMRHK, MA, MB and KE contributed to data analysis and interpretation the manuscript. All authors read the manuscript and participated in the preparation of the final version of the manuscript. Funding This work was supported by the Metabolic Diseases Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin, Iran (contract no. IR.QUMS.REC.1400.354). Data Availability The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study was approved by the Ethics Committee for Research at Qazvin University of Medical Sciences. The authors confirm that the ethical policies of the journal, as noted on the journal’s author guidelines page, have been adhered to and the appropriate ethical review committee approval has been received. While ethical approval was obtained from the university, no animals were used in this study. Participation in the study was voluntary, and all participants signed written informed consent forms Consent for publication Not applicable. Competing interests The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. References Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, Hoegfeldt CA, Powe CE, Immanuel J, Karuranga S, Divakar H. IDF diabetes atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group’s Criteria. Diabetes research and clinical practice. 2022 Jan 1;183:109050. Johns EC, Denison FC, Norman JE, Reynolds RM. Gestational diabetes mellitus: mechanisms, treatment, and complications. Trends in Endocrinology & Metabolism. 2018 Nov 1;29(11):743-54. Kouhkan A, Najafi L, Malek M, Baradaran HR, Hosseini R, Khajavi A, Khamseh ME. Gestational diabetes mellitus: Major risk factors and pregnancy-related outcomes: A cohort study. International Journal of Reproductive BioMedicine. 2021 Sep;19(9):827. Aydın H, Çelik ÖZ, Yazıcı D, Altunok C, Tarçın Ö, Deyneli O, Sancak S, Kıyıcı S, Aydın K, Yıldız BO, TURGEP Study Group. Prevalence and predictors of gestational diabetes mellitus: a nationwide multicentre prospective study. Diabetic Medicine. 2019 Feb;36(2):221-7. Fahed G, Aoun L, Bou Zerdan M, Allam S, Bou Zerdan M, Bouferraa Y, Assi HI. Metabolic syndrome: updates on pathophysiology and management in 2021. International Journal of Molecular Sciences. 2022 Jan 12;23(2):786. Sun T, Meng F, Zhao H, Yang M, Zhang R, Yu Z, Huang X, Ding H, Liu J, Zang S. Elevated first-trimester neutrophil count is closely associated with the development of maternal gestational diabetes mellitus and adverse pregnancy outcomes. Diabetes. 2020 Jul 1;69(7):1401-10. Kong M, Zhang H, Liu X, Ge Y, Zhang Z, Zhao R, Li Y, Huang S, Xiong G, Yang X, Hao L. Association of maternal neutrophil count in early pregnancy with the development of gestational diabetes mellitus: a prospective cohort study in China. Gynecological Endocrinology. 2022 Mar 4;38(3):258-62. Ye YX, Wang Y, Wu P, Yang X, Wu L, Lai Y, Ouyang J, Li Y, Li P, Hu Y, Wang YX. Blood cell parameters from early to middle pregnancy and risk of gestational diabetes mellitus. The Journal of Clinical Endocrinology & Metabolism. 2023 Jun 6:dgad336. Yang H, Zhu C, Ma Q, Long Y, Cheng Z. Variations of blood cells in prediction of gestational diabetes mellitus. Journal of Perinatal Medicine. 2015 Jan 1;43(1):89-93. Wang J, Zhu QW, Cheng XY, Sha CX, Cui YB. Clinical significance of neutrophil–lymphocyte ratio and monocyte–lymphocyte ratio in women with hyperglycemia. Postgraduate medicine. 2020 Nov 16;132(8):702-8. Khoshkerdar A, Eryasar E, Morgan HL, Watkins AJ. Reproductive Toxicology: Impacts of paternal environment and lifestyle on maternal health during pregnancy. Reproduction. 2021 Nov 1;162(5):F101-9. Hahn S, Hasler P, Vokalova L, van Breda SV, Lapaire O, Than NG, Hoesli I, Rossi SW. The role of neutrophil activation in determining the outcome of pregnancy and modulation by hormones and/or cytokines. Clinical & Experimental Immunology. 2019 Oct;198(1):24-36. Wang W, Sung N, Gilman-Sachs A, Kwak-Kim J. T helper (Th) cell profiles in pregnancy and recurrent pregnancy losses: Th1/Th2/Th9/Th17/Th22/Tfh cells. Frontiers in immunology. 2020 Aug 18;11:2025. McElwain CJ, McCarthy FP, McCarthy CM. Gestational diabetes mellitus and maternal immune dysregulation: what we know so far. International Journal of Molecular Sciences. 2021 Apr 20;22(8):4261. Zhou Z, Chen H, Sun M, Ju H. Mean platelet volume and gestational diabetes mellitus: a systematic review and meta-analysis. Journal of diabetes research. 2018 May 2;2018. Hassan B, Rayis DA, Musa IR, Eltayeb R, ALhabardi N, Adam I. Blood groups and hematological parameters do not associate with first trimester gestational diabetes mellitus (institutional experience). Annals of Clinical & Laboratory Science. 2021 Jan 1;51(1):97-101. Wang P, Guo X, Zhou Y, Li Z, Yu S, Sun Y, Hua Y. Monocyte-to-high-density lipoprotein ratio and systemic inflammation response index are associated with the risk of metabolic disorders and cardiovascular diseases in general rural population. Frontiers in Endocrinology. 2022 Sep 9;13:944991. Zhao Y, Shao W, Zhu Q, Zhang R, Sun T, Wang B, Hu X. Association between systemic immune-inflammation index and metabolic syndrome and its components: results from the National Health and Nutrition Examination Survey 2011–2016. Journal of Translational Medicine. 2023 Oct 4;21(1):691. Sannan NS. Assessment of aggregate index of systemic inflammation and systemic inflammatory response index in dry age-related macular degeneration: a retrospective study. Frontiers in Medicine. 2023 Apr 25;10:1143045. American Diabetes Association. Diabetes management guidelines. Diabetes Care 2015;38:S1-S93 Singh B, Saxena A. Surrogate markers of insulin resistance: A review. World journal of diabetes. 2010 May 5;1(2):36. Bolghanabadi N, Kharaghani R, Hosseinkhani A, Fayazi S, Mossayebnezhad R. Prevalence of Gestational Diabetes in Iran: A Systematic Review and Meta-analysis. Preventive Care in Nursing & Midwifery Journal. 2023 Jan 1;13(1). Fashami MA, Hajian S, Afrakhteh M, Khoob MK. Is there an association between platelet and blood inflammatory indices and the risk of gestational diabetes mellitus? Obstetrics & gynecology science. 2020 Feb 24;63(2):133-40. Sargın MA, Yassa M, Taymur BD, Celik A, Ergun E, Tug N. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios: are they useful for predicting gestational diabetes mellitus during pregnancy?. Therapeutics and clinical risk management. 2016 Apr 26:657-65. Liu W, Lou X, Zhang Z, Chai Y, Yu Q. Association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, mean platelet volume with the risk of gestational diabetes mellitus. Gynecological Endocrinology. 2021 Feb 1;37(2):105-7. Simsek D, Akselim B, Altekin Y. Do patients with a single abnormal OGTT value need a globally admitted definition such as “borderline GDM”? Pregnancy outcomes of these women and the evaluation of new inflammatory markers. The Journal of Maternal-Fetal & Neonatal Medicine. 2021 Nov 17;34(22):3782-9. Huang Y, Chen X, You ZS, Gu F, Li L, Wang D, Liu J, Li Y, He S. The value of first-trimester platelet parameters in predicting gestational diabetes mellitus. The Journal of Maternal-Fetal & Neonatal Medicine. 2022 Jun 3;35(11):2031-5. Zhang Y, Zhang Y, Zhao L, Shang Y, He D, Chen J. Distribution of complete blood count constituents in gestational diabetes mellitus. Medicine. 2021 Jun 6;100(23). Colak E, Ozcimen EE, Ceran MU, Tohma YA, Kulaksızoglu S. Role of mean platelet volume in pregnancy to predict gestational diabetes mellitus in the first trimester. The Journal of Maternal-Fetal & Neonatal Medicine. 2020 Nov 1;33(21):3689-94. De Luccia TP, Pendeloski KP, Ono E, Mattar R, Pares DB, Yazaki Sun S, Daher S. Unveiling the pathophysiology of gestational diabetes: studies on local and peripheral immune cells. Scandinavian journal of immunology. 2020 Apr;91(4):e12860. Yilmaz H, Celik HT, Namuslu M, Inan O, Onaran Y, Karakurt F, Ayyildiz A, Bilgic MA, Bavbek N, Akcay A. Benefits of the neutrophil-to-lymphocyte ratio for the prediction of gestational diabetes mellitus in pregnant women. Experimental and clinical endocrinology & diabetes. 2014 Jan;122(01):39-43 Giaglis S, Stoikou M, Grimolizzi F, Subramanian BY, van Breda SV, Hoesli I, Lapaire O, Hasler P, Than NG, Hahn S. Neutrophil migration into the placenta: Good, bad or deadly?. Cell adhesion & migration. 2016 Mar 3;10(1-2):208-25. Brinkman V, Reichard U, Goosmann C, Fauler B, Uhlemann Y, Weiss DS, Weinrauch Y, Zychlinsky A. Neutrophil extracellular traps kill bacteria. science. 2004 Mar 5;303(5663):1532-5. Giaglis S, Stoikou M, Sur Chowdhury C, Schaefer G, Grimolizzi F, Rossi SW, Hoesli IM, Lapaire O, Hasler P, Hahn S. Multimodal regulation of NET formation in pregnancy: progesterone antagonizes the pro-NETotic effect of estrogen and G-CSF. Frontiers in immunology. 2016 Dec 5;7:565. Stoikou M, Grimolizzi F, Giaglis S, Schäfer G, van Breda SV, Hoesli IM, Lapaire O, Huhn EA, Hasler P, Rossi SW, Hahn S. Gestational diabetes mellitus is associated with altered neutrophil activity. Frontiers in immunology. 2017 Jun 14;8:702. Asghar A, Sheikh N. Role of immune cells in obesity induced low grade inflammation and insulin resistance. Cellular immunology. 2017 May 1;315:18-26 Chen L, Chen R, Wang H, Liang F. Mechanisms linking inflammation to insulin resistance. International journal of endocrinology. 2015 Oct;2015. Szukiewicz D. Molecular Mechanisms for the Vicious Cycle between Insulin Resistance and the Inflammatory Response in Obesity. International Journal of Molecular Sciences. 2023 Jun 6;24(12):9818. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2025 Read the published version in BMC Pregnancy and Childbirth → Version 1 posted Editorial decision: Revision requested 26 Nov, 2024 Reviewers agreed at journal 25 Nov, 2024 Reviews received at journal 21 Nov, 2024 Reviewers agreed at journal 19 Nov, 2024 Reviews received at journal 29 Apr, 2024 Reviewers agreed at journal 25 Apr, 2024 Reviewers invited by journal 05 Mar, 2024 Editor assigned by journal 05 Mar, 2024 Editor invited by journal 05 Mar, 2024 Submission checks completed at journal 05 Mar, 2024 First submitted to journal 20 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-3972163\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":277165683,\"identity\":\"961b63f2-f609-4f9a-9987-8a24114a8fe6\",\"order_by\":0,\"name\":\"Sima Hashemipour\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sima\",\"middleName\":\"\",\"lastName\":\"Hashemipour\",\"suffix\":\"\"},{\"id\":277165684,\"identity\":\"0c7935d4-ef76-4dca-92ea-ae46697dab87\",\"order_by\":1,\"name\":\"Seyedeh Sareh kalantarian\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Seyedeh\",\"middleName\":\"Sareh\",\"lastName\":\"kalantarian\",\"suffix\":\"\"},{\"id\":277165685,\"identity\":\"9d556867-116a-41d0-8eb6-c9da5df82a8c\",\"order_by\":2,\"name\":\"Hamidreza Panahi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Hamidreza\",\"middleName\":\"\",\"lastName\":\"Panahi\",\"suffix\":\"\"},{\"id\":277165687,\"identity\":\"182359ca-a80a-42e8-ba18-8da5c90ec4b3\",\"order_by\":3,\"name\":\"Sara Esmaeili Kelishomi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sara\",\"middleName\":\"Esmaeili\",\"lastName\":\"Kelishomi\",\"suffix\":\"\"},{\"id\":277165688,\"identity\":\"b5847bf7-d71d-479b-adcc-a47a770fe976\",\"order_by\":4,\"name\":\"Amirabbas Ghasemi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Amirabbas\",\"middleName\":\"\",\"lastName\":\"Ghasemi\",\"suffix\":\"\"},{\"id\":277165690,\"identity\":\"90e401fd-adbb-4c57-a175-62a055081189\",\"order_by\":5,\"name\":\"Sarah Mirzaeei Chopani\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sarah\",\"middleName\":\"Mirzaeei\",\"lastName\":\"Chopani\",\"suffix\":\"\"},{\"id\":277165692,\"identity\":\"914fe4ce-43dd-40be-a02f-a0a4725e3892\",\"order_by\":6,\"name\":\"Sepideh Kolaji\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Sepideh\",\"middleName\":\"\",\"lastName\":\"Kolaji\",\"suffix\":\"\"},{\"id\":277165693,\"identity\":\"1b4c2bbd-03a0-4e5c-a7ad-9bf20209ca5c\",\"order_by\":7,\"name\":\"Milad Badri\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Milad\",\"middleName\":\"\",\"lastName\":\"Badri\",\"suffix\":\"\"},{\"id\":277165694,\"identity\":\"e8704b16-8709-4748-bef1-75c9d0e3bde5\",\"order_by\":8,\"name\":\"Arefeh Ghobadi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Arefeh\",\"middleName\":\"\",\"lastName\":\"Ghobadi\",\"suffix\":\"\"},{\"id\":277165695,\"identity\":\"6ea03c7a-630d-4711-9062-05aca8aa6fb5\",\"order_by\":9,\"name\":\"Seyyed Mohammad Reza Hadizadeh khairkhahan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Seyyed\",\"middleName\":\"Mohammad Reza Hadizadeh\",\"lastName\":\"khairkhahan\",\"suffix\":\"\"},{\"id\":277165696,\"identity\":\"cc3866a2-dce6-4176-9c32-11bb8b237308\",\"order_by\":10,\"name\":\"Fatemeh Lalooha\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Fatemeh\",\"middleName\":\"\",\"lastName\":\"Lalooha\",\"suffix\":\"\"},{\"id\":277165697,\"identity\":\"c90d5538-52fe-430e-acb9-d033cb160eb3\",\"order_by\":11,\"name\":\"Farideh Movahed\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Farideh\",\"middleName\":\"\",\"lastName\":\"Movahed\",\"suffix\":\"\"},{\"id\":277165698,\"identity\":\"4e527cb9-b1e5-448a-84cc-01ce1e64feed\",\"order_by\":12,\"name\":\"Mahnaz Abbasi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":false,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Mahnaz\",\"middleName\":\"\",\"lastName\":\"Abbasi\",\"suffix\":\"\"},{\"id\":277165699,\"identity\":\"7f475d26-1413-49fc-b049-552a1789b8c5\",\"order_by\":13,\"name\":\"Khadijeh Elmizadeh\",\"email\":\"data:image/png;base64,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\",\"orcid\":\"\",\"institution\":\"Qazvin University of Medical Sciences\",\"correspondingAuthor\":true,\"submittingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Khadijeh\",\"middleName\":\"\",\"lastName\":\"Elmizadeh\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-02-20 08:20:35\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-3972163/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-3972163/v1\",\"draftVersion\":[],\"editorialEvents\":[{\"content\":\"https://doi.org/10.1186/s12884-025-07267-y\",\"type\":\"published\",\"date\":\"2025-02-11T15:56:56+00:00\"}],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":76487427,\"identity\":\"bcbbc6d6-661a-4b34-9e3a-1d2d333457b6\",\"added_by\":\"auto\",\"created_at\":\"2025-02-17 16:05:44\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":878928,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-3972163/v1/ee86af46-32c0-43e5-9b3d-572cbe9e4a1b.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Superiority of neutrophil count over other inflammatory markers in predicting gestational diabetes: A prospective cohort study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eGestational diabetes mellitus (GDM) is one of the most common complications of pregnancy, occurring in 7\\u0026ndash;27% of pregnancies (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e). GDM affects the health of both mother and baby in various ways during pregnancy, labor, and thereafter (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e). There are traditional risk factors for GDM, including older maternal age, a positive family history of type 2 diabetes, a high body mass index (BMI), excessive gestational weight gain, and a history of GDM in previous pregnancies (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). However, GDM occurs in about 5% of pregnant women without any of these known risk factors (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e). Considering the serious complications of GDM, it is valuable to identify other predictors of GDM in early gestational weeks.\\u003c/p\\u003e \\u003cp\\u003eRegarding the profound immunological changes during pregnancy and the known metabolic consequences of low-grade inflammation (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e), predictive values of hematological parameters including various blood cell counts and some calculated blood-cell-derived inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR) for the prediction of GDM have been investigated in recent years (\\u003cspan additionalcitationids=\\\"CR6 CR7 CR8 CR9\\\" citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e). Pregnancy is associated with changes in the maternal immune system to adapt the mother to drastic changes in the body\\u0026rsquo;s environment (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e). Neutrophils play an essential role in all stages of reproduction by influencing successful implantation, fetal well-being, and successful delivery. On the other hand, altered neutrophil function is associated with gestational complications such as preeclampsia and GDM (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). When the mother\\u0026rsquo;s body is exposed to paternal antigens of the fetus, lymphocytes play a critical role in immunomodulation. T helper 1 cells (Th1) induce inflammation, while T helper 2 cells (Th2) have anti-inflammatory effects, and a higher ratio of Th1 to Th2 cells is associated with pregnancy complications such as recurrent abortion, preeclampsia, and GDM (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). In addition, over-activation of platelets may play some roles in the pathogenesis of GDM (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eData on the relationship between various blood cell counts and GDM are somewhat inconsistent. In some studies, all the parameters of platelets, neutrophil, and lymphocyte counts, and related inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR) are predictors of GDM development (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e), while in other studies, no association between these parameters and GDM has been found (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e). Furthermore, to the best of our knowledge, there are no data on the interaction of first-trimester insulin resistance (as the most important pathogenic factor of GDM) with different blood cell counts in the pathogenesis of GDM development.\\u003c/p\\u003e \\u003cp\\u003eIn recent years, the relationship of some novel blood cell-derived inflammatory indices such as systemic inflammatory response index (SIRI), systemic immune inflammation index (SII), and aggregate systemic inflammatory response index (AISI) with the severity and prognosis of some inflammatory diseases have been reported; however, except for NLR, there are no data on the predictive values of these parameters for GDM (\\u003cspan additionalcitationids=\\\"CR18\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). According to the above considerations, this study was designed to investigate the predictive values of various blood cell counts and some novel blood cell-derived inflammatory indices in early pregnancy to predict GDM development, as well as the role of insulin resistance in the relationship of these parameters with GDM.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cp\\u003eThis study is part of the Qazvin Maternal and Neonatal Metabolic Pregnancy Outcome Study (QMNMS). QMNMS is an observational prospective longitudinal study on Iranian pregnant women in Qazvin province, Iran. Pregnant women who received prenatal care at the obstetrics and gynecology clinic were recruited from September 2018 to May 2020 and from February 2021 to June 2021. The COVID-19 pandemic was the main reason for the transient interruption in the study. Inclusion criteria were age\\u0026thinsp;\\u0026ge;\\u0026thinsp;18 years and gestational age\\u0026thinsp;\\u0026le;\\u0026thinsp;14 weeks based on the date of the last menstrual period or ultrasound. Women with known diabetes before pregnancy or undiagnosed overt diabetes discovered in early pregnancy laboratory assessment were excluded from the study. Sampling was performed using the convenience method. The objectives and details of the study were explained to the participants individually. Participation in the study was voluntary, and all participants signed written informed consent forms. Data were collected at the first antenatal visit at \\u0026le;\\u0026thinsp;14th gestational week, during the 22nd -28th gestational weeks, and during the first 6 weeks postpartum. At the first visit, personal information including demographic characteristics, history of complications in previous pregnancies, and history of chronic diseases were collected using questionnaires designed before the study. Blood samples were taken after 12 hours of fasting, at most one week after the first visit. Blood cell counting was performed and all serum samples were frozen at -80\\u0026deg;C. Other laboratory measurements were done on the de-freeze samples after the completion of the study. All participants were screened for GDM using a 75gr oral glucose tolerance test (OGTT) at 24\\u0026ndash;28 weeks of pregnancy. A normal OGTT was defined as fasting blood sugar (FBS)\\u0026thinsp;\\u0026lt;\\u0026thinsp;92 mg/dl, 1-h plasma glucose\\u0026thinsp;\\u0026lt;\\u0026thinsp;180 mg/dl, and 2-h plasma glucose\\u0026thinsp;\\u0026lt;\\u0026thinsp;153 mg/dl. Having at least one measurement above these values was defined as GDM (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBlood cell counting was performed using SYSMEX XS-500i hematology analyzer. FBS and insulin were measured by enzymatic and electrochemiluminescence (ECL) methods, respectively, using the Roche/Hitachi Cobas\\u0026reg; 6000 immunoassay system and Roche Kits. The intra-assay and inter-assay CV of the insulin assay were 1.2% and 4.5%, respectively.\\u003c/p\\u003e \\u003cp\\u003eThe HOMA-IR index was calculated as follows (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e):\\u003c/p\\u003e \\u003cp\\u003eHOMA-IR\\u0026thinsp;=\\u0026thinsp;Fasting blood sugar (mg/dl) \\u0026times; Insulin (mU/l)/405\\u003c/p\\u003e \\u003cp\\u003eBlood cell-derived inflammatory indices of systemic inflammatory response index (SIRI), systemic immune-inflammation index (SII), and aggregate systemic inflammatory response index (AISI) were calculated by (N⤬M)/L, (N⤬P)/L, and (N⤬M⤬P)/L where N, P, M and L represent neutrophil counts, platelet counts, monocyte count, and lymphocyte count, respectively (\\u003cspan additionalcitationids=\\\"CR18\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSample size calculation:\\u003c/h2\\u003e \\u003cp\\u003eConsidering a prevalence of GDM of 10% in the general population (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e), power 80%, α\\u0026thinsp;=\\u0026thinsp;0.05, the lowest relative risk of 1.36 of blood cell components for GDM [relative risk of platelet quartiles in Ye et al. study (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e)], and the drop rate of 20%, the sample size calculated at least 560. According to other objectives of the QMNMS primary study, 821 participants recruited in to the study.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eThe Kolmogorov-Smirnov test was used to check the normality of quantitative data distributions. Quantitative data with normal distribution were presented as mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD and compared between GDM and non-GDM groups using t-test. Quantitative data with non-normal distribution were presented as median (interquartile range) and compared between groups using Mann-Whitney U test and Kruskal-Wallis test. Categorical data were presented as percentage and compared between groups using Chi-square test. Multivariate logistic regression test was performed to investigate the independent association of quartiles of various blood cells and inflammatory indices with the development of GDM. Model 1 was adjusted for parameters with significant difference between GDM and non-GDM groups (age, pre-pregnancy BMI, GDM history in previous pregnancies). Model 2 was adjusted for parameters in mode 1 plus HOMA-IR level.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eOf the 821 pregnant women participated in this study, 209 participants were excluded from the final analysis due to: having a history of diabetes before pregnancy (n\\u0026thinsp;=\\u0026thinsp;12), undiagnosed overt diabetes discovered in the first prenatal laboratory tests (n\\u0026thinsp;=\\u0026thinsp;6), loss to follow-up (n\\u0026thinsp;=\\u0026thinsp;124), and loss of some laboratory data (n\\u0026thinsp;=\\u0026thinsp;67). Baseline characteristics of participants who were lost to follow-up were not significantly different from those who completed the study. Finally, 612 participants were evaluated for the association of CBC and inflammatory indices with GDM.\\u003c/p\\u003e \\u003cp\\u003eGDM developed in 96 participants (15,7%). Baseline characteristics are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Age and pre-pregnancy body mass index (BMI) in the GDM group were significantly higher than the non-GDM group (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 and P\\u0026thinsp;=\\u0026thinsp;0.002, respectively). GDM developed in 46.4% of women with a past medical history of GDM in previous pregnancies compared to 14.9% of the participants without a history of GDM (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). WBC count was higher in the GDM group compared to non-GDM one [median (interquartile range): 9.56 (4.10) *10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e vs. 8.77 (2.90) *10\\u003csup\\u003e3\\u003c/sup\\u003e/ mm\\u003csup\\u003e3\\u003c/sup\\u003e, respectively, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001]. This difference was attributable to the neutrophil count, because other main component of WBC in terms of Lymphocyte and monocyte counts were not significantly different between GDM and non-GDM groups. Neutrophil count in the GDM group was significantly higher than that in the non-GDM group [median (interquartile range): 6.89 (2.73) *10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e vs.5.76 (2.52) *10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e, respectively, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001]. Platelet count was higher in the GDM group compared to non-GDM one [ median (interquartile range): 249.50 (72.75) *10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e vs. 235.00 (73.00) *10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e, respectively, P\\u0026thinsp;=\\u0026thinsp;0.006]. Other inflammatory indices in terms of CRP, NLR, SII, SIRI, and AISI were significantly higher in the GDM group compared to the non-GDM group (Tale 1).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eBaseline demographic and laboratory data of the participants categorized by GDM development in the later months.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGDM\\u003c/p\\u003e \\u003cp\\u003e96 (15.7%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNon-GDM\\u003c/p\\u003e \\u003cp\\u003e516 (84.3%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32.0 (7.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e29.0 (7.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGravidity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eParity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.164\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNulliparous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38 (13.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e244 (86.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMultiparous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e58 (17.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e272 (82.5%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHistory of GDM\\u0026dagger;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePositive\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13 (46.4%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15 (53.6%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNegative\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e45 (14.9%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e257 (85.1%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI before pregnancy\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26.12 (5.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.34 (5.17)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWeight gain\\u0026dagger;\\u0026dagger; (Kg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.21\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.51\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.89\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.589\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFBS (mg/dl)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e93 (13.25)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e90 (9.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eInsulin(mU/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13.15 (8.02)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.81 (6.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHOMA-IR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.02 (2.04)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.19 (1.65)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCRP (mg/dl)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.65 (5.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.85 (5.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRBC (*10\\u003csup\\u003e6\\u003c/sup\\u003e/mm3))\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.48 (0.55)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.47 (0.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.732\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHCT (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38.60 (3.28)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38.60 (3.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.409\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC (*10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.56 (4.10)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.77 (2.90)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeutrophils\\u003c/p\\u003e \\u003cp\\u003e(*10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.89 (2.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.76 (2.52)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLymphocyte (*10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.17 (0.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.13 (0.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.631\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMonocyte (*10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.48 (0.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.46 (0.23)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.499\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelets (*10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e249.50 (72.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e235.00 (73.00)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.97 (1.32)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.65 (1.26)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSIRI (*10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.38 (1.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.15 (0.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSII (*10\\u003csup\\u003e3\\u003c/sup\\u003e/mm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e751.11 (369.79)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e618.78 (323.46)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAISI (*10\\u003csup\\u003e6\\u003c/sup\\u003e mm\\u003csup\\u003e3\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e341.90 (325.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e277.18 (244.96)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eParametric data are presented by mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD; non-parametric data are presented by median (interquartile range); GDM: gestational diabetes mellitus.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u0026dagger; Based on the data of 420 multiparous participants.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u0026dagger;\\u0026dagger; Weight gain until the 24th -28th gestational week.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003eBMI: body mass index, NLR: neutrophil to lymphocyte ratio, SIRI: systemic inflammatory response index, SII: systemic immune-inflammation index, AISI: aggregate systemic inflammatory response index.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe association of HOMA-IR with quartiles of blood cells and inflammatory indices is shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. There was a positive association between higher levels of HOMA-IR and higher lymphocytes, platelets, and CRP quartiles (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 for lymphocyte and CRP quartiles, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.004 for platelet quartiles). The number of neutrophils, monocytes and levels of NLR, SII, SIRI, AISI had no significant associated with HOMA-IR level (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAssociation of HOMA-IR with quartiles of blood cells and inflammatory indices.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeutrophil\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.09 (1.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.21 (1.49)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.40\\u003c/p\\u003e \\u003cp\\u003e(1.73)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.49 (1.98)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.102\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLymphocytes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.90 (1.18)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.29 (1.60)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.35 (1.89)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.80 (1.78)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMonocytes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.17 (1.53)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.27 (1.88)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.60 (2.01)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.24 (1.66)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.476\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelet\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.03 (1.54)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.15 (1.63)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.37 (1.84)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.57 (2.06)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCRP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.74 (1.15)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.29 (1.44)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.74 (2.12)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.79 (1.97)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.21 (1.76)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.70 (1.87)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.22 (1.70)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.12 (1.77)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.154\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSIRI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.34\\u003c/p\\u003e \\u003cp\\u003e(1.64)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.30\\u003c/p\\u003e \\u003cp\\u003e(1.81)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.34\\u003c/p\\u003e \\u003cp\\u003e(1.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.23\\u003c/p\\u003e \\u003cp\\u003e(1.74)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.860\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSII\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.15 (1.69)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.37 (1.85)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.43 (1.50)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.19 (1.75)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.248\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAISI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.06 (1.56)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.38 (1.83)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.55 (1.95)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.21 (1.71)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.071\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eHOMA- IR values are presented as median (interquartile range). HOMA-IR levels were compared between quartiles using Kruskal-Wallis test. NLR: neutrophil to lymphocyte ratio, SIRI: systemic inflammatory response index, SII: systemic immune-inflammation index, AISI: aggregate systemic inflammatory response index.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eUnivariate logistic regression results of predictive values of blood cells and inflammatory indices for predicting GDM are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Neutrophil quartiles had the highest predictive value for the development of GDM. Neutrophil quartiles 2, 3 and 4 were associated with a 3.1, 3.3, and 5.9 higher risk of GDM occurrence compared to the first quartile (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Lymphocyte and monocytes quartiles were not associated with GDM development. Other parameters including platelets, CRP, NLR, SIRI, SII, and AISI were significant predictors for GDM, but the significance levels and relative risk values were lower than neutrophil quartiles (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eUnivariate logistic regression analysis of quartiles of blood cells and inflammatory indices as predictors of GDM development.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c6\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eRR\\u003c/p\\u003e \\u003cp\\u003e(CI95%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeutrophil\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003cp\\u003e(1.3\\u0026ndash;7.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.3\\u003c/p\\u003e \\u003cp\\u003e(1.4\\u0026ndash;7.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.9\\u003c/p\\u003e \\u003cp\\u003e(2.6\\u0026ndash;13.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLymphocytes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.2\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;2.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.6\\u003c/p\\u003e \\u003cp\\u003e(0.3\\u0026ndash;1.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.3\\u003c/p\\u003e \\u003cp\\u003e(0.7\\u0026ndash;2.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.225\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMonocytes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.9\\u003c/p\\u003e \\u003cp\\u003e(0.4\\u0026ndash;1.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.1\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;2.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003cp\\u003e(0.5\\u0026ndash;1.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.896\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelet\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003cp\\u003e(0.8\\u0026ndash;3.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.8\\u003c/p\\u003e \\u003cp\\u003e(0.9\\u0026ndash;3.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.6\\u003c/p\\u003e \\u003cp\\u003e(1.3\\u0026ndash;5.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.028\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCRP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.7\\u003c/p\\u003e \\u003cp\\u003e(1.1\\u0026ndash;6.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003cp\\u003e(1.3\\u0026ndash;7.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.6\\u003c/p\\u003e \\u003cp\\u003e(1.5\\u0026ndash;8.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.023\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.9\\u003c/p\\u003e \\u003cp\\u003e(1.3\\u0026ndash;6.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.7\\u003c/p\\u003e \\u003cp\\u003e(1.7\\u0026ndash;8.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.9\\u003c/p\\u003e \\u003cp\\u003e(1.8\\u0026ndash;8.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSIRI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.9\\u003c/p\\u003e \\u003cp\\u003e(0.4-2.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.7\\u003c/p\\u003e \\u003cp\\u003e(0.9\\u0026ndash;3.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.1\\u003c/p\\u003e \\u003cp\\u003e(1.1-4.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.031\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSII\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.4\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;3.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.2\\u003c/p\\u003e \\u003cp\\u003e(1.5\\u0026ndash;6.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.8\\u003c/p\\u003e \\u003cp\\u003e(1.8\\u0026ndash;7.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAISI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRef\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.4\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;2.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.0\\u003c/p\\u003e \\u003cp\\u003e(1.0-4.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.8\\u003c/p\\u003e \\u003cp\\u003e(1.4\\u0026ndash;5.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.012\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eGDM: gestational diabetes mellitus, NLR: neutrophil to lymphocyte ratio, SIRI: systemic inflammatory response index, SII: systemic immune-inflammation index, AISI: aggregate systemic inflammatory response index.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe results of multivariate logistic regression on the predictive values of blood cells and inflammatory indices for predicting GDM are presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. After adjusting for pre-pregnancy age, BMI, and GDM history in previous pregnancies (model 1), CRP was no longer a significant predictor of GDM, but other significant predictors of GDM in univariate analysis, remained significant in this adjusted model. After adding HOMA-IR to adjustment model 1 (model 2), the associations of quartiles of platelet, SIRI, and AISI with the occurrence of GDM became non-significant. Neutrophil quartiles remained the strongest predictors of GDM development in the model 2. The highest quartile of neutrophil was associated with 8.3 times higher risk of GDM (95% CI: 3.0-23.4, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). The predictive values of NLR and SII had no additional values compared to neutrophil (RR\\u0026thinsp;=\\u0026thinsp;3.1 (95%CI: 1.2\\u0026ndash;2.7) and RR\\u0026thinsp;=\\u0026thinsp;3.7 (95% CI:1.5\\u0026ndash;8.9) for the highest quartile NLR, and SII, respectively) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMultivariate analysis of quartiles of blood cell and inflammatory indices as predictors of GDM.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"11\\\"\\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 \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c6\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"5\\\" nameend=\\\"c11\\\" namest=\\\"c7\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeutrophil\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.9\\u003c/p\\u003e \\u003cp\\u003e(1.5\\u0026ndash;9.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.8\\u003c/p\\u003e \\u003cp\\u003e(1.5\\u0026ndash;9.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.8\\u003c/p\\u003e \\u003cp\\u003e(3.2\\u0026ndash;18.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e3.7\\u003c/p\\u003e \\u003cp\\u003e(1.2\\u0026ndash;10.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e4.2\\u003c/p\\u003e \\u003cp\\u003e(1.4\\u0026ndash;12.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e8.3\\u003c/p\\u003e \\u003cp\\u003e(3.0-23.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLymphocyte\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.3\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;2.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.7\\u003c/p\\u003e \\u003cp\\u003e(0.3\\u0026ndash;1.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.5\\u003c/p\\u003e \\u003cp\\u003e(0.7\\u0026ndash;2.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.184\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.3\\u003c/p\\u003e \\u003cp\\u003e(0.6-3.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.1\\u003c/p\\u003e \\u003cp\\u003e(0.4\\u0026ndash;2.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e2.0\\u003c/p\\u003e \\u003cp\\u003e(0.9-1.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.214\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMonocyte\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003cp\\u003e(0.5\\u0026ndash;1.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.3\\u003c/p\\u003e \\u003cp\\u003e(0.7\\u0026ndash;2.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.2\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;2.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.665\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.7\\u003c/p\\u003e \\u003cp\\u003e(0.3\\u0026ndash;1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.0\\u003c/p\\u003e \\u003cp\\u003e(0.4\\u0026ndash;2.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1.1\\u003c/p\\u003e \\u003cp\\u003e(0.5\\u0026ndash;2.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.763\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePlatelet\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.5\\u003c/p\\u003e \\u003cp\\u003e(0.7\\u0026ndash;3.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003cp\\u003e(0.8\\u0026ndash;3.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.6\\u003c/p\\u003e \\u003cp\\u003e(1.3\\u0026ndash;5.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.040\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.9\\u003c/p\\u003e \\u003cp\\u003e(0.8\\u0026ndash;4.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003cp\\u003e(0.7\\u0026ndash;3.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e2.9\\u003c/p\\u003e \\u003cp\\u003e(1.3\\u0026ndash;6.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.059\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCRP\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.4\\u003c/p\\u003e \\u003cp\\u003e(1.0-5.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.7\\u003c/p\\u003e \\u003cp\\u003e(1.1\\u0026ndash;6.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.4\\u003c/p\\u003e \\u003cp\\u003e(0.9\\u0026ndash;6.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.4\\u003c/p\\u003e \\u003cp\\u003e(1.0-5.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2.6\\u003c/p\\u003e \\u003cp\\u003e(1.0-6.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e2.4\\u003c/p\\u003e \\u003cp\\u003e(1.0-6.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.153\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.6\\u003c/p\\u003e \\u003cp\\u003e(1.5\\u0026ndash;8.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.0\\u003c/p\\u003e \\u003cp\\u003e(2.1\\u0026ndash;11.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.6\\u003c/p\\u003e \\u003cp\\u003e(1.9\\u0026ndash;10.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003cp\\u003e(1.2\\u0026ndash;7.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3.4\\u003c/p\\u003e \\u003cp\\u003e(1.4\\u0026ndash;8.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003cp\\u003e(1.2\\u0026ndash;7.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.037\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSIRI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.1\\u003c/p\\u003e \\u003cp\\u003e(0.5\\u0026ndash;2.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.9\\u003c/p\\u003e \\u003cp\\u003e(0.9-4.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.6\\u003c/p\\u003e \\u003cp\\u003e(1.3\\u0026ndash;5.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.010\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.8\\u003c/p\\u003e \\u003cp\\u003e(0.3\\u0026ndash;1.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.5\\u003c/p\\u003e \\u003cp\\u003e(0.7\\u0026ndash;3.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1.9\\u003c/p\\u003e \\u003cp\\u003e(0.9\\u0026ndash;4.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.129\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSII\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;3.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.1\\u003c/p\\u003e \\u003cp\\u003e(1.4\\u0026ndash;6.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.5\\u003c/p\\u003e \\u003cp\\u003e(2.0-9.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.6\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;4.2)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3.0\\u003c/p\\u003e \\u003cp\\u003e(1.2\\u0026ndash;7.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e3.7\\u003c/p\\u003e \\u003cp\\u003e(1.5\\u0026ndash;8.9)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.010\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAISI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.5\\u003c/p\\u003e \\u003cp\\u003e(0.7\\u0026ndash;3.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.1\\u003c/p\\u003e \\u003cp\\u003e(1.0-4.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.4\\u003c/p\\u003e \\u003cp\\u003e(1.6-7.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRef\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.4\\u003c/p\\u003e \\u003cp\\u003e(0.6\\u0026ndash;3.3)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e1.5\\u003c/p\\u003e \\u003cp\\u003e(0.7\\u0026ndash;3.5)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e2.6\\u003c/p\\u003e \\u003cp\\u003e(1.2\\u0026ndash;5.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.075\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"11\\\"\\u003eGDM: gestational diabetes mellitus, NLR: neutrophil to lymphocyte ratio, SIRI: systemic inflammatory response index, SII: systemic immune-inflammation index, AISI: aggregate systemic inflammatory response index.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"11\\\"\\u003eModel 1: Adjusted for age, pre-pregnancy BMI, GDM history in previous pregnancies. Model 2: Model 1 plus HOMA-IR\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eIn the present study, neutrophil count, platelet counts, and some inflammatory indices including CRP, NLR, SIRI, SII, and AISI in early pregnancy were associated with the development of GDM in the later months. However, in the fully adjustment model, only neutrophils and some neutrophil -related parameters (NLR and SII) were independent predictors of GDM development. Furthermore, the predictive value of neutrophil count was much higher than of each other blood cell or inflammatory index.\\u003c/p\\u003e \\u003cp\\u003eThe association of different blood cell counts with GDM has been investigated previously. The design of some of these studies is cross-sectional, so the predictive values of these parameters cannot be evaluated (\\u003cspan additionalcitationids=\\\"CR24\\\" citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e). In most longitudinal studies, higher neutrophil counts in the first trimester are associated with higher risk of GDM development in later months (\\u003cspan additionalcitationids=\\\"CR7 CR8\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e), and in some studies this association is stronger than other blood cells (\\u003cspan additionalcitationids=\\\"CR7\\\" citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). In Sun et al. study, there was a stepwise increased in the occurrence of GDM with each increased tertile of neutrophil count and the highest tertile of neutrophil count was associated with a more than three times higher GDM rate. In addition, the predictive value of neutrophil count outperformed other blood cells including lymphocyte and platelet count (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Similarly, in the studies by Kong et al. and Ye et al. the neutrophil count was a better predictor of GDM occurrence compared to other blood cells (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e). In some studies, no association was found between neutrophil count and GDM. Most of these studies are cross-sectional (\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e) or retrospective cohort using medical records (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). In a prospective longitudinal study by Hassan et al. in Sudanese women, no association was reported between neutrophil count and GDM, however also in this study, no association was found between well-known risk factors of GDM such as age or BMI and GDM development (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn our study, only the 4th quartile of platelet count was significantly associated with GDM, while in the case of neutrophil count, each increment of neutrophil quartiles was associated with a higher risk of GDM development compared to first quartile. Furthermore, after adjusting for significant risk factors of GDM and HOMA-IR, the association between platelet count and GDM became only borderline significant. The results of longitudinal studies on the association of platelet count in early pregnancy and GDM development is inconsistent. In several studies, higher platelet count is predictor of GDM development (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e), however, in some of them, this association became non-significant after adjustment for other GDM risk factors (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). In other studies, no association was found between platelet count in early pregnancy and GDM development (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eData on the association between lymphocyte count and GDM development is even more conflicting. In the present study, higher lymphocyte count was associated with higher insulin resistance assessed by HOMA-IR, however, lymphocyte count was not predictor of GDM in crude or adjusted models. Similar to our results, in the study by Sun et al. no association was found between lymphocyte count in early pregnancy and GDM development (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). In some studies, higher lymphocyte count in early pregnancy was associated with a higher risk of GDM development in later months (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). In contrast, in retrospective case-control study by Wang et al, lymphocyte count in women with hyperglycemia first time detected during pregnancy (including GDM and diabetes in pregnancy) was significantly lower than in control group (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe conflicting data on the relationship between lymphocyte count and GDM can be attributed to different and even opposite functions of various lymphocyte subsets. Th1 and Th2 Subsets have inhibitory and stimulatory effects on inflammation, respectively (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). Other CD4\\u0026thinsp;+\\u0026thinsp;lymphocyte, regulatory T cells (T regs) have immunosuppressive properties and contribute to maternal-fetal immune tolerance and seems to play some roles in maternal insulin resistance (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). Considering the contradictory effects of different lymphocyte subsets on inflammation, it does not seem that the total lymphocyte count is an appropriate parameter for predicting gestational complications such as preeclampsia and diabetes in which inflammation plays an essential role.\\u003c/p\\u003e \\u003cp\\u003eIn the recent years, some inflammatory indices including NLR, SIRI, SII, and AISI have been found as useful predictors of severity of different inflammatory disease (\\u003cspan additionalcitationids=\\\"CR18\\\" citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). Among these parameters, the positive relationship of NLR with GDM development has been reported (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e). In the present study, NLR, SIRI, SII, and AISI were associated with GDM in univariate analysis and multivariate analysis adjusted by demographic variables, but after adding HOMA-IR to the model, only SII and NLR remained significant predictors of GDM. However, their predictive values were lower than that of neutrophils. Similarly, In the study by Sun et al. the role of NLR in GDM occurrence was inferior to neutrophil count (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). The authors concluded that when lymphocyte count is not associated with GDM, the impact of neutrophils on GDM is diluted in NLR in which lymphocyte are in their formula (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). Other inflammatory indices mentioned above also have lymphocyte in their formula, so because of lack of association of lymphocytes with GDM in the present study, their association is weaker than that of neutrophil.\\u003c/p\\u003e \\u003cp\\u003eThe mechanisms by which neutrophils influence pregnancy complications have been previously investigated. Pregnancy is an inflammatory state in which neutrophils are involved from fertilization to delivery (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). In addition to phagocytosis, neutrophils produce neutrophil extracellular traps (NETs) by extrusion of DNA in to the extracellular space and trap invaded antigen (\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eDuring normal pregnancy neutrophils exhibit enhanced NETosis and in situations such as preeclampsia NETosis is exaggerated to levels even higher than sepsis (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). During a study by Giaglis et al. in normal pregnant women, the researchers found an unusual high NETosis in a serum sample taken from a wrongly supposed healthy pregnant woman (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e). By reassessment of this case, it was revealed that this pregnant woman had GDM and erroneously included into the study. This enhanced NETosis by neutrophils in pregnant women with GDM was supported by an in vitro study by Stoikou et al. (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSome of researchers hypothesized that in pregnant women with GDM, hyperglycemia stimulates NETosis by neutrophils (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). However, the direction of causality of this relationship has not been demonstrated. In the present study and other above mentioned longitudinal studies, neutrophil count was higher in participants who developed GDM later, therefore, the role of hyperglycemia as the cause of neutrophil hyperactivation is somewhat questionable.\\u003c/p\\u003e \\u003cp\\u003eThere is scanty data on the relationship of high neutrophil count with insulin resistance in the early months of pregnancy. In the present study, higher lymphocyte and platelet counts were positively associated with insulin resistance assessed by HOMA-IR. However, lymphocyte count had no association with the occurrence of GDM, and the association of platelet with GDM became non-significant after adding HOMA-IR to adjustment model in multivariate analysis.\\u003c/p\\u003e \\u003cp\\u003eSurprisingly, neutrophil count had no association with early gestational HOMA-IR level, and the association of neutrophil count with GDM development became even stronger after adjusting for HOMA \\u0026ndash;IR in early pregnancy. In the study by Sun et al. neutrophil count had positively association with HOMA-IR in GDM patients. However, in this study, HOMA-IR was assessed simultaneously with OGTT in the second trimester (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAccording to the results of our study, it seems that the increase in neutrophil count is one of the primary abnormalities in the etiological hierarchy of GDM development and can induce insulin resistance or pancreatic beta cell dysfunction in the later gestational months. Consistent with our hypothesis, in most studies on the relationship between low-grade inflammation and insulin resistance, low grade inflammation precedes and induces insulin resistance (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e), however, in a few studies, insulin resistance precedes low-grade inflammation and provokes it or there is a vicious cycle between these two abnormality (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOur studies had limitations and strengths. The main limitation was not repeating assessment of the HOMA-IR in the second trimester of pregnancy to find more pathophysiological information about the relationship of neutrophil count and insulin resistance. The strengths of our study were to investigate the predictive values of some novel inflammatory markers as well as assessment of HOMA-IR simultaneously with the measurement of blood cell count and inflammatory markers.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, the present study showed superiority of neutrophil count over other blood cells and blood cell-derived inflammatory indices for prediction GDM. Another important result of our study was causal precedence of increased neutrophil count on the insulin resistance in the pathogenesis of GDM. Designing other studies on the mechanisms involved in the relationship of neutrophil count and insulin resistance and interventional studies on the use anti-inflammatory agents as a preventive strategy for the development of GDM in high risk groups can increase our knowledge about the pathophysiology and prevention of GDM.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank members of the Metabolic Diseases Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin, Iran, for their assistance with this project.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor\\u0026rsquo;s contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eSH, and KE designed the study. SH, MB, SSK, HP, SEK, AG, SMC, and SK wrote the study manuscript. SH, SMC, SK, AG, FL, FM, SMRHK, MA, MB and KE contributed to data analysis and interpretation the manuscript. All authors read the manuscript and participated in the preparation of the final version of the manuscript.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the Metabolic Diseases Research Center, Research\\u0026nbsp;Institute for Prevention of Non-Communicable Diseases, Qazvin, Iran (contract no. IR.QUMS.REC.1400.354).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets used or analyzed during the current study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was approved by the Ethics Committee for Research at Qazvin University of Medical Sciences. The authors confirm that the ethical policies of the journal, as noted on the journal\\u0026rsquo;s author guidelines page, have been adhered to and the appropriate ethical review committee approval has been received. While ethical approval was obtained from the university, no animals were used in this study. Participation in the study was voluntary, and all participants signed written informed consent forms\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n \\u003cli\\u003eWang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, Hoegfeldt CA, Powe CE, Immanuel J, Karuranga S, Divakar H. IDF diabetes atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group\\u0026rsquo;s Criteria. Diabetes research and clinical practice. 2022 Jan 1;183:109050.\\u003c/li\\u003e\\n \\u003cli\\u003eJohns EC, Denison FC, Norman JE, Reynolds RM. Gestational diabetes mellitus: mechanisms, treatment, and complications. Trends in Endocrinology \\u0026amp; Metabolism. 2018 Nov 1;29(11):743-54.\\u003c/li\\u003e\\n \\u003cli\\u003eKouhkan A, Najafi L, Malek M, Baradaran HR, Hosseini R, Khajavi A, Khamseh ME. Gestational diabetes mellitus: Major risk factors and pregnancy-related outcomes: A cohort study. International Journal of Reproductive BioMedicine. 2021 Sep;19(9):827.\\u003c/li\\u003e\\n \\u003cli\\u003eAydın H, \\u0026Ccedil;elik \\u0026Ouml;Z, Yazıcı D, Altunok C, Tar\\u0026ccedil;ın \\u0026Ouml;, Deyneli O, Sancak S, Kıyıcı S, Aydın K, Yıldız BO, TURGEP Study Group. Prevalence and predictors of gestational diabetes mellitus: a nationwide multicentre prospective study. Diabetic Medicine. 2019 Feb;36(2):221-7.\\u003c/li\\u003e\\n \\u003cli\\u003eFahed G, Aoun L, Bou Zerdan M, Allam S, Bou Zerdan M, Bouferraa Y, Assi HI. Metabolic syndrome: updates on pathophysiology and management in 2021. International Journal of Molecular Sciences. 2022 Jan 12;23(2):786.\\u003c/li\\u003e\\n \\u003cli\\u003eSun T, Meng F, Zhao H, Yang M, Zhang R, Yu Z, Huang X, Ding H, Liu J, Zang S. Elevated first-trimester neutrophil count is closely associated with the development of maternal gestational diabetes mellitus and adverse pregnancy outcomes. Diabetes. 2020 Jul 1;69(7):1401-10.\\u003c/li\\u003e\\n \\u003cli\\u003eKong M, Zhang H, Liu X, Ge Y, Zhang Z, Zhao R, Li Y, Huang S, Xiong G, Yang X, Hao L. Association of maternal neutrophil count in early pregnancy with the development of gestational diabetes mellitus: a prospective cohort study in China. Gynecological Endocrinology. 2022 Mar 4;38(3):258-62.\\u003c/li\\u003e\\n \\u003cli\\u003eYe YX, Wang Y, Wu P, Yang X, Wu L, Lai Y, Ouyang J, Li Y, Li P, Hu Y, Wang YX. Blood cell parameters from early to middle pregnancy and risk of gestational diabetes mellitus. The Journal of Clinical Endocrinology \\u0026amp; Metabolism. 2023 Jun 6:dgad336.\\u003c/li\\u003e\\n \\u003cli\\u003eYang H, Zhu C, Ma Q, Long Y, Cheng Z. Variations of blood cells in prediction of gestational diabetes mellitus. Journal of Perinatal Medicine. 2015 Jan 1;43(1):89-93.\\u003c/li\\u003e\\n \\u003cli\\u003eWang J, Zhu QW, Cheng XY, Sha CX, Cui YB. Clinical significance of neutrophil\\u0026ndash;lymphocyte ratio and monocyte\\u0026ndash;lymphocyte ratio in women with hyperglycemia. Postgraduate medicine. 2020 Nov 16;132(8):702-8.\\u003c/li\\u003e\\n \\u003cli\\u003eKhoshkerdar A, Eryasar E, Morgan HL, Watkins AJ. Reproductive Toxicology: Impacts of paternal environment and lifestyle on maternal health during pregnancy. Reproduction. 2021 Nov 1;162(5):F101-9.\\u003c/li\\u003e\\n \\u003cli\\u003eHahn S, Hasler P, Vokalova L, van Breda SV, Lapaire O, Than NG, Hoesli I, Rossi SW. The role of neutrophil activation in determining the outcome of pregnancy and modulation by hormones and/or cytokines. Clinical \\u0026amp; Experimental Immunology. 2019 Oct;198(1):24-36.\\u003c/li\\u003e\\n \\u003cli\\u003eWang W, Sung N, Gilman-Sachs A, Kwak-Kim J. T helper (Th) cell profiles in pregnancy and recurrent pregnancy losses: Th1/Th2/Th9/Th17/Th22/Tfh cells. Frontiers in immunology. 2020 Aug 18;11:2025.\\u003c/li\\u003e\\n \\u003cli\\u003eMcElwain CJ, McCarthy FP, McCarthy CM. Gestational diabetes mellitus and maternal immune dysregulation: what we know so far. International Journal of Molecular Sciences. 2021 Apr 20;22(8):4261.\\u003c/li\\u003e\\n \\u003cli\\u003eZhou Z, Chen H, Sun M, Ju H. Mean platelet volume and gestational diabetes mellitus: a systematic review and meta-analysis. Journal of diabetes research. 2018 May 2;2018.\\u003c/li\\u003e\\n \\u003cli\\u003eHassan B, Rayis DA, Musa IR, Eltayeb R, ALhabardi N, Adam I. Blood groups and hematological parameters do not associate with first trimester gestational diabetes mellitus (institutional experience). Annals of Clinical \\u0026amp; Laboratory Science. 2021 Jan 1;51(1):97-101.\\u003c/li\\u003e\\n \\u003cli\\u003eWang P, Guo X, Zhou Y, Li Z, Yu S, Sun Y, Hua Y. Monocyte-to-high-density lipoprotein ratio and systemic inflammation response index are associated with the risk of metabolic disorders and cardiovascular diseases in general rural population. Frontiers in Endocrinology. 2022 Sep 9;13:944991.\\u003c/li\\u003e\\n \\u003cli\\u003eZhao Y, Shao W, Zhu Q, Zhang R, Sun T, Wang B, Hu X. Association between systemic immune-inflammation index and metabolic syndrome and its components: results from the National Health and Nutrition Examination Survey 2011\\u0026ndash;2016. Journal of Translational Medicine. 2023 Oct 4;21(1):691.\\u003c/li\\u003e\\n \\u003cli\\u003eSannan NS. Assessment of aggregate index of systemic inflammation and systemic inflammatory response index in dry age-related macular degeneration: a retrospective study. Frontiers in Medicine. 2023 Apr 25;10:1143045.\\u003c/li\\u003e\\n \\u003cli\\u003eAmerican Diabetes Association. Diabetes management guidelines. Diabetes Care 2015;38:S1-S93\\u003c/li\\u003e\\n \\u003cli\\u003eSingh B, Saxena A. Surrogate markers of insulin resistance: A review. World journal of diabetes. 2010 May 5;1(2):36.\\u003c/li\\u003e\\n \\u003cli\\u003eBolghanabadi N, Kharaghani R, Hosseinkhani A, Fayazi S, Mossayebnezhad R. Prevalence of Gestational Diabetes in Iran: A Systematic Review and Meta-analysis. Preventive Care in Nursing \\u0026amp; Midwifery Journal. 2023 Jan 1;13(1).\\u003c/li\\u003e\\n \\u003cli\\u003eFashami MA, Hajian S, Afrakhteh M, Khoob MK. Is there an association between platelet and blood inflammatory indices and the risk of gestational diabetes mellitus? Obstetrics \\u0026amp; gynecology science. 2020 Feb 24;63(2):133-40.\\u003c/li\\u003e\\n \\u003cli\\u003eSargın MA, Yassa M, Taymur BD, Celik A, Ergun E, Tug N. Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios: are they useful for predicting gestational diabetes mellitus during pregnancy?. Therapeutics and clinical risk management. 2016 Apr 26:657-65.\\u003c/li\\u003e\\n \\u003cli\\u003eLiu W, Lou X, Zhang Z, Chai Y, Yu Q. Association of neutrophil to lymphocyte ratio, platelet to lymphocyte ratio, mean platelet volume with the risk of gestational diabetes mellitus. Gynecological Endocrinology. 2021 Feb 1;37(2):105-7.\\u003c/li\\u003e\\n \\u003cli\\u003eSimsek D, Akselim B, Altekin Y. Do patients with a single abnormal OGTT value need a globally admitted definition such as \\u0026ldquo;borderline GDM\\u0026rdquo;? Pregnancy outcomes of these women and the evaluation of new inflammatory markers. The Journal of Maternal-Fetal \\u0026amp; Neonatal Medicine. 2021 Nov 17;34(22):3782-9.\\u003c/li\\u003e\\n \\u003cli\\u003eHuang Y, Chen X, You ZS, Gu F, Li L, Wang D, Liu J, Li Y, He S. The value of first-trimester platelet parameters in predicting gestational diabetes mellitus. The Journal of Maternal-Fetal \\u0026amp; Neonatal Medicine. 2022 Jun 3;35(11):2031-5.\\u003c/li\\u003e\\n \\u003cli\\u003eZhang Y, Zhang Y, Zhao L, Shang Y, He D, Chen J. Distribution of complete blood count constituents in gestational diabetes mellitus. Medicine. 2021 Jun 6;100(23).\\u003c/li\\u003e\\n \\u003cli\\u003eColak E, Ozcimen EE, Ceran MU, Tohma YA, Kulaksızoglu S. Role of mean platelet volume in pregnancy to predict gestational diabetes mellitus in the first trimester. The Journal of Maternal-Fetal \\u0026amp; Neonatal Medicine. 2020 Nov 1;33(21):3689-94.\\u003c/li\\u003e\\n \\u003cli\\u003eDe Luccia TP, Pendeloski KP, Ono E, Mattar R, Pares DB, Yazaki Sun S, Daher S. Unveiling the pathophysiology of gestational diabetes: studies on local and peripheral immune cells. Scandinavian journal of immunology. 2020 Apr;91(4):e12860.\\u003c/li\\u003e\\n \\u003cli\\u003eYilmaz H, Celik HT, Namuslu M, Inan O, Onaran Y, Karakurt F, Ayyildiz A, Bilgic MA, Bavbek N, Akcay A. Benefits of the neutrophil-to-lymphocyte ratio for the prediction of gestational diabetes mellitus in pregnant women. Experimental and clinical endocrinology \\u0026amp; diabetes. 2014 Jan;122(01):39-43\\u003c/li\\u003e\\n \\u003cli\\u003eGiaglis S, Stoikou M, Grimolizzi F, Subramanian BY, van Breda SV, Hoesli I, Lapaire O, Hasler P, Than NG, Hahn S. Neutrophil migration into the placenta: Good, bad or deadly?. Cell adhesion \\u0026amp; migration. 2016 Mar 3;10(1-2):208-25.\\u003c/li\\u003e\\n \\u003cli\\u003eBrinkman V, Reichard U, Goosmann C, Fauler B, Uhlemann Y, Weiss DS, Weinrauch Y, Zychlinsky A. Neutrophil extracellular traps kill bacteria. science. 2004 Mar 5;303(5663):1532-5.\\u003c/li\\u003e\\n \\u003cli\\u003eGiaglis S, Stoikou M, Sur Chowdhury C, Schaefer G, Grimolizzi F, Rossi SW, Hoesli IM, Lapaire O, Hasler P, Hahn S. Multimodal regulation of NET formation in pregnancy: progesterone antagonizes the pro-NETotic effect of estrogen and G-CSF. Frontiers in immunology. 2016 Dec 5;7:565.\\u003c/li\\u003e\\n \\u003cli\\u003eStoikou M, Grimolizzi F, Giaglis S, Sch\\u0026auml;fer G, van Breda SV, Hoesli IM, Lapaire O, Huhn EA, Hasler P, Rossi SW, Hahn S. Gestational diabetes mellitus is associated with altered neutrophil activity. Frontiers in immunology. 2017 Jun 14;8:702.\\u003c/li\\u003e\\n \\u003cli\\u003eAsghar A, Sheikh N. Role of immune cells in obesity induced low grade inflammation and insulin resistance. Cellular immunology. 2017 May 1;315:18-26\\u003c/li\\u003e\\n \\u003cli\\u003eChen L, Chen R, Wang H, Liang F. Mechanisms linking inflammation to insulin resistance. International journal of endocrinology. 2015 Oct;2015.\\u003c/li\\u003e\\n \\u003cli\\u003eSzukiewicz D. Molecular Mechanisms for the Vicious Cycle between Insulin Resistance and the Inflammatory Response in Obesity. International Journal of Molecular Sciences. 2023 Jun 6;24(12):9818.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-pregnancy-and-childbirth\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"prch\",\"sideBox\":\"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/prch/default.aspx\",\"title\":\"BMC Pregnancy and Childbirth\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Gestational diabetes mellitus, Blood cell count, Neutrophil, HOMA-IR, Systemic, Blood cell-derived inflammatory indices\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-3972163/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-3972163/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground:\\u003c/strong\\u003e To investigate the predictive values of blood cell components and blood cells-derived inflammatory indices for predicting gestational diabetes mellitus (GDM).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eMethods: \\u003c/strong\\u003eThis study is part of the Qazvin Maternal and Neonatal Metabolic Study (QMNMS) in Iran (2018-2021. The association of blood cells and blood cell-derived inflammatory indices including neutrophil to lymphocyte ratio (NLR), systemic inflammatory response index (SIRI), systemic immune inflammation index (SII), and aggregate systemic inflammatory response index (AISI) in early pregnancy with early insulin resistance and development of GDM in following months was investigated using univariate and multivariate statistical analysis.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e: The final analysis was performed on 612 participants. GDM developed in 96 participants (15,7%). In univariate analysis, neutrophil quartiles had the highest predictive value for GDM development.\\u0026nbsp; Lymphocytes and monocytes quartiles were not associated with GDM development. In the fully adjusted model, neutrophil quartiles remained the strongest predictors of GDM development with relative risks of 3.7, 4.2, and 8.3 for 2\\u003csup\\u003end\\u003c/sup\\u003e, 3\\u003csup\\u003erd\\u003c/sup\\u003e, and 4\\u003csup\\u003eth \\u003c/sup\\u003eneutrophil quartiles compared to the first quartile (P\\u0026lt;0.001). \\u0026nbsp;Other inflammatory indices including NLR, SIRI, SII, and AISI had no additional yield to predict GDM. Despite the strong associate of neutrophil quartiles with GDM, there was no association between neutrophil counts and early pregnancy insulin resistance assessed by Homeostasis of Model Assessment -Insulin Resistance (HOMA-IR).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConclusion\\u003c/strong\\u003e: Neutrophil count is the best predictor of GDM development among blood cell components and blood cell-derived inflammatory indices. The role of neutrophils in GDM development is independent of early pregnancy insulin resistance.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Superiority of neutrophil count over other inflammatory markers in predicting gestational diabetes: A prospective cohort study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-03-08 05:42:46\",\"doi\":\"10.21203/rs.3.rs-3972163/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2024-11-26T12:47:15+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"231153963241701657122505040420530871603\",\"date\":\"2024-11-25T11:23:52+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-11-22T03:00:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"331830093992953088302668554403296017581\",\"date\":\"2024-11-19T07:28:31+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2024-04-29T23:51:17+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"121045421389521953349794387217014053985\",\"date\":\"2024-04-25T08:23:46+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-03-06T02:06:09+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-03-05T23:57:10+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-03-05T10:13:39+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-03-05T09:56:23+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Pregnancy and Childbirth\",\"date\":\"2024-02-20T08:11:53+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-pregnancy-and-childbirth\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"prch\",\"sideBox\":\"Learn more about [BMC Pregnancy and Childbirth](http://bmcpregnancychildbirth.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/prch/default.aspx\",\"title\":\"BMC Pregnancy and Childbirth\",\"twitterHandle\":\"@BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"d589e873-fcaa-416e-9cd8-c4b3585c82c4\",\"owner\":[],\"postedDate\":\"March 8th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-02-17T15:58:49+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-3972163\",\"link\":\"https://doi.org/10.1186/s12884-025-07267-y\",\"journal\":{\"identity\":\"bmc-pregnancy-and-childbirth\",\"isVorOnly\":false,\"title\":\"BMC Pregnancy and Childbirth\"},\"publishedOn\":\"2025-02-11 15:56:56\",\"publishedOnDateReadable\":\"February 11th, 2025\"},\"versionCreatedAt\":\"2024-03-08 05:42:46\",\"video\":\"\",\"vorDoi\":\"10.1186/s12884-025-07267-y\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12884-025-07267-y\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-3972163\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-3972163\",\"identity\":\"rs-3972163\",\"version\":[\"v1\"]},\"buildId\":\"omnImTCwR2MFx8CMYfrG7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}