{"paper_id":"44148aeb-67bd-4e4c-baf0-532e25c87a9e","body_text":"First-trimester triglyceride glucose-body mass index as a risk marker for gestational diabetes mellitus | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article First-trimester triglyceride glucose-body mass index as a risk marker for gestational diabetes mellitus Junmiao Xiang, XueKe Guo, Yundong Pan, Zhuhua Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4587241/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Introduction: Gestational diabetes mellitus (GDM) is a significant pregnancy complication. Early identification of at-risk women is crucial for prevention. This study evaluates the first-trimester triglyceride glucose-body mass index (TyG-BMI) as a GDM predictor. Methods A retrospective study on 943 patients from The Third Affiliated Hospital of Wenzhou Medical University analyzed TyG-BMI’s correlation with GDM using logistic regression and stratified analyses. The area under the curve (AUC) assessed TyG-BMI’s diagnostic performance. Scatter plots and Pearson correlation analysis have clarified the link between TyG-BMI and neonatal birth weight, as well as the link between TyG-BMI and OGTT glycemic measures. Results In a study of 943 participants, 170 developed GDM, while 773 did not. Elevated TyG-BMI levels were linked to a higher GDM risk. The odds ratio (OR) for GDM was significant in all models, with the highest OR in the fully adjusted model (OR = 1.063, 95% CI: 1.031–1.097). TyG-BMI levels showed a linear relationship with GDM risk and outperformed other measures in diagnostic accuracy, with an AUC of 67.4% (95% CI: 62.9%-72%). TyG-BMI had a strong positive correlation with fasting blood glucose levels (r = 0.347, P < 0.001), but not with 1-hour or 2-hour levels in patients with GDM. It was also significantly higher in the triple positive group compared to single and double positive groups, although no significant link was found between TyG-BMI and neonatal birth weight. Discussion Our study indicates that the TyG-BMI index, measured in the first trimester, is an independent and effective predictor of GDM. Triglyceride glucose-body mass index Triglyceride glucose index Body mass index First-trimester Gestational diabetes mellitus Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Gestational diabetes mellitus (GDM) is defined as carbohydrate intolerance of variable severity with onset or recognition during pregnancy [ 1 ]. It represents one of the prevalent obstetric complications, with an overall incidence rate of GDM being 17.8% (ranging from 9.3–25.5%) [ 2 ]. Women with a history of GDM are frequently associated with increased incidences of preeclampsia, macrosomia, perinatal anomalies, and mortality [ 3 , 4 ]. This condition is also closely linked to the development of metabolic syndrome and hyperglycemia in both the mother and offspring [ 5 ]. Moreover, women with prior GDM at a heightened risk of developing impaired glucose tolerance and type 2 diabetes later in life. The risk within the 10-year postpartum period is approximately 40%, with the incidence peaking during the first five years following pregnancy [ 6 ]. The clinical diagnosis of GDM is conventionally established during the 24–28 weeks gestational window via a 75g oral glucose tolerance test (OGTT) [ 7 , 8 ]. Additionally, the European Board & College of Obstetrics and Gynaecology (EBCOG) recommends a selective screening predicated on risk factors identified at the initial prenatal visit [ 9 ]. Therefore, the early identification of women at risk for GDM is crucial for preventing adverse outcomes in pregnancy and halting the intergenerational transmission of metabolic disorders [ 10 ]. The triglyceride glucose index (TyG) has been advocated as a more valuable surrogate marker for insulin resistance (IR) than the homeostasis model assessment of insulin resistance (HOMA-IR) [ 11 ], and it has also been validated as a novel predictor for GDM [ 12 , 13 ]. Recently, an innovative index known as the triglyceride glucose-body mass index (TyG-BMI) has been introduced. This index integrates both TyG and body mass index (BMI), offering a potentially stronger identification of IR, particularly since obesity is a well-established risk factor for IR [ 14 ]. Multiple studies have demonstrated that TyG-BMI outperforms TyG in predicting metabolic diseases and cardiovascular disease [ 15 – 17 ]. However, literature on the association between TyG-BMI changes and GDM is scarce, and the relationship between TyG-BMI fluctuations and GDM risk remains unclear. Our study is designed to explore this association and assess the diagnostic value of TyG-BMI for predicting GDM during the first trimester. 2. Material and Methods 2.1 Patient Population A total of 943 patients from The Third Affiliated Hospital of Wenzhou Medical University between January 2019 and October 2022 were retrospectively selected and included in the study. Inclusion criteria: ultrasound confirmed intrauterine pregnancy in their first trimester of pregnancy (before 14 weeks of pregnancy according to their last menstrual period). The exclusion criteria are: a) Incomplete clinical records; b) Taking any medication that could impair insulin secretion; c) hormone or metabolic disorder (e.g., prediabetes, type1 or 2 diabetes, and polycystic ovary syndrome); d) lost to follow-up; e) had previous GDM or pre-eclampsia; F) pregnancy loss before 24 weeks. Definitions used in this study included the following: Each pregnant woman underwent a 2-h 75-g OGTT to screen for GDM after an overnight fast, between 24 and 28 weeks of gestation. A diagnosis of GDM according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) guidelines [ 7 ], if any of the following criteria were met or exceeded: a fasting blood glucose level of 5.1 mmol/L (91.90 mg/dL), a 1-h blood glucose level of 10.0 mmol/L (180.20 mg/dL), or a 2-h blood glucose level of 8.5 mmol/L (153.17 mg/dL). Single positive: Any one of the fasting, 1-hour, or 2-hour blood glucose levels met or exceeded the specified thresholds. Double positive: Any two of the fasting, 1-hour, or 2-hour blood glucose levels met or exceeded the specified thresholds. Triple positive: All three of the fasting, 1-hour, and 2-hour blood glucose levels met or exceeded the specified thresholds. All participants signed informed consent, which was reviewed and approved by the Ethics Committee of The Third Affiliated Hospital of Wenzhou Medical University. 2.2 Clinical features record The following demographic and clinical data were recorded for the study subjects: age, weight, height, reproductive history, systolic blood pressure (SBP), diastolic blood pressure (DBP), gestational week of examination and follow-up pregnancy outcomes, These outcomes included neonatal birth weight and the results of the OGTT, which measured fasting blood glucose level, 1-hour blood glucose level, and 2-hour blood glucose level. 2.3 Biochemical and routine blood test indicators measurements The chemiluminescence assay (Siemens IM1600) was employed to quantify a panel of blood lipid markers, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL). Additionally, other biochemical parameters were assessed: fasting blood glucose (FBG), uric acid (UA), total bilirubin (TBil), direct bilirubin (DBil), indirect bilirubin (IBil), total bile acid (TBA), alanine transaminase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (Scr), 25-hydroxyvitamin D (25(OH)D), and homocysteine (Hcy). Furthermore, hematological parameters such as white blood cell count (WBC), neutrophil count (Neu), red blood cell count (RBC), and hemoglobin (Hb) were determined by a blood cell analyzer (Sysmex XN, Japan). 2.4 Immune and endocrine biomarker measurements The study further included the assessment of triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH), which were determined using a chemiluminescence assay (Siemens IM1600). The automatic biochemical analyzer (Siemens CH930, Shanghai, China) was used to measure complement C3, complement C4, and complement C1q using rate turbidimetry. All assays were performed following the manufacturer’s protocol by experienced technicians. Various laboratory tests are conducted on blood samples collected from pregnant women in a fasting state during the 4th to 8th week of pregnancy. 2.5 AST/ALT, BUN/Scr and TyG-BMI calculation The aspartate aminotransferase-to-alanine transaminase ratio (AST/ALT) is calculated as follows: AST/ALT = AST (U/L)/ALT (U/L). The blood urea nitrogen to creatinine ratio (BUN/Scr) is calculated as follows: BUN/Scr = BUN (mmol/L)/ Scr (µmol/L) ×1000. TyG-BMI was calculated as follows: BMI = weight (kg)/height (m 2 ); TyG index = Ln[1/2 fasting blood glucose (mmol/L) × triglycerides (mmol/L)]; TyG-BMI = TyG index × BMI. 2.6 Statistical analysis The Shapiro–Wilk method was used to check whether the data had a normal distribution. Continuous variables that were normally distributed are presented as mean and standard deviation (X ± SD); continuous variables that were not normally distributed are presented as medians (Q1, Q3). Categorical data are reported as numbers and proportions. Means and medians were compared using the Student t test and Mann–Whitney U test. Proportions were compared using the χ 2 test. Patients were divided into three groups based on TyG-BMI levels (TyG-BMI < 12.56, 12.56 ≤ TyG-BMI < 17.69, TyG-BMI ≥ 17.69). Multivariate-adjusted models were employed to assess the robustness of the association between TyG-BMI and the incidence of GDM. Selection of variables for adjustment adhered to two criteria: an alteration in the effect estimate surpassing 10%, or a substantiated clinically significant linkage. The initial model remained unadjusted; subsequent adjustments incorporated age, pregnancy history, previous miscarriage, SBP and DBP in Model 1; Model 1 was expanded to include C3, C4, C1q, TBil, Dbil, IBil and AST/ALT in Model 2; and Model 2 was further extended to integrate TC, LDL, UA in Model 3. Outcomes are articulated as odds ratio (OR) with corresponding 95% confidence interval (CI). Non-linearity was tested by using a likelihood ratio test to compare the model with only a linear term to the model with both linear and cubic spline terms. Subgroup analyses were conducted as well. The variable TyG-BMI was treated as a categorical variable. For the continuous variables, they were initially transformed into categorical variables based on clinically established cut-off points or median values. Subsequently, the likelihood ratio test was employed to assess the interaction effects among the subgroups. To determine the diagnostic effectiveness of the variables for GDM, the receiver operating characteristic (ROC) curve was used and the area under the ROC curve (AUC) was calculated to quantify its screening value. Scatter plots and Pearson correlation analysis were employed to elucidate the associations between neonatal birth weight and the TyG-BMI index, as well as the interrelations among the TyG-BMI index and the various glycemic parameters measured by the OGTT. Statistical analyses were conducted utilizing R software (version 4.2.2; The R Foundation, http://www.R-project.org ), SPSS Statistics (version 22.0; IBM Corp., Armonk, NY), and Free Statistics (version 1.9; Beijing, China, http://www.clinicalscientists.cn/freestatistics ). All tests applied were two-tailed, and a P-value of less than 0.05 was deemed to indicate statistical significance. 3. Results 3.1 Baseline Clinical and Laboratory Characteristics of Study Participants Our study consisted of 943 patients, of which 170 experienced GDM while the remaining 773 did not develop GDM. The average age of the participants was 28.90 ± 4.43 years, and the BMI was 21.37 ± 3.10 kg/m 2 . We categorized participants into tertiles based on their TyG-BMI values, with thresholds set at < 12.56, 12.56–17.69, and ≥ 17.69. Upon stratification, no significant variances were noted across the tertiles for HDL, TBA, AST, BUN, Scr, BUN/Scr, Hcy, WBC, Neu, and TSH. Conversely, significant differences were observed ( P < 0.05) for variables including pregnancy history, previous miscarriage, SBP, DBP, TC, TG, LDL, FBG, UA, TBil, DBil, IBil, ALT, AST/ALT, 25(OH)D, RBC, Hb, C3, C4, C1q, T3, and T4 (Table 1 ). Table 1 Baseline demographic characteristics of the study population stratified by TyG-BMI Variables TyG-BMI Total (n = 943) < 12.56 (n = 314) 12.56 ∼ 17.69 (n = 315) ≥ 17.69 (n = 314) P Age (years) 28.90 ± 4.43 27.88 ± 4.05 28.50 ± 4.18 30.31 ± 4.70 < 0.001 BMI (kg/m 2 ) 21.37 ± 3.10 19.62 ± 1.94 20.86 ± 2.41 23.64 ± 3.29 < 0.001 Pregnancy history < 0.001 Nulliparous 668 (70.8%) 240 (76.4%) 235 (74.6%) 193 (61.5%) Multiparous 275 (29.2%) 74 (23.6%) 80 (25.4%) 121 (38.5%) Previous miscarriage 0.026 0 246 (26.1%) 61 (19.4%) 86 (27.3%) 99 (31.5%) 1 397 (42.1%) 147 (46.8%) 127 (40.3%) 123 (39.2%) 2 188 (19.9%) 63 (20.1%) 69 (21.9%) 56 (17.8%) ≥ 3 112 (11.9%) 43 (13.7%) 33 (10.5%) 36 (11.5%) SBP (mmHg) 115.29 ± 11.77 111.70 ± 11.01 115.50 ± 11.09 118.68 ± 12.15 < 0.001 DBP (mmHg) 69.84 ± 9.17 67.57 ± 8.64 69.68 ± 8.91 72.26 ± 9.37 < 0.001 TC (mmol/L) 4.00 ± 0.83 3.67 ± 0.69 3.96 ± 0.73 4.37 ± 0.90 < 0.001 TG (mmol/L) 1.10 ± 0.73 0.64 ± 0.14 0.96 ± 0.25 1.70 ± 0.96 < 0.001 HDL (mmol/L) 1.30 ± 0.30 1.30 ± 0.27 1.32 ± 0.31 1.29 ± 0.33 0.429 LDL (mmol/L) 2.27 ± 0.70 2.04 ± 0.61 2.25 ± 0.62 2.53 ± 0.76 < 0.001 FBG (mmol/L) 4.69 ± 0.93 4.43 ± 0.68 4.67 ± 0.82 4.99 ± 1.14 < 0.001 UA (µmol/L) 248.10 ± 63.34 235.18 ± 53.83 241.58 ± 61.42 267.58 ± 68.39 < 0.001 TBil (µmol/L) 11.06 ± 5.24 12.00 ± 5.14 11.62 ± 5.56 9.57 ± 4.68 < 0.001 DBil (µmol/L) 4.29 ± 1.93 4.82 ± 1.86 4.51 ± 2.02 3.55 ± 1.68 < 0.001 IBil (µmol/L) 6.76 ± 3.55 7.18 ± 3.51 7.09 ± 3.77 6.02 ± 3.23 < 0.001 TBA (µmol/L) 2.60 (1.60, 4.00) 2.65 (1.60, 4.00) 2.50 (1.60, 4.00) 2.60 (1.60, 4.10) 0.816 ALT (U/L) 14.00 (10.00, 22.00) 12.00 (9.00, 18.00) 14.00 (10.00, 22.00) 17.00 (11.00, 28.25) < 0.001 AST (U/L) 16.00 (13.00, 19.00) 15.00 (13.00, 19.00) 16.00 (13.00, 20.00) 15.00 (13.00, 21.00) 0.186 ALT/AST 1.21 ± 0.60 1.38 ± 0.68 1.21 ± 0.53 1.04 ± 0.52 < 0.001 BUN (mmol/L) 3.63 ± 1.09 3.63 ± 0.95 3.56 ± 0.98 3.69 ± 1.31 0.344 Scr (µmol/L) 52.47 ± 7.92 52.78 ± 6.96 52.60 ± 8.26 52.03 ± 8.46 0.464 BUN/Scr 70.07 ± 21.62 69.77 ± 20.23 68.95 ± 20.43 71.50 ± 23.99 0.320 25(OH)D (ng/mL) 20.22 ± 7.14 20.40 ± 7.55 20.91 ± 7.03 19.34 ± 6.75 0.019 Hcy (µmol/L) 6.08 ± 1.46 6.05 ± 1.23 6.22 ± 1.78 5.96 ± 1.29 0.077 WBC (*10^9/L) 9.73 ± 5.49 9.29 ± 5.88 9.65 ± 5.37 10.25 ± 5.17 0.088 Neu (*10^9/L) 7.00 ± 5.11 6.73 ± 5.43 7.01 ± 5.19 7.27 ± 4.69 0.414 RBC (*10^9/L) 4.28 ± 0.39 4.22 ± 0.36 4.29 ± 0.36 4.32 ± 0.43 0.005 Hb (g/L) 128.32 ± 10.38 127.11 ± 10.44 129.10 ± 9.78 128.75 ± 10.81 0.037 C3 (mg/dL) 9.32 ± 2.05 8.335 ± 1.56 9.07 ± 1.76 10.53 ± 2.16 < 0.001 C4 (mg/dL) 2.07 ± 0.68 1.83 ± 0.53 2.01 ± 0.62 2.38 ± 0.74 < 0.001 C1q (g/L) 174.60 ± 31.19 164.34 ± 28.27 174.86 ± 31.80 184.62 ± 30.13 < 0.001 T3 (pmol/L) 1.66 ± 0.36 1.60 ± 0.29 1.63 ± 0.35 1.77 ± 0.40 < 0.001 T4 (pmol/L) 119.42 ± 25.69 115.26 ± 22.92 118.94 ± 25.31 124.05 ± 27.91 < 0.001 TSH (mIU/L) 1.57 (1.03, 2.37) 1.61 (1.14, 2.47) 1.50 (0.97, 2.24) 1.64 (1.04, 2.40) 0.440 BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; TC: total cholesterol; TG: triglycerides; HDL: high-density lipoprotein; LDL: low-density lipoprotein; FBG: fasting blood glucose; UA: uric acid; TBil: total bilirubin; DBil: direct bilirubin; IBil: indirect bilirubin; TBA: total bile acid; ALT: alanine transaminase; AST: aspartate aminotransferase; AST/ALT: aspartate aminotransferase-to-alanine transaminase ratio; BUN: blood urea nitrogen; Scr: Serum creatinine; BUN/Scr: blood urea nitrogen to creatinine ratio; 25(OH)D: 25-hydroxyvitamin D; Hcy: homocysteine; WBC: white blood cell count; Neu: neutrophil count; RBC: red blood cell count; Hb: hemoglobin; C3: complement C3; C4: complement C4; C1q: complement C1q; T3: triiodothyronine; T4: thyroxine; TSH: thyroid stimulating hormone; 3.2 Associated between TyG-BMI and GDM in pregnancies Univariate logistic regression analysis revealed that age, BMI, FBG, TC, TG, LDL, UA, SBP, DBP, C3, C4 and C1q were positively associated with an increased risk of GDM. Conversely, TBil, DBil, IBil and AST/ALT exhibit a negative association with the occurrence of GDM (Table 2 ). In the multivariate regression analysis presented in Table 3 , we observed a positive association between the level of TyG-BMI and the risk of GDM across all four models. In the unadjusted model, the OR for GDM associated with TyG-BMI was significant (OR = 1.088; 95% CI = 1.064–1.113; P < 0.001). In the fully adjusted main model (Model 3), which accounted for age, pregnancy history, previous miscarriage, SBP and DBP, C3, C4, C1q, TBil, Dbil, IBil and AST/ALT, as well as TC, LDL and UA, the OR for TyG-BMI was 1.063 (95% CI, 1.031–1.097; P < 0.001). For analytical purposes, TyG-BMI levels were categorized from a continuous to a categorical variable. Using the first quantile as the reference category in Model 3, the ORs for the second, third, and fourth quantiles of TyG-BMI were 1.414 (95% CI, 0.763–2.619), 1.992 (95% CI, 1.089–3.644), and 2.967 (95% CI, 1.535–5.736), respectively, indicating a significant difference ( P for trend < 0.05). Table 2 Association of covariates and gestational diabetes mellitus Variables OR (95% CI) P -Value Age (years) 1.097 (1.058 ~ 1.138) < 0.001 BMI (kg/m 2 ) 1.142 (1.085 ~ 1.201) < 0.001 Pregnancy history 0.167 Nulliparous 1.00(Ref.) Multiparous 1.284 (0.901 ~ 1.831) Previous miscarriage 0.112 0 1.00(Ref.) 1 1.058 (0.695 ~ 1.610) 2 0.850 (0.505 ~ 1.431) ≥ 3 1.697 (0.991 ~ 2.905) TC (mmol/L) 1.555 (1.286 ~ 1.880) < 0.001 TG (mmol/L) 1.484 (1.198 ~ 1.838) < 0.001 LDL (mmol/L) 1.669 (1.332 ~ 2.091) < 0.001 FBG (mmol/L) 1.893 (1.588 ~ 2.256) < 0.001 UA (µmol/L) 1.001 (1.002 ~ 1.007) 0.001 TBil (µmol/L) 0.948 (0.913 ~ 0.984) 0.005 DBil (µmol/L) 0.873 (0.793 ~ 0.962) 0.006 IBil (µmol/L) 0.928 (0.878 ~ 0.981) 0.008 AST/ALT 0.522 (0.363 ~ 0.749) < 0.001 SBP (mmHg) 1.016 (1.002 ~ 1.031) 0.025 DBP (mmHg) 1.035 (1.017 ~ 1.054) < 0.001 C3 (mg/dL) 1.165 (1.078 ~ 1.259) < 0.001 C4 (mg/dL) 1.436 (1.138 ~ 1.812) 0.002 C1q (g/L) 1.007 (1.002 ~ 1.012) 0.008 BMI: body mass index; FBG: fasting blood glucose; TC: total cholesterol; TG: triglycerides; LDL: low-density lipoprotein; UA: uric acid; TBil: total bilirubin; DBil: direct bilirubin; IBil: indirect bilirubin; AST/ALT: aspartate aminotransferase-to-alanine transaminase ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; C3: complement C3; C4: complement C4; C1q: complement C1q; Table 3 Relationship between different TyG-BMI levels and gestational diabetes mellitus in different models Crude Model Model 1 Model 2 Model 3 Variable OR (95% CI) P OR (95% CI) P OR (95% CI) P OR (95% CI) P TyG-BMI 1.088 (1.064 ~ 1.113) < 0.001 1.081 (1.054 ~ 1.109) < 0.001 1.071 (1.040 ~ 1.102) < 0.001 1.063 (1.031 ~ 1.097) < 0.001 TyG-BMI, per SD 1.807 (1.541 ~ 2.119) < 0.001 1.723 (1.446 ~ 2.054) < 0.001 1.611 (1.318 ~ 1.969) < 0.001 1.533 (1.234 ~ 1.904) < 0.001 TyG-BMI (quartile) Q1 (TyG-BMI < 11.12) 1 (Ref) 1 (Ref) 1 (Ref) 1 (Ref) Q2 (11.12 ≤ TyG-BMI < 14.92) 1.529 (0.841 ~ 2.777) 0.164 1.511 (0.824 ~ 2.771) 0.182 1.503 (0.816 ~ 2.769) 0.191 1.414 (0.763 ~ 2.619) 0.271 Q3 (14.92 ≤ TyG-BMI < 19.87) 2.441 (1.389 ~ 4.287) 0.002 2.335 (1.313 ~ 4.151) 0.004 2.118 (1.166 ~ 3.848) 0.014 1.992 (1.089 ~ 3.644) 0.025 Q4 (TyG-BMI ≥ 19.87) 5.091 (2.985 ~ 8.680) < 0.001 4.397 (2.490 ~ 7.766) < 0.001 3.581 (1.921 ~ 6.673) < 0.001 2.967 (1.535 ~ 5.736) 0.001 P for trend < 0.001 < 0.001 < 0.001 0.009 Crude model: adjusted for none. Model 1: Adjusted for Age, pregnancy history, previous miscarriage, SBP and DBP. Model 2: Adjusted for the variables in Model 1 plus C3, C4, C1q, TBil, Dbil, IBil and AST/ALT. Model 3: Adjusted for the variables in Model 2 plus TC, LDL, UA TyG-BMI: triglyceride glucose-body mass index; TC: total cholesterol; LDL: low-density lipoprotein; UA: uric acid; C3: complement C3; C4: complement C4; C1q: complement C1q; TBil: total bilirubin; DBil: direct bilirubin; IBil: indirect bilirubin; AST/ALT: aspartate aminotransferase-to-alanine transaminase ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; 3.3 Diagnostic performance of variables in identifying GDM Table 4 presents the statistical efficacy of various biomarkers in differentiating between the GDM and non-GDM groups. The analysis revealed that, compared to the AUC for FBG at 0.663, TG at 0.612, BMI at 0.624, and TyG at 0.661, the TyG-BMI index demonstrated a superior AUC of 0.674, accompanied by a sensitivity of 63.5% and a specificity of 64%. The optimal threshold value identified was 16.448. However, a composite model incorporating FBG, TG, BMI, TyG, and TyG-BMI indices yielded an even higher AUC of 0.706, with an enhanced sensitivity of 72.9% and a specificity of 59.1% (Fig. 2 ). Table 4 Results of ROC analysis of the variables used to predict the development of gestational diabetes mellitus Variables AUC (95% CI), % Specificity (%) Sensitivity (%) PPV (%) NPN (%) Youden index Cutoff FBG 66.3 (61.5 ~ 71) 78.8 46.5 32.5 87 0.253 5.015 TG 61.2 (56.4 ~ 65.9) 55.4 62.9 23.7 90.6 0.183 0.935 BMI 62.4 (57.6 ~ 67.2) 73 50 28.9 86.9 0.230 22.419 TyG 66.1 (61.6 ~ 70.7) 51.4 76.5 25.7 90.8 0.279 0.692 TyG-BMI 67.4 (62.9 ~ 72.0) 64 63.5 28.0 88.9 0.275 16.448 FBG & TG & BMI & TyG & TyG-BMI 70.6 (66.1 ~ 75.1) 59.1 72.9 28.1 90.8 0.321 0.154 TyG-BMI: triglyceride glucose-body mass index; TyG: triglyceride-glucose; BMI: body mass index; FBG: fasting blood glucose; TG: triglyceride; AUC: area under the curve; PPV: positive predictive value; NPV: negative predictive value; ROC: receiver operating characteristic; 3.4 Nonlinear relationship between TyG-BMI and GDM After adjusting for a series of covariates, the relationship between TyG-BMI and GDM demonstrated a linear association ( P for non-linearity = 0.872) in restricted cubic splines (RCS), as depicted in Fig. 1 . 3.5 Subgroup analyses The stratified analyses of the associations between TyG-BMI and GDM are presented in Fig. 3 . Subgroup analyses were conducted based on confounding factors, including age, SBP, DBP, TC, LDL, UA, TBil, Dbil, IBil and AST/ALT, C3, C4 and C1q. All subgroups demonstrated a significantly elevated risk for GDM. Notably, UA level exhibited significant interactions in these subgroups, suggesting a potential modulatory effect on GDM risk. 3.6 Relationship between neonatal birth weight and the TyG-BMI Scatter plots delineating the relationship between neonatal birth weight and the TyG-BMI index. The correlation coefficients and p-values are as follows: for the entire cohort (a), r = -0.034, P = 0.261; for the GDM subgroup (b), r = 0.129, P = 0.092; and for the non-GDM subgroup (c), r = 0.018, P = 0.616. These findings suggest a lack of statistically significant association across the studied groups (Fig. 4 ). 3.7 Relationship between fasting, 1-hour, or 2-hour blood glucose levels and the UHR in patients with GDM There are 170 patients with GDM, and the OGTT results indicated that 95 patients had a single positive result, 58 patients exhibited a double positive result, and 17 patients presented with a triple positive result. Scatter plots illustrate a strong positive correlation between TyG-BMI and fasting blood glucose ((r = 0.347), ( P < 0.001)), whereas no significant correlations were observed between TyG-BMI and 1-hour or 2-hour blood glucose levels. Additionally, bar graphs indicate that the TyG-BMI is significantly higher in the triple positive group compared to the single and double positive groups ( P < 0.05) (Fig. 5 ). 4. Discussion This study represents the inaugural investigation into the relationship between the TyG-BMI index and the incidence of GDM. After adjusting for a multitude of confounding variables, our analysis revealed that the TyG-BMI index maintains an independent correlation with GDM. Notably, a one-standard deviation increase in the TyG-BMI index was associated with a 33% heightened risk of GDM in the fully adjusted model. The relationship between TyG-BMI and the onset of GDM was characterized by a linear progression. Moreover, a robust positive correlation was observed between TyG-BMI and fasting blood glucose levels in patients with GDM. Patients with a triple positive OGTT result exhibited a significantly higher TyG-BMI compared to those with single and double positive results. Conversely, no significant correlation was detected between neonatal birth weight and the TyG-BMI. These insights advocate for the implementation of TyG-BMI evaluation during the first trimester as a robust predictor of GDM, boasting a higher AUC when juxtaposed with the conventional TyG index, TG, or FBG. GDM is commonly associated with an increased risk of preeclampsia, macrosomia, perinatal complications, and mortality. Early detection of GDM is imperative to mitigate these risks. The pathophysiology of GDM involves beta-cell dysfunction and compromised insulin secretion or sensitivity during pregnancy [ 18 ]. Typically, GDM is diagnosed between 24–28 weeks of gestation, often leaving insufficient time to prevent its onset and adverse impacts. Therefore, the early identification of GDM risk through dependable IR markers is essential for averting negative outcomes. The TyG index, which integrates triglycerides and FBG, demonstrates superiority in identifying IR and the incidence of metabolic syndrome [ 19 , 20 ]. This advantage may stem from the concurrent consideration of triglycerides and FBG, both established markers in IR and GDM [ 21 , 22 ]. Initially described in 2008 as an alternative to the HOMA-IR in healthy individuals [ 23 ]. subsequent research has underscored the TyG index’s predictive value for GDM [ 12 , 24 , 25 ]. Zeng et al. [ 12 ] reported a significant positive correlation between the TyG index and GDM, which persisted even after adjusting for confounders (OR = 3.43, 95% CI: 1.20–9.85, P = 0.0216), and demonstrated its diagnostic efficacy (AUC = 0.57, 95% CI: 0.50–0.63). Kim et al. [ 13 ] found that a one standard deviation increase in the TyG index heightened GDM risk by 33% in a fully adjusted model. Theoretically, combining the TyG index with obesity metrics, such as waist circumference (WC), BMI, and waist-to-height ratio (WHtR), offers a more comprehensive reflection of IR, given the established role of obesity as a contributory factor. Lim et al. [ 14 ] discovered that the TyG-BMI index outperformed TyG, TyG-WC, and TyG-WHtR in predicting IR. Recognizing that obesity, as defined by BMI, is instrumental in IR progression [ 26 ] and implicated in GDM pathophysiology, higher maternal BMI emerges as a GDM risk factor [ 27 , 28 ]. Song et al. [ 28 ] demonstrated that GDM likelihood escalates with each standard deviation increment in BMI (OR = 1.64, P = 5.05 × 10^-17). Recent studies have established a connection between the TyG-BMI index and major adverse cardiac and cerebrovascular events (MACCEs), as well as endocrine disorders in patients [ 16 , 17 , 29 , 30 ]. Yang et al. [ 17 ] demonstrated an association between the TyG-BMI index and moderate-to-high SYNTAX scores, which quantitatively assess acute coronary syndrome patients (OR = 1.0041, 95% CI = 1.0000–1.0079, P = 0.0310). Jiang et al. [ 30 ] confirmed that the TyG-BMI index independently correlates with prediabetes. Furthermore, Wang et al. [ 16 ] identified the TyG-BMI index as an independent predictor of new-onset diabetes (HR = 1.50 per SD increase, 95% CI: 1.40–1.60, P -trend < 0.00001), with an optimal cutoff value of 213.2966 for predicting new-onset diabetes (AUC = 0.7741, sensitivity = 72.51%, specificity = 69.54%). In line with these findings, our research indicates that, within the fully adjusted primary model, the odds ratio for GDM associated with the TyG-BMI index was 1.061 (95% CI: 1.029–1.094, P < 0.001). Impaired Fasting Glucose (IFG) has been found to be closely associated with hepatic insulin resistance, whereas Impaired Glucose Tolerance (IGT) exhibits a stronger correlation with muscular insulin resistance. Our investigation revealed a significant positive correlation between TyG-BMI and fasting blood glucose levels, yet no discernible association was observed with 1-hour and 2-hour blood glucose levels. These results suggest that TyG-BMI is more indicative of IFG than IGT. Consequently, our findings suggest that TyG-BMI could potentially serve as a biomarker for hepatic insulin resistance and inadequate insulin secretion. Additionally, TyG-BMI levels were markedly elevated in the triple positive group in comparison to the single and double positive groups, which implies that individuals with higher TyG-BMI may concurrently exhibit both IFG and IGT. Therefore, the TyG-BMI index is considered a straightforward, effective, and clinically significant marker for the early detection of GDM. As metabolic indicators, FBG, TG, and BMI are theoretically posited to exhibit a significant positive correlation with neonatal birth weight. Given the human fetus’s reliance on maternal glucose, the transfer of glucose homeostasis from mother to placenta is considered a pivotal factor in fetal development, as supported by references [ 31 , 32 ]. Clinck Isabel et al. [ 33 ] reported that for each 1 mmol/L increment in TGs, birth weight (BW) increased significantly by 81.7 g, with a more pronounced effect in males (107.3 g; 95% CI 66–148) compared to females (60.5 g; 95% CI 23.6–97.4), indicating a positive correlation between TG levels and neonatal birth weight. Furthermore, studies underscore a direct association between maternal BMI and neonatal birth weight, suggesting maternal BMI as a predictive marker for neonatal birth weight [ 34 ]. However, our study revealed no significant relationship between neonatal birth weight and the TyG-BMI index. This may be attributed to our failure to exclude preterm infants and adjust for confounding factors such as gestational weight gain that influence neonatal birth weight. Additionally, dietary control during pregnancy is crucial, as modified dietary interventions have been shown to favorably influence outcomes related to maternal glycemia and birth weight [ 35 ]. Nevertheless, this study is subject to several limitations that warrant consideration. Firstly, the inherent retrospective design and the study’s restriction to a single center may affect the generalizability of the results. This is despite meticulous adjustments for all known confounders. Secondly, the absence of insulin level data precluded the comparison of the TyG-BMI with HOMA-IR as independent predictors of GDM. Thirdly, the TyG-BMI was assessed only once during early pregnancy, without subsequent monitoring for potential fluctuations, which may have resulted in overlooking variations in TyG-BMI values throughout gestation. Lastly, the lack of data on lifestyle, economic status, dietary habits, physical activity, and sleep patterns, which could serve as confounders, might influence the study outcomes. Further investigation into the potential mechanisms linking TyG-BMI and GDM, including the roles of adipokines, oxidative stress, inflammation, and insulin resistance, is imperative. Despite these limitations, our research has elucidated a significant association between the TyG-BMI index measured in the first trimester and the onset of GDM. 5. Conclusion In conclusion, our study suggests that heightened TyG-BMI levels are independent predictors of GDM. The TyG-BMI index demonstrated superior AUC values, effectively differentiating GDM cases during the first trimester. A robust positive correlation was observed between TyG-BMI and fasting blood glucose levels in patients with GDM. Notably, patients presenting with triple positive OGTT results exhibited significantly higher TyG-BMI values compared to those with single or double positive results. However, there is no significant association between TyG-BMI and neonatal birth weight. Nonetheless, further research is warranted to comprehensively ascertain the prognostic utility of the TyG-BMI index for early prediction of GDM risk. Abbreviations GDM Gestational diabetes mellitus TyG Triglyceride glucose index IR Insulin resistance HOMA-IR Homeostasis model assessment of insulin resistance TyG-BMI Triglyceride glucose-body mass index BMI Body mass index TG Triglycerides FBG Fasting blood glucose Declarations Acknowledgments The authors thank the Department of Gynecology of The Third Affiliated Hospital of Wenzhou Medical University, for their continuous support during the study Author contributions Junmiao Xiang: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Funding Acquisition, Writing - Original Draft; XueKe Guo: Data Curation, Writing - Original Draft; Yundong Pan: Visualization, Validation, Writing - Original Draft; Zhuhua Cai: Conceptualization, Resources, Supervision, Writing - Review & Editing. Funding This work was supported by Foundation of Wenzhou Municipal Health Commission (2020041) Data Availability Statement Data available on request from the authors Conflict of interest The authors declare that they have no competing interests. Consent for publication Before participating in the study, all participants signed up with informed permission. Ethics approval and consent to participate This study was reviewed and approved by the Ethics Committee of The Third Affiliated Hospital of Wenzhou Medical University. The patients/participants provided written informed consent to participate in this study. References Diagnostic criteria and classification. of hyperglycaemia first detected in pregnancy: a World Health Organization Guideline. Diabetes Res Clin Pract. 2014;103(3):341–63. Sacks DA, Hadden DR, Maresh M, Deerochanawong C, Dyer AR, Metzger BE, Lowe LP, Coustan DR, Hod M, Oats JJ, et al. Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. Diabetes Care. 2012;35(3):526–8. Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, Coustan DR, Hadden DR, McCance DR, Hod M, McIntyre HD, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358(19):1991–2002. Stacey T, Tennant PW, McCowan LM, Mitchell EA, Budd J, Li M, Thompson JM, Martin B, Roberts D, Heazell AE. Gestational diabetes and the risk of late stillbirth: a case–control study from England, UK. BJOG: Int J Obstet Gynecol. 2019;126(8):973–82. Groof Z, Garashi G, Husain H, Owayed S, AlBader S, Mouhsen H, Mohammad A, Ziyab AH. Prevalence, Risk Factors, and Fetomaternal Outcomes of Gestational Diabetes Mellitus in Kuwait: A Cross-Sectional Study. J Diabetes Res 2019, 2019:9136250. Lauenborg J, Hansen T, Jensen DM, Vestergaard H, Mølsted-Pedersen L, Hornnes P, Locht H, Pedersen O, Damm P. Increasing incidence of diabetes after gestational diabetes: a long-term follow-up in a Danish population. Diabetes Care. 2004;27(5):1194–9. Metzger BE, Gabbe SG, Persson B, Buchanan TA, Catalano PA, Damm P, Dyer AR, Leiva A, Hod M, Kitzmiler JL, et al. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes Care. 2010;33(3):676–82. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S14–31. Benhalima K, Mathieu C, Damm P, Van Assche A, Devlieger R, Desoye G, Corcoy R, Mahmood T, Nizard J, Savona-Ventura C, Dunne F. A proposal for the use of uniform diagnostic criteria for gestational diabetes in Europe: an opinion paper by the European Board & College of Obstetrics and Gynaecology (EBCOG). Diabetologia. 2015;58(7):1422–9. Retnakaran R, Shah BR. Mediating effect of vascular risk factors underlying the link between gestational diabetes and cardiovascular disease. BMC Med. 2022;20(1):389. Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, Martínez-Abundis E, Ramos-Zavala MG, Hernández-González SO, Jacques-Camarena O, Rodríguez-Morán M. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95(7):3347–51. Zeng Y, Yin L, Yin X, Zhao D. Association of triglyceride-glucose index levels with gestational diabetes mellitus in the US pregnant women: a cross-sectional study. Front Endocrinol (Lausanne). 2023;14:1241372. Kim JA, Kim J, Roh E, Hong SH, Lee YB, Baik SH, Choi KM, Noh E, Hwang SY, Cho GJ, Yoo HJ. Triglyceride and glucose index and the risk of gestational diabetes mellitus: A nationwide population-based cohort study. Diabetes Res Clin Pract. 2021;171:108533. Lim J, Kim J, Koo SH, Kwon GC. Comparison of triglyceride glucose index, and related parameters to predict insulin resistance in Korean adults: An analysis of the 2007–2010 Korean National Health and Nutrition Examination Survey. PLoS ONE. 2019;14(3):e0212963. Li Y, Gui J, Liu H, Guo LL, Li J, Lei Y, Li X, Sun L, Yang L, Yuan T, et al. Predicting metabolic syndrome by obesity- and lipid-related indices in mid-aged and elderly Chinese: a population-based cross-sectional study. Front Endocrinol (Lausanne). 2023;14:1201132. Wang X, Liu J, Cheng Z, Zhong Y, Chen X, Song W. Triglyceride glucose-body mass index and the risk of diabetes: a general population-based cohort study. Lipids Health Dis. 2021;20(1):99. Yang X, Li K, Wen J, Yang C, Li Y, Xu G, Ma Y. Association of the triglyceride glucose-body mass index with the extent of coronary artery disease in patients with acute coronary syndromes. Cardiovasc Diabetol. 2024;23(1):24. Powe CE, Allard C, Battista MC, Doyon M, Bouchard L, Ecker JL, Perron P, Florez JC, Thadhani R, Hivert MF. Heterogeneous Contribution of Insulin Sensitivity and Secretion Defects to Gestational Diabetes Mellitus. Diabetes Care. 2016;39(6):1052–5. Son D-H, Lee HS, Lee Y-J, Lee J-H, Han J-HJN, Metabolism, Diseases C. Comparison of triglyceride-glucose index and HOMA-IR for predicting prevalence and incidence of metabolic syndrome. 2022, 32(3):596–604. Unger G, Benozzi SF, Perruzza F, Pennacchiotti GLJEN. Triglycerides and glucose index: a useful indicator of insulin resistance. 2014, 61(10):533–40. Wu D, Zhang J, Xiong Y, Wang H, Lu D, Guo M, Zhang J, Chen L, Fan J, Huang H, Lin X. Effect of Maternal Glucose and Triglyceride Levels during Early Pregnancy on Pregnancy Outcomes: A Retrospective Cohort Study. Nutrients 2022, 14(16). Liang JW, Chen MX, Hu XA, Zhou M, Zhang Y, Wang LL. Potential Biomarkers in Early Pregnancy for Predicting Gestational Diabetes Mellitus and Adverse Pregnancy Outcomes. Clin Lab 2021, 67(8). Simental-Mendía LE, Rodríguez-Morán M, Guerrero-Romero F. The product of fasting glucose and triglycerides as surrogate for identifying insulin resistance in apparently healthy subjects. Metab Syndr Relat Disord. 2008;6(4):299–304. Li H, Miao C, Liu W, Gao H, Li W, Wu Z, Cao H, Zhu Y. First-Trimester Triglyceride-Glucose Index and Risk of Pregnancy-Related Complications: A Prospective Birth Cohort Study in Southeast China. Diabetes metabolic syndrome obesity: targets therapy. 2022;15:3705–15. Sánchez-García A, Rodríguez-Gutiérrez R, Saldívar-Rodríguez D, Guzmán-López A, Mancillas-Adame L, González-Nava V, Santos-Santillana K, González-González JG. Early triglyceride and glucose index as a risk marker for gestational diabetes mellitus. Int J Gynaecol Obstet. 2020;151(1):117–23. Røder ME, Porte D Jr., Schwartz RS, Kahn SE. Disproportionately elevated proinsulin levels reflect the degree of impaired B cell secretory capacity in patients with noninsulin-dependent diabetes mellitus. J Clin Endocrinol Metab. 1998;83(2):604–8. Rahnemaei FA, Abdi F, Kazemian E, Shaterian N, Shaterian N, Behesht Aeen F. Association between body mass index in the first half of pregnancy and gestational diabetes: A systematic review. SAGE open Med. 2022;10:20503121221109911. Song X, Wang C, Wang T, Zhang S, Qin J. Obesity and risk of gestational diabetes mellitus: A two-sample Mendelian randomization study. Diabetes Res Clin Pract. 2023;197:110561. Cheng Y, Fang Z, Zhang X, Wen Y, Lu J, He S, Xu B. Association between triglyceride glucose-body mass index and cardiovascular outcomes in patients undergoing percutaneous coronary intervention: a retrospective study. Cardiovasc Diabetol. 2023;22(1):75. Jiang C, Yang R, Kuang M, Yu M, Zhong M, Zou Y. Triglyceride glucose-body mass index in identifying high-risk groups of pre-diabetes. Lipids Health Dis. 2021;20(1):161. Armistead B, Johnson E, VanderKamp R, Kula-Eversole E, Kadam L, Drewlo S, Kohan-Ghadr HR. Placental Regulation of Energy Homeostasis During Human Pregnancy. Endocrinology 2020, 161(7). Zhao D, Liu D, Shi W, Shan L, Yue W, Qu P, Yin C, Mi Y. Association between Maternal Blood Glucose Levels during Pregnancy and Birth Outcomes: A Birth Cohort Study. Int J Environ Res Public Health 2023, 20(3). Isabel C, Faro Rebecca V, Vrijkotte TGM, Theodorus Bartholomeus T. Early pregnancy triglycerides and not fructosamine are associated with birth weight (with foetal sexual dimorphism). Eur J Clin Invest. 2023;53(1):e13896. Gul R, Iqbal S, Anwar Z, Ahdi SG, Ali SH, Pirzada S. Pre-pregnancy maternal BMI as predictor of neonatal birth weight. PLoS ONE. 2020;15(10):e0240748. Yamamoto JM, Kellett JE, Balsells M, García-Patterson A, Hadar E, Solà I, Gich I, van der Beek EM, Castañeda-Gutiérrez E, Heinonen S, et al. Gestational Diabetes Mellitus and Diet: A Systematic Review and Meta-analysis of Randomized Controlled Trials Examining the Impact of Modified Dietary Interventions on Maternal Glucose Control and Neonatal Birth Weight. Diabetes Care. 2018;41(7):1346–61. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 20 Jun, 2024 Editor invited by journal 18 Jun, 2024 Editor assigned by journal 18 Jun, 2024 Submission checks completed at journal 17 Jun, 2024 First submitted to journal 15 Jun, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-4587241\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":319068021,\"identity\":\"6d0e0c49-0d50-4a92-9695-21e3e2c26846\",\"order_by\":0,\"name\":\"Junmiao Xiang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Third Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Junmiao\",\"middleName\":\"\",\"lastName\":\"Xiang\",\"suffix\":\"\"},{\"id\":319068024,\"identity\":\"76fbb586-3c6e-4313-8bda-d1ae8a6ac021\",\"order_by\":1,\"name\":\"XueKe Guo\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Third Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"XueKe\",\"middleName\":\"\",\"lastName\":\"Guo\",\"suffix\":\"\"},{\"id\":319068026,\"identity\":\"6689877b-3c15-40e6-89a5-7772d428e89b\",\"order_by\":2,\"name\":\"Yundong Pan\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The Third Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Yundong\",\"middleName\":\"\",\"lastName\":\"Pan\",\"suffix\":\"\"},{\"id\":319068027,\"identity\":\"0c5e5d6d-6004-427b-901a-cac1e9e7b82b\",\"order_by\":3,\"name\":\"Zhuhua Cai\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIie3PoQoCQRCA4ZGFtaxsXUF9hgPB57lB0CRokQuGlZM1iFi16CtoMyoDl06tFwwegl0sRhWjcnc2w35xmJ9hACzrL+U0A9gAMLY9uV7vlyTP684pDLLdeSdS1IrxgKWvyyH5t4454sSHmoeaPycjNzFRIerqzFxwStCIcF16TnbLxMQB1PWCIdQEQYQhB0e1UhIZa3olC8qZNhqWIVHY91/JkhiHTImKYp+JPVVXxJlyw0Ck/iInzfNNdKk8Pxyu17vXq8jhODn5IH5btyzLsr56AByVTdfO7KP6AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"The Third Affiliated Hospital of Wenzhou Medical University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Zhuhua\",\"middleName\":\"\",\"lastName\":\"Cai\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-06-15 16:04:42\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-4587241/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-4587241/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":60344453,\"identity\":\"b78e281a-a5ba-444c-94e2-ee4e49504e0d\",\"added_by\":\"auto\",\"created_at\":\"2024-07-15 19:22:41\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":17562,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRestricted cubic spline shows the association between TyG-BMI and gestational diabetes mellitus. Data were fit by a logistic regression model based on restricted cubic splines. TyG-BMI was entered as continuous variable. Data were adjusted for all the factors of model 3 of Table 3. The curves line and shaded areas around depict the estimated values and their corresponding 95% confidence intervals.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4587241/v1/25491f8c3c5c953440cf8d73.png\"},{\"id\":60344451,\"identity\":\"ed9ffdf7-916e-4661-aba8-4a85611485d8\",\"added_by\":\"auto\",\"created_at\":\"2024-07-15 19:22:41\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":36690,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eROC curve of TyG-BMI index combined with FBG, TG, BMI, and TyG for predicting the development of gestational diabetes mellitus.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4587241/v1/e9594e167e1914a3a14186aa.png\"},{\"id\":60344498,\"identity\":\"4ae7c68e-fbbe-4c05-bfe0-2606d358aa48\",\"added_by\":\"auto\",\"created_at\":\"2024-07-15 19:22:53\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":101109,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSubgroup analyses of the association between gestational diabetes mellitus and TyG-BMI.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4587241/v1/577a811da02683e3d85e591f.png\"},{\"id\":60344454,\"identity\":\"b05ea9ea-64fb-4f08-a3f7-5dd271911225\",\"added_by\":\"auto\",\"created_at\":\"2024-07-15 19:22:41\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":24427,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eScatter plots delineating the relationship between neonatal birth weight and the TyG-BMI index.\\u003c/p\\u003e\\n\\u003cp\\u003ea. In the entire study population, the correlation coefficient (r) is -0.034 with a p-value of 0.261;\\u003c/p\\u003e\\n\\u003cp\\u003eb. In patients diagnosed with gestational diabetes mellitus (GDM), the correlation coefficient (r) is 0.129 with a p-value of 0.092;\\u003c/p\\u003e\\n\\u003cp\\u003ec. In patients without gestational diabetes mellitus (non-GDM), the correlation coefficient (r) is 0.018 with a p-value of 0.616;\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4587241/v1/43298ad0fca94a7d26192139.png\"},{\"id\":60344450,\"identity\":\"f7355558-91a2-42ab-8b79-1653621a1e5a\",\"added_by\":\"auto\",\"created_at\":\"2024-07-15 19:22:41\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":42849,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe relationship between TyG-BMI and various glycemic parameters measured by the OGTT.\\u003c/p\\u003e\\n\\u003cp\\u003ea. TyG-BMI shows a strong positive correlation with fasting blood glucose ((r = 0.347), (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001)).\\u003c/p\\u003e\\n\\u003cp\\u003eb. TyG-BMI has a weak negative correlation with 1-hour blood glucose ((r = 0.127), (\\u003cem\\u003eP\\u003c/em\\u003e = 0.099)).\\u003c/p\\u003e\\n\\u003cp\\u003ec. No correlation between TyG-BMI and 2-hour blood glucose ((r = -0.032), (\\u003cem\\u003eP\\u003c/em\\u003e = 0.682)).\\u003c/p\\u003e\\n\\u003cp\\u003ed. Significant differences in TyG-BMI were observed across groups with single, double, and triple positive outcomes from the OGTT.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4587241/v1/b3dc1ef870780df21eaf5345.png\"},{\"id\":60344510,\"identity\":\"36b613b1-eea3-47f6-af23-dc5dd2320d3a\",\"added_by\":\"auto\",\"created_at\":\"2024-07-15 19:23:05\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1330308,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4587241/v1/8f9e4672-b906-486a-ad76-6cdb4b331db0.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"First-trimester triglyceride glucose-body mass index as a risk marker for gestational diabetes mellitus\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eGestational diabetes mellitus (GDM) is defined as carbohydrate intolerance of variable severity with onset or recognition during pregnancy [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. It represents one of the prevalent obstetric complications, with an overall incidence rate of GDM being 17.8% (ranging from 9.3\\u0026ndash;25.5%) [\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]. Women with a history of GDM are frequently associated with increased incidences of preeclampsia, macrosomia, perinatal anomalies, and mortality [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]. This condition is also closely linked to the development of metabolic syndrome and hyperglycemia in both the mother and offspring [\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e]. Moreover, women with prior GDM at a heightened risk of developing impaired glucose tolerance and type 2 diabetes later in life. The risk within the 10-year postpartum period is approximately 40%, with the incidence peaking during the first five years following pregnancy [\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]. The clinical diagnosis of GDM is conventionally established during the 24\\u0026ndash;28 weeks gestational window via a 75g oral glucose tolerance test (OGTT) [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]. Additionally, the European Board \\u0026amp; College of Obstetrics and Gynaecology (EBCOG) recommends a selective screening predicated on risk factors identified at the initial prenatal visit [\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. Therefore, the early identification of women at risk for GDM is crucial for preventing adverse outcomes in pregnancy and halting the intergenerational transmission of metabolic disorders [\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e].\\u003c/p\\u003e \\u003cp\\u003eThe triglyceride glucose index (TyG) has been advocated as a more valuable surrogate marker for insulin resistance (IR) than the homeostasis model assessment of insulin resistance (HOMA-IR) [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e], and it has also been validated as a novel predictor for GDM [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]. Recently, an innovative index known as the triglyceride glucose-body mass index (TyG-BMI) has been introduced. This index integrates both TyG and body mass index (BMI), offering a potentially stronger identification of IR, particularly since obesity is a well-established risk factor for IR [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Multiple studies have demonstrated that TyG-BMI outperforms TyG in predicting metabolic diseases and cardiovascular disease [\\u003cspan additionalcitationids=\\\"CR16\\\" citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. However, literature on the association between TyG-BMI changes and GDM is scarce, and the relationship between TyG-BMI fluctuations and GDM risk remains unclear. Our study is designed to explore this association and assess the diagnostic value of TyG-BMI for predicting GDM during the first trimester.\\u003c/p\\u003e\"},{\"header\":\"2. Material and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Patient Population\\u003c/h2\\u003e \\u003cp\\u003eA total of 943 patients from The Third Affiliated Hospital of Wenzhou Medical University between January 2019 and October 2022 were retrospectively selected and included in the study. Inclusion criteria: ultrasound confirmed intrauterine pregnancy in their first trimester of pregnancy (before 14 weeks of pregnancy according to their last menstrual period). The exclusion criteria are: a) Incomplete clinical records; b) Taking any medication that could impair insulin secretion; c) hormone or metabolic disorder (e.g., prediabetes, type1 or 2 diabetes, and polycystic ovary syndrome); d) lost to follow-up; e) had previous GDM or pre-eclampsia; F) pregnancy loss before 24 weeks. Definitions used in this study included the following: Each pregnant woman underwent a 2-h 75-g OGTT to screen for GDM after an overnight fast, between 24 and 28 weeks of gestation. A diagnosis of GDM according to the International Association of Diabetes and Pregnancy Study Groups (IADPSG) guidelines [\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e], if any of the following criteria were met or exceeded: a fasting blood glucose level of 5.1 mmol/L (91.90 mg/dL), a 1-h blood glucose level of 10.0 mmol/L (180.20 mg/dL), or a 2-h blood glucose level of 8.5 mmol/L (153.17 mg/dL). Single positive: Any one of the fasting, 1-hour, or 2-hour blood glucose levels met or exceeded the specified thresholds. Double positive: Any two of the fasting, 1-hour, or 2-hour blood glucose levels met or exceeded the specified thresholds. Triple positive: All three of the fasting, 1-hour, and 2-hour blood glucose levels met or exceeded the specified thresholds. All participants signed informed consent, which was reviewed and approved by the Ethics Committee of The Third Affiliated Hospital of Wenzhou Medical University.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Clinical features record\\u003c/h2\\u003e \\u003cp\\u003eThe following demographic and clinical data were recorded for the study subjects: age, weight, height, reproductive history, systolic blood pressure (SBP), diastolic blood pressure (DBP), gestational week of examination and follow-up pregnancy outcomes,\\u003c/p\\u003e \\u003cp\\u003eThese outcomes included neonatal birth weight and the results of the OGTT, which measured fasting blood glucose level, 1-hour blood glucose level, and 2-hour blood glucose level.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Biochemical and routine blood test indicators measurements\\u003c/h2\\u003e \\u003cp\\u003eThe chemiluminescence assay (Siemens IM1600) was employed to quantify a panel of blood lipid markers, including total cholesterol (TC), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein (LDL). Additionally, other biochemical parameters were assessed: fasting blood glucose (FBG), uric acid (UA), total bilirubin (TBil), direct bilirubin (DBil), indirect bilirubin (IBil), total bile acid (TBA), alanine transaminase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum creatinine (Scr), 25-hydroxyvitamin D (25(OH)D), and homocysteine (Hcy). Furthermore, hematological parameters such as white blood cell count (WBC), neutrophil count (Neu), red blood cell count (RBC), and hemoglobin (Hb) were determined by a blood cell analyzer (Sysmex XN, Japan).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Immune and endocrine biomarker measurements\\u003c/h2\\u003e \\u003cp\\u003eThe study further included the assessment of triiodothyronine (T3), thyroxine (T4), and thyroid-stimulating hormone (TSH), which were determined using a chemiluminescence assay (Siemens IM1600). The automatic biochemical analyzer (Siemens CH930, Shanghai, China) was used to measure complement C3, complement C4, and complement C1q using rate turbidimetry. All assays were performed following the manufacturer\\u0026rsquo;s protocol by experienced technicians. Various laboratory tests are conducted on blood samples collected from pregnant women in a fasting state during the 4th to 8th week of pregnancy.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 AST/ALT, BUN/Scr and TyG-BMI calculation\\u003c/h2\\u003e \\u003cp\\u003eThe aspartate aminotransferase-to-alanine transaminase ratio (AST/ALT) is calculated as follows: AST/ALT\\u0026thinsp;=\\u0026thinsp;AST (U/L)/ALT (U/L). The blood urea nitrogen to creatinine ratio (BUN/Scr) is calculated as follows: BUN/Scr\\u0026thinsp;=\\u0026thinsp;BUN (mmol/L)/ Scr (\\u0026micro;mol/L) \\u0026times;1000. TyG-BMI was calculated as follows: BMI\\u0026thinsp;=\\u0026thinsp;weight (kg)/height (m\\u003csup\\u003e2\\u003c/sup\\u003e); TyG index\\u0026thinsp;=\\u0026thinsp;Ln[1/2 fasting blood glucose (mmol/L) \\u0026times; triglycerides (mmol/L)]; TyG-BMI\\u0026thinsp;=\\u0026thinsp;TyG index \\u0026times; BMI.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6 Statistical analysis\\u003c/h2\\u003e \\u003cp\\u003eThe Shapiro\\u0026ndash;Wilk method was used to check whether the data had a normal distribution. Continuous variables that were normally distributed are presented as mean and standard deviation (X\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD); continuous variables that were not normally distributed are presented as medians (Q1, Q3). Categorical data are reported as numbers and proportions. Means and medians were compared using the Student t test and Mann\\u0026ndash;Whitney U test. Proportions were compared using the χ\\u003csup\\u003e2\\u003c/sup\\u003e test. Patients were divided into three groups based on TyG-BMI levels (TyG-BMI\\u0026thinsp;\\u0026lt;\\u0026thinsp;12.56, 12.56\\u0026thinsp;\\u0026le;\\u0026thinsp;TyG-BMI\\u0026thinsp;\\u0026lt;\\u0026thinsp;17.69, TyG-BMI\\u0026thinsp;\\u0026ge;\\u0026thinsp;17.69).\\u003c/p\\u003e \\u003cp\\u003eMultivariate-adjusted models were employed to assess the robustness of the association between TyG-BMI and the incidence of GDM. Selection of variables for adjustment adhered to two criteria: an alteration in the effect estimate surpassing 10%, or a substantiated clinically significant linkage. The initial model remained unadjusted; subsequent adjustments incorporated age, pregnancy history, previous miscarriage, SBP and DBP in Model 1; Model 1 was expanded to include C3, C4, C1q, TBil, Dbil, IBil and AST/ALT in Model 2; and Model 2 was further extended to integrate TC, LDL, UA in Model 3. Outcomes are articulated as odds ratio (OR) with corresponding 95% confidence interval (CI). Non-linearity was tested by using a likelihood ratio test to compare the model with only a linear term to the model with both linear and cubic spline terms. Subgroup analyses were conducted as well. The variable TyG-BMI was treated as a categorical variable. For the continuous variables, they were initially transformed into categorical variables based on clinically established cut-off points or median values. Subsequently, the likelihood ratio test was employed to assess the interaction effects among the subgroups. To determine the diagnostic effectiveness of the variables for GDM, the receiver operating characteristic (ROC) curve was used and the area under the ROC curve (AUC) was calculated to quantify its screening value. Scatter plots and Pearson correlation analysis were employed to elucidate the associations between neonatal birth weight and the TyG-BMI index, as well as the interrelations among the TyG-BMI index and the various glycemic parameters measured by the OGTT.\\u003c/p\\u003e \\u003cp\\u003eStatistical analyses were conducted utilizing R software (version 4.2.2; The R Foundation, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.R-project.org\\u003c/span\\u003e\\u003cspan address=\\\"http://www.R-project.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e), SPSS Statistics (version 22.0; IBM Corp., Armonk, NY), and Free Statistics (version 1.9; Beijing, China, \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttp://www.clinicalscientists.cn/freestatistics\\u003c/span\\u003e\\u003cspan address=\\\"http://www.clinicalscientists.cn/freestatistics\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). All tests applied were two-tailed, and a P-value of less than 0.05 was deemed to indicate statistical significance.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Baseline Clinical and Laboratory Characteristics of Study Participants\\u003c/h2\\u003e \\u003cp\\u003eOur study consisted of 943 patients, of which 170 experienced GDM while the remaining 773 did not develop GDM. The average age of the participants was 28.90 ± 4.43 years, and the BMI was 21.37 ± 3.10 kg/m\\u003csup\\u003e2\\u003c/sup\\u003e. We categorized participants into tertiles based on their TyG-BMI values, with thresholds set at \\u0026lt; 12.56, 12.56–17.69, and ≥ 17.69. Upon stratification, no significant variances were noted across the tertiles for HDL, TBA, AST, BUN, Scr, BUN/Scr, Hcy, WBC, Neu, and TSH. Conversely, significant differences were observed (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05) for variables including pregnancy history, previous miscarriage, SBP, DBP, TC, TG, LDL, FBG, UA, TBil, DBil, IBil, ALT, AST/ALT, 25(OH)D, RBC, Hb, C3, C4, C1q, T3, and T4 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cdiv class=\\\"gridtable\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\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 characteristics of the study population stratified by TyG-BMI\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c5\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eTyG-BMI\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTotal (n = 943)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 12.56 (n = 314)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.56 ∼ 17.69 (n = 315)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e≥ 17.69 (n = 314)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e28.90 ± 4.43\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.88 ± 4.05\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.50 ± 4.18\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30.31 ± 4.70\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e21.37 ± 3.10\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e19.62 ± 1.94\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.86 ± 2.41\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23.64 ± 3.29\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePregnancy history\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\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=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e668 (70.8%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e240 (76.4%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e235 (74.6%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e193 (61.5%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMultiparous\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e275 (29.2%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e74 (23.6%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e80 (25.4%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e121 (38.5%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrevious miscarriage\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.026\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e246 (26.1%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e61 (19.4%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e86 (27.3%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e99 (31.5%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e397 (42.1%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e147 (46.8%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e127 (40.3%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e123 (39.2%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e188 (19.9%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e63 (20.1%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e69 (21.9%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e56 (17.8%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e≥ 3\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e112 (11.9%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e43 (13.7%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e33 (10.5%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e36 (11.5%)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP (mmHg)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e115.29 ± 11.77\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e111.70 ± 11.01\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e115.50 ± 11.09\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e118.68 ± 12.15\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP (mmHg)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e69.84 ± 9.17\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e67.57 ± 8.64\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e69.68 ± 8.91\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e72.26 ± 9.37\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTC (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.00 ± 0.83\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.67 ± 0.69\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.96 ± 0.73\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.37 ± 0.90\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTG (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.10 ± 0.73\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.64 ± 0.14\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.96 ± 0.25\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.70 ± 0.96\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHDL (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.30 ± 0.30\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.30 ± 0.27\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.32 ± 0.31\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.29 ± 0.33\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.429\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.27 ± 0.70\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.04 ± 0.61\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.25 ± 0.62\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.53 ± 0.76\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFBG (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.69 ± 0.93\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.43 ± 0.68\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.67 ± 0.82\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.99 ± 1.14\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUA (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e248.10 ± 63.34\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e235.18 ± 53.83\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e241.58 ± 61.42\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e267.58 ± 68.39\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTBil (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11.06 ± 5.24\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.00 ± 5.14\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e11.62 ± 5.56\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9.57 ± 4.68\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBil (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.29 ± 1.93\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.82 ± 1.86\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.51 ± 2.02\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.55 ± 1.68\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIBil (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.76 ± 3.55\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.18 ± 3.51\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.09 ± 3.77\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6.02 ± 3.23\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTBA (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.60 (1.60, 4.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.65 (1.60, 4.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.50 (1.60, 4.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.60 (1.60, 4.10)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.816\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eALT (U/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e14.00 (10.00, 22.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.00 (9.00, 18.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e14.00 (10.00, 22.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e17.00 (11.00, 28.25)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAST (U/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e16.00 (13.00, 19.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e15.00 (13.00, 19.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16.00 (13.00, 20.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e15.00 (13.00, 21.00)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.186\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eALT/AST\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.21 ± 0.60\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.38 ± 0.68\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.21 ± 0.53\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.04 ± 0.52\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBUN (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.63 ± 1.09\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.63 ± 0.95\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.56 ± 0.98\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.69 ± 1.31\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.344\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eScr (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e52.47 ± 7.92\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e52.78 ± 6.96\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e52.60 ± 8.26\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e52.03 ± 8.46\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.464\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBUN/Scr\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e70.07 ± 21.62\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e69.77 ± 20.23\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e68.95 ± 20.43\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e71.50 ± 23.99\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.320\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e25(OH)D (ng/mL)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20.22 ± 7.14\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e20.40 ± 7.55\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e20.91 ± 7.03\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e19.34 ± 6.75\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.019\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHcy (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6.08 ± 1.46\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.05 ± 1.23\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.22 ± 1.78\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5.96 ± 1.29\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.077\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWBC (*10^9/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.73 ± 5.49\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.29 ± 5.88\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.65 ± 5.37\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10.25 ± 5.17\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.088\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNeu (*10^9/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7.00 ± 5.11\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.73 ± 5.43\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.01 ± 5.19\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e7.27 ± 4.69\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.414\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRBC (*10^9/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.28 ± 0.39\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4.22 ± 0.36\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.29 ± 0.36\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4.32 ± 0.43\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHb (g/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e128.32 ± 10.38\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e127.11 ± 10.44\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e129.10 ± 9.78\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e128.75 ± 10.81\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.037\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC3 (mg/dL)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e9.32 ± 2.05\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8.335 ± 1.56\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e9.07 ± 1.76\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e10.53 ± 2.16\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC4 (mg/dL)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.07 ± 0.68\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.83 ± 0.53\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.01 ± 0.62\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.38 ± 0.74\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC1q (g/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e174.60 ± 31.19\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e164.34 ± 28.27\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e174.86 ± 31.80\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e184.62 ± 30.13\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT3 (pmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.66 ± 0.36\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.60 ± 0.29\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.63 ± 0.35\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.77 ± 0.40\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT4 (pmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e119.42 ± 25.69\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e115.26 ± 22.92\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e118.94 ± 25.31\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e124.05 ± 27.91\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTSH (mIU/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.57 (1.03, 2.37)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.61 (1.14, 2.47)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.50 (0.97, 2.24)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.64 (1.04, 2.40)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.440\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003eBMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; TC: total cholesterol; TG: triglycerides; HDL: high-density lipoprotein; LDL: low-density lipoprotein; FBG: fasting blood glucose; UA: uric acid; TBil: total bilirubin; DBil: direct bilirubin; IBil: indirect bilirubin; TBA: total bile acid; ALT: alanine transaminase; AST: aspartate aminotransferase; AST/ALT: aspartate aminotransferase-to-alanine transaminase ratio; BUN: blood urea nitrogen; Scr: Serum creatinine; BUN/Scr: blood urea nitrogen to creatinine ratio; 25(OH)D: 25-hydroxyvitamin D; Hcy: homocysteine; WBC: white blood cell count; Neu: neutrophil count; RBC: red blood cell count; Hb: hemoglobin; C3: complement C3; C4: complement C4; C1q: complement C1q; T3: triiodothyronine; T4: thyroxine; TSH: thyroid stimulating hormone;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Associated between TyG-BMI and GDM in pregnancies\\u003c/h2\\u003e \\u003cp\\u003eUnivariate logistic regression analysis revealed that age, BMI, FBG, TC, TG, LDL, UA, SBP, DBP, C3, C4 and C1q were positively associated with an increased risk of GDM. Conversely, TBil, DBil, IBil and AST/ALT exhibit a negative association with the occurrence of GDM (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). In the multivariate regression analysis presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, we observed a positive association between the level of TyG-BMI and the risk of GDM across all four models. In the unadjusted model, the OR for GDM associated with TyG-BMI was significant (OR = 1.088; 95% CI = 1.064–1.113; \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001). In the fully adjusted main model (Model 3), which accounted for age, pregnancy history, previous miscarriage, SBP and DBP, C3, C4, C1q, TBil, Dbil, IBil and AST/ALT, as well as TC, LDL and UA, the OR for TyG-BMI was 1.063 (95% CI, 1.031–1.097; \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001). For analytical purposes, TyG-BMI levels were categorized from a continuous to a categorical variable. Using the first quantile as the reference category in Model 3, the ORs for the second, third, and fourth quantiles of TyG-BMI were 1.414 (95% CI, 0.763–2.619), 1.992 (95% CI, 1.089–3.644), and 2.967 (95% CI, 1.535–5.736), respectively, indicating a significant difference (\\u003cem\\u003eP\\u003c/em\\u003e for trend \\u0026lt; 0.05).\\u003c/p\\u003e \\u003cdiv class=\\\"gridtable\\\"\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\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 covariates and gestational diabetes mellitus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e\\u003ccolgroup cols=\\\"3\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e-Value\\u003c/p\\u003e \\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (years)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.097 (1.058 ~ 1.138)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.142 (1.085 ~ 1.201)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePregnancy history\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.167\\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=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00(Ref.)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMultiparous\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.284 (0.901 ~ 1.831)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePrevious miscarriage\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.112\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.00(Ref.)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.058 (0.695 ~ 1.610)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.850 (0.505 ~ 1.431)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e≥ 3\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.697 (0.991 ~ 2.905)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTC (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.555 (1.286 ~ 1.880)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTG (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.484 (1.198 ~ 1.838)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLDL (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.669 (1.332 ~ 2.091)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFBG (mmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.893 (1.588 ~ 2.256)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUA (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.001 (1.002 ~ 1.007)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTBil (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.948 (0.913 ~ 0.984)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.005\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBil (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.873 (0.793 ~ 0.962)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIBil (µmol/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.928 (0.878 ~ 0.981)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAST/ALT\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.522 (0.363 ~ 0.749)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSBP (mmHg)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.016 (1.002 ~ 1.031)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.025\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDBP (mmHg)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.035 (1.017 ~ 1.054)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC3 (mg/dL)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.165 (1.078 ~ 1.259)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC4 (mg/dL)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.436 (1.138 ~ 1.812)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC1q (g/L)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.007 (1.002 ~ 1.012)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.008\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"3\\\"\\u003eBMI: body mass index; FBG: fasting blood glucose; TC: total cholesterol; TG: triglycerides; LDL: low-density lipoprotein; UA: uric acid; TBil: total bilirubin; DBil: direct bilirubin; IBil: indirect bilirubin; AST/ALT: aspartate aminotransferase-to-alanine transaminase ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure; C3: complement C3; C4: complement C4; C1q: complement C1q;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e \\u003cdiv class=\\\"gridtable\\\"\\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\\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\\u003eRelationship between different TyG-BMI levels and gestational diabetes mellitus in different models\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e\\u003ccolgroup cols=\\\"9\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eCrude Model\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eModel 1\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eModel 2\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c9\\\" namest=\\\"c8\\\"\\u003e \\u003cp\\u003eModel 3\\u003c/p\\u003e \\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eOR (95% CI)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eP\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTyG-BMI\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.088 (1.064 ~ 1.113)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.081 (1.054 ~ 1.109)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.071 (1.040 ~ 1.102)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.063 (1.031 ~ 1.097)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTyG-BMI, per SD\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.807 (1.541 ~ 2.119)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.723 (1.446 ~ 2.054)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.611 (1.318 ~ 1.969)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.533 (1.234 ~ 1.904)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTyG-BMI (quartile)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ1 (TyG-BMI \\u0026lt; 11.12)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1 (Ref)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1 (Ref)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1 (Ref)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1 (Ref)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ2 (11.12 ≤ TyG-BMI \\u0026lt; 14.92)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.529 (0.841 ~ 2.777)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.164\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.511 (0.824 ~ 2.771)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.182\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.503 (0.816 ~ 2.769)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.191\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.414 (0.763 ~ 2.619)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.271\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ3 (14.92 ≤ TyG-BMI \\u0026lt; 19.87)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.441 (1.389 ~ 4.287)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.335 (1.313 ~ 4.151)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.118 (1.166 ~ 3.848)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.014\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.992 (1.089 ~ 3.644)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.025\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ4 (TyG-BMI ≥ 19.87)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5.091 (2.985 ~ 8.680)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.397 (2.490 ~ 7.766)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.581 (1.921 ~ 6.673)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.967 (1.535 ~ 5.736)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eP for trend\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.009\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003eCrude model: adjusted for none.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003eModel 1: Adjusted for Age, pregnancy history, previous miscarriage, SBP and DBP.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003eModel 2: Adjusted for the variables in Model 1 plus C3, C4, C1q, TBil, Dbil, IBil and AST/ALT.\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003eModel 3: Adjusted for the variables in Model 2 plus TC, LDL, UA\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"9\\\"\\u003eTyG-BMI: triglyceride glucose-body mass index; TC: total cholesterol; LDL: low-density lipoprotein; UA: uric acid; C3: complement C3; C4: complement C4; C1q: complement C1q; TBil: total bilirubin; DBil: direct bilirubin; IBil: indirect bilirubin; AST/ALT: aspartate aminotransferase-to-alanine transaminase ratio; SBP: systolic blood pressure; DBP: diastolic blood pressure;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Diagnostic performance of variables in identifying GDM\\u003c/h2\\u003e \\u003cp\\u003eTable\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e presents the statistical efficacy of various biomarkers in differentiating between the GDM and non-GDM groups. The analysis revealed that, compared to the AUC for FBG at 0.663, TG at 0.612, BMI at 0.624, and TyG at 0.661, the TyG-BMI index demonstrated a superior AUC of 0.674, accompanied by a sensitivity of 63.5% and a specificity of 64%. The optimal threshold value identified was 16.448. However, a composite model incorporating FBG, TG, BMI, TyG, and TyG-BMI indices yielded an even higher AUC of 0.706, with an enhanced sensitivity of 72.9% and a specificity of 59.1% (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv class=\\\"gridtable\\\"\\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=\\\"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=\\\"char\\\" char=\\\".\\\" 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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\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\\u003eResults of ROC analysis of the variables used to predict the development of gestational diabetes mellitus\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e\\u003ccolgroup cols=\\\"8\\\"\\u003e\\u003c/colgroup\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVariables\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAUC (95% CI), %\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSpecificity (%)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eSensitivity (%)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePPV (%)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNPN (%)\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eYouden index\\u003c/p\\u003e \\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eCutoff\\u003c/p\\u003e \\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFBG\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e66.3 (61.5 ~ 71)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e78.8\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e46.5\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e32.5\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e87\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.253\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e5.015\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTG\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e61.2 (56.4 ~ 65.9)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e55.4\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62.9\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23.7\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e90.6\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.183\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.935\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e62.4 (57.6 ~ 67.2)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e73\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.9\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e86.9\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.230\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e22.419\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTyG\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e66.1 (61.6 ~ 70.7)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e51.4\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e76.5\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e25.7\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e90.8\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.279\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.692\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTyG-BMI\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e67.4 (62.9 ~ 72.0)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e64\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e63.5\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.0\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e88.9\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.275\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e16.448\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFBG \\u0026amp; TG \\u0026amp; BMI \\u0026amp; TyG \\u0026amp; TyG-BMI\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e70.6 (66.1 ~ 75.1)\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e59.1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e72.9\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e28.1\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e90.8\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.321\\u003c/p\\u003e \\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.154\\u003c/p\\u003e \\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003ctfoot\\u003e\\u003ctr\\u003e\\u003ctd colspan=\\\"8\\\"\\u003eTyG-BMI: triglyceride glucose-body mass index; TyG: triglyceride-glucose; BMI: body mass index; FBG: fasting blood glucose; TG: triglyceride; AUC: area under the curve; PPV: positive predictive value; NPV: negative predictive value; ROC: receiver operating characteristic;\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tfoot\\u003e\\u003c/table\\u003e\\u003c/div\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Nonlinear relationship between TyG-BMI and GDM\\u003c/h2\\u003e \\u003cp\\u003eAfter adjusting for a series of covariates, the relationship between TyG-BMI and GDM demonstrated a linear association (\\u003cem\\u003eP\\u003c/em\\u003e for non-linearity = 0.872) in restricted cubic splines (RCS), as depicted in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Subgroup analyses\\u003c/h2\\u003e \\u003cp\\u003eThe stratified analyses of the associations between TyG-BMI and GDM are presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e. Subgroup analyses were conducted based on confounding factors, including age, SBP, DBP, TC, LDL, UA, TBil, Dbil, IBil and AST/ALT, C3, C4 and C1q. All subgroups demonstrated a significantly elevated risk for GDM. Notably, UA level exhibited significant interactions in these subgroups, suggesting a potential modulatory effect on GDM risk.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6 Relationship between neonatal birth weight and the TyG-BMI\\u003c/h2\\u003e \\u003cp\\u003eScatter plots delineating the relationship between neonatal birth weight and the TyG-BMI index. The correlation coefficients and p-values are as follows: for the entire cohort (a), r = -0.034, \\u003cem\\u003eP\\u003c/em\\u003e = 0.261; for the GDM subgroup (b), r = 0.129, \\u003cem\\u003eP\\u003c/em\\u003e = 0.092; and for the non-GDM subgroup (c), r = 0.018, \\u003cem\\u003eP\\u003c/em\\u003e = 0.616. These findings suggest a lack of statistically significant association across the studied groups (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e\\u003ch2\\u003e3.7 Relationship between fasting, 1-hour, or 2-hour blood glucose levels and the UHR in patients with GDM\\u003c/p\\u003e\\u003cp\\u003eThere are 170 patients with GDM, and the OGTT results indicated that 95 patients had a single positive result, 58 patients exhibited a double positive result, and 17 patients presented with a triple positive result. Scatter plots illustrate a strong positive correlation between TyG-BMI and fasting blood glucose ((r = 0.347), (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001)), whereas no significant correlations were observed between TyG-BMI and 1-hour or 2-hour blood glucose levels. Additionally, bar graphs indicate that the TyG-BMI is significantly higher in the triple positive group compared to the single and double positive groups (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThis study represents the inaugural investigation into the relationship between the TyG-BMI index and the incidence of GDM. After adjusting for a multitude of confounding variables, our analysis revealed that the TyG-BMI index maintains an independent correlation with GDM. Notably, a one-standard deviation increase in the TyG-BMI index was associated with a 33% heightened risk of GDM in the fully adjusted model. The relationship between TyG-BMI and the onset of GDM was characterized by a linear progression. Moreover, a robust positive correlation was observed between TyG-BMI and fasting blood glucose levels in patients with GDM. Patients with a triple positive OGTT result exhibited a significantly higher TyG-BMI compared to those with single and double positive results. Conversely, no significant correlation was detected between neonatal birth weight and the TyG-BMI. These insights advocate for the implementation of TyG-BMI evaluation during the first trimester as a robust predictor of GDM, boasting a higher AUC when juxtaposed with the conventional TyG index, TG, or FBG.\\u003c/p\\u003e\\u003cp\\u003eGDM is commonly associated with an increased risk of preeclampsia, macrosomia, perinatal complications, and mortality. Early detection of GDM is imperative to mitigate these risks. The pathophysiology of GDM involves beta-cell dysfunction and compromised insulin secretion or sensitivity during pregnancy [\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]. Typically, GDM is diagnosed between 24–28 weeks of gestation, often leaving insufficient time to prevent its onset and adverse impacts. Therefore, the early identification of GDM risk through dependable IR markers is essential for averting negative outcomes.\\u003c/p\\u003e\\u003cp\\u003eThe TyG index, which integrates triglycerides and FBG, demonstrates superiority in identifying IR and the incidence of metabolic syndrome [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. This advantage may stem from the concurrent consideration of triglycerides and FBG, both established markers in IR and GDM [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. Initially described in 2008 as an alternative to the HOMA-IR in healthy individuals [\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. subsequent research has underscored the TyG index’s predictive value for GDM [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]. Zeng et al. [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e] reported a significant positive correlation between the TyG index and GDM, which persisted even after adjusting for confounders (OR = 3.43, 95% CI: 1.20–9.85, \\u003cem\\u003eP\\u003c/em\\u003e = 0.0216), and demonstrated its diagnostic efficacy (AUC = 0.57, 95% CI: 0.50–0.63). Kim et al. [\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e] found that a one standard deviation increase in the TyG index heightened GDM risk by 33% in a fully adjusted model. Theoretically, combining the TyG index with obesity metrics, such as waist circumference (WC), BMI, and waist-to-height ratio (WHtR), offers a more comprehensive reflection of IR, given the established role of obesity as a contributory factor. Lim et al. [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e] discovered that the TyG-BMI index outperformed TyG, TyG-WC, and TyG-WHtR in predicting IR. Recognizing that obesity, as defined by BMI, is instrumental in IR progression [\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e] and implicated in GDM pathophysiology, higher maternal BMI emerges as a GDM risk factor [\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. Song et al. [\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e] demonstrated that GDM likelihood escalates with each standard deviation increment in BMI (OR = 1.64, \\u003cem\\u003eP\\u003c/em\\u003e = 5.05 × 10^-17). Recent studies have established a connection between the TyG-BMI index and major adverse cardiac and cerebrovascular events (MACCEs), as well as endocrine disorders in patients [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]. Yang et al. [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e] demonstrated an association between the TyG-BMI index and moderate-to-high SYNTAX scores, which quantitatively assess acute coronary syndrome patients (OR = 1.0041, 95% CI = 1.0000–1.0079, \\u003cem\\u003eP\\u003c/em\\u003e = 0.0310). Jiang et al. [\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e] confirmed that the TyG-BMI index independently correlates with prediabetes. Furthermore, Wang et al. [\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e] identified the TyG-BMI index as an independent predictor of new-onset diabetes (HR = 1.50 per SD increase, 95% CI: 1.40–1.60, \\u003cem\\u003eP\\u003c/em\\u003e-trend \\u0026lt; 0.00001), with an optimal cutoff value of 213.2966 for predicting new-onset diabetes (AUC = 0.7741, sensitivity = 72.51%, specificity = 69.54%). In line with these findings, our research indicates that, within the fully adjusted primary model, the odds ratio for GDM associated with the TyG-BMI index was 1.061 (95% CI: 1.029–1.094, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001). Impaired Fasting Glucose (IFG) has been found to be closely associated with hepatic insulin resistance, whereas Impaired Glucose Tolerance (IGT) exhibits a stronger correlation with muscular insulin resistance. Our investigation revealed a significant positive correlation between TyG-BMI and fasting blood glucose levels, yet no discernible association was observed with 1-hour and 2-hour blood glucose levels. These results suggest that TyG-BMI is more indicative of IFG than IGT. Consequently, our findings suggest that TyG-BMI could potentially serve as a biomarker for hepatic insulin resistance and inadequate insulin secretion. Additionally, TyG-BMI levels were markedly elevated in the triple positive group in comparison to the single and double positive groups, which implies that individuals with higher TyG-BMI may concurrently exhibit both IFG and IGT. Therefore, the TyG-BMI index is considered a straightforward, effective, and clinically significant marker for the early detection of GDM.\\u003c/p\\u003e\\u003cp\\u003eAs metabolic indicators, FBG, TG, and BMI are theoretically posited to exhibit a significant positive correlation with neonatal birth weight. Given the human fetus’s reliance on maternal glucose, the transfer of glucose homeostasis from mother to placenta is considered a pivotal factor in fetal development, as supported by references [\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e]. Clinck Isabel et al. [\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e] reported that for each 1 mmol/L increment in TGs, birth weight (BW) increased significantly by 81.7 g, with a more pronounced effect in males (107.3 g; 95% CI 66–148) compared to females (60.5 g; 95% CI 23.6–97.4), indicating a positive correlation between TG levels and neonatal birth weight. Furthermore, studies underscore a direct association between maternal BMI and neonatal birth weight, suggesting maternal BMI as a predictive marker for neonatal birth weight [\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]. However, our study revealed no significant relationship between neonatal birth weight and the TyG-BMI index. This may be attributed to our failure to exclude preterm infants and adjust for confounding factors such as gestational weight gain that influence neonatal birth weight. Additionally, dietary control during pregnancy is crucial, as modified dietary interventions have been shown to favorably influence outcomes related to maternal glycemia and birth weight [\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eNevertheless, this study is subject to several limitations that warrant consideration. Firstly, the inherent retrospective design and the study’s restriction to a single center may affect the generalizability of the results. This is despite meticulous adjustments for all known confounders. Secondly, the absence of insulin level data precluded the comparison of the TyG-BMI with HOMA-IR as independent predictors of GDM. Thirdly, the TyG-BMI was assessed only once during early pregnancy, without subsequent monitoring for potential fluctuations, which may have resulted in overlooking variations in TyG-BMI values throughout gestation. Lastly, the lack of data on lifestyle, economic status, dietary habits, physical activity, and sleep patterns, which could serve as confounders, might influence the study outcomes. Further investigation into the potential mechanisms linking TyG-BMI and GDM, including the roles of adipokines, oxidative stress, inflammation, and insulin resistance, is imperative. Despite these limitations, our research has elucidated a significant association between the TyG-BMI index measured in the first trimester and the onset of GDM.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, our study suggests that heightened TyG-BMI levels are independent predictors of GDM. The TyG-BMI index demonstrated superior AUC values, effectively differentiating GDM cases during the first trimester. A robust positive correlation was observed between TyG-BMI and fasting blood glucose levels in patients with GDM. Notably, patients presenting with triple positive OGTT results exhibited significantly higher TyG-BMI values compared to those with single or double positive results. However, there is no significant association between TyG-BMI and neonatal birth weight. Nonetheless, further research is warranted to comprehensively ascertain the prognostic utility of the TyG-BMI index for early prediction of GDM risk.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\" \\u003cp\\u003eGDM Gestational diabetes mellitus\\u003c/p\\u003e \\u003cp\\u003eTyG Triglyceride glucose index\\u003c/p\\u003e \\u003cp\\u003eIR Insulin resistance\\u003c/p\\u003e \\u003cp\\u003eHOMA-IR Homeostasis model assessment of insulin resistance\\u003c/p\\u003e \\u003cp\\u003eTyG-BMI Triglyceride glucose-body mass index\\u003c/p\\u003e \\u003cp\\u003eBMI Body mass index\\u003c/p\\u003e \\u003cp\\u003eTG Triglycerides\\u003c/p\\u003e \\u003cp\\u003eFBG Fasting blood glucose\\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors thank the Department of Gynecology of The Third Affiliated Hospital of Wenzhou Medical University, for their continuous support during the study\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJunmiao Xiang: Conceptualization, Methodology, Software, Investigation, Formal Analysis, Funding Acquisition, Writing - Original Draft;\\u0026nbsp;XueKe Guo: Data Curation, Writing - Original Draft;\\u0026nbsp;Yundong Pan: Visualization, Validation, Writing - Original Draft; Zhuhua Cai: Conceptualization, Resources, Supervision, Writing - Review \\u0026amp; Editing.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by Foundation of Wenzhou Municipal Health Commission (2020041)\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData Availability Statement\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eData available on request from the authors\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBefore participating in the study, all participants signed up with informed permission.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was reviewed and approved by the Ethics Committee of The Third Affiliated Hospital of Wenzhou Medical University. The patients/participants provided written informed consent to participate in this study.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eDiagnostic criteria and classification. of hyperglycaemia first detected in pregnancy: a World Health Organization Guideline. Diabetes Res Clin Pract. 2014;103(3):341\\u0026ndash;63.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSacks DA, Hadden DR, Maresh M, Deerochanawong C, Dyer AR, Metzger BE, Lowe LP, Coustan DR, Hod M, Oats JJ, et al. Frequency of gestational diabetes mellitus at collaborating centers based on IADPSG consensus panel-recommended criteria: the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study. Diabetes Care. 2012;35(3):526\\u0026ndash;8.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMetzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, Coustan DR, Hadden DR, McCance DR, Hod M, McIntyre HD, et al. 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PLoS ONE. 2019;14(3):e0212963.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi Y, Gui J, Liu H, Guo LL, Li J, Lei Y, Li X, Sun L, Yang L, Yuan T, et al. Predicting metabolic syndrome by obesity- and lipid-related indices in mid-aged and elderly Chinese: a population-based cross-sectional study. Front Endocrinol (Lausanne). 2023;14:1201132.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang X, Liu J, Cheng Z, Zhong Y, Chen X, Song W. Triglyceride glucose-body mass index and the risk of diabetes: a general population-based cohort study. Lipids Health Dis. 2021;20(1):99.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang X, Li K, Wen J, Yang C, Li Y, Xu G, Ma Y. Association of the triglyceride glucose-body mass index with the extent of coronary artery disease in patients with acute coronary syndromes. 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Metab Syndr Relat Disord. 2008;6(4):299\\u0026ndash;304.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi H, Miao C, Liu W, Gao H, Li W, Wu Z, Cao H, Zhu Y. First-Trimester Triglyceride-Glucose Index and Risk of Pregnancy-Related Complications: A Prospective Birth Cohort Study in Southeast China. Diabetes metabolic syndrome obesity: targets therapy. 2022;15:3705\\u0026ndash;15.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eS\\u0026aacute;nchez-Garc\\u0026iacute;a A, Rodr\\u0026iacute;guez-Guti\\u0026eacute;rrez R, Sald\\u0026iacute;var-Rodr\\u0026iacute;guez D, Guzm\\u0026aacute;n-L\\u0026oacute;pez A, Mancillas-Adame L, Gonz\\u0026aacute;lez-Nava V, Santos-Santillana K, Gonz\\u0026aacute;lez-Gonz\\u0026aacute;lez JG. Early triglyceride and glucose index as a risk marker for gestational diabetes mellitus. 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Diabetes Res Clin Pract. 2023;197:110561.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCheng Y, Fang Z, Zhang X, Wen Y, Lu J, He S, Xu B. Association between triglyceride glucose-body mass index and cardiovascular outcomes in patients undergoing percutaneous coronary intervention: a retrospective study. Cardiovasc Diabetol. 2023;22(1):75.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJiang C, Yang R, Kuang M, Yu M, Zhong M, Zou Y. Triglyceride glucose-body mass index in identifying high-risk groups of pre-diabetes. Lipids Health Dis. 2021;20(1):161.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eArmistead B, Johnson E, VanderKamp R, Kula-Eversole E, Kadam L, Drewlo S, Kohan-Ghadr HR. Placental Regulation of Energy Homeostasis During Human Pregnancy. Endocrinology 2020, 161(7).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhao D, Liu D, Shi W, Shan L, Yue W, Qu P, Yin C, Mi Y. 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Diabetes Care. 2018;41(7):1346\\u0026ndash;61.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"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\":\"Triglyceride glucose-body mass index, Triglyceride glucose index, Body mass index, First-trimester, Gestational diabetes mellitus\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4587241/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4587241/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eIntroduction:\\u003c/h2\\u003e \\u003cp\\u003eGestational diabetes mellitus (GDM) is a significant pregnancy complication. Early identification of at-risk women is crucial for prevention. This study evaluates the first-trimester triglyceride glucose-body mass index (TyG-BMI) as a GDM predictor.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eA retrospective study on 943 patients from The Third Affiliated Hospital of Wenzhou Medical University analyzed TyG-BMI\\u0026rsquo;s correlation with GDM using logistic regression and stratified analyses. The area under the curve (AUC) assessed TyG-BMI\\u0026rsquo;s diagnostic performance. Scatter plots and Pearson correlation analysis have clarified the link between TyG-BMI and neonatal birth weight, as well as the link between TyG-BMI and OGTT glycemic measures.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eIn a study of 943 participants, 170 developed GDM, while 773 did not. Elevated TyG-BMI levels were linked to a higher GDM risk. The odds ratio (OR) for GDM was significant in all models, with the highest OR in the fully adjusted model (OR\\u0026thinsp;=\\u0026thinsp;1.063, 95% CI: 1.031\\u0026ndash;1.097). TyG-BMI levels showed a linear relationship with GDM risk and outperformed other measures in diagnostic accuracy, with an AUC of 67.4% (95% CI: 62.9%-72%). TyG-BMI had a strong positive correlation with fasting blood glucose levels (r\\u0026thinsp;=\\u0026thinsp;0.347, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), but not with 1-hour or 2-hour levels in patients with GDM. It was also significantly higher in the triple positive group compared to single and double positive groups, although no significant link was found between TyG-BMI and neonatal birth weight.\\u003c/p\\u003e\\u003ch2\\u003eDiscussion\\u003c/h2\\u003e \\u003cp\\u003eOur study indicates that the TyG-BMI index, measured in the first trimester, is an independent and effective predictor of GDM.\\u003c/p\\u003e\",\"manuscriptTitle\":\"First-trimester triglyceride glucose-body mass index as a risk marker for gestational diabetes mellitus\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-07-15 19:22:22\",\"doi\":\"10.21203/rs.3.rs-4587241/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2024-06-21T02:02:08+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2024-06-18T12:53:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2024-06-18T11:28:17+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2024-06-17T11:22:14+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Pregnancy and Childbirth\",\"date\":\"2024-06-15T16:03:30+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\":\"51b999ae-9725-4a46-a138-5b0d675bfd18\",\"owner\":[],\"postedDate\":\"July 15th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2024-07-15T19:22:22+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-07-15 19:22:22\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4587241\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4587241\",\"identity\":\"rs-4587241\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}