Gestational Diabetes Mellitus Predictors Based on First Trimester Lipid and Glucose- Derived Indices: A Single Center Retrospective Cohort Study

Gestational Diabetes Mellitus Predictors Based on First Trimester Lipid and Glucose- Derived Indices: A Single Center Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gestational Diabetes Mellitus Predictors Based on First Trimester Lipid and Glucose- Derived Indices: A Single Center Retrospective Cohort Study Mücahit Furkan BALCI, Celal AKDEMİR, Fatih YILDIRIM, Suna YILDIRIM KARACA, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7941905/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Feb, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted 12 You are reading this latest preprint version Abstract Background: Gestational diabetes mellitus (GDM) is associated with substantial maternal and neonatal morbidity. Early identification of women at risk remains a clinical priority. This study aimed to evaluate whether simple indices derived from first-trimester fasting triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and glucose—namely the triglyceride-glucose (TyG) index, TG/HDL-C ratio, and a lipid-based composite insulin resistance surrogate (Lipid-IR)—predict subsequent development of GDM. Methods: This single-center retrospective cohort included 679 pregnant women with first-trimester fasting lipid and glucose measurements. Of these, 342 developed GDM (diagnosed at 24–28 weeks using a two-step approach and Carpenter–Coustan criteria), and 337 remained normoglycemic. Baseline demographic, anthropometric, laboratory, and obstetric/neonatal variables were compared. TyG was calculated as ln[(TG (mg/dL) × fasting glucose (mg/dL))/2], TG/HDL-C as TG divided by HDL-C (mg/dL), and Lipid-IR as ln(2×TG (mg/dL) + total cholesterol (mg/dL)). Independent predictors were identified through multivariable logistic regression. Receiver operating characteristic (ROC) analysis evaluated discriminative ability and optimal cut-off points using Youden’s J statistic. Results: Compared with controls, women who developed GDM were older and had higher pre-pregnancy and current body weights, body mass index (BMI), and waist circumference (all p ≤ 0.003). They also exhibited higher fasting glucose, HbA1c, insulin, TG, total cholesterol, and liver enzyme levels, and lower HDL-C concentrations (all p ≤ 0.03). Cesarean delivery was more frequent among women with GDM (58% vs. 32%; p = 0.001), with higher birth weights and lower 5-minute Apgar scores. In multivariable models, TyG (OR 3.10), Lipid-IR (OR 1.85), and TG/HDL-C (OR 2.12) were independently associated with GDM (all p < 0.001). Discrimination was strong, with AUCs of 0.88 for TyG, 0.82 for Lipid-IR, and 0.79 for TG/HDL-C; combining all three indices increased the AUC to 0.92 (sensitivity 89%, specificity 85%). Conclusion: First-trimester lipid–glucose indices, particularly the TyG index, show robust predictive performance for GDM and may enable early risk stratification and preventive interventions before oral glucose tolerance testing. Trial registration: Not applicable (observational, non-interventional study). Gestational diabetes mellitus triglyceride–glucose index TG/HDL-C ratio Lipid-IR insulin resistance early prediction Figures Figure 1 Highlights Early lipid–glucose indices predict gestational diabetes: First-trimester TyG index, TG/HDL-C ratio, and Lipid-IR were identified as independent predictors of gestational diabetes mellitus (GDM). Strong predictive accuracy of the TyG index: The TyG index achieved the highest discriminative performance (AUC = 0.88; sensitivity 85%; specificity 82%). Combined model provides superior performance: Integration of TyG, TG/HDL-C, and Lipid-IR indices enhanced diagnostic accuracy (AUC = 0.92; sensitivity 89%; specificity 85%). Feasibility in routine prenatal care: These indices can be automatically calculated from standard first-trimester lipid and glucose tests, allowing pre-OGTT risk stratification. Clinical implications: Early identification of at-risk women may enable timely lifestyle interventions, reducing macrosomia and adverse obstetric outcomes. Introduction Gestational diabetes mellitus (GDM) is defined as glucose intolerance that develops during pregnancy, typically diagnosed in the second half of gestation. It exerts significant short- and long-term effects on both maternal and fetal health, increasing the risk of preeclampsia, macrosomia, cesarean delivery, and congenital metabolic disorders ( 1 ). Recent meta-analyses have reported that the prevalence of GDM varies between 5.2% and 13.7%, depending on diagnostic thresholds and study populations, with a prevalence of 13.7% in cohorts utilizing a one-step screening approach ( 2 ). Data from Canada and the United States indicate that the prevalence of GDM increased from 6.1% to 10.4% between 2005 and 2019 ( 3 ). Traditionally, GDM screening is performed using the oral glucose tolerance test (OGTT) at 24–28 weeks of gestation. However, the clinical utility of shifting this screening to earlier stages of pregnancy has become an emerging topic of interest. The 2024 American Diabetes Association (ADA) Standards of Care recommend assessing HbA1c and glucose parameters during the first trimester in high-risk women ( 4 ). Metabolic alterations occurring early in pregnancy have been investigated for their association with subsequent GDM development. Several studies have demonstrated that first-trimester serum triglyceride levels and the HDL-cholesterol ratio are significant predictors of GDM risk. Ma et al. reported that both the first-trimester TyG index and the TG/HDL-C ratio exhibit high diagnostic performance for predicting GDM, with areas under the curve (AUCs) of 0.88 and 0.79, respectively ( 5 ). Similarly, a multicenter study published in BMC Lipidology found that the TyG index measured at the first prenatal visit was a useful predictor of GDM, with an AUC of 0.686 ( 6 ). The present study aims to investigate the diagnostic value of the TyG index, Lipid-IR, and TG/HDL-C ratio derived from the maternal serum lipid profile, as well as the predictive performance of a combined model incorporating these three parameters for early detection of GDM risk. The application of these simple and routinely measurable lipid–glucose indices during the first trimester may provide an opportunity for earlier intervention and represent a valuable strategy for reducing maternal and neonatal complications. Materials and Methods Study Design and Ethical Approval This study was designed as a single-center retrospective cohort investigation. The study protocol was approved by the Ethics Committee of Health Sciences University Izmir Tepecik Training and Research Hospital (approval number: 2025-8-5) and conducted in accordance with the principles of the Declaration of Helsinki. Participants Between June 2022 and January 2025, pregnant women whose fasting triglyceride (TG), glucose, and high-density lipoprotein cholesterol (HDL-C) levels were measured during the first trimester and who were subsequently diagnosed with GDM during the second trimester were included in the GDM group. During the same period, women with first-trimester measurements of these parameters who maintained normal glycemic levels and completed pregnancy without complications were included as controls. Women with pregestational type 1 or type 2 diabetes, multiple gestations, or chronic systemic diseases (e.g., hypertension, thyroid disorders) were excluded. Additionally, those without first-trimester follow-up data or whose delivery occurred at another institution after initial monitoring at our center were excluded from analysis. Diagnosis of Gestational Diabetes Mellitus At our institution, GDM is diagnosed using a two-step (2-step) approach. All pregnant women undergo a 50 g oral glucose tolerance test (OGTT) between 24 and 28 gestational weeks. Those with plasma glucose levels ≥ 140 mg/dL at one hour are subjected to a 100 g OGTT, performed regardless of fasting status. GDM is diagnosed when at least two threshold values meet or exceed the Carpenter–Coustan criteria. Following diagnosis, all patients initially receive dietary therapy; those who fail to achieve glycemic control within two weeks are transitioned to insulin therapy. Both diet-controlled and insulin-requiring cases were classified within the GDM group. Data Collection For each participant, demographic and clinical variables including age, parity, smoking status, use of assisted reproductive techniques, fasting blood glucose (FBG), first-trimester systolic and diastolic blood pressure, HDL-C, and TG levels were recorded. Gestational age was calculated based on the last menstrual period and confirmed by crown–rump length measurement during first-trimester ultrasonography. Laboratory data were obtained from routine venous blood samples collected during standard prenatal visits at our institution. Calculation of Indices A total of 679 participants were analyzed, comprising 342 women with GDM and 337 healthy controls, using first-trimester fasting TG, HDL-C, and glucose measurements. TyG index was calculated according to the formula proposed by Simental-Mendía and Guerrero-Romero ( 7 ): $$\:\text{TyG index}=\text{l}\text{n}\left[\frac{\text{Fasting triglyceride (mg/dL)}\times\:\text{Fasting glucose (mg/dL)}}{2}\right]$$ TG/HDL-C ratio was obtained by dividing TG (mg/dL) by HDL-C (mg/dL) ( 8 ). Lipid-IR index was defined as a composite indicator of lipid and glucose metabolism, calculated as ( 8 ): $$\:\text{Lipid-IR}=\text{l}\text{n}(2\times\:\text{TG (mg/dL)}+\text{Total cholesterol (mg/dL)})$$ This study was conducted and reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines; the completed checklist is provided as a supplementary file. Statistical Analysis All analyses were performed using IBM SPSS Statistics version 22.0 (Armonk, NY, USA). Continuous variables were expressed as mean ± standard deviation (SD) or median [interquartile range, IQR], and categorical variables as number (percentage). The distribution of continuous variables was assessed using the Shapiro–Wilk test and graphical inspection. Comparisons between two groups were performed using the independent samples t -test for normally distributed variables and the Mann–Whitney U test for non-normally distributed variables. Categorical variables were compared using Pearson’s chi-square or Fisher’s exact test when expected frequencies were < 5. Effect sizes were presented as mean/median differences with 95% confidence intervals (CI) for continuous variables and as odds ratios (OR) with 95% CI for categorical variables. Multivariable logistic regression analysis was conducted with GDM status (yes/no) as the dependent variable. Variables that were clinically relevant and/or showed a univariate association with p 5 were excluded or adjusted. Model calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test, and discrimination by the area under the receiver operating characteristic (ROC) curve (AUC). ROC analyses were performed for the TyG index, Lipid-IR, and TG/HDL-C ratio. Optimal cut-off points were determined using Youden’s J statistic, and corresponding sensitivity, specificity, and positive/negative likelihood ratios were reported. Diagnostic performance of the combined model was evaluated based on AUC values derived from predicted probabilities of the multivariable logistic regression model. A two-sided significance level of α = 0.05 was adopted for all tests. Cases with missing data were excluded using the listwise deletion method; no additional imputation was performed. Results A total of 679 pregnant women were included, comprising 342 with gestational diabetes mellitus (GDM) and 337 with normal glucose tolerance. Women with GDM were significantly older than controls (31.8 ± 5.9 vs 28.5 ± 5.2 years, p < 0.001). There was no difference in height ( p = 0.12). Pre-pregnancy weight (70.5 ± 12.4 vs 64.2 ± 8.7 kg, p = 0.003), current weight (81.2 ± 13.1 vs 74.8 ± 9.5 kg, p = 0.001), BMI (29.8 ± 4.1 vs 27.1 ± 3.5 kg/m², p < 0.001), and waist circumference (83.6 ± 8.9 vs 78.4 ± 7.3 cm, p = 0.001) were all higher in the GDM group. While gravida, parity, and abortions tended to be higher in GDM, only the number of curettages reached statistical significance (0.4 ± 0.6 vs 0.2 ± 0.4, p = 0.03). Gestational age at assessment was similar between groups (24.3 ± 1.2 vs 24.6 ± 0.8 weeks, p = 0.22). Baseline demographic and clinical characteristics are summarized in Table 1 . Table 1 Baseline demographic and clinical characteristics of the study population Variable Control (n = 337) GDM (n = 342) p -value Age (years) 28.5 ± 5.2 31.8 ± 5.9 < 0.001 Height (cm) 162.3 ± 5.1 163.7 ± 6.3 0.12 Pre-pregnancy weight (kg) 64.2 ± 8.7 70.5 ± 12.4 0.003 Current weight (kg) 74.8 ± 9.5 81.2 ± 13.1 0.001 BMI (kg/m²) 27.1 ± 3.5 29.8 ± 4.1 < 0.001 Waist circumference (cm) 78.4 ± 7.3 83.6 ± 8.9 0.001 Gravida (number) 2.8 ± 1.5 3.1 ± 1.7 0.09 Gestational week (at assessment) 24.6 ± 0.8 24.3 ± 1.2 0.22 Parity 1.6 ± 1.1 1.9 ± 1.3 0.07 Abortions 0.4 ± 0.7 0.6 ± 0.9 0.06 Curettages 0.2 ± 0.4 0.4 ± 0.6 0.03 Data are expressed as mean ± SD. GDM: gestational diabetes mellitus; BMI: body mass index. Comparisons were made using independent-samples t -test or Mann–Whitney U test where appropriate. Two-tailed p < 0.05 was considered statistically significant. OGTT glucose levels were significantly higher at all time points among women with GDM (0 h: 112.6 ± 25.8 vs 85.2 ± 10.4 mg/dL; 1 h: 158.4 ± 32.7 vs 110.5 ± 18.3 mg/dL; 2 h: 135.9 ± 28.5 vs 92.7 ± 12.6 mg/dL; all p < 0.001). Fasting glucose (108.2 ± 22.4 vs 84.3 ± 9.8 mg/dL, p < 0.001) and HbA1c (6.9 ± 1.2 vs 5.3 ± 0.6%, p < 0.001) were higher in the GDM group. Serum creatinine was modestly higher (0.61 ± 0.18 vs 0.54 ± 0.12 mg/dL, p = 0.02), with a trend for BUN ( p = 0.08). The lipid profile was more atherogenic in GDM: triglycerides (234.6 ± 98.5 vs 182.4 ± 75.3 mg/dL, p = 0.004) and total cholesterol (238.7 ± 67.8 vs 219.5 ± 45.2 mg/dL, p = 0.01) were higher, and HDL-C was lower (58.3 ± 12.7 vs 68.5 ± 14.2 mg/dL, p < 0.001); LDL-C showed a non-significant increase ( p = 0.09). Insulin levels were higher in GDM (15.6 ± 7.8 vs 12.1 ± 4.3 µIU/mL, p = 0.006). Liver enzymes (GGT, ALT, AST) were also higher in the GDM group ( p = 0.03, p = 0.01, and p = 0.02, respectively). Composite metabolic indices differed significantly: Lipid-IR (10.3 ± 1.8 vs 8.9 ± 1.2, p < 0.001), TG/HDL-C (4.1 ± 2.3 vs 2.8 ± 1.5, p < 0.001), and the TyG index (4.3 ± 0.6 vs 3.8 ± 0.4, p < 0.001) were all higher in GDM. Laboratory and metabolic parameters are detailed in Table 2 . Table 2 First-trimester laboratory and metabolic parameters according to GDM status Variable Control (n = 337) GDM (n = 342) p -value OGTT 0 h (mg/dL) 85.2 ± 10.4 112.6 ± 25.8 < 0.001 OGTT 1 h (mg/dL) 110.5 ± 18.3 158.4 ± 32.7 < 0.001 OGTT 2 h (mg/dL) 92.7 ± 12.6 135.9 ± 28.5 < 0.001 Fasting glucose (mg/dL) 84.3 ± 9.8 108.2 ± 22.4 < 0.001 BUN (mg/dL) 14.2 ± 4.1 15.8 ± 5.3 0.08 Creatinine (mg/dL) 0.54 ± 0.12 0.61 ± 0.18 0.02 Triglycerides (mg/dL) 182.4 ± 75.3 234.6 ± 98.5 0.004 Total cholesterol (mg/dL) 219.5 ± 45.2 238.7 ± 67.8 0.01 HDL-C (mg/dL) 68.5 ± 14.2 58.3 ± 12.7 < 0.001 LDL-C (mg/dL) 124.6 ± 38.7 136.2 ± 52.4 0.09 HbA1c (%) 5.3 ± 0.6 6.9 ± 1.2 < 0.001 Insulin (µIU/mL) 12.1 ± 4.3 15.6 ± 7.8 0.006 GGT (U/L) 16.2 ± 6.5 19.4 ± 9.1 0.03 ALT (U/L) 14.8 ± 5.2 18.3 ± 7.6 0.01 AST (U/L) 18.3 ± 6.7 21.5 ± 9.4 0.02 Lipid-IR 8.9 ± 1.2 10.3 ± 1.8 < 0.001 TG/HDL-C ratio 2.8 ± 1.5 4.1 ± 2.3 < 0.001 TyG index 3.8 ± 0.4 4.3 ± 0.6 < 0.001 Data are presented as mean ± SD. OGTT: oral glucose tolerance test; BUN: blood urea nitrogen; HDL-C/LDL-C: high-/low-density lipoprotein cholesterol; HbA1c: glycated hemoglobin; GGT: γ-glutamyl transferase; ALT: alanine aminotransferase; AST: aspartate aminotransferase. Indices calculated as: TyG = ln\(TG × fasting glucose\)/2; TG/HDL-C = TG ÷ HDL-C; Lipid-IR = ln(2×TG + total cholesterol). Two-tailed p < 0.05 indicates significance. Although gestational age at delivery was slightly lower in GDM, the difference was not significant (36.8 ± 1.9 vs 37.2 ± 1.5 weeks, p = 0.06). Birth weight was higher in GDM (3580 ± 550 vs 3250 ± 420 g, p = 0.008). The 1-minute Apgar score did not differ ( p = 0.06), whereas the 5-minute Apgar score was lower in GDM (7.5 ± 1.4 vs 8.1 ± 1.1, p = 0.022). Cesarean delivery was more frequent (58% vs 32%, p = 0.001) and IUGR was more common (22% vs 8%, p = 0.03) in the GDM group. NICU admission (12% vs 5%, p = 0.15) and instrumental/complicated delivery (6% vs 2%, p = 0.08) were higher without reaching significance; sex distribution was similar ( p = 0.45). Perinatal outcomes are summarized in Table 3 . Table 3 Delivery and neonatal outcomes Variable Control (n = 337) GDM (n = 342) p -value Gestational age at delivery (weeks) 37.2 ± 1.5 36.8 ± 1.9 0.06 Birth weight (g) 3250 ± 420 3580 ± 550 0.008 Apgar 1 min 7.2 ± 1.3 6.8 ± 1.5 0.06 Apgar 5 min 8.1 ± 1.1 7.5 ± 1.4 0.022 Cesarean delivery (%) 32 58 0.001 IUGR (%) 8 22 0.03 NICU admission (%) 5 12 0.15 Instrumental/complicated delivery (%) 2 6 0.08 Sex (male/female, %) 54/46 58/42 0.45 Continuous variables are expressed as mean ± SD; categorical variables as n (%). GA: gestational age; IUGR: intrauterine growth restriction; NICU: neonatal intensive care unit. Group comparisons: independent-samples t -test or Mann–Whitney U (continuous), Pearson chi-square or Fisher’s exact (categorical). Two-tailed p < 0.05 considered significant. In multivariable logistic regression (Table 4 ), all three indices were independently and positively associated with GDM: Lipid-IR (OR = 1.85, 95% CI 1.42–2.41; p < 0.001), TG/HDL-C (OR = 2.12, 95% CI 1.68–2.67; p < 0.001), and TyG (OR = 3.10, 95% CI 2.15–4.48; p < 0.001), with the largest effect size observed for TyG. Table 4 Multivariable logistic regression analysis for predictors of GDM Variable Odds Ratio (OR) 95% Confidence Interval p -value Lipid-IR 1.85 1.42–2.41 < 0.001 TG/HDL-C ratio 2.12 1.68–2.67 < 0.001 TyG index 3.10 2.15–4.48 < 0.001 ORs correspond to one-unit increases in each index. CI: confidence interval. Model adjusted for age, BMI, parity, and family history of diabetes. Two-tailed p < 0.05 denotes statistical significance. ROC analyses demonstrated good to excellent discrimination (Table 5 , Fig. 1 ). The TyG index yielded the highest individual AUC (0.88; optimal cut-off ≥ 4.0; sensitivity 85%; specificity 82%), followed by Lipid-IR (AUC 0.82; cut-off ≥ 9.5; sensitivity 78%; specificity 76%) and TG/HDL-C (AUC 0.79; cut-off ≥ 3.2; sensitivity 72%; specificity 74%). A combined model incorporating all three indices achieved an AUC of 0.92 (sensitivity 89%, specificity 85%), outperforming single-index models. Table 5 Receiver operating characteristic (ROC) analysis and diagnostic performance of lipid–glucose indices Parameter AUC (95% CI) Optimal Cut-off Sensitivity (%) Specificity (%) Lipid-IR 0.82 (0.78–0.86) ≥ 9.5 78 76 TG/HDL-C ratio 0.79 (0.74–0.83) ≥ 3.2 72 74 TyG index 0.88 (0.84–0.91) ≥ 4.0 85 82 Combined model 0.92 (0.89–0.94) — 89 85 AUC: area under the ROC curve; CI: confidence interval. Optimal cut-off points determined by Youden’s J statistic. Sensitivity and specificity values correspond to respective thresholds. The combined model AUC derived from predicted probabilities of multivariable logistic regression incorporating TyG, TG/HDL-C, and Lipid-IR indices. Discussion This study evaluated the predictive value of first-trimester serum lipid parameters specifically, the triglyceride-glucose (TyG) index, TG/HDL-C ratio, and Lipid-IR index for GDM. Our findings demonstrate that these indices are independent risk factors for GDM and that their combined use substantially improves diagnostic performance. In the present study, both maternal age and pregestational BMI were significantly higher among women with GDM. A previous systematic review and meta-analysis reported that higher pregestational BMI is strongly associated with an increased risk of GDM, with each 1 kg/m² increment conferring approximately a 10% rise in risk and obesity doubling the risk compared to women with normal weight ( 9 ). This reinforces the pivotal role of age and BMI in the pathogenesis of GDM. We also observed higher neonatal birth weight, lower 5-minute Apgar scores, and a higher cesarean section rate in the GDM group, consistent with the adverse impact of intrauterine hyperglycemia and early dysmetabolic milieu on perinatal outcomes. The 2024 ADA Standards of Medical Care in Diabetes emphasize that maternal hyperglycemia is associated with increased risks of macrosomia and obstetric intervention ( 4 ). The dyslipidemia observed in GDM characterized by elevated TG and reduced HDL-C has likewise been associated with macrosomia and perinatal morbidity in large-scale reviews and meta-analyses ( 10 , 11 ). Our results suggest that early metabolic indices not only predict GDM risk but may also have clinical relevance for neonatal outcomes. Consistent with prior research, triglyceride levels were significantly higher and HDL-C levels lower in the GDM group (TG: 234.6 ± 98.5 mg/dL vs. 182.4 ± 75.3 mg/dL, p = 0.004; HDL-C: 58.3 ± 12.7 mg/dL vs. 68.5 ± 14.2 mg/dL, p < 0.001). A large meta-analysis by Hu et al. involving 97,880 women from 292 studies found that TG levels were approximately 20% higher across all trimesters in GDM pregnancies ( 10 ). Similarly, Rahnemaei et al. analyzed 33 studies (n = 23,792) and reported significant increases in both total cholesterol and TG levels among women with GDM (SMD = 0.23 mg/dL and SMD = 1.14 mg/dL, respectively) ( 11 ). Ryckman et al., in a meta-analysis of 60 studies, also demonstrated consistently higher TG and lower HDL-C levels across all trimesters in GDM cases ( 12 ). Collectively, these findings underscore the critical role of early lipid regulation in glycemic control during pregnancy. In our study, ALT, AST, and GGT levels were significantly elevated in the GDM group, suggesting hepatic insulin resistance as part of the metabolic response to pregnancy. This observation aligns with previous studies linking hepatic enzyme elevation with lipid-based insulin resistance markers, including TG/HDL-C and Lipid-IR ( 8 , 10 ). Mild hepatic enzyme elevation in early pregnancy may thus serve as an early indicator of later glycemic dysregulation. The TyG index exhibited the strongest predictive association with GDM in logistic regression (OR = 3.10; 95% CI 2.15–4.48; p < 0.001) and yielded the highest AUC (0.88). These findings are nearly identical to those of Ning Ma et al., who reported an AUC of 0.88, sensitivity 85%, and specificity 82% for a TyG cut-off ≥ 4.0 in predicting GDM ( 5 ). Likewise, the TG/HDL-C ratio was independently associated with GDM (AUC = 0.79), consistent with Ma’s study and with a prospective cohort published in BMC Pregnancy and Childbirth , which showed a 2.3-fold higher GDM risk among women with TG/HDL-C ≥ 3.0 in early pregnancy ( 13 ). Higher insulin concentrations in the GDM group further support the presence of early insulin resistance reflected by TyG, TG/HDL-C, and Lipid-IR. The TyG index, in particular, serves as a practical and reliable surrogate marker of insulin resistance in early pregnancy ( 5 , 8 , 11 ). When all three indices were combined, the model achieved an AUC of 0.92 with 89% sensitivity and 85% specificity, outperforming each parameter individually. Although no prior meta-analysis has assessed this exact combination, Hu et al. ( 10 ) suggested that simultaneous evaluation of multiple lipid-based markers may provide additive predictive benefit. The superior performance of the combined model likely reflects the complementary metabolic information captured by these indices, supporting the rationale for multivariable lipid-based screening models. From a clinical perspective, these indices can be automatically calculated from routine first-trimester biochemistry panels to provide pre-OGTT risk stratification. The 2024 ADA guidelines recommend early evaluation in high-risk pregnancies ( 4 ); our findings suggest that incorporating lipid–glucose–derived indices into this framework could enhance early discrimination and enable timely lifestyle interventions, ultimately improving maternal and neonatal outcomes ( 4 , 10 ). Conclusion The present study demonstrates that simple indices derived from routinely measured first-trimester lipid and glucose parameters particularly the TyG index, TG/HDL-C ratio, and Lipid-IR are effective tools for early prediction of GDM. Incorporating these indices into early screening protocols may enable risk stratification before OGTT and facilitate timely lifestyle modification, potentially reducing rates of neonatal macrosomia and obstetric complications. Future multicenter, prospective studies are warranted to validate these findings across diverse populations and to standardize optimal cut-off thresholds. Study Limitations The findings of this study should be interpreted within the context of several limitations. The retrospective, single-center design may restrict generalizability. Dynamic changes in biochemical markers across pregnancy could not be evaluated, and intervention efficacy was not assessed. Prospective, multicenter, and interventional trials are needed to confirm optimal thresholds and enhance external validity. Declarations Acknowledgments We sincerely thank all the women who participated in this study. Authors’ Contributions Mücahit Furkan Balcı: conceptualization, data acquisition, manuscript drafting. Celal Akdemir: data collection, critical review, approval of final draft. Fatih Yıldırım: data acquisition. İbrahim Karaca: statistical analysis, manuscript editing. Suna Yıldırım Karaca: writing and statistical analysis support. All authors reviewed and approved the final version of the manuscript. Funding This study received no external financial support. Availability of Data and Materials All data generated or analyzed in this study are included in this article. Further inquiries can be directed to the corresponding author, Dr. Mücahit Furkan Balcı. Ethics Approval and Consent to Participate The study was conducted in accordance with the principles of the Declaration of Helsinki. It was approved by the Ethics Committee of the Health Sciences University, Izmir Tepecik Training and Research Hospital (Approval date: 21 Aug 2025; Ref. No. 05/28). Given the retrospective design and use of anonymized data, the requirement for individual informed consent was waived. Consent for Publication All authors have consented to publication of this article. Competing Interests The authors declare no commercial or financial conflicts of interest. Declaration of Interest The authors declare that they have no competing interests —financial, institutional, or personal—that could have influenced the conduct or reporting of this research. This study was conducted independently at the Health Sciences University, Izmir Tepecik Training and Research Hospital , without any external sponsorship, financial assistance, or industry involvement. The institution had no role in study design, data collection, data analysis, interpretation, or manuscript preparation. All authors have read and approved the final version of the manuscript and take full responsibility for the accuracy, integrity, and originality of the reported data. Graphical Abstract Concept: Gestational diabetes mellitus (GDM) remains a major cause of maternal and neonatal morbidity. Early detection before the oral glucose tolerance test (OGTT) may improve outcomes. Study Design: Retrospective cohort of 679 pregnant women (342 GDM, 337 controls) with first-trimester fasting lipid and glucose data. Key Findings: TyG index, TG/HDL-C ratio, and Lipid-IR were all independently associated with GDM. TyG showed the strongest individual predictive ability (AUC 0.88). The combined model of all three indices achieved AUC 0.92, outperforming individual parameters. Clinical Implication: Simple lipid–glucose–derived indices can be incorporated into early prenatal screening to identify women at high risk for GDM, enabling preventive strategies before OGTT. References American Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2024. Diabetes Care. 2024;47(Suppl 1):S20–42. 10.2337/dc24-S002 . Eades CE, Burrows KA, Andreeva R, Stansfield DR, Evans JM. Prevalence of gestational diabetes in the United States and Canada: a systematic review and meta-analysis. BMC Pregnancy Childbirth. 2024;24(1):204. 10.1186/s12884-024-06378-2 . Luo R, Fell DB, Corsi DJ, Taljaard M, Wen SW, Walker MC. Temporal trends in gestational diabetes mellitus and associated risk factors in Ontario, Canada, 2012–2020: a population-based cohort study. J Obstet Gynaecol Can. 2024;46(8):102573. 10.1016/j.jogc.2024.102573 . Elsayed NA, Aleppo G, Bannuru RR, Bruemmer D, Collins BS, Ekhlaspour L, et al. 15. Management of Diabetes in Pregnancy: Standards of Care in Diabetes—2024. Diabetes Care. 2024;47(Suppl 1):S282–94. 10.2337/dc24-S015 . Ma N, Bai L, Lu Q. First-trimester triglyceride–glucose index and triglyceride/high-density lipoprotein cholesterol are predictors of gestational diabetes mellitus among the four surrogate biomarkers of insulin resistance. Diabetes Metab Syndr Obes. 2024;17:1575–83. 10.2147/DMSO.S445728 . Guo Y, Lu J, Bahani M, Ding G, Wang L, Zhang Y, et al. Triglyceride–glucose index in early pregnancy predicts the risk of gestational diabetes: a prospective cohort study. Lipids Health Dis. 2024;23(1):97. 10.1186/s12944-024-02076-2 . Alizargar J, Hsieh N-C, Wu S-FV. The correct formula to calculate triglyceride–glucose index (TyG). J Pediatr Endocrinol Metab. 2020;33(7):945–6. 10.1515/jpem-2019-0579 . Murguía-Romero M, Jiménez-Flores JR, Sigrist-Flores SC, Espinoza-Camacho MA, Jiménez-Morales M, Piña E, et al. Plasma triglyceride/HDL-cholesterol ratio, insulin resistance, and cardiometabolic risk in young adults. J Lipid Res. 2013;54(10):2795–9. 10.1194/jlr.M040584 . Torloni MR, Betrán AP, Horta BL, Nakamura MU, Atallah AN, Moron AF, et al. Prepregnancy BMI and the risk of gestational diabetes: a systematic review of the literature with meta-analysis. Obes Rev. 2009;10(2):194–203. 10.1111/j.1467-789X.2008.00541.x . Hu J, Gillies CL, Lin S, Stewart ZA, Melford SE, Abrams KR, et al. Association of maternal lipid profile and gestational diabetes mellitus: a systematic review and meta-analysis of 292 studies and 97,880 women. EClinicalMedicine. 2021;34:100830. 10.1016/j.eclinm.2021.100830 . Rahnemaei FA, Pakzad R, Amirian A, Pakzad I, Abdi F. Effect of gestational diabetes mellitus on lipid profile: a systematic review and meta-analysis. Open Med (Wars). 2021;17(1):70–86. 10.1515/med-2021-0408 . Ryckman KK, Spracklen CN, Smith CJ, Robinson JG, Saftlas AF. Maternal lipid levels during pregnancy and gestational diabetes: a systematic review and meta-analysis. BJOG. 2015;122(5):643–51. 10.1111/1471-0528.13261 . Xu X, Luo S, Lin J, Zhou J, Zheng L, Yang L, et al. Association between maternal lipid profiles and lipid ratios in early to middle pregnancy as well as their dynamic changes and gestational diabetes mellitus. BMC Pregnancy Childbirth. 2024;24(1):510. 10.1186/s12884-024-06692-9 . Additional Declarations No competing interests reported. Supplementary Files flowchard.png Cite Share Download PDF Status: Published Journal Publication published 11 Feb, 2026 Read the published version in BMC Endocrine Disorders → Version 1 posted Editorial decision: Revision requested 26 Dec, 2025 Reviews received at journal 23 Dec, 2025 Reviewers agreed at journal 12 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviews received at journal 30 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 09 Nov, 2025 Reviewers invited by journal 29 Oct, 2025 Editor assigned by journal 28 Oct, 2025 Submission checks completed at journal 28 Oct, 2025 First submitted to journal 24 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":30789,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) analysis and diagnostic performance of lipid–glucose indices\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7941905/v1/59661bc9d3fa6ee7dd4dc8c6.png"},{"id":102785365,"identity":"032df97e-1fbc-43cc-b8f4-1b4c8c09bf61","added_by":"auto","created_at":"2026-02-16 16:05:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1071250,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7941905/v1/01f21875-22ea-4f87-80ae-75fc186dc34f.pdf"},{"id":95691317,"identity":"ac3ae98b-fd41-4fdb-8405-72acb199e2fd","added_by":"auto","created_at":"2025-11-12 02:18:57","extension":"png","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1688330,"visible":true,"origin":"","legend":"","description":"","filename":"flowchard.png","url":"https://assets-eu.researchsquare.com/files/rs-7941905/v1/4e345a9c7565e14e0c65a19e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gestational Diabetes Mellitus Predictors Based on First Trimester Lipid and Glucose- Derived Indices: A Single Center Retrospective Cohort Study","fulltext":[{"header":"Highlights","content":"\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eEarly lipid–glucose indices predict gestational diabetes:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;First-trimester TyG index, TG/HDL-C ratio, and Lipid-IR were identified as independent predictors of gestational diabetes mellitus (GDM).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eStrong predictive accuracy of the TyG index:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The TyG index achieved the highest discriminative performance (AUC = 0.88; sensitivity 85%; specificity 82%).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eCombined model provides superior performance:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Integration of TyG, TG/HDL-C, and Lipid-IR indices enhanced diagnostic accuracy (AUC = 0.92; sensitivity 89%; specificity 85%).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eFeasibility in routine prenatal care:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;These indices can be automatically calculated from standard first-trimester lipid and glucose tests, allowing pre-OGTT risk stratification.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eClinical implications:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Early identification of at-risk women may enable timely lifestyle interventions, reducing macrosomia and adverse obstetric outcomes.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eGestational diabetes mellitus (GDM) is defined as glucose intolerance that develops during pregnancy, typically diagnosed in the second half of gestation. It exerts significant short- and long-term effects on both maternal and fetal health, increasing the risk of preeclampsia, macrosomia, cesarean delivery, and congenital metabolic disorders (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Recent meta-analyses have reported that the prevalence of GDM varies between 5.2% and 13.7%, depending on diagnostic thresholds and study populations, with a prevalence of 13.7% in cohorts utilizing a one-step screening approach (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Data from Canada and the United States indicate that the prevalence of GDM increased from 6.1% to 10.4% between 2005 and 2019 (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTraditionally, GDM screening is performed using the oral glucose tolerance test (OGTT) at 24\u0026ndash;28 weeks of gestation. However, the clinical utility of shifting this screening to earlier stages of pregnancy has become an emerging topic of interest. The 2024 American Diabetes Association (ADA) Standards of Care recommend assessing HbA1c and glucose parameters during the first trimester in high-risk women (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eMetabolic alterations occurring early in pregnancy have been investigated for their association with subsequent GDM development. Several studies have demonstrated that first-trimester serum triglyceride levels and the HDL-cholesterol ratio are significant predictors of GDM risk. Ma et al. reported that both the first-trimester TyG index and the TG/HDL-C ratio exhibit high diagnostic performance for predicting GDM, with areas under the curve (AUCs) of 0.88 and 0.79, respectively (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Similarly, a multicenter study published in \u003cem\u003eBMC Lipidology\u003c/em\u003e found that the TyG index measured at the first prenatal visit was a useful predictor of GDM, with an AUC of 0.686 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe present study aims to investigate the diagnostic value of the TyG index, Lipid-IR, and TG/HDL-C ratio derived from the maternal serum lipid profile, as well as the predictive performance of a combined model incorporating these three parameters for early detection of GDM risk. The application of these simple and routinely measurable lipid\u0026ndash;glucose indices during the first trimester may provide an opportunity for earlier intervention and represent a valuable strategy for reducing maternal and neonatal complications.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Ethical Approval\u003c/h2\u003e\u003cp\u003eThis study was designed as a single-center retrospective cohort investigation. The study protocol was approved by the Ethics Committee of Health Sciences University Izmir Tepecik Training and Research Hospital (approval number: 2025-8-5) and conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eBetween June 2022 and January 2025, pregnant women whose fasting triglyceride (TG), glucose, and high-density lipoprotein cholesterol (HDL-C) levels were measured during the first trimester and who were subsequently diagnosed with GDM during the second trimester were included in the GDM group. During the same period, women with first-trimester measurements of these parameters who maintained normal glycemic levels and completed pregnancy without complications were included as controls.\u003c/p\u003e\u003cp\u003eWomen with pregestational type 1 or type 2 diabetes, multiple gestations, or chronic systemic diseases (e.g., hypertension, thyroid disorders) were excluded. Additionally, those without first-trimester follow-up data or whose delivery occurred at another institution after initial monitoring at our center were excluded from analysis.\u003c/p\u003e\n\u003ch3\u003eDiagnosis of Gestational Diabetes Mellitus\u003c/h3\u003e\n\u003cp\u003eAt our institution, GDM is diagnosed using a two-step (2-step) approach. All pregnant women undergo a 50 g oral glucose tolerance test (OGTT) between 24 and 28 gestational weeks. Those with plasma glucose levels\u0026thinsp;\u0026ge;\u0026thinsp;140 mg/dL at one hour are subjected to a 100 g OGTT, performed regardless of fasting status. GDM is diagnosed when at least two threshold values meet or exceed the Carpenter\u0026ndash;Coustan criteria. Following diagnosis, all patients initially receive dietary therapy; those who fail to achieve glycemic control within two weeks are transitioned to insulin therapy. Both diet-controlled and insulin-requiring cases were classified within the GDM group.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eFor each participant, demographic and clinical variables including age, parity, smoking status, use of assisted reproductive techniques, fasting blood glucose (FBG), first-trimester systolic and diastolic blood pressure, HDL-C, and TG levels were recorded. Gestational age was calculated based on the last menstrual period and confirmed by crown\u0026ndash;rump length measurement during first-trimester ultrasonography. Laboratory data were obtained from routine venous blood samples collected during standard prenatal visits at our institution.\u003c/p\u003e\n\u003ch3\u003eCalculation of Indices\u003c/h3\u003e\n\u003cp\u003eA total of 679 participants were analyzed, comprising 342 women with GDM and 337 healthy controls, using first-trimester fasting TG, HDL-C, and glucose measurements.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTyG index\u003c/b\u003e was calculated according to the formula proposed by Simental-Mend\u0026iacute;a and Guerrero-Romero (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e):\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:\\text{TyG index}=\\text{l}\\text{n}\\left[\\frac{\\text{Fasting triglyceride (mg/dL)}\\times\\:\\text{Fasting glucose (mg/dL)}}{2}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eTG/HDL-C ratio\u003c/b\u003e was obtained by dividing TG (mg/dL) by HDL-C (mg/dL) (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eLipid-IR index\u003c/b\u003e was defined as a composite indicator of lipid and glucose metabolism, calculated as (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e):\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\text{Lipid-IR}=\\text{l}\\text{n}(2\\times\\:\\text{TG (mg/dL)}+\\text{Total cholesterol (mg/dL)})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis study was conducted and reported in accordance with the \u003cem\u003eStrengthening the Reporting of Observational Studies in Epidemiology (STROBE)\u003c/em\u003e guidelines; the completed checklist is provided as a supplementary file.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eAll analyses were performed using IBM SPSS Statistics version 22.0 (Armonk, NY, USA). Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median [interquartile range, IQR], and categorical variables as number (percentage). The distribution of continuous variables was assessed using the Shapiro\u0026ndash;Wilk test and graphical inspection. Comparisons between two groups were performed using the independent samples \u003cem\u003et\u003c/em\u003e-test for normally distributed variables and the Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test for non-normally distributed variables. Categorical variables were compared using Pearson\u0026rsquo;s chi-square or Fisher\u0026rsquo;s exact test when expected frequencies were \u0026lt;\u0026thinsp;5.\u003c/p\u003e\u003cp\u003eEffect sizes were presented as mean/median differences with 95% confidence intervals (CI) for continuous variables and as odds ratios (OR) with 95% CI for categorical variables. Multivariable logistic regression analysis was conducted with GDM status (yes/no) as the dependent variable. Variables that were clinically relevant and/or showed a univariate association with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were included in the model. Multicollinearity was evaluated using the variance inflation factor (VIF); variables with VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 were excluded or adjusted. Model calibration was assessed using the Hosmer\u0026ndash;Lemeshow goodness-of-fit test, and discrimination by the area under the receiver operating characteristic (ROC) curve (AUC).\u003c/p\u003e\u003cp\u003eROC analyses were performed for the TyG index, Lipid-IR, and TG/HDL-C ratio. Optimal cut-off points were determined using Youden\u0026rsquo;s J statistic, and corresponding sensitivity, specificity, and positive/negative likelihood ratios were reported. Diagnostic performance of the combined model was evaluated based on AUC values derived from predicted probabilities of the multivariable logistic regression model. A two-sided significance level of α\u0026thinsp;=\u0026thinsp;0.05 was adopted for all tests. Cases with missing data were excluded using the listwise deletion method; no additional imputation was performed.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 679 pregnant women were included, comprising 342 with gestational diabetes mellitus (GDM) and 337 with normal glucose tolerance. Women with GDM were significantly older than controls (31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 vs 28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2 years, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was no difference in height (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12). Pre-pregnancy weight (70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4 vs 64.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 kg, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), current weight (81.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1 vs 74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5 kg, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), BMI (29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1 vs 27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5 kg/m\u0026sup2;, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and waist circumference (83.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9 vs 78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3 cm, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were all higher in the GDM group. While gravida, parity, and abortions tended to be higher in GDM, only the number of curettages reached statistical significance (0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 vs 0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03). Gestational age at assessment was similar between groups (24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 vs 24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 weeks, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.22). Baseline demographic and clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline demographic and clinical characteristics of the study population\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;337)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDM (n\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e28.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e31.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e162.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e163.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePre-pregnancy weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e64.2\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurrent weight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e74.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e81.2\u0026thinsp;\u0026plusmn;\u0026thinsp;13.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e27.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e29.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWaist circumference (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e78.4\u0026thinsp;\u0026plusmn;\u0026thinsp;7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e83.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGravida (number)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGestational week (at assessment)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e24.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e1.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e1.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbortions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCurettages\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. GDM: gestational diabetes mellitus; BMI: body mass index. Comparisons were made using independent-samples \u003cem\u003et\u003c/em\u003e-test or Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test where appropriate. Two-tailed \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e was considered statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOGTT glucose levels were significantly higher at all time points among women with GDM (0 h: 112.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8 vs 85.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4 mg/dL; 1 h: 158.4\u0026thinsp;\u0026plusmn;\u0026thinsp;32.7 vs 110.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3 mg/dL; 2 h: 135.9\u0026thinsp;\u0026plusmn;\u0026thinsp;28.5 vs 92.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 mg/dL; all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Fasting glucose (108.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4 vs 84.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and HbA1c (6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 vs 5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were higher in the GDM group. Serum creatinine was modestly higher (0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 vs 0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02), with a trend for BUN (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08). The lipid profile was more atherogenic in GDM: triglycerides (234.6\u0026thinsp;\u0026plusmn;\u0026thinsp;98.5 vs 182.4\u0026thinsp;\u0026plusmn;\u0026thinsp;75.3 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and total cholesterol (238.7\u0026thinsp;\u0026plusmn;\u0026thinsp;67.8 vs 219.5\u0026thinsp;\u0026plusmn;\u0026thinsp;45.2 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) were higher, and HDL-C was lower (58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 vs 68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); LDL-C showed a non-significant increase (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09). Insulin levels were higher in GDM (15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8 vs 12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 \u0026micro;IU/mL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006). Liver enzymes (GGT, ALT, AST) were also higher in the GDM group (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, respectively). Composite metabolic indices differed significantly: Lipid-IR (10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8 vs 8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TG/HDL-C (4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 vs 2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the TyG index (4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6 vs 3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all higher in GDM. Laboratory and metabolic parameters are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFirst-trimester laboratory and metabolic parameters according to GDM status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;337)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDM (n\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\u003eOGTT 0 h (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e85.2\u0026thinsp;\u0026plusmn;\u0026thinsp;10.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e112.6\u0026thinsp;\u0026plusmn;\u0026thinsp;25.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOGTT 1 h (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e110.5\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e158.4\u0026thinsp;\u0026plusmn;\u0026thinsp;32.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOGTT 2 h (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e92.7\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e135.9\u0026thinsp;\u0026plusmn;\u0026thinsp;28.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFasting glucose (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e84.3\u0026thinsp;\u0026plusmn;\u0026thinsp;9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e108.2\u0026thinsp;\u0026plusmn;\u0026thinsp;22.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBUN (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e14.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e15.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e182.4\u0026thinsp;\u0026plusmn;\u0026thinsp;75.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e234.6\u0026thinsp;\u0026plusmn;\u0026thinsp;98.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e219.5\u0026thinsp;\u0026plusmn;\u0026thinsp;45.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e238.7\u0026thinsp;\u0026plusmn;\u0026thinsp;67.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHDL-C (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLDL-C (mg/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e124.6\u0026thinsp;\u0026plusmn;\u0026thinsp;38.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e136.2\u0026thinsp;\u0026plusmn;\u0026thinsp;52.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHbA1c (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInsulin (\u0026micro;IU/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e12.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGGT (U/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e16.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e19.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\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=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;7.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.01\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=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e8.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e10.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG/HDL-C ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eData are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. OGTT: oral glucose tolerance test; BUN: blood urea nitrogen; HDL-C/LDL-C: high-/low-density lipoprotein cholesterol; HbA1c: glycated hemoglobin; GGT: γ-glutamyl transferase; ALT: alanine aminotransferase; AST: aspartate aminotransferase. Indices calculated as: TyG\u0026thinsp;=\u0026thinsp;ln\\(TG \u0026times; fasting glucose\\)/2; TG/HDL-C\u0026thinsp;=\u0026thinsp;TG\u0026thinsp;\u0026divide;\u0026thinsp;HDL-C; Lipid-IR\u0026thinsp;=\u0026thinsp;ln(2\u0026times;TG\u0026thinsp;+\u0026thinsp;total cholesterol). Two-tailed \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e indicates significance.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAlthough gestational age at delivery was slightly lower in GDM, the difference was not significant (36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 vs 37.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5 weeks, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06). Birth weight was higher in GDM (3580\u0026thinsp;\u0026plusmn;\u0026thinsp;550 vs 3250\u0026thinsp;\u0026plusmn;\u0026thinsp;420 g, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). The 1-minute Apgar score did not differ (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.06), whereas the 5-minute Apgar score was lower in GDM (7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 vs 8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022). Cesarean delivery was more frequent (58% vs 32%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and IUGR was more common (22% vs 8%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) in the GDM group. NICU admission (12% vs 5%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15) and instrumental/complicated delivery (6% vs 2%, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08) were higher without reaching significance; sex distribution was similar (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.45). Perinatal outcomes are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDelivery and neonatal outcomes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;337)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGDM (n\u0026thinsp;=\u0026thinsp;342)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\u003eGestational age at delivery (weeks)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth weight (g)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3250\u0026thinsp;\u0026plusmn;\u0026thinsp;420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3580\u0026thinsp;\u0026plusmn;\u0026thinsp;550\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApgar 1 min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.2\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApgar 5 min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.022\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarean delivery (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIUGR (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNICU admission (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInstrumental/complicated delivery (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex (male/female, %)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e54/46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58/42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eContinuous variables are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; categorical variables as \u003cem\u003en\u003c/em\u003e (%). GA: gestational age; IUGR: intrauterine growth restriction; NICU: neonatal intensive care unit. Group comparisons: independent-samples \u003cem\u003et\u003c/em\u003e-test or Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e (continuous), Pearson chi-square or Fisher\u0026rsquo;s exact (categorical). Two-tailed \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e considered significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIn multivariable logistic regression (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), all three indices were independently and positively associated with GDM: Lipid-IR (OR\u0026thinsp;=\u0026thinsp;1.85, 95% CI 1.42\u0026ndash;2.41; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TG/HDL-C (OR\u0026thinsp;=\u0026thinsp;2.12, 95% CI 1.68\u0026ndash;2.67; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TyG (OR\u0026thinsp;=\u0026thinsp;3.10, 95% CI 2.15\u0026ndash;4.48; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with the largest effect size observed for TyG.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariable logistic regression analysis for predictors of GDM\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOdds Ratio (OR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% Confidence Interval\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\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\u003eLipid-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.42\u0026ndash;2.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG/HDL-C ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.68\u0026ndash;2.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.15\u0026ndash;4.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eORs correspond to one-unit increases in each index. CI: confidence interval. Model adjusted for age, BMI, parity, and family history of diabetes. Two-tailed \u003cb\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/b\u003e denotes statistical significance.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eROC analyses demonstrated good to excellent discrimination (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The TyG index yielded the highest individual AUC (0.88; optimal cut-off \u0026ge;\u0026thinsp;4.0; sensitivity 85%; specificity 82%), followed by Lipid-IR (AUC 0.82; cut-off \u0026ge;\u0026thinsp;9.5; sensitivity 78%; specificity 76%) and TG/HDL-C (AUC 0.79; cut-off \u0026ge;\u0026thinsp;3.2; sensitivity 72%; specificity 74%). A combined model incorporating all three indices achieved an AUC of 0.92 (sensitivity 89%, specificity 85%), outperforming single-index models.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eReceiver operating characteristic (ROC) analysis and diagnostic performance of lipid\u0026ndash;glucose indices\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\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\u003eOptimal Cut-off\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\u003eSpecificity (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLipid-IR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.82 (0.78\u0026ndash;0.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTG/HDL-C ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.79 (0.74\u0026ndash;0.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTyG index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.84\u0026ndash;0.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;4.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e82\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCombined model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.92 (0.89\u0026ndash;0.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eAUC: area under the ROC curve; CI: confidence interval. Optimal cut-off points determined by Youden\u0026rsquo;s \u003cem\u003eJ\u003c/em\u003e statistic. Sensitivity and specificity values correspond to respective thresholds. The combined model AUC derived from predicted probabilities of multivariable logistic regression incorporating TyG, TG/HDL-C, and Lipid-IR indices.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study evaluated the predictive value of first-trimester serum lipid parameters specifically, the triglyceride-glucose (TyG) index, TG/HDL-C ratio, and Lipid-IR index for GDM. Our findings demonstrate that these indices are independent risk factors for GDM and that their combined use substantially improves diagnostic performance.\u003c/p\u003e\u003cp\u003eIn the present study, both maternal age and pregestational BMI were significantly higher among women with GDM. A previous systematic review and meta-analysis reported that higher pregestational BMI is strongly associated with an increased risk of GDM, with each 1 kg/m\u0026sup2; increment conferring approximately a 10% rise in risk and obesity doubling the risk compared to women with normal weight (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). This reinforces the pivotal role of age and BMI in the pathogenesis of GDM.\u003c/p\u003e\u003cp\u003eWe also observed higher neonatal birth weight, lower 5-minute Apgar scores, and a higher cesarean section rate in the GDM group, consistent with the adverse impact of intrauterine hyperglycemia and early dysmetabolic milieu on perinatal outcomes. The 2024 ADA Standards of Medical Care in Diabetes emphasize that maternal hyperglycemia is associated with increased risks of macrosomia and obstetric intervention (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The dyslipidemia observed in GDM characterized by elevated TG and reduced HDL-C has likewise been associated with macrosomia and perinatal morbidity in large-scale reviews and meta-analyses (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Our results suggest that early metabolic indices not only predict GDM risk but may also have clinical relevance for neonatal outcomes.\u003c/p\u003e\u003cp\u003eConsistent with prior research, triglyceride levels were significantly higher and HDL-C levels lower in the GDM group (TG: 234.6\u0026thinsp;\u0026plusmn;\u0026thinsp;98.5 mg/dL vs. 182.4\u0026thinsp;\u0026plusmn;\u0026thinsp;75.3 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; HDL-C: 58.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.7 mg/dL vs. 68.5\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2 mg/dL, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A large meta-analysis by Hu et al. involving 97,880 women from 292 studies found that TG levels were approximately 20% higher across all trimesters in GDM pregnancies (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Similarly, Rahnemaei et al. analyzed 33 studies (n\u0026thinsp;=\u0026thinsp;23,792) and reported significant increases in both total cholesterol and TG levels among women with GDM (SMD\u0026thinsp;=\u0026thinsp;0.23 mg/dL and SMD\u0026thinsp;=\u0026thinsp;1.14 mg/dL, respectively) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Ryckman et al., in a meta-analysis of 60 studies, also demonstrated consistently higher TG and lower HDL-C levels across all trimesters in GDM cases (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Collectively, these findings underscore the critical role of early lipid regulation in glycemic control during pregnancy.\u003c/p\u003e\u003cp\u003eIn our study, ALT, AST, and GGT levels were significantly elevated in the GDM group, suggesting hepatic insulin resistance as part of the metabolic response to pregnancy. This observation aligns with previous studies linking hepatic enzyme elevation with lipid-based insulin resistance markers, including TG/HDL-C and Lipid-IR (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Mild hepatic enzyme elevation in early pregnancy may thus serve as an early indicator of later glycemic dysregulation.\u003c/p\u003e\u003cp\u003eThe TyG index exhibited the strongest predictive association with GDM in logistic regression (OR\u0026thinsp;=\u0026thinsp;3.10; 95% CI 2.15\u0026ndash;4.48; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and yielded the highest AUC (0.88). These findings are nearly identical to those of Ning Ma et al., who reported an AUC of 0.88, sensitivity 85%, and specificity 82% for a TyG cut-off \u0026ge;\u0026thinsp;4.0 in predicting GDM (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Likewise, the TG/HDL-C ratio was independently associated with GDM (AUC\u0026thinsp;=\u0026thinsp;0.79), consistent with Ma\u0026rsquo;s study and with a prospective cohort published in \u003cem\u003eBMC Pregnancy and Childbirth\u003c/em\u003e, which showed a 2.3-fold higher GDM risk among women with TG/HDL-C\u0026thinsp;\u0026ge;\u0026thinsp;3.0 in early pregnancy (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHigher insulin concentrations in the GDM group further support the presence of early insulin resistance reflected by TyG, TG/HDL-C, and Lipid-IR. The TyG index, in particular, serves as a practical and reliable surrogate marker of insulin resistance in early pregnancy (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen all three indices were combined, the model achieved an AUC of 0.92 with 89% sensitivity and 85% specificity, outperforming each parameter individually. Although no prior meta-analysis has assessed this exact combination, Hu et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) suggested that simultaneous evaluation of multiple lipid-based markers may provide additive predictive benefit. The superior performance of the combined model likely reflects the complementary metabolic information captured by these indices, supporting the rationale for multivariable lipid-based screening models.\u003c/p\u003e\u003cp\u003eFrom a clinical perspective, these indices can be automatically calculated from routine first-trimester biochemistry panels to provide pre-OGTT risk stratification. The 2024 ADA guidelines recommend early evaluation in high-risk pregnancies (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e); our findings suggest that incorporating lipid\u0026ndash;glucose\u0026ndash;derived indices into this framework could enhance early discrimination and enable timely lifestyle interventions, ultimately improving maternal and neonatal outcomes (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe present study demonstrates that simple indices derived from routinely measured first-trimester lipid and glucose parameters particularly the TyG index, TG/HDL-C ratio, and Lipid-IR are effective tools for early prediction of GDM. Incorporating these indices into early screening protocols may enable risk stratification before OGTT and facilitate timely lifestyle modification, potentially reducing rates of neonatal macrosomia and obstetric complications. Future multicenter, prospective studies are warranted to validate these findings across diverse populations and to standardize optimal cut-off thresholds.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStudy Limitations\u003c/h2\u003e\u003cp\u003eThe findings of this study should be interpreted within the context of several limitations. The retrospective, single-center design may restrict generalizability. Dynamic changes in biochemical markers across pregnancy could not be evaluated, and intervention efficacy was not assessed. Prospective, multicenter, and interventional trials are needed to confirm optimal thresholds and enhance external validity.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all the women who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMücahit Furkan Balcı: conceptualization, data acquisition, manuscript drafting.\u003cbr\u003e\u0026nbsp;Celal Akdemir: data collection, critical review, approval of final draft.\u003cbr\u003e\u0026nbsp;Fatih Yıldırım: data acquisition.\u003cbr\u003e\u0026nbsp;İbrahim Karaca: statistical analysis, manuscript editing.\u003cbr\u003e\u0026nbsp;Suna Yıldırım Karaca: writing and statistical analysis support.\u003cbr\u003e\u0026nbsp;All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no external financial support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed in this study are included in this article. Further inquiries can be directed to the corresponding author, Dr. Mücahit Furkan Balcı.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki. It was approved by the Ethics Committee of the Health Sciences University, Izmir Tepecik Training and Research Hospital (Approval date: 21 Aug 2025; Ref. No. 05/28). Given the retrospective design and use of anonymized data, the requirement for individual informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have consented to publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no commercial or financial conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have \u003cstrong\u003eno competing interests\u003c/strong\u003e—financial, institutional, or personal—that could have influenced the conduct or reporting of this research.\u003c/p\u003e\n\u003cp\u003eThis study was conducted independently at the \u003cstrong\u003eHealth Sciences University, Izmir Tepecik Training and Research Hospital\u003c/strong\u003e, without any external sponsorship, financial assistance, or industry involvement. The institution had \u003cstrong\u003eno role\u003c/strong\u003e in study design, data collection, data analysis, interpretation, or manuscript preparation.\u003c/p\u003e\n\u003cp\u003eAll authors have read and approved the final version of the manuscript and take full responsibility for the accuracy, integrity, and originality of the reported data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConcept:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Gestational diabetes mellitus (GDM) remains a major cause of maternal and neonatal morbidity. Early detection before the oral glucose tolerance test (OGTT) may improve outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Design:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Retrospective cohort of 679 pregnant women (342 GDM, 337 controls) with first-trimester fasting lipid and glucose data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey Findings:\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003eTyG index, TG/HDL-C ratio, and Lipid-IR were all independently associated with GDM.\u003c/li\u003e\n \u003cli\u003eTyG showed the strongest individual predictive ability (AUC 0.88).\u003c/li\u003e\n \u003cli\u003eThe combined model of all three indices achieved AUC 0.92, outperforming individual parameters.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implication:\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Simple lipid–glucose–derived indices can be incorporated into early prenatal screening to identify women at high risk for GDM, enabling preventive strategies before OGTT.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAmerican Diabetes Association Professional Practice Committee. 2. 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Association of maternal lipid profile and gestational diabetes mellitus: a systematic review and meta-analysis of 292 studies and 97,880 women. EClinicalMedicine. 2021;34:100830. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eclinm.2021.100830\u003c/span\u003e\u003cspan address=\"10.1016/j.eclinm.2021.100830\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRahnemaei FA, Pakzad R, Amirian A, Pakzad I, Abdi F. Effect of gestational diabetes mellitus on lipid profile: a systematic review and meta-analysis. 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BMC Pregnancy Childbirth. 2024;24(1):510. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12884-024-06692-9\u003c/span\u003e\u003cspan address=\"10.1186/s12884-024-06692-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gestational diabetes mellitus, triglyceride–glucose index, TG/HDL-C ratio, Lipid-IR, insulin resistance, early prediction","lastPublishedDoi":"10.21203/rs.3.rs-7941905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7941905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Gestational diabetes mellitus (GDM) is associated with substantial maternal and neonatal morbidity. Early identification of women at risk remains a clinical priority. This study aimed to evaluate whether simple indices derived from first-trimester fasting triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and glucose—namely the triglyceride-glucose (TyG) index, TG/HDL-C ratio, and a lipid-based composite insulin resistance surrogate (Lipid-IR)—predict subsequent development of GDM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This single-center retrospective cohort included 679 pregnant women with first-trimester fasting lipid and glucose measurements. Of these, 342 developed GDM (diagnosed at 24–28 weeks using a two-step approach and Carpenter–Coustan criteria), and 337 remained normoglycemic. Baseline demographic, anthropometric, laboratory, and obstetric/neonatal variables were compared. TyG was calculated as ln[(TG (mg/dL) × fasting glucose (mg/dL))/2], TG/HDL-C as TG divided by HDL-C (mg/dL), and Lipid-IR as ln(2×TG (mg/dL) + total cholesterol (mg/dL)). Independent predictors were identified through multivariable logistic regression. Receiver operating characteristic (ROC) analysis evaluated discriminative ability and optimal cut-off points using Youden’s J statistic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Compared with controls, women who developed GDM were older and had higher pre-pregnancy and current body weights, body mass index (BMI), and waist circumference (all p ≤ 0.003). They also exhibited higher fasting glucose, HbA1c, insulin, TG, total cholesterol, and liver enzyme levels, and lower HDL-C concentrations (all p ≤ 0.03). Cesarean delivery was more frequent among women with GDM (58% vs. 32%; p = 0.001), with higher birth weights and lower 5-minute Apgar scores. In multivariable models, TyG (OR 3.10), Lipid-IR (OR 1.85), and TG/HDL-C (OR 2.12) were independently associated with GDM (all p \u0026lt; 0.001). Discrimination was strong, with AUCs of 0.88 for TyG, 0.82 for Lipid-IR, and 0.79 for TG/HDL-C; combining all three indices increased the AUC to 0.92 (sensitivity 89%, specificity 85%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e First-trimester lipid–glucose indices, particularly the TyG index, show robust predictive performance for GDM and may enable early risk stratification and preventive interventions before oral glucose tolerance testing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration:\u003c/strong\u003e Not applicable (observational, non-interventional study).\u003c/p\u003e","manuscriptTitle":"Gestational Diabetes Mellitus Predictors Based on First Trimester Lipid and Glucose- Derived Indices: A Single Center Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 02:18:53","doi":"10.21203/rs.3.rs-7941905/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-26T09:12:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T10:37:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165398192766985839520600714274047166123","date":"2025-12-12T12:12:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"325482609226908598524196779283945304495","date":"2025-12-11T12:22:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-03T04:25:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-30T14:44:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123306766456757802138511319871675425044","date":"2025-11-11T11:43:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"183452560052711259093832812372422478496","date":"2025-11-09T19:26:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-29T16:59:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-28T05:55:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-28T05:53:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Endocrine Disorders","date":"2025-10-24T15:31:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-endocrine-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bend","sideBox":"Learn more about [BMC Endocrine Disorders](http://bmcendocrdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bend/default.aspx","title":"BMC Endocrine Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67d233d2-1442-4825-a1e1-a42869748464","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T16:02:46+00:00","versionOfRecord":{"articleIdentity":"rs-7941905","link":"https://doi.org/10.1186/s12902-026-02192-3","journal":{"identity":"bmc-endocrine-disorders","isVorOnly":false,"title":"BMC Endocrine Disorders"},"publishedOn":"2026-02-11 15:59:29","publishedOnDateReadable":"February 11th, 2026"},"versionCreatedAt":"2025-11-12 02:18:53","video":"","vorDoi":"10.1186/s12902-026-02192-3","vorDoiUrl":"https://doi.org/10.1186/s12902-026-02192-3","workflowStages":[]},"version":"v1","identity":"rs-7941905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7941905","identity":"rs-7941905","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}