Introduction: Gestational diabetes mellitus (GDM) is one of the most prevalent metabolic complications of pregnancy, posing significant risks to both maternal and neonatal health. Multiple maternal factors, including pre-pregnancy body mass index (BMI), gestational weight gain (GWG), height, and metabolic status, have been implicated in the development of macrosomia in GDM pregnancies. In recent years, novel inflammation-based hematological indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-HDL cholesterol ratio (NHR) have emerged as cost-effective biomarkers reflecting systemic inflammatory and metabolic alterations. However, their potential role in predicting macrosomia in GDM remains insufficiently explored. Thus, the purpose of our study was to asses the association between maternal risk factors, blood parameters, novel inflammatory markers, and macrosomia in patients with GDM. In addition, we aimed to develop and validate new predictive models for macrosomia in pregnancies complicated by GDM.Methods: A total of 200 patients diagnosed with GDM using a 75-gram oral glucose tolerance test (OGTT) between the 24th and 28th weeks of gestation were included in the study. Macrosomia was identified if birth weight exceeded 4,000 grams or the 90th percentile for gestational age. Patients were divided into two groups: 42 GDM patients with macrosomic offspring (GDM-M) and 158 GDM patients without macrosomia (GDM-N). Maternal characteristics, blood parameters, and novel inflammatory markers were compared between the GDM-M and GDM-N groups using statistical analyses. Point-biserial (Pearson-equivalent) correlation analysis was used to examine the relationships between various maternal, metabolic, and inflammatory parameters and macrosomia. Independent associations were then evaluated using multivariable logistic regression, with calibration and explained variance assessed. Discrimination was examined using receiver operating characteristic (ROC) curves for single predictors and a combined model; optimal cut-offs were derived by the Youden index.Results: GWG (p = 0.028), pre-pregnancy BMI (p = 0.023), maternal height (p 0.001), fasting plasma glucose (FPG) (p 0.001), glycated hemoglobin (HbA1c) (p 0.001), triglyceride (TG) (p = 0.034), NLR (p = 0.012), and NHR (p = 0.018) levels were significantly higher in the GDM-M group than in the GDM-N group. In contrast, insulin (p 0.001) and high-density lipoprotein cholesterol (HDL-C) (p = 0.022) levels were significantly lower in the GDM-M group compared with the GDM-N group. In adjusted analyses, higher pre-pregnancy BMI, maternal height, GWG, FPG, HbA1c, NLR, and NHR were associated with increased odds of macrosomia, whereas insulin and HDL-C were inversely associated. ROC analyses showed the highest single-marker area under curve (AUC)’s for maternal height (0.78; cut-off ≥162 cm), NHR (0.76; cut-off ≥3.2), and HbA1c (0.75; cut-off ≥7.4%), with NLR (0.74), GWG (0.73), FPG (0.72), and HDL-C (0.71; cut-off ≤42 mg/dL) performing similarly; the combined model achieved AUC 0.85 (95% CI 0.77–0.92), sensitivity 80.9%, specificity 78.5%, positive predictive value (PPV) 63.2%, and negative predictive value (NPV) 90.4%.Conclusion: There is an association between higher GWG, pre-pregnancy BMI, maternal height, FPG, and HbA1c levels, as well as lower insulin and HDL-C levels, and the occurrence of macrosomia in patients with GDM. These findings suggest that GWG, pre-pregnancy BMI, maternal height, FPG, HbA1c, insulin, HDL-C, NLR, and NHR may be useful in predicting macrosomia in pregnancies complicated by GDM. A compact multivariable model combining these routine measures demonstrated good discrimination with a high NPV, supporting potential use for early risk stratification.
Bulu et al. (Wed,) studied this question.