Abstract:
Introduction: Fetal macrosomia, defined as birth weight exceeding 4000 grams, is associated
with increased perinatal morbidity and mortality, particularly in pregnancies complicated by
gestational diabetes mellitus (GDM). Conventional methods of estimating fetal weight often
lack accuracy in predicting macrosomia in diabetic pregnancies, where abnormal fat
distribution patterns may confound standard biometric measurements. This prospective study
aimed to evaluate the efficacy of three sonographic parameters—umbilical cord thickness, fetal
fat layer thickness, and interventricular septal thickness—as predictors of fetal macrosomia in
women with GDM.
Methods: A total of 123 pregnant women with GDM between 34-40 weeks of gestation were
enrolled in this prospective study. Comprehensive maternal data including age, BMI, and
glycemic parameters were recorded. Sonographic measurements of umbilical cord thickness,
fetal fat layer, and interventricular septal thickness were performed, and their association with
actual birth weight and delivery outcomes was analyzed.
Results: Out of 123 pregnancies, 77 (62.6%) resulted in macrosomic babies. Significant
associations were found between macrosomia and maternal BMI (p<0.001), HbA1c levels
(p<0.001), umbilical cord thickness ≥25 mm (p<0.001), fetal fat layer ≥4.5 mm (p<0.001), and
interventricular septal thickness ≥3.9 mm (p<0.001). Umbilical cord thickness demonstrated
the strongest correlation with birth weight (r=0.792, p<0.001) and showed excellent diagnostic
accuracy with sensitivity of 93.3% and specificity of 85.4%. Fetal fat layer thickness exhibited
high specificity (93.3%) and positive predictive value (97.3%), while interventricular septal
thickness showed good specificity (85%) but lower sensitivity (71.8%).
Conclusion: Sonographic measurements of umbilical cord thickness, fetal fat layer, and
interventricular septal thickness are valuable predictors of fetal macrosomia in GDM
pregnancies. Integration of these parameters with maternal factors may enhance the accuracy
of macrosomia prediction, potentially improving clinical decision-making and optimizing
maternal and neonatal outcomes.
