Prediction of gestational diabetes mellitus using clinical and ultrasonographic parameters: development of independent maternal and fetal models
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVES: To develop predictive models for gestational diabetes mellitus (GDM) using maternal clinical and ultrasonographic parameters, as well as fetal ultrasonographic measurements obtained during routine second-trimester obstetric examination. METHODS: Our prospective case-control study included 80 pregnant participants (32 with GDM and 48 healthy controls) between 24 and 28 weeks of gestation. We evaluated maternal parameters including anthropometric measurements such as neck circumference and waist-hip ratio, ultrasonographic measurements like maternal adipose thickness, as well as fetal ultrasonographic parameters including pancreatic hyperechogenicity, soft tissue measurements, and cardiac parameters. Following ultrasonographic examination, all participants underwent a 75-g oral glucose tolerance test (OGTT). Two separate logistic regression models were developed for maternal and fetal parameters to evaluate their association with the presence or absence of GDM. RESULTS: The maternal model achieved 90.0 % accuracy through a stepwise integration of suprapubic subcutaneous adipose tissue thickness (OR=1.35, 95 % CI: 1.11-1.64), visceral adipose tissue (OR=1.68, 95 % CI: 1.18-2.40), gravidity (OR=2.09, 95 % CI: 1.32-3.33), and family history of diabetes. The fetal model reached 82.5 % accuracy using two parameters: pancreatic hyperechogenicity (OR=0.02, 95 % CI: 0.00-0.14) and fetal abdominal subcutaneous tissue thickness (OR=5.09, 95 % CI: 1.75-14.78). The maternal model demonstrated excellent discriminative ability with an AUC of 0.95. CONCLUSIONS: Both maternal and fetal ultrasonographic parameters can serve as effective predictors of GDM when combined with clinical risk factors. These models, which can be easily incorporated into routine second-trimester ultrasound examinations, offer potential for early identification of high-risk patients without additional patient visits or invasive testing.
Source: Journal of Perinatal Medicine
Authors: Sinem Tekin, Aydın Ocal, Filiz Yarsilikal Guleroglu, Cagseli Göksu Ozgün Selcuk, Omer Gökhan Eyisoy, Emine Ufuk Büyükkaya Ocal, Ali Cetin
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411264995