Phd in ENDOCRINOLOGICAL SCIENCES

Thesis title: Predizione precoce del diabete mellito gestazionale nel primo trimestre di gravidanza: ruolo della Pregnancy-Associated Plasma Protein-A e sviluppo di un modello multifattoriale

Background. Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy and is associated with an increased risk of adverse maternal and neonatal outcomes. Early identification of women at risk is a major priority in maternal–fetal medicine, as GDM is currently generally diagnosed by oral glucose tolerance testing between 24 and 28 weeks of gestation, when metabolic alterations are already established. Pregnancy-Associated Plasma Protein-A (PAPP-A), a biomarker used in first-trimester combined screening, is involved in the regulation of the PAPP-A–IGF–IGFBP-4 axis and placental development, making it a potential early marker of GDM risk. Objective. To evaluate the association between first-trimester PAPP-A levels and the subsequent development of gestational diabetes mellitus, assessing its predictive ability both as an isolated biomarker and within multifactorial models incorporating maternal characteristics and early metabolic parameters. Materials and Methods. A prospective observational study was conducted including 279 singleton pregnancies followed at the Prenatal Diagnosis Centre of the Department of Maternal and Child Health and Urological Sciences, Policlinico Umberto I, Rome, between December 1, 2023, and March 1, 2026. First-trimester demographic, medical history, anthropometric, biochemical, and ultrasound data were collected, including PAPP-A levels expressed as multiples of the median (MoM). GDM was diagnosed according to IADPSG criteria using a 75-g oral glucose tolerance test performed at 24–28 weeks of gestation. Univariate and multivariate analyses, logistic regression models, and receiver operating characteristic (ROC) curve analyses were performed. Model performance was assessed using the area under the ROC curve (AUC) and internally validated by bootstrap resampling. Results. In the present study, women who developed GDM already exhibited a different clinical and metabolic profile in the first trimester compared with controls, characterized by a higher pre-pregnancy BMI, significantly higher fasting glucose levels, and a different distribution of ethnic origin. Although PAPP-A levels were lower in pregnancies complicated by GDM, PAPP-A alone showed limited discriminative ability (AUC 0.561) and was not confirmed as an independent predictor in multivariate analysis (OR 0.92; 95% CI 0.54–1.47; p=0.743). In contrast, pre-pregnancy BMI (OR 1.13; 95% CI 1.06–1.22; p=0.001), non-Caucasian ethnicity (OR 7.10; 95% CI 3.24–16.36; p<0.001), and first-trimester fasting glucose (OR 1.12; 95% CI 1.08–1.17; p<0.001) were confirmed as the main independent predictors of GDM. The integration of maternal risk factors with first-trimester clinical, metabolic, and biochemical parameters enabled the development of a multifactorial predictive model with an AUC of 0.891, confirmed by internal bootstrap validation (mean AUC 0.898). The improvement in discriminative performance was mainly attributable to first-trimester fasting glucose, whereas PAPP-A did not provide a significant independent predictive contribution. The model showed even better performance in predicting forms of GDM requiring pharmacological treatment (GDMA2), with an AUC of 0.927. Regarding obstetric outcomes, pregnancies complicated by GDM were associated with delivery at a significantly earlier gestational age (37.81 vs 38.53 weeks; p=0.008) and a significantly higher birthweight percentile (p=0.049), with no significant differences in mode of delivery or indicators of neonatal well-being. Conclusions. Although PAPP-A levels were lower in pregnancies subsequently complicated by gestational diabetes mellitus, PAPP-A was neither an independent predictor nor a sufficiently accurate biomarker when used alone and did not significantly improve predictive performance when added to maternal risk factors alone. In contrast, the integration of maternal risk factors with first-trimester clinical and metabolic parameters, particularly fasting glucose, provided improved early risk stratification for GDM. These findings therefore support the use of multifactorial approaches for the early prediction of GDM and provide a rationale for future prospective multicentre validation studies aimed at assessing their applicability in clinical practice.