First Trimester Combined Test (FTCT) as a Predictor of Gestational Diabetes Mellitus.
Federica ViscontiPaola QuaresimaEusebio ChiefariPatrizia CaroleoBiagio ArcidiaconoLuigi PuccioMaria MirabelliDaniela P FotiCostantino Di CarloRaffaella VeroAntonio BrunettiPublished in: International journal of environmental research and public health (2019)
Background-The first trimester combined test (FTCT) is an effective screening tool to estimate the risk of fetal aneuploidy. It is obtained by the combination of maternal age, ultrasound fetal nuchal translucency (NT) measurement, and the maternal serum markers free β-human chorionic gonadotropin (β-hCG) and pregnancy-associated plasma protein A (PAPP-A). However, conflicting data have been reported about the association of FTCT, β-hCG, or PAPP-A with the subsequent diagnosis of gestational diabetes mellitus (GDM). Research design and methods-2410 consecutive singleton pregnant women were retrospectively enrolled in Calabria, Southern Italy. All participants underwent examinations for FTCT at 11-13 weeks (plus 6 days) of gestation, and screening for GDM at 16-18 and/or 24-28 weeks of gestation, in accordance with current Italian guidelines and the International Association Diabetes Pregnancy Study Groups (IADPSG) glycemic cut-offs. Data were examined by univariate and logistic regression analyses. Results-1814 (75.3%) pregnant women were normal glucose tolerant, while 596 (24.7%) were diagnosed with GDM. Spearman univariate analysis demonstrated a correlation between FTCT values and subsequent GDM diagnosis (ρ = 0.048, p = 0.018). The logistic regression analysis showed that women with a FTCT <1:10000 had a major GDM risk (p = 0.016), similar to women with a PAPP-A <1 multiple of the expected normal median (MoM, p = 0.014). Conversely, women with β-hCG ≥2.0 MoM had a reduced risk of GDM (p = 0.014). Conclusions-Our findings indicate that GDM susceptibility increases with fetal aneuploidy risk, and that FTCT and its related maternal serum parameters can be used as early predictors of GDM.
Keyphrases
- pregnancy outcomes
- pregnant women
- gestational age
- birth weight
- preterm birth
- type diabetes
- endothelial cells
- preterm infants
- computed tomography
- electronic health record
- magnetic resonance imaging
- cardiovascular disease
- big data
- machine learning
- glycemic control
- metabolic syndrome
- blood pressure
- data analysis
- insulin resistance
- body mass index
- protein protein
- artificial intelligence