The serotonin receptor 2A gene (HTR2A) is a strong candidate for the fetal programming of future behavior and metabolism. Maternal obesity and gestational diabetes mellitus (GDM) have been associated with an increased risk of metabolic and psychological problems in offspring. We tested the hypothesis that maternal metabolic status affects methylation of HTR2A in the placenta. The prospective study included 199 pairs of mothers and healthy full-term newborns. Genomic DNA was extracted from feto-placental samples and analyzed for genotypes of two polymorphisms (rs6311, rs6306) and methylation of four cytosine residues (−1665, −1439, −1421, −1224) in the HTR2A promoter region. Placental HTR2A promoter methylation was higher in male than female placentas and depended on both rs6311 and rs6306 genotypes. A higher maternal pre-gestational body mass index (pBMI) and, to a lesser extent, diagnosis of GDM were associated with reduced HTR2A promoter methylation in female but not male placentas. Higher pBMI was associated with reduced methylation both directly and indirectly through increased GDM incidence. Tobacco use during pregnancy was associated with reduced HTR2A promoter methylation in male but not female placentas. The obtained results suggest that HTR2A is a sexually dimorphic epigenetic target of intrauterine exposures. The findings may contribute to a better understanding of the early developmental origins of neurobehavioral and metabolic disorders associated with altered HTR2A function.

In immunoglobulin G (IgG), N-glycosylation plays a pivotal role in structure and function. It is often altered in different diseases, suggesting that it could be a promising health biomarker. Studies indicate that IgG glycosylation not only associates with various diseases but also has predictive capabilities. Additionally, changes in IgG glycosylation correlate with physiological and biochemical traits known to reflect overall health state. This study aimed to investigate the power of IgG glycans to predict physiological and biochemical parameters. We developed two models using IgG N-glycan data as an input: a regression model using elastic net and a machine learning model using deep learning. Data were obtained from the Korčula and Vis cohorts. The Korčula cohort data were used to train both models, while the Vis cohort was used exclusively for validation. Our results demonstrated that IgG glycome composition effectively predicts several biochemical and physiological parameters, especially those related to lipid and glucose metabolism and cardiovascular events. Both models performed similarly on the Korčula cohort; however, the deep learning model showed a higher potential for generalization when validated on the Vis cohort. This study reinforces the idea that IgG glycosylation reflects individuals’ health state and brings us one step closer to implementing glycan-based diagnostics in personalized medicine. Additionally, it shows that the predictive power of IgG glycans can be used for imputing missing covariate data in deep learning frameworks.