Recent studies have indicated that exogenously administered neurotrophins produce antidepressant-like behavioral effects. We have here investigated the role of endogenous brain-derived neurotrophic factor (BDNF) and its receptor trkB in the mechanism of action of antidepressant drugs. We found that trkB.T1-overexpressing transgenic mice, which show reduced trkB activation in brain, as well as heterozygous BDNF null (BDNF(+/)-) mice, were resistant to the effects of antidepressants in the forced swim test, indicating that normal trkB signaling is required for the behavioral effects typically produced by antidepressants. In contrast, neurotrophin-3(+/)- mice showed a normal behavioral response to antidepressants. Furthermore, acute as well as chronic antidepressant treatment induced autophosphorylation and activation of trkB in cerebral cortex, particularly in the prefrontal and anterior cingulate cortex and hippocampus. Tyrosines in the trkB autophosphorylation site were phosphorylated in response to antidepressants, but phosphorylation of the shc binding site was not observed. Nevertheless, phosphorylation of cAMP response element-binding protein was increased by antidepressants in the prefrontal cortex concomitantly with trkB phosphorylation and this response was reduced in trkB.T1-overexpressing mice. Our data suggest that antidepressants acutely increase trkB signaling in a BDNF-dependent manner in cerebral cortex and that this signaling is required for the behavioral effects typical of antidepressant drugs. Neurotrophin signaling increased by antidepressants may induce formation and stabilization of synaptic connectivity, which gradually leads to the clinical antidepressive effects and mood recovery.

Antidepressants increase proliferation of neuronal progenitor cells and expression of brain-derived neurotrophic factor (BDNF) in the hippocampus. We investigated the role of BDNF signaling in antidepressant-induced neurogenesis by using transgenic mice with either reduced BDNF levels (BDNF ϩ/Ϫ ) or impaired trkB activation (trkB.T1-overexpressing mice). In both transgenic strains, chronic (21 d) imipramine treatment increased the number of bromodeoxyuridine (BrdU)-positive cells to degree similar to that seen in wild-type mice 24 h after BrdU administration, although the basal proliferation rate was increased in both transgenic strains. Three weeks after BrdU administration and the last antidepressant injection, the amount of newborn (BrdU-or TUC-4-positive) cells was significantly reduced in both BDNF ϩ/Ϫ and trkB.T1-overexpressing mice, which suggests that normal BDNF signaling is required for the long-term survival of newborn hippocampal neurons. Moreover, the antidepressant-induced increase in the surviving BrdU-positive neurons seen in wild-type mice 3 weeks after treatment was essentially lost in mice with reduced BDNF signaling. Furthermore, we observed that chronic treatment with imipramine or fluoxetine produced a temporally similar increase in both BrdU-positive and terminal deoxynucleotidyl transferasemediated biotinylated UTP nick end-labeled neurons in the dentate gyrus, indicating that these drugs simultaneously increase both neurogenesis and neuronal elimination. These data suggest that antidepressants increase turnover of hippocampal neurons rather than neurogenesis per se and that BDNF signaling is required for the long-term survival of newborn neurons in mouse hippocampus.

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Introduction: Accurate early risk prediction for gestational diabetes mellitus (GDM) would target intervention and prevention in women at the highest risk. We evaluated novel biomarker predictors to develop a first-trimester risk prediction model in a large multiethnic cohort. Methods: Maternal clinical, aneuploidy and pre-eclampsia screening markers (PAPP-A, free hCGβ, mean arterial pressure, uterine artery pulsatility index) were measured prospectively at 11–13⁺⁶ weeks’ gestation in 980 women (248 with GDM; 732 controls). Nonfasting glucose, lipids, adiponectin, leptin, lipocalin-2, and plasminogen activator inhibitor-2 were measured on banked serum. The relationship between marker multiples-of-the-median and GDM was examined with multivariate regression. Model predictive performance for early (< 24 weeks’ gestation) and overall GDM diagnosis was evaluated by receiver operating characteristic curves. Results: Glucose, triglycerides, leptin, and lipocalin-2 were higher, while adiponectin was lower, in GDM (p < 0.05). Lipocalin-2 performed best in Caucasians, and triglycerides in South Asians with GDM. Family history of diabetes, previous GDM, South/East Asian ethnicity, parity, BMI, PAPP-A, triglycerides, and lipocalin-2 were significant independent GDM predictors (all p < 0.01), achieving an area under the curve of 0.91 (95% confidence interval [CI] 0.89–0.94) overall, and 0.93 (95% CI 0.89–0.96) for early GDM, in a combined multivariate prediction model. Conclusions: A first-trimester risk prediction model, which incorporates novel maternal lipid markers, accurately identifies women at high risk of GDM, including early GDM.

Modelling the risk of abnormal pregnancy-related outcomes such as stillbirth and preterm birth have been proposed in the past. Commonly they utilize maternal demographic and medical history information as predictors, and they are based on conventional statistical modelling techniques. In this study, we utilize state-of-the-art machine learning methods in the task of predicting early stillbirth, late stillbirth and preterm birth pregnancies. The aim of this experimentation is to discover novel risk models that could be utilized in a clinical setting. A CDC data set of almost sixteen million observations was used conduct feature selection, parameter optimization and verification of proposed models. An additional NYC data set was used for external validation. Algorithms such as logistic regression, artificial neural network and gradient boosting decision tree were used to construct individual classifiers. Ensemble learning strategies of these classifiers were also experimented with. The best performing machine learning models achieved 0.76 AUC for early stillbirth, 0.63 for late stillbirth and 0.64 for preterm birth while using a external NYC test data. The repeatable performance of our models demonstrates robustness that is required in this context. Our proposed novel models provide a solid foundation for risk prediction and could be further improved with the addition of biochemical and/or biophysical markers.