Perinatal depressive and anxiety symptoms are associated with gut microbiota in pregnant women with overweight and obesity.

The associations of gut microbiota with depressive and anxiety symptoms have been investigated mainly in non-pregnant humans, and currently there is a significant gap in research on pregnant women, especially those who are living with overweight and thus at a higher risk for experiencing perinatal mental health problems. In this study, we used shotgun metagenomic sequencing to analyze the gut microbiota of pregnant women with overweight and obesity, both in early and late pregnancy. We compared gut microbial diversity, composition, and function across groups with different trajectories of depressive (n=419) and anxiety (n=408) symptoms.

Links between gut microbiota with specific serum metabolite groups in pregnant women with overweight or obesity.

Background And Aim: Gut microbiota may regulate metabolism but is incompletely characterized in pregnancy. Our objective was to investigate the relations using omics techniques.

Methods And Results: In a cross-sectional setting, fecal and serum samples of 361 healthy pregnant women with overweight or obesity were analyzed with a combinatorial approach of metagenomics and targeted NMR-based metabolomics, with statistical and machine learning techniques to identify and analyze the extent to which the gut microbiota composition and predicted functions would be reflected in the serum metabolome.

Diet-Gut Microbiota Relations: Critical Appraisal of Evidence From Studies Using Metagenomics.

Diet may influence the gut microbiota and subsequently affect the host's health. Recent developments in methods analyzing the composition and function of the gut microbiota allow a deeper understanding of diet-gut microbiota relationships. A state-of-the-art methodology, shotgun metagenomics sequencing, offers a higher taxonomic resolution of the gut microbiota at the bacterial species and strain levels, and more accurate information regarding the functional potential of gut microbiota.

Gut microbiota composition and function in pregnancy as determinants of prediabetes at two-year postpartum.

Aims: Deep metagenomics offers an advanced tool for examining the relationship between gut microbiota composition and function and the onset of disease; in this case, does the composition and function of gut microbiota during pregnancy differ in women who develop prediabetes and those who do not at two-year postpartum, and whether the gut microbiota composition associates with glycemic traits.

Methods: In total, 439 women were recruited in early pregnancy. Gut microbiota was assessed by metagenomics analysis in early (13.

Aberrations in the early pregnancy serum metabolic profile in women with prediabetes at two years postpartum.

Introduction: Aberrations in circulating metabolites have been associated with diabetes and cardiovascular risk.

Objectives: To investigate if early and late pregnancy serum metabolomic profiles differ in women who develop prediabetes by two years postpartum compared to those who remain normoglycemic.

Methods: An NMR metabolomics platform was used to measure 228 serum metabolite variables from women with pre-pregnancy overweight in early and late pregnancy.

Distinct Diet-Microbiota-Metabolism Interactions in Overweight and Obese Pregnant Women: a Metagenomics Approach.

Diet and gut microbiota are known to modulate metabolic health. Our aim was to apply a metagenomics approach to investigate whether the diet-gut microbiota-metabolism and inflammation relationships differ in pregnant overweight and obese women. This cross-sectional study was conducted in overweight ( = 234) and obese ( = 152) women during early pregnancy.

Global phylogeography of Dengue type 1 and 2 viruses reveals the role of India.

Patterns in virus dispersal and epidemiology of viral diseases can be revealed by phylogeographic studies. Currently knowledge about phylogeography of Dengue virus (DENV) Types 1 and 2 is limited. We carried out the phylogeographic analyses for DENV-1 and DENV-2, by the Bayesian Markov Chain Monte Carlo (MCMC) approach, with emphasis on Indian isolates in relation to the global evolutionary dynamics of the viruses.