| Literature DB >> 30405455 |
Rachael Horne1, Jane A Foster1,2.
Abstract
Advances in understanding the role of the microbiome in physical and mental health are at the forefront of medical research and hold potential to have a direct impact on precision medicine approaches. In the past 7 years, we have studied the role of microbiota-brain communication on behavior in mouse models using germ-free mice, mice exposed to antibiotics, and healthy specific pathogen free mice. Through our work and that of others, we have seen an amazing increase in our knowledge of how bacteria signal to the brain and the implications this has for psychiatry. Gut microbiota composition and function are influenced both by genetics, age, sex, diet, life experiences, and many other factors of psychiatric and bodily disorders and thus may act as potential biomarkers of the gut-brain axis that could be used in psychiatry and co-morbid conditions. There is a particular need in major depressive disorder and other mental illness to identify biomarkers that can stratify patients into more homogeneous groups to provide better treatment and for development of new therapeutic approaches. Peripheral outcome measures of host-microbe bidirectional communication have significant translational value as biomarkers. Enabling stratification of clinical populations, based on individual biological differences, to predict treatment response to pharmacological and non-pharmacological interventions. Here we consider the links between co-morbid metabolic syndrome and depression, focusing on biomarkers including leptin and ghrelin in combination with assessing gut microbiota composition, as a potential tool to help identify individual differences in depressed population.Entities:
Keywords: ghrelin; gut-brain axis; leptin; major depression (MDD); microbiome
Year: 2018 PMID: 30405455 PMCID: PMC6204462 DOI: 10.3389/fpsyt.2018.00513
Source DB: PubMed Journal: Front Psychiatry ISSN: 1664-0640 Impact factor: 4.157
Bacterial taxa differences at the family and genus level observed in individuals with major depressive disorder.
| Naseribafrouei et al. ( | Lacnopiraceae (down) | |
| Jiang et al. ( | Acidaminoccocaceae (up) | |
| Mothur metastats | Enterobacteriaceae (up) | |
| Fusobacteriaceae (up) | ||
| Porphyromonadaceae (up) | ||
| Rikenellaceae (up) | ||
| Bacteroidaceae (down) | ||
| Erysipelotrichaceae (down) | ||
| Lacnopiraceae (down) | ||
| Prevotellaceae (down) | ||
| Ruminococcaceae (down) | ||
| Veillonellaceae (down) | ||
| Jiang et al. ( | ||
| LefSe LDA | ||
| Alpha leve = 0.05 | ||
| Effect size threshold = 2 | ||
| Kelly et al. ( | Prevoellaceae (down) | |
| Mann-Whitney U test | Thermoanaerobacteriaceae (up) | |
| FDR adjusted 10% | ||
| Lin et al. ( | ||
| Wilcoxon's sign rank test | ||
| Zheng et al. ( | Actinomycineae (up) | |
| Random forest classifier | Coriobacterineae (up) | |
| Lactobacillaceae (up) | ||
| Streptococcaceae (up) | ||
| Clostridales incertae sedis XI (up) | ||
| Eubacteriaceae (up) | ||
| Lachnospiraceae (up) | ||
| Ruminococcaceae (up) | ||
| Erysipelotrichaceae in certae sedis (up) | ||
| Bacteroidaceae (down) | ||
| Rikenellaceae (down) | ||
| Lachnospiraceae (down) | ||
| Acidaminococcaceae (down) | ||
| Vellonellaceae (down) | ||
| Sutterellaceae (down) | ||