Gut microbiota and obesity: implications for fecal microbiota transplantation therapy

Abstract

Obesity is a major public health issue as it is causally associated with several chronic disorders, including type-2 diabetes, cerebrovascular disease (CVD), and cancer. In the United States and other countries worldwide, the obesity epidemic has drastically impacted the status of health of millions as well as healthcare costs. Aside from poor diet, hygiene, and genetics, there are many other factors thought to play an important role in the emergence of obesity. Nowadays, accumulating evidence is elucidating the relation of dysbiosis of intestinal bacteria with obesity and metabolic disorders. Certain gut microbial strains have been shown to inhibit or attenuate immune responses related to chronic inflammation in experimental models, suggesting that specific species among gut microbiota may play either a protective or a pathogenic role in the progression of obesity. Fecal microbiota transplantation (FMT) can therefore represent a therapeutic approach for obesity treatment. FMT is a relatively straightforward therapy that manipulates the human gastrointestinal (GI) microbiota by transferring healthy donor microbiota into an existing but disturbed microbial ecosystem. However, the relevant scientific work is still in its early stages. In this review, we summarize the cutting-edge research being done into FMT treatment of obesity, current issues in FMT treatment, and the future of FMT and microbial therapeutics.

Gut Microbiota and Liver Interaction through Immune System Cross-Talk: A Comprehensive Review at the Time of the SARS-CoV-2 Pandemic

“…In fact, obesity itself, with or without a high fat-diet intake, can shape the gut microbiota. On the other hand, this “obese” microbiota can reprogram the gut as well as the entire body to maximize nutrient absorption and an accumulative metabolism [ 74 ]. Furthermore, this shift in microbial populations has been associated with a metabolic endotoxaemia due to higher LPS passage through an impaired IP [ 75 ].…”

Analysis of the Relationship between Gut Flora Levels in Childhood Obese Population and Normal Healthy Population Based on Machine Learning

“…Compared with classical statistical methods, machine learning is more suitable for dealing with large-scale learning problems and shows excellent performance when dealing with high-dimensional structured data obtained by metagenomic sequencing [ 7 ]. Machine learning methods can deeply mine the interaction information contained in the flora data by extracting features, which can deal with a variety of biological information problems, identify relevant biomarker variables based on sparse data, and prevent overfitting [ 8 ]. For multitask problems, the performance is improved by using information from various related tasks, for clustering feature analysis of unlabeled data [ 9 ].…”

Fecal microbiota transplantation in the metabolic diseases: Current status and perspectives

“…The oral cavity is an important location of intestinal microbiota, which has an important effect on human health. Studies have shown that children who grow up on farms have a lower risk of asthma; a phenomenon that may be linked to changes in their gut microbiota[ 8 ]. In addition, babies born by cesarean section are at increased risk of developing autoimmune diseases, mainly because the initial microbes passed from the vagina to the baby at birth are replaced by skin microbes from the mother and surgical team members, which alter the baby’s gut microbes[ 9 ].…”