论文题目:Persistent microbiome alterations modulate the rate of post-dieting weight regain
scholar 引用:169
页数:23
发表时间:2016.12
发表刊物:nature
作者:Christoph A. Thaiss, Shlomik Itav,...,Eran Segal & Eran Elinav
摘要:
In tackling the obesity pandemic, considerable efforts are devoted to the development of effective weight reduction strategies, yet many dieting individuals fail to maintain a long-term weight reduction, and instead undergo excessive weight regain cycles. The mechanisms driving recurrent post-dieting obesity remain largely elusive. Here we identify an intestinal microbiome signature that persists after successful dieting of obese mice and contributes to faster weight regain and metabolic aberrations upon re-exposure to obesity-promoting conditions. Faecal transfer experiments show that the accelerated weight regain phenotype can be transmitted to germ-free mice. We develop a machine-learning algorithm that enables personalized microbiome-based prediction of the extent of post-dieting weight regain. Additionally, we find that the microbiome contributes to diminished post-dieting flavonoid levels and reduced energy expenditure, and demonstrate that flavonoid-based ‘post-biotic’ intervention ameliorates excessive secondary weight gain. Together, our data highlight a possible microbiome contribution to accelerated post-dieting weight regain, and suggest that microbiome-targeting approaches may help to diagnose and treat this common disorder.
正文组织架构:
1. Main
2. Enhanced weight regain after dieting
3. Persistence of post-dieting microbiome alterations
4. The post-dieting microbiome contributes to weight regain
5. Microbiota composition predicts weight regain
6. Metabolites contribute to post-dieting weight regain
7. Flavonoids modulate weight regain and UCP1 expression
8. Discussion
9. Methods
正文部分内容摘录:
1. Biological Problem: What biological problems have been solved in this paper?
2. Main discoveries: What is the main discoveries in this paper?
3. ML(Machine Learning) Methods: What are the ML methods applied in this paper?
4. ML Advantages: Why are these ML methods better than the traditional methods in these biological problems?