Ning Li, Lifang Peng, Huixin He
Management Science and Engineering Department, Management School, Xiamen University, Xiamen, China.
Computer Science and Engineering Department, Computer Science and Engineering School, Huaqiao University, Quanzhou, China.
Front Mol Biosci. 2020 Dec 15;7:600720. doi: 10.3389/fmolb.2020.600720. eCollection 2020.
The gut microbiota is composed of a large number of different bacteria, that play a key role in the construction of a metabolic signaling network. Deepening the link between metabolic pathways of the gut microbiota and human health, it seems increasingly essential to evolutionarily define the principal technologies applied in the field and their future trends. We use a topic analysis tool, Latent Dirichlet Allocation, to extract themes as a probabilistic distribution of latent topics from literature dataset. We also use the Prophet neural network prediction tool to predict future trend of this area of study. A total of 1,271 abstracts (from 2006 to 2020) were retrieved from MEDLINE with the query on "gut microbiota" and "metabolic pathway." Our study found 10 topics covering current research types: dietary health, inflammation and liver cancer, fatty and diabetes, microbiota community, hepatic metabolism, metabolomics-based approach and SFCAs, allergic and immune disorders, gut dysbiosis, obesity, brain reaction, and cardiovascular disease. The analysis indicates that, with the rapid development of gut microbiota research, the metabolomics-based approach and SCFAs (topic 6) and dietary health (topic 1) have more studies being reported in the last 15 years. We also conclude from the data that, three other topics could be heavily focused in the future: metabolomics-based approach and SCFAs (topic 6), obesity (topic 8) and brain reaction and cardiovascular disease (topic 10), to unravel microbial affecting human health.
肠道微生物群由大量不同的细菌组成，它们在代谢信号网络的构建中起着关键作用。随着肠道微生物群代谢途径与人类健康之间的联系日益加深，从进化角度定义该领域应用的主要技术及其未来趋势显得越发重要。我们使用主题分析工具潜在狄利克雷分配（Latent Dirichlet Allocation）从文献数据集中提取主题，作为潜在主题的概率分布。我们还使用先知神经网络预测工具来预测该研究领域的未来趋势。通过在MEDLINE上检索，以“肠道微生物群”和“代谢途径”为关键词，共检索到1271篇摘要（2006年至2020年）。我们的研究发现了涵盖当前研究类型的10个主题：饮食健康、炎症与肝癌、脂肪与糖尿病、微生物群落、肝脏代谢、基于代谢组学的方法与短链脂肪酸、过敏与免疫紊乱、肠道菌群失调、肥胖、大脑反应和心血管疾病。分析表明，随着肠道微生物群研究的迅速发展，在过去15年中，基于代谢组学的方法与短链脂肪酸（主题6）和饮食健康（主题1）有更多的研究报道。我们还从数据中得出结论，未来可能会重点关注另外三个主题：基于代谢组学的方法与短链脂肪酸（主题6）、肥胖（主题8）以及大脑反应和心血管疾病（主题10），以揭示微生物对人类健康的影响。
