Branched-chain amino acids (BCAAs), composed of leucine, isoleucine, and valine, are important essential amino acids in human physiology. Decades of studies have revealed their roles in protein synthesis, regulating neurotransmitter synthesis, and the mechanistic target of rapamycin (mTOR). BCAAs are found to be related to many metabolic disorders, such as insulin resistance, obesity, and heart failure. Also, many diseases are related to the alteration of the BCAA catabolism enzyme branched-chain α-keto acid dehydrogenase kinase (BCKDK), including maple syrup urine disease, human autism with epilepsy, and so on. In this review, diseases and the corresponding therapies are discussed after the introduction of the catabolism and detection methods of BCAAs and BCKDK. Also, the interaction between microbiota and BCAAs is highlighted.

Background: Non-alcoholic fatty liver disease (NAFLD) represents a severe public health problem. Dysbiosis of gut microbiome has been identified as one of the key environmental factors contributing to NAFLD. As an essential nutrition, Vitamin D (VD) plays an important role in regulating gut microbiota based on its receptor (Vitamin D Receptor, VDR) which is highly expressed in the gastrointestinal tract.Methods: Rats were fed with HFD (high-fat diet) for 12 weeks. And the rats were treated with VD two times a week by intraperitoneal injection for 12 weeks. H&E staining combined with plasma biochemical index was performed to characterize pathological changes and function of the liver. Fecal microbiota 16S rRNA gene sequencing and metabolomics were taken to reveal the altered gut microbiota and metabolites.Result: The VD alleviates the HFD-induced lipid accumulation in the liver as well as decreases the levels of amlodipine besylate (ALT) and amlodipine aspartate (AST). VD supplement decreased the ratio of phylum Firmicutes/Bacteroidetes (F/B) but increased alpha diversity. In addition, the VD treatment improved the HFD-induced gut microbiota by increasing the Prevotella and Porphyromonadaceae and decreasing Mucispirillum, Acetatifactor, Desulfovibrio, and Oscillospira abundance. Furthermore, the capability of tyrosine metabolism, tryptophan metabolism, arginine biosynthesis, and sphingolipid metabolism was enhanced after VD treatment. Consistently, Prevotella positively correlated with tryptophan metabolism and sphingolipid metabolism. Importantly, the Prevotella abundance was positively associated with serotonin, melatonin, tryptamine, L-arginine, and 3-dehydrosphinganine which synthesize from tryptophan, tyrosine, arginosuccinate, and serine, respectively.Conclusion: VD treatment inhibited HFD-induced NAFLD accompany by dysbiosis gut microbiota and metabolites, suggesting that VD supplement could be a potential intervention used for NAFLD treatment by targeting the specific microbiota.

A growing number of studies have shown the connection between gut microbiota, obesity, and cancer. However, to our knowledge, the association between gut microbiota and endometrial cancer progression in humans has not been studied.

Research on obesity and related diseases has received attention from government policymakers; interventions targeting nutrient intake, dietary patterns, and physical activity are deployed globally. An urgent issue now is how can we improve the efficiency of obesity research or obesity interventions. Currently, machine learning (ML) methods have been widely applied in obesity-related studies to detect obesity disease biomarkers or discover intervention strategies to optimize weight loss results. In addition, an open source of these algorithms is necessary to check the reproducibility of the research results. Furthermore, appropriate applications of these algorithms could greatly improve the efficiency of similar studies by other researchers. Here, we proposed a mini-review of several open-source ML algorithms, platforms, or related databases that are of particular interest or can be applied in the field of obesity research. We focus our topic on nutrition, environment and social factor, genetics or genomics, and microbiome-adopting ML algorithms.