Metabolic syndrome is associated with usual dietary patterns that may be involved in enterotypes. We aimed to understand the potential relationship of enterotypes and dietary patterns to influence metabolic syndrome in the Koreans. Using the Korea National Health and Nutrition Examination Survey (KNHANES)-VI in 2014, metabolic parameters were also analyzed among the dietary patterns classified by principal component analysis in Korean adults. The fecal microbiota data of 1199 Korean adults collected in 2014 were obtained from the Korea Centers for Disease Control and Prevention. Enterotypes were classified based on Dirichlet multinomial mixtures (DMM) by Mothur v.1.36. The functional abundance of fecal bacteria was analyzed using the PICRUSt2 pipeline. Korean adults were clustered into three dietary patterns including Korean-style balanced diets (KBD, 20.4%), rice-based diets (RBD, 17.2%), and Western-style diets (WSD, 62.4%) in KNHANES. The incidence of metabolic syndrome was lowered in the order of RBD, WSD, and KBD. The participants having a KBD had lower serum C-reactive protein and triglyceride concentrations than those with RBD and WSD (p < 0.05). Three types of fecal bacteria were classified as Ruminococcaceae type (ET-R, 28.7%), Prevotella type (ET-P, 52.2%), and Bacteroides type (ET-B, 42.1%; p < 0.05). ET-P had a higher abundance of Prevotella copri, while ET-R contained a higher abundance of Alistipes, Akkermansia muciniphila, Bifidobacterium adolescentis, and Faecalibacterium prausnitzii. ET-B had a higher abundance of the order Bilophila (p < 0.05). Metabolism of propanoate, starch, and sucrose in fecal microbiome was higher in ET-P and ET-R, whereas fatty acid metabolism was enhanced in ET-B. Fecal microbiota in ET-P and ET-B had higher lipopolysaccharide biosynthesis activity than that in ET-R. The metabolic results of KBD and RBD were consistent with ET-R and ET-P’s gut microbiota metabolism, respectively. In conclusion, Korean enterotypes of ET-P, ET-B, and ET-R were associated with RBD, WSD, and KBD, respectively. This study suggests a potential link between dietary patterns, metabolic syndrome, and enterotypes among Korean adults.

The gut microbiota has been demonstrated to play a critical role in maintaining cognitive function via the gut-brain axis, which may be related to the parasympathetic nervous system (PNS). However, the exact mechanism remains to be determined. We investigated that patients with mild cognitive impairment (MCI) and Alzheimer’s disease (AD) could exhibit an altered gut microbiota through the suppression of the PNS, compared to the healthy individuals, using the combined gut microbiota data from previous human studies. The hypothesis was validated in rats to suppress the PNS by scopolamine injections. The human fecal bacterial FASTA/Q files were selected and combined from four different AD studies (n = 410). All rats had a high-fat diet and treatments for six weeks. The MD rats had memory impairment by scopolamine injection (2 mg/kg body weight; MD, Control) or no memory impairment by saline injection. The scopolamine-injected rats had a donepezil intake as the positive group. In the optimal model generated from the XGboost analysis, Blautia luti, Pseudomonas mucidoiens, Escherichia marmotae, and Gemmiger formicillis showed a positive correlation with MCI while Escherichia fergusonii, Mycobacterium neglectum, and Lawsonibacter asaccharolyticus were positively correlated with AD in the participants with enterotype Bacteroides (ET-B, n = 369). The predominant bacteria in the AD group were negatively associated in the networking analysis with the bacteria in the healthy group of ET-B participants. From the animal study, the relative abundance of Bacteroides and Bilophilia was lower, and that of Escherichia, Blautia, and Clostridium was higher in the scopolamine-induced memory deficit (MD) group than in the normal group. These results suggest that MCI was associated with the PNS suppression and could progress to AD by exacerbating the gut dysbiosis. MCI increased Clostridium and Blautia, and its progression to AD elevated Escherichia and Pseudomonas. Therefore, the modulation of the PNS might be linked to an altered gut microbiota and brain function, potentially through the gut-brain axis.

Wu X, et al. J Korean Med Sci. 2023 Apr;38(21):e162. https://doi.org/10.3346/jkms.2023.38.e162

Bile acid metabolism, involved with the digestion and absorption of nutrients in the gut, is linked to the gut microbiota community, greatly impacting the host’s metabolism. We examined the hypothesis that the modulation of bile acid metabolism by dietary fat contents, gallbladder removal (GBX; cholecystectomy), and bile acid sequestrant (BAS; cholestyramine) treatment could alter energy, glucose, and lipid metabolism through the changes in the gut microbiota. Mice were randomly assigned to the following six groups: (1) Sham GBX surgery (Sham) + low fat/high carbohydrate diet (LFD), (2) Sham + high fat diet (HFD), (3) Sham + HFD + BAS, (4) GBX + LFD, (5) GBX + HFD, and (6) GBX + HFD + BAS. BAS groups received 2% cholestyramine. After an 8-week intervention, energy, glucose, and lipid metabolism, and the gut microbiota community were measured. HFD groups exhibited higher body weight gain than LFD, and GBX increased the weight gain comped to Sham groups regardless of BAS in HFD (p < 0.05). Homeostatic model assessment for insulin resistance (HOMA-IR) was higher in HFD than LFD, and GBX increased it regardless of BAS. Serum lipid profiles were worsened in GBX + HFD compared to Sham + LFD, whereas BAS alleviated them, except for serum HDL cholesterol. Hepatic tumor-necrosis-factor-α (TNF-α) mRNA expression and lipid peroxide contents increased with GBX and BAS treatment compared to Sham and no BAS treatment (p < 0.05). Hepatic mRNA expression of sterol regulatory element-binding transcription factor 1c (SREBP1c) and peroxisome proliferator-activated receptor gamma (PPAR-γ) exhibited the same trend as that of tumor necrosis factor-α (TNF-α). The α-diversity of gut bacteria decreased in GBX + HFD and increased in GBX + HFD + BAS. Akkermentia, Dehalobacterium, SMB53, and Megamonas were high in the Sham + LFD, and Veillonella and Streptococcus were rich in the Sham + HFD, while Oscillospira and Olsenella were high in Sham + HFD + BAS (p < 0.05). GBX + LFD increased Lactobacillus and Sutterella while GBX + HFD + BAS elevated Clostridium, Alistipes, Blautia, Eubacterium, and Coprobacillus (p < 0.05). In conclusion, the modulation of bile acid metabolism influences energy, glucose, and lipid metabolisms, and it might be linked to changes in the gut microbiota by bile acid metabolism modulation.

Background: Insulin resistance is a common etiology of metabolic syndrome, but receiver operating characteristic (ROC) curve analysis shows a weak association in Koreans. Using a machine learning (ML) approach, we aimed to generate the best model for predicting insulin resistance in Korean adults aged > 40 of the Ansan/Ansung cohort using a machine learning (ML) approach. Methods: The demographic, anthropometric, biochemical, genetic, nutrient, and lifestyle variables of 8842 participants were included. The polygenetic risk scores (PRS) generated by a genome-wide association study were added to represent the genetic impact of insulin resistance. They were divided randomly into the training (n = 7037) and test (n = 1769) sets. Potentially important features were selected in the highest area under the curve (AUC) of the ROC curve from 99 features using seven different ML algorithms. The AUC target was ≥0.85 for the best prediction of insulin resistance with the lowest number of features. Results: The cutoff of insulin resistance defined with HOMA-IR was 2.31 using logistic regression before conducting ML. XGBoost and logistic regression algorithms generated the highest AUC (0.86) of the prediction models using 99 features, while the random forest algorithm generated a model with 0.82 AUC. These models showed high accuracy and k-fold values (>0.85). The prediction model containing 15 features had the highest AUC of the ROC curve in XGBoost and random forest algorithms. PRS was one of 15 features. The final prediction models for insulin resistance were generated with the same nine features in the XGBoost (AUC = 0.86), random forest (AUC = 0.84), and artificial neural network (AUC = 0.86) algorithms. The model included the fasting serum glucose, ALT, total bilirubin, HDL concentrations, waist circumference, body fat, pulse, season to enroll in the study, and gender. Conclusion: The liver function, regular pulse checking, and seasonal variation in addition to metabolic syndrome components should be considered to predict insulin resistance in Koreans aged over 40 years.

Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is characterized by hepatic fat accumulation by metabolic dysfunction. The rising prevalence of MAFLD, especially among Asians, may be associated with changes in gut microbiota. We investigated gut microbiota characteristics and potential mechanisms leading to MAFLD development according to enterotypes. Case-control studies examining the gut microbiota composition between MAFLD and non-MAFLD participants were searched in public databases until July 2023. Gut microbiota was categorized into two enterotypes by principal component analysis. According to the enterotypes, LEfSe, ALDEx2, XGBoost, and DCiPatho were utilized to identify differential abundances and pathogenic microbes in the gut between the MAFLD and non-MAFLD groups. We analyzed microbial community networks with the SprCC module and predicted microbial functions. In the Prevotella enterotype (ET-P), 98.6% of Asians and 65.1% of Caucasians were associated with MAFLD (p = 0.049). MAFLD incidence was correlated with enterotype, age, obesity, and ethnicity (p < 0.05). Asian MAFLD patients exhibited decreased Firmicutes and Akkermansia muciniphila and increased Bacteroidetes and P. copri. The pathogenicity scores were 0.006 for A. muciniphila and 0.868 for P. copri. The Asian MAFLD group showed decreased stability and complexity in the gut microbiota network. Metagenome function analysis revealed higher fructose metabolism and lipopolysaccharide (LPS) biosynthesis and lower animal proteins and α-linolenic acid metabolism in Asians with MAFLD compared with the non-MAFLD group. LPS biosynthesis was positively correlated with P. copri (p < 0.05). In conclusion, P. copri emerged as a potential microbial biomarker for MAFLD. These findings enhance our understanding of the pathological mechanisms of MAFLD mediated through the gut microbiota, providing insights for future interventions.