The benefits of ketone production regimens remain controversial. Here, we hypothesized that the ketone-producing regimens modulated cognitive impairment, glucose metabolism, and inflammation while altering the gut microbiome. The hypothesis and the mechanism were explored in amyloid-β infused rats. Rats that received an amyloid-β(25–35) infusion into the hippocampus had either ketogenic diet (AD-KD), intermittent fasting (AD-IMF), 30 energy percent fat diet (AD-CON), or high carbohydrate (starch) diet (AD-CHO) for 8 weeks. AD-IMF and AD-CHO, but not AD-KD, lowered the hippocampal amyloid-β deposition compared to the AD-CON despite serum ketone concentrations being elevated in both AD-KD and AD-IMF. AD-IMF and AD-CHO, but not AD-KD, improved memory function in passive avoidance, Y maze, and water maze tests compared to the AD-CON. Hippocampal insulin signaling (pAkt→pGSK-3β) was potentiated and pTau was attenuated in AD-IMF and AD-CHO much more than AD-CON. AD-IMF and AD-CON had similar glucose tolerance results during OGTT, but AD-KD and AD-IMF exhibited glucose intolerance. AD-KD exacerbated gut dysbiosis by increasing Proteobacteria , and AD-CHO improved it by elevating Bacteriodetes . In conclusion, ketone production itself might not improve memory function, insulin resistance, neuroinflammation or the gut microbiome when induced by ketone-producing remedies. Intermittent fasting and a high carbohydrate diet containing high starch may be beneficial for people with dementia.

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.

Chronic alcohol intake causes hepatic steatosis and changes the body composition and glucose metabolism. We examined whether water extracts of mulberry (WMB) and white flower dandelion ( Taraxacum coreanum Nakai, WTC) can prevent and/or delay the symptoms of chronic ethanol-induced hepatic steatosis in male Sprague Dawley rats, and explored the mechanisms. Ethanol degradation was examined by orally administering 3 g ethanol/kg bw after giving them 0.3 g/kg bw WMB or WTC. All rats were continuously provided about 7 g ethanol/kg bw/day for four weeks and were given either of 0.1% dextrin (control), WMB, WTC, or water extracts of Hovenia dulcis Thunb fruit (positive-control) in high-fat diets. Area under the curve of serum ethanol levels was lowered in descending order of control, WTC and positive-control, and WMB in acute ethanol challenge. WMB and WTC prevented alcohol intake-related decrease in bone mineral density and lean body mass compared to the control. After glucose challenge, serum glucose levels increased more in the control group than other groups in the first part and the rate of decrease after 40 min was similar among all groups. These changes were associated with decreasing serum insulin levels. WMB had the greatest efficacy for decreasing triglyceride and increasing glycogen deposits. WMB and WTC prevented the disruption of the hepatic cells and nuclei while reducing malondialdehyde contents in rats fed alcohol, but the prevention was not as much as the normal-control. The ratio of Firmicutes to Bacteroidetes in the gut was much higher in the control than the normal-control, but WTC and WMB decreased the ratio compared to the control. WMB and WTC separated the gut microbiota community from the control. In conclusion, WMB and WTC protected against alcoholic liver steatosis by accelerating ethanol degradation and also improved body composition and glucose metabolism while alleviating the dysbiosis of gut microbiome by chronic alcohol intake. Impact statement Excessive alcohol consumption is associated with serious pathologies and is common in much of the world. Pathologies include liver damage, glucose intolerance, and loss of lean body mass and bone mass. These pathologies are mediated by changes in metabolism as well as toxic metabolic byproducts, and possibly by gut dysbiosis. In this study, we demonstrate that aqueous extracts of mulberry and dandelion protected rats against ethanol-induced losses in lean body and bone masses, improved glucose tolerance and partially normalized gut bacterial populations, with mulberry extract being generally more effective. This research suggests that mulberry and dandelion extracts may have the potential to improve some of the pathologies associated with excess alcohol consumption, and that further clinical research is warranted.

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Rice porridge containing Allium fistulosum (Welsh onion) root water extract (RAFR) has anti-inflammatory bioactive compounds. We examined whether the long-term administration of rice porridge with RAFR would prevent or delay the progression of osteoarthritis and menopausal symptoms in estrogen-deficient animals by ovariectomy. The rats consumed 40% fat energy diets containing 250 mg RAFR (rice: Allium fistulosum root = 13:1)/kg body weight (bw) (OVX-OA-RAFR-Low), 750 mg RAFR/kg bw (OVX-OA-RAFR-High) and 750 mg starch and protein/kg bw(OVX), respectively. After consuming the assigned diets for eight weeks, monoiodoacetate (OVX-OA) or saline (OVX) were injected into the knee joints of the rats for an additional three weeks. Sham rats were administered saline injections (normal-control). OVX-OA-RAFR improved oral glucose tolerance and also protected against decreases in bone mineral density and lean body mass in the legs and increases in fat mass in the abdomen, compared to the OVX and OVX-OA. OVX-OA-RAFR improved swelling and limping scores, normalized weight distribution between the osteoarthritic and normal limbs, and increased maximum running speeds compared to the OVX-OA. The OVX-OA deteriorated the articular cartilage by reducing the articular matrix and bone loss in the knee joint and it prevented knee joint deterioration when compared to the OVX. The improvement in osteoarthritis symptoms in OVX-OA-RAFR decreased the mRNA expression of matrix metallo-proteinase-1 and matrix metalloproteinase-13, tumor necrosis factor-α, and interleukin-1β and interleukin-6 in the articular cartilage compared to OVX-OA rats. In conclusions, RAFR is effective in treating osteoarthritis symptoms and it may be used for a therapeutic agent in osteoarthritis-induced menopausal women.

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.

Background: Skeletal muscle mass (SMM) and fat mass (FM) are essentially required for health and quality of life in older adults. Objective: To generate the best SMM and FM prediction models using machine learning models incorporating socioeconomic, lifestyle, and biochemical parameters and the urban hospital-based Ansan/Ansung cohort, and to determine relations between SMM and FM and metabolic syndrome and its components in this cohort. Methods: SMM and FM data measured using an Inbody 4.0 unit in 90% of Ansan/Ansung cohort participants were used to train seven machine learning algorithms. The ten most essential predictors from 1411 variables were selected by: (1) Manually filtering out 48 variables, (2) generating best models by random grid mode in a training set, and (3) comparing the accuracy of the models in a test set. The seven trained models’ accuracy was evaluated using mean-square errors (MSE), mean absolute errors (MAE), and R² values in 10% of the test set. SMM and FM of the 31,025 participants in the Ansan/Ansung cohort were predicted using the best prediction models (XGBoost for SMM and artificial neural network for FM). Metabolic syndrome and its components were compared between four groups categorized by 50 percentiles of predicted SMM and FM values in the cohort. Results: The best prediction models for SMM and FM were constructed using XGBoost (R2 = 0.82) and artificial neural network (ANN; R2 = 0.89) algorithms, respectively; both models had a low MSE. Serum platelet concentrations and GFR were identified as new biomarkers of SMM, and serum platelet and bilirubin concentrations were found to predict FM. Predicted SMM and FM values were significantly and positively correlated with grip strength (r = 0.726) and BMI (r = 0.915, p < 0.05), respectively. Grip strengths in the high-SMM groups of both genders were significantly higher than in low-SMM groups (p < 0.05), and blood glucose and hemoglobin A1c in high-FM groups were higher than in low-FM groups for both genders (p < 0.05). Conclusion: The models generated by XGBoost and ANN algorithms exhibited good accuracy for estimating SMM and FM, respectively. The prediction models take into account the actual clinical use since they included a small number of required features, and the features can be obtained in outpatients. SMM and FM predicted using the two models well represented the risk of low SMM and high fat in a clinical setting.