Metabolome analysis reveals that cyclic adenosine diphosphate ribose contributes to the regulation of differentiation in mice adipocyte.

Adipocytes play a key role in energy storage and homeostasis. Although the role of transcription factors in adipocyte differentiation is known, the effect of endogenous metabolites of low molecular weight remains unclear. Here, we analyzed time-dependent changes in the levels of these metabolites throughout adipocyte differentiation, using metabolome analysis, and demonstrated that there is a positive correlation between cyclic adenosine diphosphate ribose (cADPR) and Pparγ mRNA expression used as a marker of differentiation. We also found that the treatment of C3H10T1/2 adipocytes with cADPR increased the mRNA expression of those marker genes and the accumulation of triglycerides. Furthermore, inhibition of ryanodine receptors (RyR), which are activated by cADPR, caused a significant reduction in mRNA expression levels of the marker genes and triglyceride accumulation in adipocytes. Our findings show that cADPR accelerates adipocytic differentiation via RyR pathway.

Impact of dietary supplementation with resistant dextrin (NUTRIOSE®) on satiety, glycaemia, and related endpoints, in healthy adults.

PURPOSE: Resistant dextrin (RD) supplementation has been shown to alter satiety, glycaemia, and body weight, in overweight Chinese men; however, there are limited data on its effects in other demographic groups. Here, we investigated the effects of RD on satiety in healthy adults living in the United Kingdom. METHODS: 20 normal weight and 16 overweight adults completed this randomised controlled cross-over study. Either RD (14 g/day NUTRIOSE® FB06) or maltodextrin control was consumed in mid-morning and mid-afternoon preload beverages over a 28-day treatment period with crossover after a 28-day washout. During 10-h study visits (on days 1, 14, and 28 of each treatment period), satietogenic, glycaemic and anorectic hormonal responses to provided meals were assessed. RESULTS: Chronic supplementation with RD was associated with higher fasted satiety scores at day 14 (P = 0.006) and day 28 (P = 0.040), compared to control. RD also increased satiety after the mid-morning intervention drink, but it was associated with a reduction in post-meal satiety following both the lunch and evening meals (P < 0.01). The glycaemic response to the mid-morning intervention drink (0-30 min) was attenuated following RD supplementation (P < 0.01). Whilst not a primary endpoint we also observed lower systolic blood pressure at day 14 (P = 0.035) and 28 (P = 0.030), compared to day 1, following RD supplementation in the normal weight group. Energy intake and anthropometrics were unaffected. CONCLUSIONS: RD supplementation modified satiety and glycaemic responses in this cohort, further studies are required to determine longer-term effects on body weight control and metabolic markers. CLINICALTRIALS. GOV REGISTRATION: NCT02041975 (22/01/2014).

Machine Learning Approaches for Stroke Risk Prediction: Findings from the Suita Study.

Stroke constitutes a significant public health concern due to its impact on mortality and morbidity. This study investigates the utility of machine learning algorithms in predicting stroke and identifying key risk factors using data from the Suita study, comprising 7389 participants and 53 variables. Initially, unsupervised k-prototype clustering categorized participants into risk clusters, while five supervised models including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosted Machine (LightGBM) were employed to predict stroke outcomes. Stroke incidence disparities among identified risk clusters using the unsupervised k-prototype clustering method are substantial, according to the findings. Supervised learning, particularly RF, was a preferable option because of the higher levels of performance metrics. The Shapley Additive Explanations (SHAP) method identified age, systolic blood pressure, hypertension, estimated glomerular filtration rate, metabolic syndrome, and blood glucose level as key predictors of stroke, aligning with findings from the unsupervised clustering approach in high-risk groups. Additionally, previously unidentified risk factors such as elbow joint thickness, fructosamine, hemoglobin, and calcium level demonstrate potential for stroke prediction. In conclusion, machine learning facilitated accurate stroke risk predictions and highlighted potential biomarkers, offering a data-driven framework for risk assessment and biomarker discovery.

Predicting Cardiovascular Disease Mortality: Leveraging Machine Learning for Comprehensive Assessment of Health and Nutrition Variables.

Cardiovascular disease (CVD) is one of the primary causes of death around the world. This study aimed to identify risk factors associated with CVD mortality using data from the National Health and Nutrition Examination Survey (NHANES). We created three models focusing on dietary data, non-diet-related health data, and a combination of both. Machine learning (ML) models, particularly the random forest algorithm, demonstrated robust consistency across health, nutrition, and mixed categories in predicting death from CVD. Shapley additive explanation (SHAP) values showed age, systolic blood pressure, and several other health factors as crucial variables, while fiber, calcium, and vitamin E, among others, were significant nutritional variables. Our research emphasizes the importance of comprehensive health evaluation and dietary intake in predicting CVD mortality. The inclusion of nutrition variables improved the performance of our models, underscoring the utility of dietary intake in ML-based data analysis. Further investigation using large datasets with recurring dietary recalls is necessary to enhance the effectiveness and interpretability of such models.

Phenethylamine in chlorella alleviates high-fat diet-induced mouse liver damage by regulating generation of methylglyoxal.

This study examined the effects of oral administration of water extract of chlorella (WEC) (100 mg/kg bodyweight) and phenethylamine (10 µg/kg bodyweight) on high-fat diet (HFD)-induced liver damage in mice. Phenethylamine significantly mitigated HFD-induced lipid oxidation (generation of malondialdehyde) and liver damage without markedly decreasing hepatic lipid accumulation. WEC exerted similar effects although with decreased efficacy. In addition, WEC and phenethylamine decreased the methylglyoxal levels and increased the glyceraldehyde 3-phosphate dehydrogenase (GAPDH) protein levels in the liver. Methylglyoxal is generated from substrates of GAPDH, dihydroxyacetone phosphate and glyceraldehyde 3-phosphate. These facts indicate that methylglyoxal triggers oxidation of accumulated lipid, which generates malondialdehyde and consequently induces liver damage. Suppression of generation of toxic aldehydes by WEC and phenethylamine was also confirmed by maintaining hepatic cysteine, highly reactive to aldehydes. Thus, trace amounts of phenethylamine alleviate HFD-induced liver damage by regulating methylglyoxal via increase of GAPDH.

Development of a Method for Quantitation of Glyceraldehyde in Various Body Compartments of Rodents and Humans.

There is limited information available about the physiological content of glyceraldehyde, a precursor of toxic advanced glycation end products. The conventional derivatization method for aldoses using 1-phenyl-3-methyl-5-pyrazolone did not allow reproducible quantification of glyceraldehyde due to the instability of glyceraldehyde compared to other aldoses. We optimized the derivatization condition to achieve high and reproducible recovery of derivatives for liquid chromatography tandem mass spectrometry quantification. Based on the stability of glyceraldehyde during sample preparation and high recovery of spiked standard, the present method provides reproducible quantification of glyceraldehyde in the body. The glyceraldehyde contents in fasting conditions in the rodent liver (mice: 50.0 ± 3.9 nmol/g; rats: 35.5 ± 4.9 nmol/g) were higher than those in plasma (9.4 ± 1.7 and 7.2 ± 1.2 nmol/mL). The liver glyceraldehyde levels significantly increased after food consumption (p < 0.05) but remained constant in the plasma. High fat diet feeding significantly increased plasma glyceraldehyde levels in mice (p < 0.005). In healthy human volunteers, the plasma glyceraldehyde levels remained unchanged after the consumption of steamed rice. In patients with type 2 diabetes, the plasma glyceraldehyde level was positively correlated with the plasma glucose level (r = 0.84; p < 0.0001).

In vitro fermentation of NUTRIOSE(®) FB06, a wheat dextrin soluble fibre, in a continuous culture human colonic model system.

Wheat dextrin soluble fibre may have metabolic and health benefits, potentially acting via mechanisms governed by the selective modulation of the human gut microbiota. Our aim was to examine the impact of wheat dextrin on the composition and metabolic activity of the gut microbiota. We used a validated in vitro three-stage continuous culture human colonic model (gut model) system comprised of vessels simulating anatomical regions of the human colon. To mimic human ingestion, 7 g of wheat dextrin (NUTRIOSE(®) FB06) was administered to three gut models, twice daily at 10.00 and 15.00, for a total of 18 days. Samples were collected and analysed for microbial composition and organic acid concentrations by 16S rRNA-based fluorescence in situ hybridisation and gas chromatography approaches, respectively. Wheat dextrin mediated a significant increase in total bacteria in vessels simulating the transverse and distal colon, and a significant increase in key butyrate-producing bacteria Clostridium cluster XIVa and Roseburia genus in all vessels of the gut model. The production of principal short-chain fatty acids, acetate, propionate and butyrate, which have been purported to have protective, trophic and metabolic host benefits, were increased. Specifically, wheat dextrin fermentation had a significant butyrogenic effect in all vessels of the gut model and significantly increased production of acetate (vessels 2 and 3) and propionate (vessel 3), simulating the transverse and distal regions of the human colon, respectively. In conclusion, wheat dextrin NUTRIOSE(®) FB06 is selectively fermented in vitro by Clostridium cluster XIVa and Roseburia genus and beneficially alters the metabolic profile of the human gut microbiota.