Identifying Glucose Metabolism Status in Nondiabetic Japanese Adults Using Machine Learning Model with Simple Questionnaire.

We aimed to identify the glucose metabolism statuses of nondiabetic Japanese adults using a machine learning model with a questionnaire. In this cross-sectional study, Japanese adults (aged 20-64 years) from Tokyo and surrounding areas were recruited. Participants underwent an oral glucose tolerance test (OGTT) and completed a questionnaire regarding lifestyle and physical characteristics. They were classified into four glycometabolic categories based on the OGTT results: category 1: best glucose metabolism, category 2: low insulin sensitivity, category 3: low insulin secretion, and category 4: combined characteristics of categories 2 and 3. A total of 977 individuals were included; the ratios of participants in categories 1, 2, 3, and 4 were 46%, 21%, 14%, and 19%, respectively. Machine learning models (decision tree, support vector machine, random forest, and XGBoost) were developed for identifying the glycometabolic category using questionnaire responses. Then, the top 10 most important variables in the random forest model were selected, and another random forest model was developed using these variables. Its areas under the receiver operating characteristic curve (AUCs) to classify category 1 and the others, category 2 and the others, category 3 and the others, and category 4 and the others were 0.68 (95% confidence intervals: 0.62-0.75), 0.66 (0.58-0.73), 0.61 (0.51-0.70), and 0.70 (0.62-0.77). For external validation of the model, the same dataset of 452 Japanese adults in Hokkaido was obtained. The AUCs to classify categories 1, 2, 3, and 4 and the others were 0.66 (0.61-0.71), 0.57 (0.51-0.62), 0.60 (0.50-0.69), and 0.64 (0.57-0.71). In conclusion, our model could identify the glucose metabolism status using only 10 factors of lifestyle and physical characteristics. This model may help the larger general population without diabetes to understand their glucose metabolism status and encourage lifestyle improvement to prevent diabetes.

Genomic predictions and genome-wide association studies based on RAD-seq of quality-related metabolites for the genomics-assisted breeding of tea plants.

Effectively using genomic information greatly accelerates conventional breeding and applying it to long-lived crops promotes the conversion to genomic breeding. Because tea plants are bred using conventional methods, we evaluated the potential of genomic predictions (GPs) and genome-wide association studies (GWASs) for the genetic breeding of tea quality-related metabolites using genome-wide single nucleotide polymorphisms (SNPs) detected from restriction site-associated DNA sequencing of 150 tea accessions. The present GP, based on genome-wide SNPs, and six models produced moderate prediction accuracy values (r) for the levels of most catechins, represented by ( -)-epigallocatechin gallate (r = 0.32-0.41) and caffeine (r = 0.44-0.51), but low r values for free amino acids and chlorophylls. Integrated analysis of GWAS and GP detected potential candidate genes for each metabolite using 80-160 top-ranked SNPs that resulted in the maximum cumulative prediction value. Applying GPs and GWASs to tea accession traits will contribute to genomics-assisted tea breeding.

Dipeptide tyrosyl-leucine exhibits antidepressant-like activity in mice.

Depression is a worldwide health problem. In the present study, we found that a dipeptide, tyrosyl leucine (Tyr-Leu, YL), administered orally, intracerebroventricularly, or intraperitoneally exhibited a potent antidepressant-like activity in the forced swim and tail suspension tests in naïve mice. YL increased the amount of cells expressing c-Fos, a marker for neuronal activity, in the dentate gyrus of the hippocampus. YL increased bromo-2'-deoxyuridine-positive cells and doublecortin expression in the dentate gyrus of the hippocampus, suggesting that YL enhanced the proliferation of hippocampal progenitor cells in vivo and in vitro. YL did not affect hippocampal mRNA and protein expression of BDNF, which is a regulatory factor of both neurogenesis and depression-like behavior. Intriguingly, YL suppressed activation of the hypothalamo-pituitary-adrenal axis by forced swim stress. Moreover, other aromatic amino acid-leucines, Phe-Leu and Trp-Leu, also exhibited antidepressant-like activities, suggesting that the structure of aromatic amino acid-leucine may be important for antidepressant activity. In addition, bovine milk casein-derived peptide, Tyr-Leu-Gly (YLG), an anxiolytic peptide, exhibited an antidepressant-like activity. Our findings demonstrate that YL exhibits an antidepressant-like effect, moderates the stress response, and induces hippocampal neuronal proliferation through a signal pathway independent of BDNF.

Semi-quantitative Assessment Using [18F]FDG Tracer in Patients with Severe Brain Injury.

Patients with severe traumatic brain injury (sTBI) have difficulty knowing whether they are accurately expressing their thoughts and emotions because of disorders of consciousness, disrupted higher brain function, and verbal disturbances. As a consequence of an insufficient ability to communicate, objective evaluations are needed from family members, medical staff, and caregivers. One such evaluation is the assessment of functioning brain areas. Recently, multimodal brain imaging has been used to explore the function of damaged brain areas. [18F]-fluorodeoxyglucose positron emission tomography-computed tomography ([18F]FDG-PET/CT) is a successful tool for examining brain function. However, the assessment of brain glucose metabolism based on [18F]FDG-PET/CT is not standardized and depends on several varying parameters, as well as the patient's condition. Here, we describe a series of semiquantitative assessment protocols for a region-of-interest (ROI) image analysis using self-produced [18F]FDG tracers in patients with sTBI. The protocol focuses on screening the participants, preparing the [18F]FDG tracer in the hot lab, scheduling the acquisition of [18F]FDG-PET/CT brain images, and measuring glucose metabolism using the ROI analysis from a targeted brain area.

Identification of Rubisco anxiolytic-like peptides (rALPs) by comprehensive analysis of spinach green leaf protein digest.

Rubisco, an enzyme for photosynthetic carbon dioxide fixation, is a major green leaf protein and known as the most abundant protein on the Earth. We found that Rubisco digested mimicking gastrointestinal enzymatic conditions exhibited anxiolytic-like effects after oral administration in mice. Based on a comprehensive peptide analysis of the digest using nanoLC-Orbitrap-MS and the structure-activity relationship of known anxiolytic-like peptides, we identified SYLPPLTT, SYLPPLT and YHIEPV [termed Rubisco anxiolytic-like peptide (rALP)-1, rALP-1(1-7) and rALP-2, respectively], which exhibited potent anxiolytic-like effects after oral administration. The anxiolytic-like effects of rALP-1/rALP-1(1-7) were blocked by a serotonin 5-HT1A receptor antagonist, whereas rALP-2-induced effects were inhibited by a δ-opioid receptor antagonist. In conclusion, novel Rubisco-derived anxiolytic-like peptides, rALP-1/rALP-1(1-7) and rALP-2, act via independent neural pathways.

Increased brain glucose metabolism in chronic severe traumatic brain injury as determined by longitudinal 18F-FDG PET/CT.

Little is known about changes in glucose metabolism in patients with chronic severe traumatic brain injury (sTBI). It remains to be elucidated how neurological manifestations of sTBI are associated with brain glucose metabolism during longitudinal follow-up. We show here that neurological manifestations are associated with changes of brain glucose metabolism by using two serial 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) images. In this longitudinal observational study, two serial 18F-FDG PET/CT images from each of 45 patients were analyzed for whole-brain maximum standardized uptake values (SUVmax). For clinical assessment, we applied two different scales: the coma recovery scale-revised and the original Chiba score with additional information regarding nutrition, excretion, facial expression, and position change of the patient's relative immobility and bedridden state. As a result, the increased FDG uptake group was associated with a high level of wakefulness (first PET, p = 0.04; second PET, p = 0.01) and small ventricular size (first PET, p = 0.01; second PET, p = 0.01). In addition, anticonvulsant withdrawal (p = 0.001), improvement of total Chiba score (p = 0.01), language expression (p = 0.03), position change (p = 0.03), and communication (p = 0.03) were accelerated in the increased FDG uptake group. Spearman's rank correlation coefficients of change in SUVmax and language expression between the first and second PET were 0.4 (p = 0.01). Our results indicate that chronic severe traumatic head injury patients have changed brain glucose metabolism.