Irritable bowel syndrome (IBS) is diagnosed by subjective clinical symptoms. We aimed to establish an objective IBS prediction model based on gut microbiome analyses employing machine learning. We collected fecal samples and clinical data from 85 adult patients who met the Rome III criteria for IBS, as well as from 26 healthy controls. The fecal gut microbiome profiles were analyzed by 16S ribosomal RNA sequencing, and the determination of short-chain fatty acids was performed by gas chromatography–mass spectrometry. The IBS prediction model based on gut microbiome data after machine learning was validated for its consistency for clinical diagnosis. The fecal microbiome alpha-diversity indices were significantly smaller in the IBS group than in the healthy controls. The amount of propionic acid and the difference between butyric acid and valerate were significantly higher in the IBS group than in the healthy controls (p < 0.05). Using LASSO logistic regression, we extracted a featured group of bacteria to distinguish IBS patients from healthy controls. Using the data for these featured bacteria, we established a prediction model for identifying IBS patients by machine learning (sensitivity >80%; specificity >90%). Gut microbiome analysis using machine learning is useful for identifying patients with IBS.

The purpose of this study was to establish reference ranges for gut microbial indices by collecting real-world Japanese microbiome data from a Mykinso cohort. Although several large cohort studies have focused on the human gut microbiome, large cohort studies of the gut microbiome from Japanese populations are scarce, especially from healthy or non-diseased individuals. We collected stool samples and original survey lifestyle information from 5,843 Japanese individuals through the Mykinso gut microbiome testing service. From the obtained 16S rRNA sequence data derived from stool samples, the ratio and distribution of each taxon were analyzed. The relationship between different epidemiological attributes and gut microbial indicators were statistically analyzed. The qualitative and quantitative indicators of these common gut microbiota were confirmed to be strongly correlated with age, sex, constipation/diarrhea, and history of lifestyle-related diseases. Therefore, we set up a healthy sub-cohort that controlled for these attribute factors and defined reference ranges from the distribution of gut microbial index in that population. Taken together, these results show that the gut microbiota of Japanese people had high beta-diversity, with no single "typical" gut microbiota type. We believe that the reference ranges for the gut microbial indices obtained in this study can be new reference values for determining the balance and health of the gut microbiota of an individual. In the future, it is necessary to clarify the clinical validity of these reference values by comparing them with a clinical disease cohort.

Prognostic significance of procalcitonin in small cell lung cancer Kosuke Ichikawa, Satoshi Watanabe, Satoru Miura, Aya Ohtsubo, Satoshi Shoji, Koichiro Nozaki, Tomohiro Tanaka, Yu Saida, Rie Kondo, Satoshi Hokari, Nobumasa Aoki, Yasuyoshi Ohshima, Toshiyuki Koya, Toshiaki Kikuchi

Ketogenic diets, which are carbohydrate-restricted high-fat diets, may have therapeutic effects on various diseases, including cancer. However, ketogenic diets are often not standardized and, therefore, results are difficult to interpret. We previously investigated the usefulness of ketogenic diets in cancer therapy, where ketogenic formulas (KF) were used as supplements to enhance blood ketone bodies; however, the amount of KF was determined empirically with reference to blood ketone bodies levels. Here, to determine a standardized optimal amount of KF, we investigated temporal changes in blood ketone bodies (acetoacetic acid (AcAc), β-hydroxybutyrate (BHB)) and safety in 20 healthy individuals when KF was taken repeatedly under the conditions of a ketogenic diet (UMIN000034216). The diurnal variation in total ketone bodies, and AcAc and BHB levels significantly increased after lunch and after dinner, on the 4th day of KF administration. There were no significant safety issues related to KF in the context of anthropometric, metabolic, nutritional, urological and gastrointestinal parameters. In addition, ketogenic diets lead to changes in gut microbiota. KF showed a decrease in phylum Firmicutes. Our study provides baseline data of the usefulness of KF in a ketogenic diet.

Executive function (EF) consists of explicit emotion regulation (EER) and cognitive control (CC). Childhood EER in particular predicts mental and physical health in adulthood. Identifying factors affecting EER development has implications for lifelong physical and mental health. Gut microbiota (GM) has attracted attention as a potential biomarker for risk of physical and mental problems in adulthood. Furthermore, GM is related to brain function/structure, which plays a crucial role in emotional processing. However, little is known about how GM compositions are associated with the development of emotion regulation in early childhood. Therefore, in this study, we examined 257 children aged 3–4 to investigate links between GM and risk to EF. EF was measured using the Mother-Reported Behavior Rating Inventory of Executive Function–Preschool version. GM composition (alpha/beta diversity and genus abundance) was evaluated using 16S rRNA gene sequencing and compared between EF-risk and non-risk groups. Our results show that children with EER-risk (an index of inhibitory self-control) had a higher abundance of the genera Actinomyces and Sutterella. Although we have not established a direct link between GM and CC risk, our findings indicate that GM of preschoolers is closely associated with emotional processing and that EERrisk children have more inflammation-related bacteria.