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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.

We determined the effect of risedronate on the trabecular microstructure of ovariectomized rat tibiae, using micro-computed tomography, in order to investigate how changes in microstructure contribute to biomechanical properties. Fifty 18-week-old rats underwent sham operation (n=10) or ovariectomy (OVX) (n=40). The OVX rats were further divided into four groups (n=10 for each group) and treated with risedronate at doses of 0, 0.1, 0.5 or 2.5 mg/kg for 9 months. OVX caused deterioration of three-dimensional trabecular microstructure, notably structure model index (SMI) and connectivity density, while treatment of OVX rats with risedronate at 0.5 and 2.5 mg/kg improved those deleterious microstructural changes. Biomechanical property, as assessed by finite element analysis (FEA), correlated significantly with trabecular bone volume fraction (BV/TV), and the correlation further increased substantially when microstructural parameters were added, especially SMI and connectivity density, with risedronate therapy. Thus, it is suggested that, in addition to increasing bone mass, risedronate improves biomechanical property by maintaining a plate-like structure as well as connectivity of trabeculae.