The prevalence of diabetes mellitus is increasing globally. Probiotics have been shown to be an effective intervention for diabetes. This study focused on the relieving effects and possible mechanisms of 16 strains of two dominant Bifidobacterium species (B. bifidum and B. adolescentis, which exist in the human gut at different life stages) on type 2 diabetes (T2D). The results indicated that more B. adolescentis strains appeared to be superior in alleviating T2D symptoms than B. bifidum strains. This effect was closely related to the ability of B. adolescentis to restore the homeostasis of the gut microbiota, increase the abundance of short-chain fatty acid-producing flora, and alleviate inflammation in mice with T2D. In addition, compared with B. bifidum, B. adolescentis had a higher number of core genes, and these genes were more evolutionarily stable, including unique environmental tolerance, carbon and nitrogen utilization genes, and a blood sugar regulation gene, glgP. This may be one of the reasons why B. adolescentis is more likely to colonize in the adult gut and show a superior ability to relieve T2D. This study provides insights into future studies aimed at investigating probiotics for the treatment of metabolic diseases.

Purpose: Type 2 diabetes mellitus (T2DM) is a persistent metabolic condition with an unknown pathophysiology. Moreover, T2DM remains a serious health risk despite advances in medication and preventive care. Randomised controlled trials (RCTs) have provided evidence that probiotics may have positive effects on glucolipid metabolism. Therefore, we performed a meta-analysis of RCTs to measure the effect of probiotic therapy on glucolipid metabolism in patients with T2DM. Methods: With no constraints on the language used in the literature, Excerpta Medica Database, PubMed, the Cochrane Library, and the Web of Science were searched for pertinent RCTs published between the date of creation and 18 August 2022. Stringent inclusion and exclusion criteria were applied by two reviewers to independently examine the literature. The risk of bias associated with the inclusion of the original studies was assessed using the Cochrane risk-of-bias tool, and Stata 15.0 was used to perform the meta-analysis. Results: Thirty-seven publications containing a total of 2502 research participants were included in the meta-analysis. The results showed that after a probiotic intervention, the experimental group showed a significant decrease in body mass index (standardised mean difference (SMD) = −0.42, 95% confidence interval (CI) [−0.76, −0.08]), fasting glucose concentration (SMD = −0.73, 95% CI [−0.97, −0.48]), fasting insulin concentration (SMD = −0.67, 95% CI [−0.99, −0.36]), glycated haemoglobin concentration (SMD = −0.55, 95% CI [−0.75, −0.35]), Homeostatic Model Assessment for Insulin Resistance score (SMD = −0.88, 95% CI [−1.17, −0.59]), triglyceride concentration (SMD = −0.30, 95% CI [−0.43, −0.17]), total cholesterol concentration (SMD = −0.27, 95% CI [−0.43, −0.11]), and low-density lipoprotein concentration (SMD = −0.20, 95% CI [−0.37, −0.04]), and an increase in high-density lipoprotein concentration (SMD = 0.31, 95% CI [0.08, 0.54]). Moreover, subgroup analyses showed that patients with a longer intervention time, or those who were treated with multiple strains of probiotics, may benefit more than those with a shorter intervention time or those who were treated with a single probiotic strain, respectively. Conclusion: Probiotic supplementation improves glucolipid metabolism in patients with T2DM, offering an alternative approach for the treatment of these patients.

Objective: Studies on the relationship between oral microbial diversity and diabetes were limited. This study analyzed the oral microbial composition and diversity using NHANES data to explore its potential role in diabetes pathogenesis; Methods: A cross-sectional design was employed, utilizing NHANES data (2009–2012), including oral microbiota samples and diabetes-related indicators. Oral samples were collected via mouthwash and analyzed using 16 S rRNA gene sequencing. The Shannon-Wiener Index represented microbial diversity (Only bacteria). Multivariate logistic regression, restricted cubic splines, and subgroup analyses were employed to evaluate associations. Results: A significant association was found between oral microbial diversity and diabetes. According to the completely adjusted model, a one-unit increment in the Shannon-Wiener Index was associated with a 12.1% increase in the likelihood of developing diabetes (OR = 1.121, 95%CI: 1.120 ~ 1.122). Subgroup analyses showed divergent findings. In subgroups with lower body weight and BMI, increased microbial diversity correlated with a decreased likelihood of developing diabetes (OR = 0.68 (0.68–0.68)); Conclusions: Oral microbial diversity exhibits a complex relationship with diabetes risk. The increase and subsequent decrease of oral microbiota diversity in relation to diabetes risk. This suggests that certain specific microbes or interactions between microbes may influence the development of diabetes. However, due to the many limitations of this study, it cannot prove the causal relationship between oral microbial richness and diabetes. Further longitudinal and mechanistic studies are essential to elucidate the causal links and dynamic alterations between the oral microbiome and the progression of diabetes.

Atrial fibrillation (AF), the most prevalent critical care arrhythmia, demonstrates substantial mortality associations where renal dysfunction management plays a pivotal therapeutic role. We examined the prognostic capacity of admission blood urea nitrogen-to-creatinine ratio (BUN/Cr) - a low-cost renal biomarker - for 28-/365-day mortality prediction in AF through multidimensional survival analyses leveraging the MIMIC-IV 3.1 database. Data relevant to AF patients were extracted from the publicly available MIMIC-IV 3.1 database based on predefined inclusion and exclusion criteria. Cox proportional hazards regression, Kaplan-Meier survival analysis, and Restricted Cubic Spline (RCS) models were used to assess the association between the BUN/Cr and the risk of 28-day and 365-day mortality. Subsequently, a short-term and long-term mortality risk prediction model for AF patients was developed using interpretable machine learning algorithms, incorporating the BUN/Cr and other clinical features. The MIMIC-IV analysis included 14,725 AF patients (72.9 ± 11.7 years, 60.3% male). Cox regression identified BUN/Cr as an independent predictor of 28-day and 365-day mortality, with risk quintiles showing a non-linear pattern: Q5 (> 27.8), Q4 (22.0–27.8), Q1 (≤ 15.0), Q3 (18.5–22.0), and Q2 (15.0–18.5). Kaplan-Meier curves confirmed decreasing survival with elevated BUN/Cr. Restricted cubic splines revealed U-shaped mortality relationships (P < 0.001), with inflection points at BUN/Cr = 16.49 (28-day) and 16.67 (365-day). Among machine learning models, XGBoost outperformed others in predicting mortality (28-day: AUC = 0.793 [0.776–0.810], Accuracy = 73.1%; 365-day: AUC = 0.778 [0.764–0.793], Accuracy = 69.8%). SHAP analysis ranked BUN/Cr fourth among predictors for both endpoints. The BUN/Cr emerged as a robust independent predictor of short- and long-term mortality in AF. The interpretable XGBoost model, integrating BUN/Cr with clinical variables, achieved superior predictive accuracy for 28-/365-day outcomes while maintaining generalizability. BUN/Cr constituted a fourth-ranked feature across mortality timelines. These findings underscore its clinical utility for AF risk stratification and treatment optimization, supporting biomarker-guided therapeutic interventions.

Background: Postoperative nausea and vomiting (PONV) continue to be some of the most common and troublesome complications following anesthesia. Oliceridine, a G protein-biased µ-opioid receptor agonist, has the potential to provide effective pain relief while reducing the incidence of opioid-associated side effects. Objectives: The aim of this study is to evaluate the effectiveness and safety of oliceridine for preventing PONV compared with morphine and a placebo. Methods: A comprehensive search was performed across PubMed/MEDLINE, Embase (Ovid), and the Cochrane Central Register of Controlled Trials (CENTRAL) from their inception up to 30 September 2025, without any language limitations. Randomized controlled trials (RCTs) comparing oliceridine with morphine or placebo were included. Outcomes included incidence of nausea, vomiting, and other opioid-related adverse events (ORAEs). Pooled risk ratios (RRs) and their corresponding 95% confidence intervals (CIs) were estimated using random-effects models. Results: This meta-analysis included five studies, with a total of 1,767 patients. The pooled analysis showed that oliceridine significantly reduced the incidence of postoperative nausea compared with morphine (risk ratio [RR] = 0.80; 95% confidence interval [CI] = 0.70–0.90). In dose-specific analyses, the RRs were 0.58 (95% CI = 0.50–0.67) for the 0.1 mg group, 0.82 (95% CI = 0.74–0.92) for the 0.35 mg group, and 1.00 (95% CI = 0.91–1.11) for the 0.5 mg group. Oliceridine also decreased the risk of postoperative vomiting (RR = 0.55; 95% CI = 0.45–0.67) relative to morphine. Subgroup analyses yielded RRs of 0.39 (95% CI = 0.31–0.50), 0.54 (95% CI = 0.38–0.78), and 0.80 (95% CI = 0.68–0.95) for the 0.1 mg, 0.35 mg, and 0.5 mg groups, respectively. Moreover, oliceridine appeared to lower the incidence of opioid-related adverse events, including dizziness (RR = 0.89; 95% CI = 0.76–1.04), dry mouth (RR = 0.50; 95% CI = 0.37–0.69), pruritus (RR = 0.50; 95% CI = 0.35–0.70), and somnolence (RR = 0.61; 95% CI = 0.45–0.82). Based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system, the certainty of evidence ranged from moderate to low. Conclusion: Oliceridine, particularly at 0.1–0.35 mg demand doses, reduces PONV and several opioid-related adverse events compared with morphine while maintaining effective analgesia. However, compared with placebo, typical opioid adverse effects remain. Further large-scale RCTs are warranted. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024604703 , identifier PROSPERO (CRD42024604703).