Hyperuricaemia (HUA) is a metabolic disorder resulting from the dysregulation of purine metabolism. It is closely associated with gout and various metabolic syndromes, representing an increasing global public health challenge. Current treatment approaches for HUA and gout generally involve the lifelong administration of urate-lowering agents to maintain optimal serum urate concentrations. However, poor patient adherence, often due to potential hepatorenal toxicity, frequently leads to disease relapse. Recent evidence indicates that the gut microbiota plays a significant role in maintaining urate homeostasis through multiple mechanisms, including the modulation of purine metabolism, urate catabolism and excretion, regulation of inflammatory responses, and preservation of intestinal barrier integrity. These findings highlight the gut microbiota as a promising novel therapeutic target. This review synthesizes recent progress in three key areas: (1) the relationship between the gut microbiota and HUA; (2) microbial mechanisms underlying urate-lowering effects, such as microbial purine and urate metabolism, regulation of urate transporters like ABCG2, and production of anti-inflammatory metabolites; and (3) microbiota-based therapeutic interventions, including probiotics, engineered bacterial strains, fecal microbiota transplantation, and pharmabiotic strategies. Additionally, we explore the translational potential of microbiota modulation in clinical settings and outline directions for future research. By integrating mechanistic understanding with therapeutic innovation, this review offers researchers and clinicians a comprehensive framework for advancing microbiota-targeted approaches in the management of hyperuricaemia.

The accurate identification of flight fatigue is crucial for managing pilot training intensity and preventing aviation accidents. However, as a subjective perception, flight fatigue is often difficult to evaluate objectively. Heart rate variability (HRV), derived from electrocardiogram signals and regulated by the autonomic nervous system, is recognized as an effective biomarker for assessing fatigue status. This study proposes a novel HRV-based method for the automatic and objective classification of flight fatigue. This study involved an experimental investigation conducted with a cohort of 90 pilots. First, we conducted statistical analyses to investigate whether HRV features and respiratory rate indicators significantly differed across various fatigue levels. A subset of HRV features and the respiratory metric were used as input variables for four machine learning algorithms: decision tree, support vector machine, K-nearest neighbor, and light gradient-boosting machine (LightGBM). These models were applied to perform a three-level classification of flight fatigue. Finally, classification performance was evaluated using average accuracy, precision, recall, and F1 score. Among these models, LightGBM demonstrated the best performance, achieving an accuracy of 0.886 ± 0.057, precision of 0.837 ± 0.064, recall of 0.861 ± 0.086, and F1 score of 0.849 ± 0.067. These findings indicate that a LightGBM model trained on 12 selected HRV features and one respiratory indicator can accurately categorize flight fatigue into three levels. Fatigue can be detected even when mild, enabling real-time monitoring and early warning of flight fatigue. This approach holds potential for reducing fatigue-related flight accidents.

Background/Objectives: Gout, a complex metabolic disorder of increasing global incidence, remains incompletely understood in its pathogenesis. Current diagnostic approaches exhibit significant limitations, including insufficient specificity and the requirement for invasive joint aspiration, highlighting the need for non-invasive, sensitive biomarkers for early detection. Methods: Urine metabolites were extracted from 28 healthy controls, 13 asymptomatic hyperuricemia (HUA) patients, and 29 acute gouty arthritis (AGA) patients. The extracted metabolites were analyzed by UHPLC-MS/MS for untargeted metabolomics. Differential metabolites were screened by partial least squares discriminant analysis (PLS-DA) and volcano plot analysis. Pathway analysis determined the core disorder pathway of gout progression. Results: A total of 278 differential metabolites associated with gout progression were identified. The most pronounced metabolic alterations were observed between the AGA and control groups, indicative of substantial metabolic reprogramming during disease transition. Metabolic pathway analysis revealed four significantly dysregulated pathways: histidine metabolism, nicotinate and nicotinamide metabolism, phenylalanine metabolism, and tyrosine metabolism. Receiver operating characteristic (ROC) curve analysis revealed that three urine markers with high diagnostic efficacy—oxoamide, 3-methylindole, and palmitic acid—exhibited progressive alterations across the disease continuum. Conclusions: This metabolomics study identified core regulatory metabolites and newly discovered metabolic pathways underlying gout pathogenesis, along with novel urinary biomarkers capable of predicting HUA-to-AGA progression. The aberrant levels of key metabolites in the disordered pathway implicate neuroimmune dysregulation, energy metabolism disruption, and oxidative stress in gout pathogenesis. These findings provide new foundations and strategies for the daily monitoring and prevention of gout.

The acute gouty arthritis (AGA) to chronic gouty arthritis (CGA) transition is a critical phase leading to irreversible joint damage and systemic complications. However, current molecular mechanism investigations have remained limited to single-omics approaches that lack comprehensive multi-omics explorations. We integrate high-depth data-independent acquisition (DIA) proteomics and untargeted metabolomics to analyze serum samples from healthy controls (n =28), AGA (n = 31), and CGA (n = 14) patients to address this gap. Through differential expression analysis, we identified nine persistently dysregulated pivotal proteins with robust discriminative capacity, including the urate excretion regulator ZBTB20 and inflammation/immune-related proteins (GUCY1A2, CNDP1, LYZ, SERPINA5, GSN). Additionally, 11 consistently altered core metabolites with diagnostic potential were detected, indicating perturbations in sex hormones, thyroid hormones, gut microbiota-derived metabolites, environmental exposures, and nutritional factors. Multi-omics KEGG enrichment analysis highlighted thyroid hormone synthesis, AMPK signaling pathway, and taurine and hypotaurine metabolism as central pathways. Correlation network analysis further revealed significant immune dysregulation, illustrating an evolution from acute immune activation to chronic inflammation during AGA-to-CGA progression. Our study establishes that a coordinated disruption of the thyroid hormone–AMPK–taurine metabolic axis and concomitant immune microenvironment remodeling is associated with chronic gout development. These findings provide critical targets for developing early diagnostic indicators and targeted interventions for CGA.