Osteoarthritis (OA) represents the foremost degenerative joint disease observed in a clinical context. The escalating issue of population aging significantly exacerbates the prevalence of OA, thereby imposing an immense annual economic burden on societies worldwide. The current therapeutic landscape falls short in offering reliable pharmaceutical interventions and efficient treatment methodologies to tackle this growing problem. However, the scientific community continues to dedicate significant efforts towards advancing OA treatment research. Contemporary studies have discovered that the progression of OA may be slowed through the strategic influence on peroxisome proliferator-activated receptors (PPARs). PPARs are ligand-activated receptors within the nuclear hormone receptor family. The three distinctive subtypes—PPARα, PPARβ/δ, and PPARγ—find expression across a broad range of cellular terminals, thus managing a multitude of intracellular metabolic operations. The activation of PPARγ and PPARα has been shown to efficaciously modulate the NF-κB signaling pathway, AP-1, and other oxidative stress-responsive signaling conduits, leading to the inhibition of inflammatory responses. Furthermore, the activation of PPARγ and PPARα may confer protection to chondrocytes by exerting control over its autophagic behavior. In summation, both PPARγ and PPARα have emerged as promising potential targets for the development of effective OA treatments.

Insulin resistance (IR) and arthritis are strongly associated, and the triglyceride–glucose (TyG) index combinations with obesity indicators [including TyG–BMI (glucose triglyceride–body mass index), TyG–WC (glucose triglyceride–waist circumference), and TyG–WHtR (glucose triglyceride–waist height ratio)] has recently been recognized as a more effective indicator for assessing IR. However, there is a lack of research on its association with arthritis, and it is also important to assess in different populations. The analysis utilized data from the China Health and Retirement Longitudinal Study (CHARLS) and the National Health and Nutrition Examination Survey (NHANES). Arthritis diagnosis relied on self-reporting confirmed by physicians. The association of TyG–BMI, TyG–WC, and TyG–WHtR with arthritis was analyzed through weighted logistic regression models, and exploring nonlinear effects with restricted cubic spline (RCS) models. Secondary and sensitivity analyses included receiver operating characteristic curve (ROC) analysis, comparisons of z score-related odds ratios, subgroup analyses, and multiple imputation. The study involved 6141 CHARLS participants and 17,091 NHANES participants. Adjusting for confounding variables, TyG–BMI and TyG–WHtR demonstrate a positive correlation with arthritis prevalence in both CHARLS (TyG–BMI: OR = 1.02, 95% CI 1.00–1.04; TyG–WHtR: OR = 1.13, 95% CI 1.03–1.24) and NHANES (TyG–BMI: OR = 1.07, 95% CI 1.06–1.08; TyG–WHtR: OR = 1.50, 95% CI 1.40–1.60). RCS regression analysis demonstrated a significant nonlinear association. ROC analysis indicated that TyG–BMI and TyG–WHtR were superior to TyG for the diagnosis of arthritis in both CHARLS and NHANES. TyG–BMI and TyG–WHtR demonstrate a positive correlation with arthritis prevalence in both Chinese and the U.S. populations, displaying superior diagnostic relevance compared to TyG.

Background: Copper is an indispensable mineral element involved in many physiological metabolic processes. Cuproptosis is associated with a variety of cancer such as hepatocellular carcinoma (HCC). The objective of this study was to examine the relationships between the expression of cuproptosis-related genes (CRGs) and tumor characteristics, including prognosis and microenvironment of HCC.Methods: The differentially expressed genes (DEGs) between high and low CRGs expression groups in HCC samples were identified, and further were analyzed for functional enrichment analysis. Then, CRGs signature of HCC was constructed and analyzed utilizing LASSO and univariate and multivariate Cox regression analysis. Prognostic values of CRGs signature were evaluated by Kaplan-Meier analysis, independent prognostic analysis and nomograph. The expression of prognostic CRGs was verified by Real-time quantitative PCR (RT-qPCR) in HCC cell lines. In addition, the relationships between prognostic CRGs expression and the immune infiltration, tumor microenvironment, antitumor drugs response and m6A modifications were further explored using a series of algorithms in HCC. Finally, ceRNA regulatory network based on prognostic CRGs was constructed.Results: The DEGs between high and low CRG expression groups in HCC were mainly enriched in focal adhesion and extracellular matrix organization. Besides, we constructed a prognostic model that consists of CDKN2A, DLAT, DLST, GLS, and PDHA1 CRGs for predicting the survival likelihood of HCC patients. And the elevated expression of these five prognostic CRGs was substantially in HCC cell lines and associated with poor prognosis. Moreover, immune score and m6A gene expression were higher in the high CRG expression group of HCC patients. Furthermore, prognostic CRGs have higher mutation rates in HCC, and are significantly correlated with immune cell infiltration, tumor mutational burden, microsatellite instability, and anti-tumor drug sensitivity. Then, eight lncRNA-miRNA-mRNA regulatory axes that affected the progression of HCC were predicted.Conclusion: This study demonstrated that the CRGs signature could effectively evaluate prognosis, tumor immune microenvironment, immunotherapy response and predict lncRNA-miRNA-mRNA regulatory axes in HCC. These findings extend our knowledge of cuproptosis in HCC and may inform novel therapeutic strategies for HCC.