Proteoglycan 4 (PRG4), also known as lubricin, plays a critical role in maintaining joint homeostasis by reducing friction between articular cartilage surfaces and preventing cartilage degradation. Its deficiency leads to early-onset osteoarthritis (OA), while overexpression can protect against cartilage degeneration. Beyond its lubricating properties, PRG4 exerts anti-inflammatory effects by interacting with Toll-like receptors, modulating inflammatory responses within the joint. The expression of Prg4 is regulated by various factors, including mechanical stimuli, inflammatory cytokines, transcription factors such as Creb5 and FoxO, and signaling pathways like TGF-β, EGFR, and Wnt/β-catenin. Therapeutic strategies targeting PRG4 in OA have shown promising results, including recombinant PRG4 protein injections, gene therapies, and small molecules that enhance endogenous Prg4 expression or mimic its function. Further research into the molecular mechanisms regulating Prg4 expression will be essential in developing more effective OA treatments. Understanding the interplay between Prg4 and other signaling pathways could reveal novel therapeutic targets. Additionally, advancements in gene therapy and biomaterials designed to deliver PRG4 in a controlled manner may hold potential for the long-term management of OA, improving patient outcomes and delaying disease progression.

Background: Osteoporosis (OP) is a systemic bone disease often undiagnosed until fractures occur. Metabolites may influence OP, offering potential biomarkers or therapeutic targets. This study investigates the causal relationship between circulating metabolites and OP-related phenotypes using Mendelian Randomization (MR). Methods: GWAS data on 233 metabolic traits from 136,016 participants were analyzed through two-sample MR. Linkage disequilibrium score regression (LDCS) was used to estimate genetic correlations between metabolic traits and OP-related phenotypes, leveraging European ancestry linkage disequilibrium scores to account for polygenicity and stratification. MR employed the inverse-variance weighted (IVW) method, with sensitivity analyses via MR-Egger, MR-PRESSO, and weighted median methods to address pleiotropy and confounders. Results: LDCS identified significant genetic correlations between metabolites and bone mineral density (BMD) phenotypes, with total body BMD (toBMD) showing the strongest associations. Thirty-five metabolite traits, including apolipoprotein A-I, exhibited significant linkages. Among 79 metabolites influencing BMD, serum acetate levels were significantly associated with femoral neck BMD (OR: 1.28, 95% CI: 1.02–1.62), lumbar spine BMD (OR: 1.73, 95% CI: 1.32–2.27), and total body BMD (OR: 1.21, 95% CI: 1.04–1.42). Creatinine levels were consistently linked to reduced BMD, including lumbar spine BMD (OR: 0.88, 95% CI: 0.79–0.99). Triglycerides in IDL and VLDL particles also contributed to BMD variation. Conclusions: Significant genetic correlations and causal relationships were observed between specific metabolites and OP, highlighting key traits as potential biomarkers of bone health. These findings enhance the understanding of OP pathogenesis and suggest future preventive strategies.

The causal impact of blood metabolites on OA has yet to be definitively established, further studies are needed to explore the specific roles of metabolites in OA. This is a genetic correlation and two-sample bidirectional mendelian randomization study. GWAS summary data of metabolites and OA were extracted from large-scale GWAS study based on Europeans and Asians. LDSC was conducted to estimate the genetic correlations between 233 circulating metabolites and 11 OA phenotypes, MR was then performed to explore the casual association. 41.20% of the metabolic traits showed genetic correlation with All OA, 15.88% with Knee/Hip OA, 51.50% with Knee OA, and 52.79% with Spine OA. No significant genetic correlations were detected between the metabolic traits and other OA phenotypes. Lactate levels was associated with increased odds of All OA (OR: 1.1558, P<0.001), Hip OA (OR: 1.1446, P=0.004), Knee/Hip OA (OR: 1.1820, P<0.001), Knee OA (OR: 1.1375, P=0.001), Spine OA (OR: 1.3179, P<0.001), THR (OR: 1.5290, P<0.001), and TJR (OR: 1.2827, P<0.001), except for Thumb OA (OR: 0.9429, P<0.001). Ratio of conjugated linoleic acid to total fatty acids was associated 6 OA phenotypes: Hip OA (OR: 0.9522, P=0.035), Knee/Hip OA (OR: 1.0890, P<0.001), Knee OA (OR: 1.1429, P<0.001), THR (OR: 1.3800, P<0.001), TJR (OR: 1.3102, P<0.001), and TKR (OR: 1.2555, P<0.001). Glycerol levels exhibited significant MR associations with four OA phenotypes: Finger OA (OR: 0.6669, P<0.001), Hand OA (OR: 0.8682, P=0.011), Hip OA (OR: 0.9395, P<0.001), and Knee OA (OR: 1.1409, P=0.036). This study underscores genetic and causal connections between specific metabolites and OA. These findings could inform future therapeutic metabolic pathways involved in OA.

Background: This study utilized data from the Global Burden of Disease (GBD) to systematically analyze the years lived with disability (YLDs) and direct economic costs attributable to rheumatoid arthritis (RA), osteoarthritis (OA), neck pain (NP), and low back pain (LBP) among women aged 15– 49 between 1990 and 2021, aiming to inform public health intervention strategies. Methods: Using GBD 2021 data, we reported the YLDs and age-standardized rates (ASYR) for four major musculoskeletal diseases (MSD) in women of childbearing age (WCA) from 1990 to 2021. The temporal trends in disease burden were assessed by estimated annual percentage change (EAPC), and a non-linear frontier model was used to examine the association between the sociodemographic index (SDI) and disease burden. Additionally, the direct economic burden imposed by the four diseases in WCA was estimated. Results: In 2021, the YLDs among WCA worldwide due to RA, OA, NP, and LBP were 610,000, 1.61 million, 6.35 million, and 19.51 million, respectively, reflecting increases of 86.8%, 111%, 58%, and 40% compared to 1990. The ASYR for RA and OA increased by 13.6% and 11.2%, respectively, while the ASYR for NP and LBP slightly decreased. High SDI regions bear the heaviest burden, but regions with low-middle SDI show a faster growth rate. The global economic burden of these four diseases totaled $43.66 billion, with LBP accounting for the largest portion ($16.7 billion), and about 80% of the expenditure concentrated in high-SDI and high-middle SDI countries. Conclusion: MSD pose a persistent and growing health and economic challenge for WCA, with RA and OA showing a clear trend toward younger age groups. Although the ASYR for LBP has decreased, its absolute disability burden has still increased due to population growth. High-income countries bear the majority of the economic cost, reflecting the unequal distribution of global health resources.