Objective: Observational studies have shown the association between iron status and osteoarthritis (OA). However, due to difficulties of determining sequential temporality, their causal association is still elusive. Based on the summary data of genome-wide association studies (GWASs) of a large-scale population, this study explored the genetic causal association between iron status and OA. Methods: First, we took a series of quality control steps to select eligible instrumental SNPs which were strongly associated with exposure. The genetic causal association between iron status and OA was analyzed using the two-sample Mendelian randomization (MR). Inverse-variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode methods were used for analysis. The results were mainly based on IVW (random effects), followed by sensitivity analysis. IVW and MR-Egger were used for heterogeneity testing. MR-Egger was also used for pleiotropy testing. Leave-one-SNP-out analysis was used to identify single nucleotide polymorphisms (SNPs) with potential impact. Maximum likelihood, penalized weighted median, and IVW (fixed effects) were performed to further validate the reliability of results. Results: IVW results showed that transferrin saturation had a positive causal association with knee osteoarthritis (KOA), hip osteoarthritis (HOA) and KOA or HOA (p < 0.05, OR > 1), and there was a negative causal association between transferrin and HOA and KOA or HOA (p < 0.05, OR < 1). The results of heterogeneity test showed that our IVW analysis results were basically free of heterogeneity (p > 0.05). The results of the pleiotropy test showed that there was no pleiotropy in our IVW analysis (p > 0.05). The analysis results of maximum likelihood, penalized weighted median and IVW (fixed effects) were consistent with our IVW results. No genetic causal association was found between serum iron and ferritin and OA. Conclusions: This study provides evidence of the causal association between iron status and OA, which provides novel insights to the genetic research of OA.

Ferroptosis, a recently discovered regulated cell death modality, is characterised by iron-dependent accumulation of lipid hydroperoxides, which can reach lethal levels but can be specifically reversed by ferroptosis inhibitors. Osteoarthritis (OA), the most common degenerative joint disease, is characterised by a complex pathogenesis involving mechanical overload, increased inflammatory mediator levels, metabolic alterations, and cell senescence and death. Since iron accumulation and oxidative stress are the universal pathological features of OA, the role played by ferroptosis in OA has been extensively explored. Increasing evidence has shown that iron dyshomeostasis and lipid peroxidation are closely associated with OA pathogenesis. Therefore, in this review, we summarize recent evidence by focusing on ferroptotic mechanisms and the role played by ferroptosis in OA pathogenesis from the perspectives of clinical findings, animal models, and cell research. By summarizing recent research advances that characterize the relationship between ferroptosis and OA, we highlight avenues for further research and potential therapeutic targets.

Background: Blood biomarkers are first-line tools for identifying periprosthetic joint infection (PJI). C-reactive protein (CRP) is currently recognized as the standard biomarker for PJI diagnosis. Other recently reported novel biomarkers, including plasma fibrinogen, platelet count, monocyte/lymphocyte ratio (MLR), neutrophil/lymphocyte ratio (NLR), and platelet count/lymphocyte ratio (PLR), have also shown promise in diagnosing PJI. This study aimed to evaluate whether these biomarkers were superior to CRP for identifying PJI. Methods: Patients who underwent revision hip or knee arthroplasty at our hospital from January 2008 to September 2020 were included consecutively and divided into infected and non-infected groups according to the 2013 International Consensus Meeting Criteria. Blood samples were collected preoperatively, and erythrocyte sedimentation rate (ESR), CRP, interleukin-6, fibrinogen, platelet count, MLR, NLR, and PLR were analyzed. The diagnostic values of the tested biomarkers and their combinations were compared with CRP based on the area under the receiver operating characteristic curve (AUC) using the z-test. Classification trees were constructed to explore more accurate combinations of the tested markers for identifying PJI. Results: A total of 543 patients were included, of whom 245 had PJI. Among the tested biomarkers, CRP with a cutoff of 7.39 mg/L showed the highest AUC, which gave a sensitivity of 79.1% and specificity of 86.0%. The AUCs of pairwise combinations of tested markers including CRP also were inferior to CRP itself, as were combinations derived from classification trees. Conclusions: Preoperative serum CRP with a low cutoff may be the best reliable blood biomarker for identifying PJI, and those traditional or novel available blood biomarkers could not further improve the diagnostic ability on the basis of CRP.

Objective: Osteoporosis (OP) is the most common bone disease. The genetic and metabolic factors play important roles in OP development. However, the genetic basis of OP is still elusive. The study aimed to explore the relationships between OP and dietary habits. Methods: This study used large-scale genome-wide association study (GWAS) summary statistics from the UK Biobank to explore potential associations between OP and 143 dietary habits. The GWAS summary data of OP included 9434 self-reported OP cases and 444,941 controls, and the GWAS summary data of the dietary habits included 455,146 participants of European ancestry. Linkage disequilibrium score regression (LDSC) was used to detect the genetic correlations between OP and each of the 143 dietary habits, followed by Mendelian randomization (MR) analysis to further assess the causal relationship between OP and candidate dietary habits identified by LDSC. Results: The LDSC analysis identified seven candidate dietary habits that showed genetic associations with OP including cereal type such as biscuit cereal (coefficient = −0.1693, p value = 0.0183), servings of raw vegetables per day (coefficient = 0.0837, p value = 0.0379), and spirits measured per month (coefficient = 0.115, p value = 0.0353). MR analysis found that OP and PC17 (butter) (odds ratio [OR] = 0.974, 95% confidence interval [CI] = (0.973, 0.976), p value = 0.000970), PC35 (decaffeinated coffee) (OR = 0.985, 95% CI = (0.983, 0.987), p value = 0.00126), PC36 (overall processed meat intake) (OR = 1.035, 95% CI = (1.033, 1.037), p value = 0.000976), PC39 (spirits measured per month) (OR = 1.014, 95% CI = (1.011, 1.015), p value = 0.00153), and servings of raw vegetables per day (OR = 0.978, 95% CI = (0.977, 0.979), p value = 0.000563) were clearly causal. Conclusions: Our findings provide new clues for understanding the genetic mechanisms of OP, which focus on the possible role of dietary habits in OP pathogenesis.

Background: Diabetes mellitus (DM) and osteoarthritis (OA) are common diseases that are predicted to increase in prevalence, and DM is a risk factor for OA progression and has a negative impact on the outcome. However, the evidence remains unclear on how it affects patients’ clinical results of total knee arthroplasty (TKA) under enhanced recovery after surgery (ERAS). Methods: A retrospective single-center study was conducted comparing diabetic and non-diabetic patients who underwent TKA in West China Hospital of Sichuan University between September 2016 to December 2017 under ERAS. Consecutive propensity score matching (PSM) was conducted by 1:1 (DM: non-DM) matching analysis with all baselines as covariates. The primary clinical results were the improvement of knee joint function, the incidence of postoperative complications, and the FJS-12 sensory results 5 years after the operation between DM and Non-DM groups. The secondary clinical results were the postoperative length of stay (LOS), postoperative blood test and total blood loss (TBL). Result: After PSM, the final analysis included 84 diabetic patients and 84 non-diabetic patients. Diabetic patients were more likely to experience early postoperative complications (21.4% vs. 4.8%, P = 0.003), of which wound complications are the most significant (10.7% vs. 1.2%, P = 0.022). Diabetic patients experienced longer postoperative LOS with a significant increase in patients with LOS exceeding 3 days (66.7% vs. 50%, P = 0.028) and showed less postoperative range of motion (ROM) (106.43 ± 7.88 vs. 109.50 ± 6.33 degrees, P = 0. 011). Diabetic patients also reported lower Forgotten joint score (FJS-12) than non-diabetic patients (68.16 + 12.16 vs. 71.57 + 10.75, P = 0.020) in the 5-year follow-up and were less likely to achieve a forgotten knee joint (10.7% vs. 1.2%, P = 0.022). In additional, Compared with non-diabetics, diabetic patients showed lower hemoglobin (Hb) (P < 0.001) and hematocrit (HCT) (P < 0.001) and were more likely to suffer from hypertension before TKA (P < 0.001). Conclusion: Diabetic patients show increased risk for postoperative complications, and have lower lower postoperative ROM and lower FJS-12 compared with non-diabetic patients after TKA under ERAS. More perioperative protocols are still needed to be investigated and optimized for diabetic patients.