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Background: Osteoarthritis is an important health issue for the elderly. Many studies indicate that genetics is an important risk factor for osteoarthritis, and a disintegrin and metalloproteinase with thrombospondin motifs 5 (ADAMTS5) is one gene that is most frequently implicated. Many recent studies have examined the relationship between a polymorphism in the ADAMTS5 gene (rs226794) and the risk for developing osteoarthritis without definitive results. Objective: In this case-control study, we examined the correlation between the ADAMTS5 gene polymorphism, rs226794, and knee osteoarthritis. We used a meta-analysis and trial sequential analysis to determine whether ADAMTS5 rs226794 expression increases susceptibility to osteoarthritis. Methods: This study consisted of two parts: a case-control study and a meta-analysis. The case-control study included subjects who underwent knee radiography at the Health Examination Center of the Tri Service General Hospital from 2015 to 2019. The Kellgren–Lawrence (KL) grading system was used as diagnostic criteria. Patients with unsuccessful gene sequencing were excluded. There were 606 subjects in the knee osteoarthritis group (KL ≥ 2) and 564 in the control group (KL < 2). Gene sequencing was performed using iPLEX Gold to determine the association between the gene polymorphism of ADAMTS5 rs226794 and knee osteoarthritis. For the meta-analysis, databases such as PubMed, Embase, and Cochrane were queried to identify studies that examined the relationship between ADAMTS5 rs226794 and osteoarthritis. Next, the findings of the meta-analysis were incorporated with the results of the case-control study and samples from the published studies to estimate the association between the genetic polymorphism and osteoarthritis using an odds ratio and a 95% confidence interval. Results: We found a non-significant association between the G allele and knee OA (crude-OR: 0.93 (95% CI: 0.79–1.10) and adjusted-OR: 1.02 (95% CI: 0.76–1.36) in the allele model) in the present study, and the analysis of other genetic models revealed a similar trend. After including five published studies and our case-control study, the results with 2866 Asians indicated a conclusively null association between ADAMTS5 rs226794 and knee OA) OR: 1.09 (95% CI: 0.93–1.26). The results for Caucasians also revealed a null association (OR: 1.21 (95% CI: 0.81–1.82)). Conclusions: This study indicates that the gene polymorphism, ADAMTS5 rs226794, is not significantly associated with knee osteoarthritis. Additionally, assuming that the cumulative sample size in the allele model is sufficient, we confirmed that the G allele is not a risk factor for osteoarthritis. This study integrated all available evidence to arrive at this conclusion, and it suggests that no additional studies are necessary.

Background Osteoarthritis (OA) is a multifactorial disease that is associated with several genetic factors. TFAP2A with a motif of C allele at rs6426749 demonstrates a higher binding ability, thereby increasing CDC42 expression, potentially affecting OA occurrence. In this study, we evaluated the role of rs6426749 polymorphisms on knee OA in a female Taiwanese population. Methods We performed a case-control study of 368 OA cases and 379 controls between March 2017 and October 2018. Knee OA was defined using the Kellgren-Lawrence grading system, and genotypes were determined using the Sequenom MassArray iPLEX Gold assay. Stratified sex and body mass index (BMI) analyses were performed using logistic regression to explore interactions between genes and the environment. We also used expression quantitative trait loci data from the genotype-tissue expression project to conduct functional analyses. Results The C allele of rs6426749 was associated with the risk of knee OA (odds ratio [OR] = 1.31, 95% confidence interval [CI], 1.01-1.71; p = 0.042), after adjusting for gender, age, and BMI. In addition, subgroup analyses indicated that females expressing C alleles showed an increased risk for knee OA (OR = 1.56; 95% CI, 1.12-2.18; p = 0.009). Females with a normal BMI and the C allele had the highest OA risk (OR = 1.73; 95% CI, 1.08-2.76; p = 0.022). Conclusion Our findings indicated that rs6426749 may be related to OA susceptibility in the Taiwanese population. This was particularly true for women with normal BMI.

Supplemental Digital Content is available in the text, Bioelectrical impedance analysis (BIA) is currently the most commonly used method in clinical practice to measure body composition. However, the bioelectrical impedance analyzer is not designed according to different countries, races, and elderly populations. Because different races may have different body compositions, a prediction model for the elderly population in Taiwan should be developed to avoid population bias, thereby improving the accuracy of community evaluation surveys. Dual energy X-ray absorptiometry (DXA) was used as a standard method for comparison, and impedance analysis was used for the development of a highly accurate predictive model that is suitable for assessing the body composition of elderly people. This study employed a cross-sectional design and recruited 438 elderly people who were undergoing health examinations at the health management center in the Tri-Service General Hospital as study subjects. Basic demographic variables and impedance analysis values were used in four predictive models, namely, linear regression, random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) models, to predict DXA body composition. The data from 354 study subjects were used to develop the predictive model, while the data from 84 study subjects were used to validate the accuracy of the predictive model. The body composition of elderly people as estimated by InBody 720 was highly correlated with that estimated by DXA. The correlation coefficient between InBody 720 and DXA for muscle mass was 0.969, and that for fat mass was 0.935. Consistency analysis results showed that InBody 720 tends to underestimate muscle mass and fat mass. A comparison of the accuracy of the linear regression, random forest, SVM, and XGBoost models showed that the linear regression has the highest accuracy. The correlation coefficient between the new model and DXA for muscle mass and fat mass were 0.977 and 0.978, respectively. The new predictive model can be used to monitor the nutrition status of elderly people and identify people with sarcopenia in the community.