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BACKGROUND: In recent years, epidemiological studies have shown that sleep patterns are risk factors for osteoarthritis, but the causal relationship between sleep characteristics and osteoarthritis remains unknown. OBJECTIVE: To investigate the causal relationship between seven sleep phenotypes and osteoarthritis, thereby providing a theoretical foundation for clinical prevention and intervention of osteoarthritis. METHODS: Seven sleep-related features, namely sleep duration, wake-up time, daytime napping, morning/evening preference, snoring, insomnia, and hypersomnia, were selected from published genome-wide association studies. Instrumental variables for these sleep-related features were extracted. Instrumental variables for knee osteoarthritis and hip osteoarthritis were obtained from publicly available genome-wide association studies. Causal relationships between sleep characteristics and outcome risks were evaluated using two-sample and multivariable Mendelian randomization analyses. The inverse variance weighted method was employed as the primary Mendelian randomization approach. Various methods, including weighted median, weighted mode, Mendelian randomization-Egger regression, Mendelian randomization pleiotropy-residual sum and outlier, were utilized to detect and correct for the presence of pleiotropy. RESULTS AND CONCLUSION: The results of the inverse variance-weighted method in the two-sample Mendelian randomization study revealed a detrimental causal association between the duration of sleep and the incidence risk of knee osteoarthritis [odds ratio (OR)=0.621, 95% confidence interval (CI): 0.470-0.822, P=0.001]. Concurrently, insomnia displayed a positive causal connection with hip osteoarthritis risk (OR=2.016, 95% CI: 1.249-3.254, P=0.005). Sensitivity analysis affirmed the robustness of these causal relationships, and Mendelian randomization-Egger intercept analysis found no evidence of potential horizontal pleiotropy (knee osteoarthritis: P=0.468, hip osteoarthritis: P=0.551). Moreover, the results from the multivariable Mendelian randomization analysis showed that the causal association between insomnia and hip osteoarthritis lacked statistical significance (P=0.715). In contrast, sleep duration exhibited a direct negative causal relationship with the incidence risk of knee osteoarthritis (OR=0.526, 95% CI: 0.336-0.824, P=0.005). Reverse Mendelian randomization analysis indicated that knee osteoarthritis did not influence sleep duration (P=0.757). These findings indicate a negative correlation between sleep duration and incidence risk of knee osteoarthritis, suggesting that correcting insufficient sleep might mitigate the incidence risk of knee osteoarthritis. [ABSTRACT FROM AUTHOR]

Objective: To analyze the causal relationship between gut microbiota and gestational diabetes, and to clarify its mechanism. Methods: Two-sample Mendelian randomization (MR) analysis was conducted by using summary data from genome-wide association study (GWAS) for gut microbiota and gestational diabetes. The GWAS data of gut microbiota were obtained from a GWAS study from the MiBioGen consortium; the GWAS data on gestational diabetes were sourced from the FinnGen consortium’s publicly available R8 dataset; inverse variance weighted (IVW) method was used as the primary method to detect the causal association between the gut microbiota and the gestational diabetes. Sensitivity analysis was performed by Weighted Median and MR Egger methods; heterogeneity and pleiotropy were detected by Cochran’s Q, MR-PRESSO, Egger intercept tests and Leave-One-Out analysis; multivariable MR was used to adjust for the effect of body mass index (BMI); reverse MR was used to explore the presence of reverse causal associations; Gene Ontology (GO) fuctional and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling enrichment analyses were used to explore the potential pathways through which gut microbiota may have impact on gestational diabetes. Results: Four gut microbes were found to be causally associated with gestational diabetes: the genus Methanobrevibacter and the phylum Euryarchaeota displayed negative causal relationships with the risk of gestational diabetes, while the genus Olsenella and genus Lachnoclostridium exhibited positive causal associations. No significant heterogeneity or horizontal pleiotropy was detected in the analysis. The reverse MR analysis did not reveal any causal relationship. After adjusting for BMI, the multivariable MR analysis results showed there were the causal associations between the genus Olsenella and the phylum Euryarchaeota with the risk of gestational diabetes. The GO fuctional and KEGG signaling pathway enrichment analyses results showed that axon development, axon production, insulin secretion and other pathways were significantly enriched. Conclusion: There are causal associations between four gut microbes and gestational diabetes. Among them, the significant correlations with gestational diabetes are still observed in phylum Euryarchaeota and genus Olsenella after adjusting for BMI. [ABSTRACT FROM AUTHOR]

Objective To explore the bidirectional causal relationships between periodontitis and asthma using the two-sample Mendelian randomization (MR) method to provide a basis for exploring the etiology and formulating preventive and therapeutic measures of periodontitis and asthma. Methods We performed two-sample bidirectional Mendelian randomization analysis using publicly released European genome-wide association studies (GWAS) statistics for periodontitis (n = 34 615) and asthma (n = 408 422). The inverse variance weighted (IVW) method was employed as the main approach to estimate the bidirectional causal relationships between periodontitis and asthma. In addition, weighted median (WM), MR-Egger regression, maximum likelihood, and Mendelian randomization robust adjusted profile score (MR-RAPS) were used as supplementary analyses. Sensitivity analyses were conducted using Cochran's Q test, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO), and leave-one-out analysis. Results A total of 12 and 43 single-nucleotide polymorphisms (SNPs) were included as instrumental variables for periodontitis and asthma, respectively. The results of IVW, WM, MR-Egger regression, maximum likelihood, and MR-RAPS showed that periodontitis was not causally related to the risk of asthma (IVW: OR: 1.003, 95% CI: 0.973-1.035, P = 0.828, WM: OR: 0.990, 95% CI: 0.951-1.031, P = 0.641; MR-Egger regression: OR: 0.988, 95% CI: 0.960-1.028, P = 0.573; maximum likelihood: OR: 1.003, 95% CI: 0.972-1.035, P = 0.834; MR-RAPS: OR: 1.002, 95% CI: 0.970-1.036, P = 0.890) among the European population, and no causal effect of asthma on periodontitis was found (IVW: OR: 1.021, 95% CI: 0.938-1.111, P = 0.633, WM: OR: 1.011, 95% CI: 0.894-1.142, P = 0.866; MR-Egger regression: OR: 1.042, 95% CI: 0.824-1.319, P = 0.731; maximum likelihood: OR: 1.021, 95% CI: 0.938-1.112, P = 0.631; MR-RAPS: OR: 1.017, 95% CI: 0.931-1.110, P = 0.713) among the European population. Cochran's Q test showed no heterogeneity among the included instrumental variables, MR-PRESSO test found no horizontal pleiotropy, and the leave-one-out method did not identify outlier SNPs. Conclusion The results of this study, based on European genetic data, do not support a bidirectional causal association between periodontitis and asthma in the European population.

Copyright of Journal of Prevention & Treatment For Stomatological Diseases is the property of Journal of Prevention & Treatment For Stomatological Diseases Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BACKGROUND: Many clinical research observations have indicated a close association between rheumatoid arthritis and osteoporosis as well as bone mineral density (BMD). However, it remains unclear whether there is a causal genetic relationship between rheumatoid arthritis and the development of osteoporosis and alterations of BMD. OBJECTIVE: To assess the potential causal relationship between rheumatoid arthritis and osteoporosis as well as BMD using a two-sample Mendelian randomization approach, provide meaningful insights from a genetic perspective into the underlying mechanisms and offer a reference for early prevention of osteoporosis and improvement in the progression of the disease. METHODS: We conducted a study using data from publicly available genome-wide association studies databases to identify single nucleotide polymorphisms associated with rheumatoid arthritis as instrumental variables (P < 5×10-8). The main outcomes of the study included osteoporosis and BMD at five different sites, including total body BMD, lumbar spine BMD, femoral neck BMD, heel BMD, and forearm BMD. The inverse variance-weighted method was used as the primary analysis method to evaluate causal effects. Weighted median, simple median, weighted mode and MR-Egger regression were used as supplementary analyses. Causal relationships between rheumatoid arthritis and the risk of osteoporosis and BMD were assessed using odds ratios (OR) and 95% confidence intervals (CI). Heterogeneity was assessed using Cochran’s Q test and horizontal pleiotropy was evaluated using MR-Egger intercept tests. RESULTS AND CONCLUSION: The inverse variance-weighted analysis demonstrated a positive association between genetically predicted rheumatoid arthritis and osteoporosis (OR=1.123, 95% CI: 1.077-1.171; P=4.02×10-8). Heterogeneity test (P=0.388) indicated no significant heterogeneity among the single nucleotide polymorphisms. MR-Egger intercept (P=0.571) tests did not detect horizontal pleiotropy, and sensitivity analysis showed no evidence of bias in the study results. There was no causal relationship between rheumatoid arthritis and BMD at the five different sites. The total body BMD (OR=1.000, 95% CI: 0.988-1.012; P=0.925), lumbar spine BMD (OR=0.999, 95% CI: 0.982-1.016; P=0.937), femoral neck BMD (OR=1.001, 95% CI: 0.986-1.016; P=0.866), heel BMD (OR=0.996, 95% CI: 0.989-1.004; P=0.419), and forearm BMD (OR=1.063, 95% CI: 0.970-1.031; P=0.996) indicated no significant association. MREgger intercept analysis did not detect potential horizontal pleiotropy (total body BMD: P=0.253; lumbar spine BMD: P=0.638; femoral neck BMD: P=0.553; heel BMD: P=0.444; forearm BMD: P=0.079). Rheumatoid arthritis may contribute to the development of osteoporosis through the interaction between chronic inflammation and bone formation, resorption, and absorption. Additionally, the use of glucocorticoids and the presence of autoantibodies (such as anti-citrullinated protein antibody) in patients with rheumatoid arthritis showed associations with osteoporosis. Future research should focus on monitoring systemic inflammatory markers, standardized use of glucocorticoids, and regular screening for osteoporosis risk in patients with rheumatoid arthritis. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Prevention & Treatment For Stomatological Diseases is the property of Journal of Prevention & Treatment For Stomatological Diseases Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Negative control methods (NCM) are developed based on the idea that negative controls should be tested with negative results. It also extends to the multiple comparison groups and used for specificity of association in population studies. Negative controls do not take part in studying the causal hypothesis but sharing the same potential bias structures. Under different purposes, NCM can be divided into methods on negative exposure, negative period or negative outcome controls. They are mainly used to detect and control bias, such as: selection bias, confounding bias and measurement bias, but leaving the sample size at secondary importance. Rational application of NCM can help improving the accuracy of causal inference in both experimental and observational processes.