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Epidemiological studies have consistently shown that Sjögren's disease (SjD) increases the risk of dental caries. Despite similar evidence indicating an elevated risk of periodontitis, SjD remains a disputed risk factor for this disease. The risk of bias in observational research is a major impediment to confirming this link. Within an instrumental variable framework, genetic variants associated with a risk factor can be used to proxy its effect on an outcome while avoiding common sources of observational study bias. In this study, we leveraged an instrumental variable approach to investigate whether SjD affects the risk of caries and periodontitis. A total of 57 genetic variants strongly associated with SjD were identified from a genome-wide association study of 2,247 European descent cases and 332,115 controls. We tested for associations of these genetic instruments with caries (measured as the number of decayed, missing, and filled surfaces in 26,792 individuals) and periodontitis (17,353 clinical periodontitis cases and 28,210 European controls). Several sensitivity analyses were used to further validate the primary inverse variance weighted (IVW) estimate. IVW analysis revealed an adverse effect of SjD on caries (β = 0.039, P = 6.3e-16) and periodontitis (odds ratio = 1.033, P = 2.3e-05). Sensitivity analyses, conducted to assess the robustness to potential violations of instrumental variable assumptions, further support these findings. Our results showed that SjD has a detrimental effect on caries and also suggest that SjD promotes periodontitis. [ABSTRACT FROM AUTHOR]

The article in the Journal of Dental Research responds to a critique of a Mendelian randomization study on the relationship between Sjögren's Disease (SjD) and periodontitis (PD). The study found a positive effect of SjD on PD, using a different analytic strategy to strengthen the evidence. The authors argue that well-designed longitudinal studies support the link between SjD and PD, contrary to previous findings. The article emphasizes the importance of understanding causal pathways and avoiding bias in research design. [Extracted from the article]