Association between genetically predicted rheumatoid arthritis and alopecia areata: a two-sample Mendelian randomization study.

Background: Although numerous observational studies have indicated a potential association between autoimmune diseases, such as rheumatoid arthritis (RA) and alopecia areata (AA), the research reports lack a clear causal relationship. In this study, our objective is to utilize the Mendelian randomization (MR) design to examine the potential causal association between RA and AA. Methods: To investigate the causal relationship between RA and AA, we utilized large-scale gene aggregation data from genome-wide association studies (GWAS), including RA (n=58,284) and AA (n=361,822) based on previous observational studies. In our analysis, we mainly employed the inverse variance-weighted (IVW) method of the random effects model, supplemented by the weighted median (WM) method and the MR Egger method. Results: The findings from the IVW methods revealed a significant association between genetically predicted RA and an increased likelihood of AA, as evidenced by an odds ratio of 1.21 (95%CI = 1.11-1.32; P < 0.001. Both the WM method and MR-Egger regression consistently showed significant directional outcomes (Both P < 0.05), indicating a robust association between RA and AA. Additionally, both the funnel plot and the MR-Egger intercepts provided evidence of the absence of directional pleiotropy, suggesting that the observed association is not influenced by other common genetic factors. Conclusions: The results of the study suggest a possible link between genetically predicted RA and AA. This finding highlights the importance for individuals diagnosed with RA to remain vigilant and aware of the potential development of AA. Regular monitoring and early detection can be crucial in managing and addressing this potential complication.

Evaluating the effect of childhood sunburn on the risk of cutaneous melanoma through Mendelian randomization.

Despite numerous observational studies indicating an increased risk of cutaneous melanoma (CM) due to childhood sunburn, no studies have established a definitive cause-and-effect relationship. Therefore, our objective was to employ a Mendelian randomization (MR) design to explore a possible causal association between childhood sunburn and the risk of CM. To investigate the causal relationship between childhood sunburn and CM, we used large-scale genetic summary-level data from genome-wide association studies (GWAS), including childhood sunburn (n = 346,955) and CM (n = 262,288), building upon previous observational studies. In the analysis, we mainly used the inverse-variance weighted (IVW) method of the random effects model, supplemented by the weighted median method and MR-Egger method. The results of the IVW method demonstrated that genetically predicted childhood sunburn was significantly associated with higher odds of CM, with an odds ratio (OR) of 2.418 (95%CI, 1.426-4.099; p = .001). The weighted median method and MR-Egger regression also demonstrated directionally similar results (both p < .05). Furthermore, both the funnel plot and the MR-Egger intercepts showed the absence of directional pleiotropy between childhood sunburn and CM. Our study offers potential evidence linking genetically predicted childhood sunburn with CM, underscoring the need for individuals with a history of childhood sunburn to be extra vigilant regarding the occurrence of CM.

The synergistic effect of diabetes mellitus and osteoporosis on the all-cause mortality: a cohort study of an American population.

Background: The increasing incidence of diabetes mellitus (DM) and osteoporosis have different effects on prognosis. The two often co-occur, so we aimed to investigate whether DM and osteoporosis have an effect on all-cause death and whether DM and osteoporosis have a synergistic effect. Methods: This study analyzed 18,658 subjects from five cycles of the National Health and Nutrition Examination Survey (NHANES). The primary endpoint was all-cause death. The subjects were divided into four groups based on the presence or absence of DM and osteoporosis. Survival curves and Cox regression analysis based on NHANES recommended weights were used to assess the risk of all-cause death between the diseased and non-diseased groups and to calculate additive interactions to assess whether there was a synergistic effect between diabetes and osteoporosis. Results: The group with DM and osteoporosis had the lowest survival rate. After full adjustment for confounders, patients with DM alone had a 30% higher risk of all-cause death compared with those without DM and osteoporosis (HR: 1.30, 95%CI: 1.09-1.55). Patients with osteoporosis alone had a 67% higher risk of all-cause death (HR: 1.67, 95%CI:1.16-2.43) and patients with combined DM and osteoporosis had a 127% higher risk of all-cause death (HR:2.27, 95%CI: 1.57-3.27). There was an additive interaction between DM and osteoporosis [RERI (95%CI): 1.03(0.55-1.50)] and excess mortality risk of 38% [AP (95% CI) 0.38(0.30-0.46)]. Conclusions: There might be a synergistic effect of DM and osteoporosis on all-cause mortality, and patients with both conditions have a higher risk of death.