Objective To analyze the potential causal relationship between sunscreen/ultraviolet protection and the risk of non-Hodgkin lymphoma using a two sample Mendelian randomization (MR) study method. Methods The summary data of genome-wide association study was used to select three types of non-Hodgkin lymphoma, namely diffuse large B-cell lymphoma (DLBCL), follicular lymphoma, T/NK cell lymphoma, and sunscreen/ultraviolet protection highly correlated genetic loci, namely single nucleotide polymorphism (SNP), as instrumental variables. The reverse variance weighting method was used as the main method for MR analysis, MR Egger and MR-PRESO were used to detect level pleiotropy, and leave-one-out method was used for sensitivity analysis to ensure the robustness of the results. Results A total of 132 SNPs were included in the analysis. The results of the inverse variance weighted analysis showed that sunscreen/ultraviolet protection increased the incidence of DLBCL [odds ratio=2.439, 95% confidence interval (1.109, 5.362), P=0.027]. The heterogeneity test results showed that there was no heterogeneity in the causal relationship between sunscreen/ultraviolet protection and DLBCL (P>0.05). The results of the horizontal pleiotropy test showed that SNP did not exhibit horizontal pleiotropy (P>0.05). The leave-one-out method showed that no SNP with a significant impact on the results was found. There was no causal relationship between sunscreen/ultraviolet protection and follicular lymphoma and T/NK cell lymphoma. Conclusion There is a positive causal relationship between sunscreen/ultraviolet protection and the incidence of DLBCL.
ObjectiveTo systematically review the dose-response relationship between body mass index (BMI) and the risk of stroke. MethodsPubMed, EMbase, Web of Science, The Cochrane Library, CBM, VIP, WanFang Data and CNKI databases were electronically searched to collect studies on BMI and the risk of stroke from inception to December 2021. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies; then, meta-analysis was performed by using Stata 16.0 software, and the dose-response relationship between BMI and risk of stroke was analyzed by using restricted cubic spline function and generalized least squares estimation (GLST). ResultsA total of 19 studies involving 3 689 589 patients were included. The results of meta-analysis showed that compared with normal BMI, overweight (RR=1.28, 95%CI 1.19 to 1.39, P<0.01) and obesity (RR=1.41, 95%CI 1.15 to 1.72, P<0.01) had a higher risk of stroke. Dose-response meta-analysis suggested that there was no significant non-linear relationship between BMI and stroke risk (nonlinear test P=0.318), and linear trend showed that the risk of stroke increased by 4% for each unit increase in BMI (RR=1.04, 95%CI 1.03 to 1.05, P<0.01). ConclusionCurrent evidence suggests that increased BMI is associated with an increased risk of stroke. Due to limited quality and quantity of the included studies, more high-quality studies are needed to verify the above conclusion.
ObjectiveTo investigate the causal relationship between gastroesophageal reflux disease (GERD) and obstructive sleep apnea (OSA) with its typical symptoms (snoring and daytime sleepiness) by using Mendelian randomization (MR). MethodsThe inverse-variance weighted method was used as the main analysis method to assess the causal effect. Sensitivity and pleiotropy analyses were carried out using leave-one-out and MR-Egger analysis, and then heterogeneity tests were conducted. ResultsIn the MR analysis, genetically predicted GERD was associated with a greater risk of OSA (IVW: OR=1.528, 95%CI 1.374 to 1.699, P=5.315E‒15). Additional MR results were consistent with the IVW results, and no pleiotropy or heterogeneity was found. We also discovered a significant causal relationship between GRED and snoring (IVW: OR=0.959, 95%CI 0.949 to 0.969, P=1.507E‒15), and daytime sleepiness (IVW: OR=1.024, 95%CI 1.021 to 1.036, P=4.580E‒5), with no evidence of pleiotropy. ConclusionThe MR study supports a causal effect between GERD and OSA with its typical symptoms (daytime sleepiness and snoring).
ObjectiveThyroid nodules are an exceptionally common thyroid disorder. Past studies suggested a possible link between thyroid diseases and breast neoplasms. However, few studies have delved into the causal relationship between thyroid nodules and breast neoplasms. This study conducted a Mendelian randomization (MR) analysis to further investigate the causal relationship between them. MethodsThis study was conducted using data sourced from genome-wide association study (GWAS) summary datasets. The study focused on thyroid nodules, benign breast tumors, and malignant breast cancers as the research objects, and relevant single nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs). The inverse-variance weighted (IVW) was primarily used to assess the causal relationship between thyroid nodules and breast neoplasms. Cochran’s Q test was employed to detect heterogeneity, while MR-Egger intercept and MR-PRESSO were used to test for pleiotropy. Sensitivity analysis was conducted using the leave-one-out method. ResultsThere was a significant causal relationship between thyroid nodules and malignant neoplasm of breast (OR=0.88, 95%CI 0.83 to 0.95, P<0.01), with no evidence of reverse causality between them (OR=1.01, 95%CI 0.99 to 1.03, P=0.16). No causal relationship was found between thyroid nodules and benign neoplasm of breast, as indicated by both forward MR analysis (OR=0.97, 95%CI 0.89 to 1.06, P=0.51) and reverse MR analysis (OR=0.97, 95%CI 0.92 to 1.04, P=0.40). Sensitivity analyses suggested that the study findings were accurate and reliable. ConclusionThe present study identifies thyroid nodules as a potential protective factor for malignant neoplasm of breast.
ObjectiveTo systematically evaluate the dose-response relationship between coffee consumption and liver cancer risk. MethodsThe PubMed, Web of Science, Cochrane Library, EMbase, CNKI, VIP, WanFang Data, and CBM databases were searched from inception to December 2022. Two reviewers independently screened literature, extracted data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using Stata 17.0 software. ResultsFifteen studies (11 cohort studies and 4 case-control studies) involving 557 259 participants were included. The results of meta-analysis showed that coffee consumption was significantly negatively associated with the risk of liver cancer (RR=0.39, 95%CI 0.27 to 0.57, P<0.01). The dose-response meta-analysis showed a non-linear dose-response relationship between coffee consumption and the risk of liver cancer (P<0.01). Compared with people who did not drink coffee, people who drank 1 cup of coffee a day had a 25% lower risk of liver cancer (RR=0.75, 95%CI 0.67 to 0.83), and people who drank 2 cups of coffee a day had a 38% lower risk of liver cancer (RR=0.62, 95%CI 0.56 to 0.70). The risk of liver cancer decreased by 45% (RR=0.55, 95%CI 0.48 to 0.62) for 3 cups of coffee and by 51% (RR=0.49, 95%CI 0.43 to 0.56) for 4 cups of coffee. ConclusionCurrent evidence suggests that there is a nonlinear dose-response relationship between coffee consumption and the risk of liver cancer. These results indicate that habitual coffee consumption is a protective factor for liver cancer. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
ObjectiveTo investigate the causal relationship between gut microbiota and cholelithiasis using a two-sample Mendelian randomization method. MethodsThe genome-wide association studies (GWAS) data of gut microbiota from the MiBioGen study and the GWAS data of cholelithiasis from the FinnGen Biobank were utilized. Genetic variants significantly associated with the relative abundance of gut microbiota were identified as instrumental variables (IVs) based on a specified threshold. The inverse variance weighted (IVW) method was employed as the primary analytical approach, with results assessed based on the odds ratio (OR) and 95% confidence interval (CI). The robustness and reliability of the findings were ensured through quality control measures, including sensitivity analysis, assessment of heterogeneity, and evaluation for horizontal gene pleiotropy. ResultsClostridiumsensustricto1 [OR=1.160, 95%CI (1.023, 1.314), P=0.020], Coprococcus3 [OR=1.136, 95%CI (1.014, 1.272), P=0.028] and Peptococcus [OR=1.074, 95%CI (1.023, 1.128) , P=0.004] increased the risk of cholelithiasis. Bacilli [OR=0.897, 95%CI (0.818, 0.984), P=0.022], Family Ⅹ ⅢAD3011group [OR=0.908, 95%CI (0.830, 0.992), P=0.033] and Lactobacillales [OR=0.884, 95%CI (0.802, 0.974), P=0.013] were protective factors for cholelithiasis. ConclusionThe study has identified 6 kinds of specific gut microbiota that are causally linked to the development of cholelithiasis, providing new ideas for the diagnosis and treatment of cholelithiasis.
Medical behaviors involve multi level and multi subject legal relations. In the process of medical and nursing care, there are two categories of legal relations concerning medical behaviors. They are external and internal legal relations. External legal relations involve legal relations between hospitals, between physicians and patients and between major medical accidents and penalties; internal legal relations involve physicians, nurses and hospital authorities.
As a valid method in systematic review, dose-response meta-analysis is widely used in investigating the relationship between independent variable and dependent variable, and which usually based on observational studies. With large sample size, observational studies can provide a reasonable amount of statistical power for meta-analysis. However, due to the design defects of observational studies, they tend to introduce many kinds of biases, which may influence the final results that make them deviation from the truth. Given the dead zone of methodology, there is no any bias adjusting method in dose-response meta-analysis. In this article, we will introduce some bias adjusting methods from other observational-study-based meta-analysis and make them suit for dose-response meta-analysis, and then compare the advantages and disadvantages of these methods.
Objective To analyze the causal relationship between cerebrospinal fluid (CSF) metabolites and tic disorder (TD) based on two-sample Mendelian randomization (MR). Methods CSF metabolites data from humans were downloaded from genome-wide association study databases, and CSF metabolites were selected as exposure factors. single nucleotide polymorphisms (SNPs) strongly associated with the exposure factors and independent of each other were selected as instrumental variables. The TD dataset from the Finngen database was downloaded, including 365 cases of TD and 411 816 controls. Analysis was conducted using inverse variance weighting, MR-Egger, weighted median, weighted mode, and simple mode. Sensitivity analysis was conducted using leave-one-out, and multiple-effects testing was conducted using MR-Egger and MR-PRESSO. Heterogeneity was detected using Cochran’s Q. Results A total of 9 CSF metabolites were found to have a causal relationship with the occurrence and development of TD (P<0.05), with a total of 394 SNPs included in the analysis. Inverse variance weighting results showed that N-acetylneuraminic acid [odds ratio (OR)=2.715, 95% confidence interval (CI) (1.102, 6.961), P=0.030], γ-glutamylglutamine [OR=1.402, 95%CI (1.053, 1.868), P=0.021], lysine [OR=2.816, 95%CI (1.084, 7.319), P=0.034] could increase the risk of TD. Cysteinylglycine disulfide [OR=0.437, 95%CI (0.216, 0.885), P=0.021], propionylcarnitine [OR=0.762, 95%CI (0.616, 0.941), P=0.012], pantothenate [OR=0.706, 95%CI (0.523, 0.952), P=0.023], gulareic acid [OR=0.758, 95%CI (0.579, 0.992), P=0.044], and cysteine-glycine [OR=0.799, 95%CI (0.684, 0.934), P=0.005] could reduce the risk of TD. The results of leave-one-out sensitivity analysis were stable, and no horizontal pleiotropy or heterogeneity was observed. Conclusions N-acetylneuraminic acid, γ-glutamylglutamine, and lysine can increase the risk of TD, but cysteinylglycine disulfide, propionylcarnitine, pantothenate, gulagic acid and cysteine-glycine can reduce the risk of TD. However, the mechanism of their effects on TD still needs to be further explored.