Background Alterations in the brain cortical structures of patients with chronic kidney disease (CKD) have been reported; however, the cause has not been determined yet. Herein, we used Mendelian randomization (MR) to reveal the causal effect of kidney damage on brain cortical structure. Methods Genome-wide association studies summary data of estimated glomerular filtration rate (eGFR) in 480,698 participants from the CKDGen Consortium were used to identify genetically predicted eGFR. Data from 567,460 individuals from the CKDGen Consortium were used to assess genetically determined CKD; 302,687 participants from the UK Biobank were used to evaluate genetically predicted albuminuria. Further, data from 51,665 patients from the ENIGMA Consortium were used to assess the relationship between genetic predisposition and reduced eGFR, CKD, and progressive albuminuria with alterations in cortical thickness (TH) or surficial area (SA) of the brain. Magnetic resonance imaging was used to measure the SA and TH globally and in 34 functional regions. Inverse-variance weighted was used as the primary estimate whereas MR Pleiotropy RESidual Sum and Outlier, MR-Egger and weighted median were used to detect heterogeneity and pleiotropy. Findings At the global level, albuminuria decreased TH (β = −0.07 mm, 95% CI: −0.12 mm to −0.02 mm, P = 0.004); at the functional level, albuminuria reduced TH of pars opercularis gyrus without global weighted (β = −0.11 mm, 95% CI: −0.16 mm to −0.07 mm, P = 3.74×10 −6 ). No pleiotropy was detected. Interpretation Kidney damage causally influences the cortex structure which suggests the existence of a kidney-brain axis. Funding This study was supported by the Science and Technology Planning Project of Guangdong Province (Grant No. 2020A1515111119 and 2017B020227007), the National Key Research and Development Program of China (Grant No. 2018YFA0902803), the National Natural Science Foundation of China (Grant No. 81825016, 81961128027, 81772719, 81772728), the Key Areas Research and Development Program of Guangdong (Grant No. 2018B010109006), Guangdong Special Support Program (2017TX04R246), Grant KLB09001 from the Key Laboratory of Malignant Tumor Gene Regulation and Target Therapy of Guangdong Higher Education Institutes, and Grants from the Guangdong Science and Technology Department (2020B1212060018).

Background The 2019 coronavirus disease pandemic (COVID-19) poses an enormous threat to public health worldwide, and the ensuing management of social isolation has greatly decreased opportunities for physical activity (PA) and increased opportunities for leisure sedentary behaviors (LSB). Given that both PA and LSB have been established as major influencing factors for obesity, diabetes and cardiometabolic syndrome, whether PA/LSB in turn affects the susceptibility to COVID-19 by disrupting metabolic homeostasis remains to be explored. In this study, we aimed to systematically evaluate the causal relationship between PA/LSB and COVID-19 susceptibility, hospitalization and severity using a Mendelian randomization study. Methods Data were obtained from a large-scale PA dataset (N = 377,000), LSB dataset (N = 422,218) and COVID-19 Host Genetics Initiative (N = 2,586,691). The causal effects were estimated with inverse variance weighted, MR-Egger, weighted median and MR-PRESSO. Sensitivity analyses were implemented with Cochran’s Q test, MR-Egger intercept test, MR-PRESSO, leave-one-out analysis and the funnel plot. Risk factor analyses were further conducted to investigate the potential mediators. Results Genetically predicted accelerometer-assessed PA decreased the risk for COVID-19 hospitalization (OR = 0.93, 95% CI 0.88–0.97; P = 0.002), while leisure television watching significantly increased the risk of COVID-19 hospitalization (OR = 1.55, 95% CI 1.29–1.88; P = 4.68 × 10–6) and disease severity (OR = 1.85, 95% CI 1.33–2.56; P = 0.0002) after Bonferroni correction. No causal effects of self-reported moderate to vigorous physical activity (MVPA), accelerometer fraction of accelerations > 425 milligravities, computer use or driving on COVID-19 progression were observed. Risk factor analyses indicated that the above causal associations might be mediated by several metabolic risk factors, including smoking, high body mass index, elevated serum triglyceride levels, insulin resistance and the occurrence of type 2 diabetes. Conclusion Our findings supported a causal effect of accelerometer-assessed PA on the reduced risk of COVID-19 hospitalization as well as television watching on the increased risk of COVID-19 hospitalization and severity, which was potentially mediated by smoking, obesity and type 2 diabetes-related phenotypes. Particular attention should be given to reducing leisure sedentary behaviors and encouraging proper exercise during isolation and quarantine for COVID-19.

Background The association between depression and prostate carcinogenesis has been reported in observational studies but the causality from depression on prostate cancer (PCa) remained unknown. We aimed to assess the causal effect of depression on PCa using the two‐sample Mendelian randomization (MR) method. Methods Two sets of genetics instruments were used for analysis, derived from publicly available genetic summary data. One was 44 single‐nucleotide polymorphisms (SNPs) robustly associated with major depressive disorder (MDD) and the other was two SNPs related with depressive status as ever depressed for a whole week. Inverse‐variance weighted method, weighted median method, MR‐Egger regression, MR Pleiotropy RESidual Sum, and Outlier test were used for MR analyses. Results No evidence for an effect of MDD on PCa risk was found in inverse‐variance weighted (OR: 1.12, 95% CI: 0.97‐1.30, p = 0.135), MR‐Egger (OR 0.89, 95% CI: 0.29‐2.68, p = 0.833), and weighted median (OR: 1.08, 95% CI: 0.92‐1.27, p = 0.350). Also, no strong evidence for an effect of depressive status on PCa incidence was found using the inverse‐variance weighted method (OR 0.72, 95% CI: 0.35‐1.47, p = 0.364). Conclusions The large MR analysis indicated that depression may not be causally associated with a risk of PCa.

Background Metabolic disturbance has been reported in patients with epilepsy. Still, the evidence about the causal role of metabolites in facilitating or preventing epilepsy is lacking. Systematically investigating the causality between blood metabolites and epilepsy would help provide novel targets for epilepsy screening and prevention. Methods We conducted two-sample Mendelian randomization (MR) analysis. Data for 486 human blood metabolites came from a genome-wide association study (GWAS) comprising 7824 participants. GWAS data for epilepsy were obtained from the International League Against Epilepsy (ILAE) consortium for primary analysis and the FinnGen consortium for replication and meta-analysis. Sensitivity analyses were conducted to evaluate heterogeneity and pleiotropy. Results 482 out of 486 metabolites were included for MR analysis following rigorous genetic variants selection. After IVW and sensitivity analysis filtration, six metabolites with causal effects on epilepsy were identified from the ILAE consortium. Only four metabolites remained significant associations with epilepsy when combined with the FinnGen consortium [uridine: odds ratio (OR) = 2.34, 95% confidence interval (CI) = 1.48–3.71, P = 0.0003; 2-hydroxystearate: OR = 1.61, 95% CI = 1.19–2.18, P = 0.002; decanoylcarnitine: OR = 0.82, 95% CI = 0.72–0.94, P = 0.004; myo-inositol: OR = 0.77, 95% CI = 0.62–0.96, P = 0.02]. Conclusion The evidence that the four metabolites mentioned above are associated with epilepsy in a causal way provides a novel insight into the underlying mechanisms of epilepsy by integrating genomics with metabolism, and has an implication for epilepsy screening and prevention.

Background and aims Observational studies have yielded conflicting results on the association of prescription opioid use (POU) with the risk of cardiovascular diseases (CVD). Residual confounding and potential reverse causality are inevitable in such conventional observational studies. This study used Mendelian randomization (MR) design to estimate the causal effect of POU on the risk of CVD, including coronary heart disease (CHD), myocardial infarction (MI) and ischemic stroke (IS), as well as their common risk factors. Design We estimated the causal effect of genetic liability for POU on CVD in a two‐sample MR framework. Complementary sensitivity analyses were conducted to test the robustness of our results. Setting Genome‐wide association studies (GWAS) that were based on predominantly European ancestry. Participants The sample sizes of the GWAS used in this study ranged from 69 033 to 757 601 participants. Measurements Genetic variants predictive of the POU and their corresponding summary‐level information in the outcomes were retrieved and extracted from the respective GWAS. Findings Using univariable MR, we found evidence for a causal effect of genetic liability for POU on an increased risk of CHD [odds ratio (OR) = 1.09; 95% confidence interval (CI) = 1.02–1.16; P = 0.008] and MI (OR = 1.13; 95% CI = 1.04–1.22; P = 0.002). In multivariable MR, the association remained after accounting for comorbid pain conditions, but was attenuated with adjustment for potential mediators, including body mass index (BMI), waist circumference (WC) and type 2 diabetes (T2D). Conclusion Mendelian randomization estimates provide robust evidence for the causal effects of genetic liability for prescription opioid use on an increased risk of coronary heart disease and myocardial infarction, which might be mediated by obesity‐related traits.