scholarly journals Causal effect of atrial fibrillation/flutter on chronic kidney disease: A bidirectional two-sample Mendelian randomization study

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261020
Author(s):  
Masahiro Yoshikawa ◽  
Kensuke Asaba ◽  
Tomohiro Nakayama
Keyword(s):  
Atrial Fibrillation ◽  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Odds Ratio ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
European Population ◽  
Genome Wide Association Studies ◽  
The Uk

Chronic kidney disease (CKD) and atrial fibrillation are both major burdens on the health care system worldwide. Several observational studies have reported clinical associations between CKD and atrial fibrillation; however, causal relationships between these conditions remain to be elucidated due to possible bias by confounders and reverse causations. Here, we conducted bidirectional two-sample Mendelian randomization analyses using publicly available summary statistics of genome-wide association studies (the CKDGen consortium and the UK Biobank) to investigate causal associations between CKD and atrial fibrillation/flutter in the European population. Our study suggested a causal effect of the risk of atrial fibrillation/flutter on the decrease in serum creatinine-based estimated glomerular filtration rate (eGFR) and revealed a causal effect of the risk of atrial fibrillation/flutter on the risk of CKD (odds ratio, 9.39 per doubling odds ratio of atrial fibrillation/flutter; 95% coefficient interval, 2.39–37.0; P = 0.001), while the causal effect of the decrease in eGFR on the risk of atrial fibrillation/flutter was unlikely. However, careful interpretation and further studies are warranted, as the underlying mechanisms remain unknown. Further, our sample size was relatively small and selection bias was possible.

scholarly journals Causal Inference Between Chronic Periodontitis and Chronic Kidney Disease: A Bidirectional Mendelian Randomization Analysis in a European Population

2021 ◽  
Vol 12 ◽  
Author(s):  
Jie Yang ◽  
Tianyi Chen ◽  
Yahong Zhu ◽  
Mingxia Bai ◽  
Xingang Li
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Causal Inference ◽  
Causal Relationship ◽  
Instrumental Variables ◽  
Chronic Periodontitis ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Summary Statistics

BackgroundPrevious epidemiological studies have shown significant associations between chronic periodontitis (CP) and chronic kidney disease (CKD), but the causal relationship remains uncertain. Aiming to examine the causal relationship between these two diseases, we conducted a bidirectional two-sample Mendelian randomization (MR) analysis with multiple MR methods.MethodsFor the casual effect of CP on CKD, we selected seven single-nucleotide polymorphisms (SNPs) specific to CP as genetic instrumental variables from the genome-wide association studies (GWAS) in the GLIDE Consortium. The summary statistics of complementary kidney function measures, i.e., estimated glomerular filtration rate (eGFR) and blood urea nitrogen (BUN), were derived from the GWAS in the CKDGen Consortium. For the reversed causal inference, six SNPs associated with eGFR and nine with BUN from the CKDGen Consortium were included and the summary statistics were extracted from the CLIDE Consortium.ResultsNo significant causal association between genetically determined CP and eGFR or BUN was found (all p > 0.05). Based on the conventional inverse variance-weighted method, one of seven instrumental variables supported genetically predicted CP being associated with a higher risk of eGFR (estimate = 0.019, 95% CI: 0.012–0.026, p < 0.001).ConclusionEvidence from our bidirectional causal inference does not support a causal relation between CP and CKD risk and therefore suggests that associations reported by previous observational studies may represent confounding.

P0770IMPAIRED FASTING GLUCOSE AND CHRONIC KIDNEY DISEASE: A MENDELIAN RANDOMIZATION STUDY

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Hyunjin Noh ◽  
Hyoungnae Kim ◽  
Su Yeon Park ◽  
Dohui Hwang ◽  
Kyoungin Choi
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Fasting Glucose ◽  
Association Studies ◽  
Genetic Risk Score ◽  
Genome Wide Association Studies ◽  
Standard Deviation Increase ◽  
Inverse Variance

Abstract Background and Aims Diabetes mellitus is a risk factor of chronic kidney disease (CKD); however, the relationship between fasting glucose and CKD remains controversial in non-diabetic population. Method This study included 6,354 participants without diabetes and CKD from the KoreanGenome Epidemiology Study. The genetic risk score (GRS9) was calculated using nine genetic variants associated with fasting glucose in previous genome wide association studies. Incident CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 or proteinuria (≥1+). The causal relationship between fasting glucose and CKD was evaluated using Mendelian randomization (MR) approach. Results The GRS9 was strongly associated with fasting glucose (β, 1.01; P < 0.001). During a median follow-up of 11.6 years, 531 (8.4%) CKD events occurred. After adjusting for confounding factors, fasting glucose was not associated with incident CKD (odds ratio [OR], 0.991; 95% confidence interval [CI], 0.980–1.003; P =0.139). In the MR analysis, GRS9 was not associated with CKD development (OR per 1 standard deviation increase, 1.179; 95% CI, 0.819–1.696; P = 0.376). Further evaluation with various other MR methods using inverse-variance weighted, MR-Egger, and weighted median methods for multiple genetic variants and strict CKD criteria (decrease in the eGFR of ≥ 30% to a value of < 60 mL/min/1.73m2) found no significant relationship between GRS9 and incident CKD. Conclusion Impaired fasting glucose was not causally associated with CKD development in nondiabetic population.

scholarly journals Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis

PLoS Medicine ◽  
2021 ◽  
Vol 18 (3) ◽  
pp. e1003553
Author(s):  
Aaron Leong ◽  
Joanne B. Cole ◽  
Laura N. Brenner ◽  
James B. Meigs ◽  
Jose C. Florez ◽  
...  
Keyword(s):  
Odds Ratio ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Statistical Significance ◽  
Epidemiological Studies ◽  
Population Based ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Uk Biobank ◽  
The Uk

Background Epidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses. Methods and findings We selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10−8 from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m2; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10−5; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m2, p = 2.1 × 10−5). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null. Conclusions In this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes.

scholarly journals Impaired fasting glucose and development of chronic kidney disease in non-diabetic population: a Mendelian randomization study

2020 ◽  
Vol 8 (1) ◽  
pp. e001395
Author(s):  
Hyoungnae Kim ◽  
Suyeon Park ◽  
Soon Hyo Kwon ◽  
Jin Seok Jeon ◽  
Dong Cheol Han ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Causal Relationship ◽  
Mendelian Randomization ◽  
Fasting Glucose ◽  
Association Studies ◽  
Genetic Risk Score ◽  
Genome Wide Association Studies ◽  
Diabetic Population ◽  
Design And Methods

IntroductionDiabetes mellitus is a risk factor of chronic kidney disease (CKD); however, the relationship between fasting glucose and CKD remains controversial in non-diabetic population. This study aimed to assess causal relationship between genetically predicted fasting glucose and incident CKD.Research design and methodsThis study included 5909 participants without diabetes and CKD from the Korean Genome Epidemiology Study. The genetic risk score (GRS9) was calculated using nine genetic variants associated with fasting glucose in previous genome-wide association studies. Incident CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 and/or proteinuria (≥1+). The causal relationship between fasting glucose and CKD was evaluated using the Mendelian randomization (MR) approach.ResultsThe GRS9 was strongly associated with fasting glucose (β, 1.01; p<0.001). During a median follow-up of 11.6 years, 490 (8.3%) CKD events occurred. However, GRS9 was not significantly different between participants with CKD events and those without. After adjusting for confounding factors, fasting glucose was not associated with incident CKD (OR 0.990; 95% CI 0.977 to 1.002; p=0.098). In the MR analysis, GRS9 was not associated with CKD development (OR per 1 SD increase, 1.179; 95% CI 0.819 to 1.696; p=0.376). Further evaluation using various other MR methods and strict CKD criteria (decrease in the eGFR of ≥30% to a value of <60 mL/min/1.73 m2) found no significant relationship between GRS9 and incident CKD.ConclusionsFasting glucose was not causally associated with CKD development in non-diabetic population.

scholarly journals Genetics of Height and Risk of Atrial Fibrillation: A Mendelian Randomization Study

2019 ◽  
Author(s):  
Michael G. Levin ◽  
Renae Judy ◽  
Dipender Gill ◽  
Marijana Vujkovic ◽  
Matthew C. Hyman ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Odds Ratio ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Genome Wide Association ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Standard Deviation Increase ◽  
Genome Wide ◽  
Predicted Height

ABSTRACTObjectiveTo determine whether height has a causal effect on risk of atrial fibrillationDesignMendelian randomization studySettingGenome-wide association studies of height and atrial fibrillation; Penn Medicine BiobankParticipantsMultiethnic (predominantly European ancestry) participants in genome-wide association studies of height (693,529 individuals) and atrial fibrillation (65,446 cases and 522,744 controls); 7,023 Penn Medicine Biobank participants of European ancestryExposuresHeight, cardiometabolic risk factors for atrial fibrillation, and randomly allocated genetic variants strongly associated with these traitsMain outcome measureRisk of atrial fibrillation (measured in odds ratio)ResultsAt the population level, a 1 standard deviation increase in genetically-predicted height was associated with increased odds of AF (Odds ratio [OR] 1.34; 95% Confidence Interval [CI] 1.29 to 1.40; p = 5×10−42). These findings remained consistent in sensitivity analyses that were robust to the presence of pleiotropic variants. Results from analyses considering individual-participant data were similar, even after adjustment for clinical covariates, including left atrial size.ConclusionGenetically predicted height is a positive causal risk factor for AF. This finding raises the possibility of investigating height/growth-related pathways as a means for gaining novel mechanistic insights to atrial fibrillation, as well as incorporating height into population screening strategies for atrial fibrillation.

scholarly journals Bias in two-sample Mendelian randomization when using heritable covariable-adjusted summary associations

2021 ◽  
Author(s):  
Fernando Pires Hartwig ◽  
Kate Tilling ◽  
George Davey Smith ◽  
Deborah A Lawlor ◽  
Maria Carolina Borges
Keyword(s):  
Waist Circumference ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Real Data ◽  
Sensitivity Analyses ◽  
Effect Estimate ◽  
Genome Wide Association Studies ◽  
Residual Confounding

Abstract Background Two-sample Mendelian randomization (MR) allows the use of freely accessible summary association results from genome-wide association studies (GWAS) to estimate causal effects of modifiable exposures on outcomes. Some GWAS adjust for heritable covariables in an attempt to estimate direct effects of genetic variants on the trait of interest. One, both or neither of the exposure GWAS and outcome GWAS may have been adjusted for covariables. Methods We performed a simulation study comprising different scenarios that could motivate covariable adjustment in a GWAS and analysed real data to assess the influence of using covariable-adjusted summary association results in two-sample MR. Results In the absence of residual confounding between exposure and covariable, between exposure and outcome, and between covariable and outcome, using covariable-adjusted summary associations for two-sample MR eliminated bias due to horizontal pleiotropy. However, covariable adjustment led to bias in the presence of residual confounding (especially between the covariable and the outcome), even in the absence of horizontal pleiotropy (when the genetic variants would be valid instruments without covariable adjustment). In an analysis using real data from the Genetic Investigation of ANthropometric Traits (GIANT) consortium and UK Biobank, the causal effect estimate of waist circumference on blood pressure changed direction upon adjustment of waist circumference for body mass index. Conclusions Our findings indicate that using covariable-adjusted summary associations in MR should generally be avoided. When that is not possible, careful consideration of the causal relationships underlying the data (including potentially unmeasured confounders) is required to direct sensitivity analyses and interpret results with appropriate caution.

scholarly journals A Novel Method for Mendelian Randomization Analyses With Pleiotropy and Linkage Disequilibrium in Genetic Variants From Individual Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuquan Wang ◽  
Tingting Li ◽  
Liwan Fu ◽  
Siqian Yang ◽  
Yue-Qing Hu
Keyword(s):  
Linkage Disequilibrium ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Random Effect ◽  
Pleiotropic Effect ◽  
Genome Wide Association Studies ◽  
The Novel ◽  
Novel Method

Mendelian randomization makes use of genetic variants as instrumental variables to eliminate the influence induced by unknown confounders on causal estimation in epidemiology studies. However, with the soaring genetic variants identified in genome-wide association studies, the pleiotropy, and linkage disequilibrium in genetic variants are unavoidable and may produce severe bias in causal inference. In this study, by modeling the pleiotropic effect as a normally distributed random effect, we propose a novel mixed-effects regression model-based method PLDMR, pleiotropy and linkage disequilibrium adaptive Mendelian randomization, which takes linkage disequilibrium into account and also corrects for the pleiotropic effect in causal effect estimation and statistical inference. We conduct voluminous simulation studies to evaluate the performance of the proposed and existing methods. Simulation results illustrate the validity and advantage of the novel method, especially in the case of linkage disequilibrium and directional pleiotropic effects, compared with other methods. In addition, by applying this novel method to the data on Atherosclerosis Risk in Communications Study, we conclude that body mass index has a significant causal effect on and thus might be a potential risk factor of systolic blood pressure. The novel method is implemented in R and the corresponding R code is provided for free download.

scholarly journals Association of Physical Activity with Body Composition: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Ferris Alaa Ramadan ◽  
Katherine Ellingson ◽  
Yann Klimentidis
Keyword(s):  
Physical Activity ◽  
Body Composition ◽  
Sedentary Behavior ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Sensitivity Analyses ◽  
Genome Wide Association Studies ◽  
Protective Effects ◽  
Dietary Interventions ◽  
The Uk

Background. Studies suggest that body composition can be improved through physical activity (PA) independently of dietary interventions. A separate line of evidence suggests that PA may reduce high-risk visceral adipose tissue (VAT), without clinically meaningful weight change. Genome-wide association studies have previously identified genetic markers associated with PA behaviors and may provide an opportunity to evaluate hypothesized causal relationships with body composition. Methods. We performed a Mendelian randomization (MR) study to test the incremental benefits of various PA exposures on body composition outcomes as assessed by anthropometric indices, lean body mass (LBM) (kg), body fat (%), and VAT (kg). Genetic instruments were identified for both self-reported and accelerometer-measured PA, including sedentary behavior. Outcomes included anthropometric and dual-energy X-ray absorptiometry measures of adiposity, extracted from the UK Biobank and the largest publicly available consortia. Multivariable MR (MVMR) included educational attainment as a covariate to address potential confounding. Sensitivity analyses were evaluated for weak instrument bias and pleiotropic effects.Results. We did not identify associations between genetically-predicted sedentary behavior (self-reported or accelerometer) and body composition outcomes in MVMR analyses. All analyses for self-reported moderate PA were null for body composition outcomes, including BMI, LBM and VAT. Genetically-predicted PA at higher intensities was protective against VAT in MR and MVMR analyses of both accelerometer-measured vigorous PA (MVMR β = -0.15, 95% Confidence Interval (CI): -0.24, -0.07, p&lt;0.001) and self-reported participation in strenuous sports or other exercises (MVMR β = -0.27, 95%CI: -0.52, -0.01, p=0.034), and was robust across several sensitivity analyses. Conclusions. We did not identify evidence of a causal relationship between genetically-predicted PA and body composition, with the exception of a putatively protective effect of higher-intensity PA on VAT. Protective effects of PA against VAT may support prior evidence of biological pathways through which PA decreases risk of downstream cardiometabolic diseases.

scholarly journals bGWAS: an R package to perform Bayesian genome wide association studies

2020 ◽  
Vol 36 (15) ◽  
pp. 4374-4376
Author(s):  
Ninon Mounier ◽  
Zoltán Kutalik
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
R Package ◽  
Genome Wide Association ◽  
Supplementary Information ◽  
Genome Wide Association Studies ◽  
Biological Mechanisms ◽  
Genome Wide ◽  
Related Risk

Abstract Summary Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms. Availability and implementation bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS. Supplementary information Supplementary data are available at Bioinformatics online.

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