scholarly journals Causality of Abdominal Obesity on Cognition: a Trans-ethnic Mendelian Randomization study

2021 ◽  
Author(s):  
Shi-Heng Wang ◽  
Mei-Hsin Su ◽  
Chia-Yen Chen ◽  
Yen-Feng Lin ◽  
Yen-Chen Anne Feng ◽  
...  
Keyword(s):  
Cognitive Aging ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
European Ancestry ◽  
P Value ◽  
Abdominal Adiposity ◽  
Uk Biobank ◽  
Reverse Causality ◽  
Summary Data

Obesity has been associated with cognition in observational studies; however, whether its effect is confounding, reverse causality, or causal remains inconclusive. Using two-sample Mendelian randomization (MR) analyses, we investigated the causality of overall obesity, measured by BMI, and abdominal adiposity, measured by waist-hip ratio adjusted for BMI (WHRadjBMI), on cognition. Using summary data from the GIANT consortium, COGENT consortium, and UK Biobank of European ancestry, there was no causal effect of BMI on cognition performance (beta[95% CI]=-0.04[-0.12,0.04], p-value=0.35); however, a 1-SD increase in WHRadjBMI was associated with 0.07 standardized decrease in cognition performance (beta[95% CI]=-0.07[-0.12,-0.02], p=0.006). Using raw data from the Taiwan Biobank of Asian ancestry, there was no causal effect of BMI on cognitive aging (beta[95% CI]=0.00[-0.09,0.09], p-value=0.95); however, a 1-SD increase in WHRadjBMI was associated with a 0.17 standardized decrease in cognitive aging (beta[95% CI]=-0.17[-0.30,-0.03], p=0.02). This trans-ethnic MR study reveals that abdominal adiposity impairs cognition.

scholarly journals Does Obesity Cause Thyroid Cancer? A Mendelian Randomization Study

2020 ◽  
Vol 105 (7) ◽  
pp. e2398-e2407
Author(s):  
Jonathan Mark Fussey ◽  
Robin N Beaumont ◽  
Andrew R Wood ◽  
Bijay Vaidya ◽  
Joel Smith ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Thyroid Cancer ◽  
Thyroid Disease ◽  
Observational Studies ◽  
Mendelian Randomization ◽  
European Ancestry ◽  
Uk Biobank ◽  
Reverse Causality ◽  
The Uk

Abstract Background The incidence of thyroid cancer is rising, and relatively little is known about modifiable risk factors for the condition. Observational studies have suggested a link between adiposity and thyroid cancer; however, these are subject to confounding and reverse causality. Here, we used data from the UK Biobank and Mendelian randomization approaches to investigate whether adiposity causes benign nodular thyroid disease and differentiated thyroid cancer. Methods We analyzed data from 379 708 unrelated participants of European ancestry in the UK Biobank and identified 1812 participants with benign nodular thyroid disease and 425 with differentiated thyroid carcinoma. We tested observational associations with measures of adiposity and type 2 diabetes mellitus. One and 2-sample Mendelian randomization approaches were used to investigate causal relationships. Results Observationally, there were positive associations between higher body mass index (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.08-1.22), higher waist-hip ratio (OR, 1.16; 95% CI, 1.09-1.23), and benign nodular thyroid disease, but not thyroid cancer. Mendelian randomization did not support a causal link for obesity with benign nodular thyroid disease or thyroid cancer, although it did provide some evidence that individuals in the highest quartile for genetic liability of type 2 diabetes had higher odds of thyroid cancer than those in the lowest quartile (OR, 1.45; CI, 1.11-1.90). Conclusions Contrary to the findings of observational studies, our results do not confirm a causal role for obesity in benign nodular thyroid disease or thyroid cancer. They do, however, suggest a link between type 2 diabetes and thyroid cancer.

scholarly journals Depression and Osteoporosis: A Mendelian Randomization Study

2021 ◽  
Author(s):  
Bin He ◽  
Qiong Lyu ◽  
Lifeng Yin ◽  
Muzi Zhang ◽  
Zhengxue Quan ◽  
...  
Keyword(s):  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Association Studies ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Genome Wide Association Studies ◽  
Reverse Causality ◽  
Mineral Density

AbstractObservational studies suggest a link between depression and osteoporosis, but these may be subject to confounding and reverse causality. In this two-sample Mendelian randomization analysis, we included the large meta-analysis of genome-wide association studies for depression among 807,553 individuals (246,363 cases and 561,190 controls) of European descent, the large meta-analysis to identify genetic variants associated with femoral neck bone mineral density (FN-BMD), forearm BMD (FA-BMD) and lumbar spine BMD (LS-BMD) among 53,236 individuals of European ancestry, and the GWAS summary data of heel BMD (HE-BMD) and fracture among 426,824 individuals of European ancestry. The results revealed that genetic predisposition towards depression showed no causal effect on FA-BMD (beta-estimate: 0.091, 95% confidence interval [CI] − 0.088 to 0.269, SE:0.091, P value = 0.320), FN-BMD (beta-estimate: 0.066, 95% CI − 0.016 to 0.148, SE:0.042, P value = 0.113), LS-BMD (beta-estimate: 0.074, 95% CI − 0.029 to 0.177, SE:0.052, P value = 0.159), HE-BMD (beta-estimate: 0.009, 95% CI − 0.043 to 0.061, SE:0.027, P value = 0.727), or fracture (beta-estimate: 0.008, 95% CI − 0.071 to 0.087, SE:0.041, P value = 0.844). These results were also confirmed by multiple sensitivity analyses. Contrary to the findings of observational studies, our results do not reveal a causal role of depression in osteoporosis or fracture.

scholarly journals Smoking and COVID-19: A two-sample Mendelian randomization study

2021 ◽  
Author(s):  
Bing-Kun Zheng ◽  
Na Li
Keyword(s):  
Observational Studies ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Dose Effect ◽  
Genetic Studies ◽  
Host Genetics ◽  
Causal Risk Factor ◽  
Increased Risk ◽  
Summary Data ◽  
Randomization Method

AbstractEvidence from observational studies suggested that smokers are at increased risk of coronavirus disease 2019 (COVID-19). We aimed to assess the causal effect of smoking on risk for COVID-19 susceptibility and severity using two-sample Mendelian randomization method. Smoking-associated variants were selected as instrument variables from two largest genetic studies. The latest summary data of COVID-19 that shared on Jan 18, 2021 by the COVID-19 Host Genetics Initiative was used. The present Mendelian randomization study provided genetic evidence that smoking was a causal risk factor for COVID-19 susceptibility and severity. In addition, there may be a dose-effect relationship between smoking and COVID-19 severity.

scholarly journals Causal Effect of the Triglyceride-Glucose Index and the Joint Exposure of Higher Glucose and Triglyceride With Extensive Cardio-Cerebrovascular Metabolic Outcomes in the UK Biobank: A Mendelian Randomization Study

2021 ◽  
Vol 7 ◽  
Author(s):  
Shucheng Si ◽  
Jiqing Li ◽  
Yunxia Li ◽  
Wenchao Li ◽  
Xiaolu Chen ◽  
...  
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Heart Diseases ◽  
Cerebrovascular Diseases ◽  
European Ancestry ◽  
Uk Biobank ◽  
Tyg Index ◽  
Metabolic Outcomes ◽  
Meta Regression ◽  
The Uk

Background: The causal evidence of the triglyceride–glucose (TyG) index, as well as the joint exposure of higher glucose and triglyceride on the risk of cardio-cerebrovascular diseases (CVD), was lacking.Methods: A comprehensive factorial Mendelian randomization (MR) was performed in the UK Biobank cohort involving 273,368 individuals with European ancestry to assess and quantify these effects. The factorial MR, MR-PRESSO, MR-Egger, meta-regression, sensitivity analysis, positive control, and external verification were utilized. Outcomes include major outcomes [overall CVD, ischemic heart diseases (IHD), and cerebrovascular diseases (CED)] and minor outcomes [angina pectoris (AP), acute myocardial infarction (AMI), chronic IHD (CIHD), heart failure (HF), hemorrhagic stroke (HS), and ischemic stroke (IS)].Results: The TyG index significantly increased the risk of overall CVD [OR (95% CI): 1.20 (1.14–1.25)], IHD [OR (95% CI): 1.22 (1.15–1.29)], CED [OR (95% CI): 1.14 (1.05–1.23)], AP [OR (95% CI): 1.29 (1.20–1.39)], AMI [OR (95% CI): 1.27 (1.16–1.39)], CIHD [OR (95% CI): 1.21 (1.13–1.29)], and IS [OR (95% CI): 1.22 (1.06–1.40)]. Joint exposure to genetically higher GLU and TG was significantly associated with a higher risk of overall CVD [OR (95% CI): 1.17 (1.12–1.23)] and IHD [OR (95% CI): 1.22 (1.16–1.29)], but not with CED. The effect of GLU and TG was independent of each other genetically and presented dose–response effects in bivariate meta-regression analysis.Conclusions: Lifelong genetic exposure to higher GLU and TG was jointly associated with higher cardiac metabolic risk while the TyG index additionally associated with several cerebrovascular diseases. The TyG index could serve as a more sensitive pre-diagnostic indicator for CVD while the joint GLU and TG could offer a quantitative risk for cardiac metabolic outcomes.

Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization

2019 ◽  
Vol 48 (5) ◽  
pp. 1478-1492 ◽  
Author(s):  
Qingyuan Zhao ◽  
Yang Chen ◽  
Jingshu Wang ◽  
Dylan S Small
Keyword(s):  
Odds Ratio ◽  
Statistical Power ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Density Lipoprotein ◽  
P Value ◽  
Genome Wide ◽  
A Genome ◽  
Summary Data ◽  
Genetic Instruments

Abstract Background Summary-data Mendelian randomization (MR) has become a popular research design to estimate the causal effect of risk exposures. With the sample size of GWAS continuing to increase, it is now possible to use genetic instruments that are only weakly associated with the exposure. Development We propose a three-sample genome-wide design where typically 1000 independent genetic instruments across the whole genome are used. We develop an empirical partially Bayes statistical analysis approach where instruments are weighted according to their strength; thus weak instruments bring less variation to the estimator. The estimator is highly efficient with many weak genetic instruments and is robust to balanced and/or sparse pleiotropy. Application We apply our method to estimate the causal effect of body mass index (BMI) and major blood lipids on cardiovascular disease outcomes, and obtain substantially shorter confidence intervals (CIs). In particular, the estimated causal odds ratio of BMI on ischaemic stroke is 1.19 (95% CI: 1.07–1.32, P-value <0.001); the estimated causal odds ratio of high-density lipoprotein cholesterol (HDL-C) on coronary artery disease (CAD) is 0.78 (95% CI: 0.73–0.84, P-value <0.001). However, the estimated effect of HDL-C attenuates and become statistically non-significant when we only use strong instruments. Conclusions A genome-wide design can greatly improve the statistical power of MR studies. Robust statistical methods may alleviate but not solve the problem of horizontal pleiotropy. Our empirical results suggest that the relationship between HDL-C and CAD is heterogeneous, and it may be too soon to completely dismiss the HDL hypothesis.

scholarly journals Searching for the causal effects of BMI in over 300 000 individuals, using Mendelian randomization

2017 ◽  
Author(s):  
Louise A C Millard ◽  
Neil M Davies ◽  
Kate Tilling ◽  
Tom R Gaunt ◽  
George Davey Smith
Keyword(s):  
Body Mass Index ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
P Value ◽  
Protective Effects ◽  
Independent Subset ◽  
Uk Biobank ◽  
False Discovery ◽  
Causal Risk Factor

ABSTRACTMendelian randomization (MR) has been used to estimate the causal effect of body mass index (BMI) on particular traits thought to be affected by BMI. However, BMI may also be a modifiable, causal risk factor for outcomes where there is no prior reason to suggest that a causal effect exists. We perform a MR phenome-wide association study (MR-pheWAS) to search for the causal effects of BMI in UK Biobank (n=334 968), using the PHESANT open-source phenome scan tool. Of the 20 461 tests performed, our MR-pheWAS identified 519 associations below a stringent P value threshold corresponding to a 5% estimated false discovery rate, including many previously identified causal effects. We also identified several novel effects, including protective effects of higher BMI on a set of psychosocial traits, identified initially in our preliminary MR-pheWAS and replicated in an independent subset of UK Biobank. Such associations need replicating in an independent sample.

scholarly journals An examination of multivariable Mendelian randomization in the single sample and two-sample summary data settings

2018 ◽  
Author(s):  
Eleanor Sanderson ◽  
George Davey Smith ◽  
Frank Windmeijer ◽  
Jack Bowden
Keyword(s):  
Mendelian Randomization ◽  
Causal Effect ◽  
Causal Effects ◽  
Single Sample ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
Level Data ◽  
Wide Range ◽  
Using Data ◽  
Summary Data

AbstractBackgroundMendelian Randomisation (MR) is a powerful tool in epidemiology which can be used to estimate the causal effect of an exposure on an outcome in the presence of unobserved confounding, by utilising genetic variants that are instrumental variables (IVs) for the exposure. This has been extended to Multivariable MR (MVMR) to estimate the effect of two or more exposures on an outcome.Methods/ResultsWe use simulations and theory to clarify the interpretation of estimated effects in a MVMR analysis under a range of underlying scenarios, where a secondary exposure acts variously as a confounder, a mediator, a pleiotropic pathway and a collider. We then describe how instrument strength and validity can be assessed for an MVMR analysis in the single sample setting, and develop tests to assess these assumptions in the popular two-sample summary data setting. We illustrate our methods using data from UK biobank to estimate the effect of education and cognitive ability on body mass index.ConclusionMVMR analysis consistently estimates the effect of an exposure, or exposures, of interest and provides a powerful tool for determining causal effects in a wide range of scenarios with either individual or summary level data.

scholarly journals The Effect of Circulating Zinc, Selenium, Copper and Vitamin K1 on COVID-19 Outcomes: A Mendelian Randomization Study

Nutrients ◽  
2022 ◽  
Vol 14 (2) ◽  
pp. 233
Author(s):  
Maria K. Sobczyk ◽  
Tom R. Gaunt
Keyword(s):  
Critical Illness ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
P Value ◽  
Vitamin K1 ◽  
Inverse Variance ◽  
A Genome

Background & Aims: Previous results from observational, interventional studies and in vitro experiments suggest that certain micronutrients possess anti-viral and immunomodulatory activities. In particular, it has been hypothesized that zinc, selenium, copper and vitamin K1 have strong potential for prophylaxis and treatment of COVID-19. We aimed to test whether genetically predicted Zn, Se, Cu or vitamin K1 levels have a causal effect on COVID-19 related outcomes, including risk of infection, hospitalization and critical illness. Methods: We employed a two-sample Mendelian Randomization (MR) analysis. Our genetic variants derived from European-ancestry GWAS reflected circulating levels of Zn, Cu, Se in red blood cells as well as Se and vitamin K1 in serum/plasma. For the COVID-19 outcome GWAS, we used infection, hospitalization or critical illness. Our inverse-variance weighted (IVW) MR analysis was complemented by sensitivity analyses including a more liberal selection of variants at a genome-wide sub-significant threshold, MR-Egger and weighted median/mode tests. Results: Circulating micronutrient levels show limited evidence of association with COVID-19 infection, with the odds ratio [OR] ranging from 0.97 (95% CI: 0.87–1.08, p-value = 0.55) for zinc to 1.07 (95% CI: 1.00–1.14, p-value = 0.06)—i.e., no beneficial effect for copper was observed per 1 SD increase in exposure. Similarly minimal evidence was obtained for the hospitalization and critical illness outcomes with OR from 0.98 (95% CI: 0.87–1.09, p-value = 0.66) for vitamin K1 to 1.07 (95% CI: 0.88–1.29, p-value = 0.49) for copper, and from 0.93 (95% CI: 0.72–1.19, p-value = 0.55) for vitamin K1 to 1.21 (95% CI: 0.79–1.86, p-value = 0.39) for zinc, respectively. Conclusions: This study does not provide evidence that supplementation with zinc, selenium, copper or vitamin K1 can prevent SARS-CoV-2 infection, critical illness or hospitalization for COVID-19.

scholarly journals Association between coffee consumption and overall risk of being diagnosed with or dying from cancer among >300 000 UK Biobank participants in a large-scale Mendelian randomization study

2019 ◽  
Vol 48 (5) ◽  
pp. 1447-1456 ◽  
Author(s):  
Jue-Sheng Ong ◽  
Matthew H Law ◽  
Jiyuan An ◽  
Xikun Han ◽  
Puya Gharahkhani ◽  
...  
Keyword(s):  
Cancer Risk ◽  
Observational Studies ◽  
Large Scale ◽  
Mendelian Randomization ◽  
Coffee Consumption ◽  
Cancer Death ◽  
Uk Biobank ◽  
Coffee Intake ◽  
Summary Data ◽  
Overall Risk

Abstract Background Previous observational studies have suggested that coffee intake may be associated with a reduction in cancer risk. Mendelian randomization (MR) studies can help clarify whether the observed associations are likely to be causal. Here we evaluated whether coffee intake is associated with: (i) overall risk of being diagnosed with/dying from any cancer; and (ii) risk of individual cancers. Methods We identified 46 155 cases (of which 6998 were fatal) and 270 342 controls of White British ancestry from the UK Biobank cohort (UKB), based on ICD10 diagnoses. Individuals with benign tumours were excluded. Coffee intake was self-reported and recorded based on cup/day consumption. We conducted both observational and summary data MR analyses. Results There was no observational association between coffee intake and overall cancer risk [odds ratio (OR) per one cup/day increase = 0.99, 95% confidence interval (CI) 0.98, 1.00] or cancer death (OR = 1.01, 0.99, 1.03); the estimated OR from MR is 1.01 (0.94, 1.08) for overall cancer risk and 1.11 (0.95, 1.31) for cancer death. The relationship between coffee intake and individual cancer risks were consistent with a null effect, with most cancers showing little or no associations with coffee. Meta-analysis of our MR findings with publicly available summary data on various cancers do not support a strong causal relationship between coffee and risk of breast, ovarian, lung or prostate cancer, upon correction for multiple testing. Conclusions Taken together, coffee intake is not associated with overall risk of being diagnosed with or dying from cancer in UKB. For individual cancers, our findings were not statistically inconsistent with earlier observational studies, although for these we were unable to rule out a small effect on specific types of cancer.

scholarly journals Mendelian randomization analysis of C-reactive protein on colorectal cancer risk

2018 ◽  
Vol 48 (3) ◽  
pp. 767-780 ◽  
Author(s):  
Xiaoliang Wang ◽  
James Y Dai ◽  
Demetrius Albanes ◽  
Volker Arndt ◽  
Sonja I Berndt ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Instrumental Variables ◽  
Statistical Power ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
European Ancestry ◽  
Reverse Causality ◽  
C Reactive Protein ◽  
Reactive Protein ◽  
Increased Risk

Abstract Background Chronic inflammation is a risk factor for colorectal cancer (CRC). Circulating C-reactive protein (CRP) is also moderately associated with CRC risk. However, observational studies are susceptible to unmeasured confounding or reverse causality. Using genetic risk variants as instrumental variables, we investigated the causal relationship between genetically elevated CRP concentration and CRC risk, using a Mendelian randomization approach. Methods Individual-level data from 30 480 CRC cases and 22 844 controls from 33 participating studies in three international consortia were used: the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colorectal Transdisciplinary Study (CORECT) and the Colon Cancer Family Registry (CCFR). As instrumental variables, we included 19 single nucleotide polymorphisms (SNPs) previously associated with CRP concentration. The SNP-CRC associations were estimated using a logistic regression model adjusted for age, sex, principal components and genotyping phases. An inverse-variance weighted method was applied to estimate the causal effect of CRP on CRC risk. Results Among the 19 CRP-associated SNPs, rs1260326 and rs6734238 were significantly associated with CRC risk (P = 7.5 × 10–4, and P = 0.003, respectively). A genetically predicted one-unit increase in the log-transformed CRP concentrations (mg/l) was not associated with increased risk of CRC [odds ratio (OR) = 1.04; 95% confidence interval (CI): 0.97, 1.12; P = 0.256). No evidence of association was observed in subgroup analyses stratified by other risk factors. Conclusions In spite of adequate statistical power to detect moderate association, we found genetically elevated CRP concentration was not associated with increased risk of CRC among individuals of European ancestry. Our findings suggested that circulating CRP is unlikely to be a causal factor in CRC development.

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