Novel insights into the consequences of obesity: a phenotype-wide Mendelian randomization study

AbstractObesity is thought to significantly impact the quality of life. In this study, we sought to evaluate the health consequences of obesity on the risk of a broad spectrum of human diseases. The causal effects of exposing to obesity on health outcomes were inferred using Mendelian randomization (MR) analyses using a fixed effects inverse-variance weighted model. The instrumental variables were SNPs associated with obesity as measured by body mass index (BMI) reported by GIANT consortium. The spectrum of outcome consisted of the phenotypes from published GWAS and the UK Biobank. The MR-Egger intercept test was applied to estimate horizontal pleiotropic effects, along with Cochran’s Q test to assess heterogeneity among the causal effects of instrumental variables. Our MR results confirmed many putative disease risks due to obesity, such as diabetes, dyslipidemia, sleep disorder, gout, smoking behaviors, arthritis, myocardial infarction, and diabetes-related eye disease. The novel findings indicated that elevated red blood cell count was inferred as a mediator of BMI-induced type 2 diabetes in our bidirectional MR analysis. Intriguingly, the effects that higher BMI could decrease the risk of both skin and prostate cancers, reduce calorie intake, and increase the portion size warrant further studies. Our results shed light on a novel mechanism of the disease-causing roles of obesity.

Download Full-text

A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures

10.1101/2021.10.01.462221 ◽

2021 ◽

Author(s):

Chen Mo ◽

Zhenyao Ye ◽

Hongjie Ke ◽

Tong Lu ◽

Travis Canida ◽

...

Keyword(s):

Cognitive Function ◽

Brain Imaging ◽

Instrumental Variables ◽

Mendelian Randomization ◽

Causal Effect ◽

Imaging Genetics ◽

Causal Effects ◽

Imaging Data ◽

Multiple Exposures ◽

The Uk

The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables. However, classic Mendelian Randomization methods are limited when handling high-throughput imaging traits as exposures to identify causal effects. We propose a new Mendelian Randomization framework to jointly select instrumental variables and imaging exposures, and then estimate the causal effect of multivariable imaging data on the outcome. We validate the proposed method with extensive data analyses and compare it with existing methods. We further apply our method to evaluate the causal effect of white matter microstructure integrity on cognitive function. The findings suggest that our method achieved better performance regarding sensitivity, bias, and false discovery rate compared to individually assessing the causal effect of a single exposure and jointly assessing the causal effect of multiple exposures without dimension reduction. Our application results indicated that WM measures across different tracts have a joint causal effect that significantly impacts the cognitive function among the participants from the UK Biobank.

Download Full-text

Causal Associations Between Blood Lipids and COVID-19 Risk: A Two-Sample Mendelian Randomization Study

Arteriosclerosis Thrombosis and Vascular Biology ◽

10.1161/atvbaha.121.316324 ◽

2021 ◽

Author(s):

Kun Zhang ◽

Shan-Shan Dong ◽

Yan Guo ◽

Shi-Hao Tang ◽

Hao Wu ◽

...

Keyword(s):

Total Cholesterol ◽

Blood Lipids ◽

Mendelian Randomization ◽

Low Density Lipoprotein ◽

Density Lipoprotein ◽

Outcome Data ◽

Causal Effects ◽

Lipoprotein Cholesterol ◽

Global Pandemic ◽

The Uk

Objective: Coronavirus disease 2019 (COVID-19) is a global pandemic caused by the severe acute respiratory syndrome coronavirus 2. It has been reported that dyslipidemia is correlated with COVID-19, and blood lipids levels, including total cholesterol, HDL-C (high-density lipoprotein cholesterol), and LDL-C (low-density lipoprotein cholesterol) levels, were significantly associated with disease severity. However, the causalities of blood lipids on COVID-19 are not clear. Approach and Results: We performed 2-sample Mendelian randomization (MR) analyses to explore the causal effects of blood lipids on COVID-19 susceptibility and severity. Using the outcome data from the UK Biobank (1221 cases and 4117 controls), we observed potential positive causal effects of dyslipidemia (odds ratio [OR], 1.27 [95% CI, 1.08–1.49], P =3.18×10 −3 ), total cholesterol (OR, 1.19 [95% CI, 1.07–1.32], P =8.54×10 −4 ), and ApoB (apolipoprotein B; OR, 1.18 [95% CI, 1.07–1.29], P =1.01×10 −3 ) on COVID-19 susceptibility after Bonferroni correction. In addition, the effects of total cholesterol (OR, 1.01 [95% CI, 1.00–1.02], P =2.29×10 −2 ) and ApoB (OR, 1.01 [95% CI, 1.00–1.02], P =2.22×10 −2 ) on COVID-19 susceptibility were also identified using outcome data from the host genetics initiative (14 134 cases and 1 284 876 controls). Conclusions: In conclusion, we found that higher total cholesterol and ApoB levels might increase the risk of COVID-19 infection.

Download Full-text

Circulating adiponectin levels and systemic lupus erythematosus: a two-sample Mendelian randomization study

Rheumatology ◽

10.1093/rheumatology/keaa506 ◽

2020 ◽

Author(s):

Yi-Lin Dan ◽

Peng Wang ◽

Zhongle Cheng ◽

Qian Wu ◽

Xue-Rong Wang ◽

...

Keyword(s):

Single Nucleotide Polymorphisms ◽

Instrumental Variables ◽

Mendelian Randomization ◽

Causal Effect ◽

Causal Effects ◽

European Ancestry ◽

Nucleotide Polymorphisms ◽

Single Nucleotide ◽

Inverse Variance ◽

Weighted Median

Abstract Objectives Several studies have reported increased serum/plasma adiponectin levels in SLE patients. This study was performed to estimate the causal effects of circulating adiponectin levels on SLE. Methods We selected nine independent single-nucleotide polymorphisms that were associated with circulating adiponectin levels (P < 5 × 10−8) as instrumental variables from a published genome-wide association study (GWAS) meta-analysis. The corresponding effects between instrumental variables and outcome (SLE) were obtained from an SLE GWAS analysis, including 7219 cases with 15 991 controls of European ancestry. Two-sample Mendelian randomization (MR) analyses with inverse-variance weighted, MR-Egger regression, weighted median and weight mode methods were used to evaluate the causal effects. Results The results of inverse-variance weighted methods showed no significantly causal associations of genetically predicted circulating adiponectin levels and the risk for SLE, with an odds ratio (OR) of 1.38 (95% CI 0.91, 1.35; P = 0.130). MR-Egger [OR 1.62 (95% CI 0.85, 1.54), P = 0.195], weighted median [OR 1.37 (95% CI 0.82, 1.35), P = 0.235) and weighted mode methods [OR 1.39 (95% CI 0.86, 1.38), P = 0.219] also supported no significant associations of circulating adiponectin levels and the risk for SLE. Furthermore, MR analyses in using SLE-associated single-nucleotide polymorphisms as an instrumental variable showed no associations of genetically predicted risk of SLE with circulating adiponectin levels. Conclusion Our study did not find evidence for a causal relationship between circulating adiponectin levels and the risk of SLE or of a causal effect of SLE on circulating adiponectin levels.

Download Full-text

Genetically Determined Physical Activity and Its Association with Circulating Blood Cells

Genes ◽

10.3390/genes10110908 ◽

2019 ◽

Vol 10 (11) ◽

pp. 908 ◽

Cited By ~ 2

Author(s):

Femke M. Prins ◽

M. Abdullah Said ◽

Yordi J. van de Vegte ◽

Niek Verweij ◽

Hilde E. Groot ◽

...

Keyword(s):

Physical Activity ◽

Blood Cells ◽

Fixed Effects ◽

Mendelian Randomization ◽

P Value ◽

Nucleotide Polymorphisms ◽

Increased Risk ◽

Inflammatory State ◽

The Uk ◽

Genetically Determined

Lower levels of physical activity (PA) have been associated with increased risk of cardiovascular disease. Worldwide, there is a shift towards a lifestyle with less PA, posing a serious threat to public health. One of the suggested mechanisms behind the association between PA and disease development is through systemic inflammation, in which circulating blood cells play a pivotal role. In this study we investigated the relationship between genetically determined PA and circulating blood cells. We used 68 single nucleotide polymorphisms associated with objectively measured PA levels to perform a Mendelian randomization analysis on circulating blood cells in 222,645 participants of the UK Biobank. For inverse variance fixed effects Mendelian randomization analyses, p < 1.85 × 10−3 (Bonferroni-adjusted p-value of 0.05/27 tests) was considered statistically significant. Genetically determined increased PA was associated with decreased lymphocytes (β = –0.03, SE = 0.008, p = 1.35 × 10−3) and decreased eosinophils (β = –0.008, SE = 0.002, p = 1.36 × 10−3). Although further mechanistic studies are warranted, these findings suggest increased physical activity is associated with an improved inflammatory state with fewer lymphocytes and eosinophils.

Download Full-text

Pleiotropy-robust Mendelian Randomization

10.1101/072603 ◽

2016 ◽

Cited By ~ 3

Author(s):

Hans van Kippersluis ◽

Cornelius A Rietveld

Keyword(s):

Sensitivity Analysis ◽

Instrumental Variables ◽

Genetic Variants ◽

Large Scale ◽

Mendelian Randomization ◽

Causal Effect ◽

Pleiotropic Effects ◽

List Type ◽

Exclusion Restriction ◽

The Uk

AbstractBackgroundThe potential of Mendelian Randomization studies is rapidly expanding due to (i) the growing power of GWAS meta-analyses to detect genetic variants associated with several exposures, and (ii) the increasing availability of these genetic variants in large-scale surveys. However, without a proper biological understanding of the pleiotropic working of genetic variants, a fundamental assumption of Mendelian Randomization (the exclusion restriction) can always be contested.MethodsWe build upon and synthesize recent advances in the econometric literature on instrumental variables (IV) estimation that test and relax the exclusion restriction. Our Pleiotropy-robust Mendelian Randomization (PRMR) method first estimates the degree of pleiotropy, and in turn corrects for it. If a sample exists for which the genetic variants do not affect the exposure, and pleiotropic effects are homogenous, PRMR obtains unbiased estimates of causal effects in case of pleiotropy.ResultsSimulations show that existing MR methods produce biased estimators for realistic forms of pleiotropy. Under the aforementioned assumptions, PRMR produces unbiased estimators. We illustrate the practical use of PRMR by estimating the causal effect of (i) cigarettes smoked per day on Body Mass Index (BMI); (ii) prostate cancer on self-reported health, and (iii) educational attainment on BMI in the UK Biobank data.ConclusionsPRMR allows for instrumental variables that violate the exclusion restriction due to pleiotropy, and corrects for pleiotropy in the estimation of the causal effect. If the degree of pleiotropy is unknown, PRMR can still be used as a sensitivity analysis.Key messagesIf genetic variants have pleiotropic effects, causal estimates of Mendelian Randomization studies will be biased.Pleiotropy-robust Mendelian Randomization (PRMR) produces unbiased causal estimates in case (i) a subsample can be identified for which the genetic variants do not affect the exposure, and (ii) pleiotropic effects are homogenous.If such a subsample does not exist, PRMR can still routinely be reported as a sensitivity analysis in any MR analysis.If pleiotropic effects are not homogenous, PRMR can be used as an informal test to gauge the exclusion restriction.

Download Full-text

The effect of education on adult mortality, health, and income: triangulating across genetic and policy reforms

10.1101/250068 ◽

2018 ◽

Cited By ~ 5

Author(s):

Neil M Davies ◽

Matt Dickson ◽

George Davey Smith ◽

Frank Windmeijer ◽

Gerard J van den Berg

Keyword(s):

Genetic Variation ◽

Life Expectancy ◽

Mendelian Randomization ◽

Policy Reform ◽

Adult Mortality ◽

Causal Effects ◽

Natural Experiments ◽

Policy Reforms ◽

Sources Of Variation ◽

The Uk

1AbstractOn average, educated people are healthier, wealthier and have higher life expectancy than those with less education. Numerous studies have attempted to determine whether these differences are caused by education, or are merely correlated with it and are ultimately caused by another factor. Previous studies have used a range of natural experiments to provide causal evidence. Here we exploit two natural experiments, perturbation of germline genetic variation associated with education which occurs at conception, known as Mendelian randomization, and a policy reform, the raising of the school leaving age in the UK in 1972. Previous studies have suggested that the differences in outcomes associated with education may be due to confounding. However, the two independent sources of variation we exploit largely imply consistent causal effects of education on outcomes much later in life.

Download Full-text

Phenome-wide investigation of the causal associations between childhood BMI and adult trait outcomes: a two-sample Mendelian randomization study

Genome Medicine ◽

10.1186/s13073-021-00865-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Shan-Shan Dong ◽

Kun Zhang ◽

Yan Guo ◽

Jing-Miao Ding ◽

Yu Rong ◽

...

Keyword(s):

Childhood Obesity ◽

Dietary Habits ◽

Mendelian Randomization ◽

Later Life ◽

Causal Effects ◽

Inverse Variance ◽

Weighted Median ◽

Artery Disease ◽

The Uk ◽

Health Rating

Abstract Background Childhood obesity is reported to be associated with the risk of many diseases in adulthood. However, observational studies cannot fully account for confounding factors. We aimed to systematically assess the causal associations between childhood body mass index (BMI) and various adult traits/diseases using two-sample Mendelian randomization (MR). Methods After data filtering, 263 adult traits genetically correlated with childhood BMI (P < 0.05) were subjected to MR analyses. Inverse-variance weighted, MR-Egger, weighted median, and weighted mode methods were used to estimate the causal effects. Multivariable MR analysis was performed to test whether the effects of childhood BMI on adult traits are independent from adult BMI. Results We identified potential causal effects of childhood obesity on 60 adult traits (27 disease-related traits, 27 lifestyle factors, and 6 other traits). Higher childhood BMI was associated with a reduced overall health rating (β = − 0.10, 95% CI − 0.13 to − 0.07, P = 6.26 × 10−11). Specifically, higher childhood BMI was associated with increased odds of coronary artery disease (OR = 1.09, 95% CI 1.06 to 1.11, P = 4.28 × 10−11), essential hypertension (OR = 1.12, 95% CI 1.08 to 1.16, P = 1.27 × 10−11), type 2 diabetes (OR = 1.36, 95% CI 1.30 to 1.43, P = 1.57 × 10−34), and arthrosis (OR = 1.09, 95% CI 1.06 to 1.12, P = 8.80 × 10−9). However, after accounting for adult BMI, the detrimental effects of childhood BMI on disease-related traits were no longer present (P > 0.05). For dietary habits, different from conventional understanding, we found that higher childhood BMI was associated with low calorie density food intake. However, this association might be specific to the UK Biobank population. Conclusions In summary, we provided a phenome-wide view of the effects of childhood BMI on adult traits. Multivariable MR analysis suggested that the associations between childhood BMI and increased risks of diseases in adulthood are likely attributed to individuals remaining obese in later life. Therefore, ensuring that childhood obesity does not persist into later life might be useful for reducing the detrimental effects of childhood obesity on adult diseases.

Download Full-text

Causal Effects of Gut Microbiome on Systemic Lupus Erythematosus: A Two-Sample Mendelian Randomization Study

Frontiers in Immunology ◽

10.3389/fimmu.2021.667097 ◽

2021 ◽

Vol 12 ◽

Author(s):

Kun Xiang ◽

Peng Wang ◽

Zhiwei Xu ◽

Yu-Qian Hu ◽

Yi-Sheng He ◽

...

Keyword(s):

Instrumental Variables ◽

Lupus Erythematosus ◽

Gut Microbiome ◽

Mendelian Randomization ◽

Statistical Significance ◽

Causal Effects ◽

Systemic Lupus ◽

Significance Level ◽

Significance Threshold ◽

Genome Wide

The observational association between gut microbiome and systemic lupus erythematosus (SLE) has been well documented. However, whether the association is causal remains unclear. The present study used publicly available genome-wide association study (GWAS) summary data to perform two-sample Mendelian randomization (MR), aiming to examine the causal links between gut microbiome and SLE. Two sets of MR analyses were conducted. A group of single nucleotide polymorphisms (SNPs) that less than the genome-wide statistical significance threshold (5 × 10-8) served as instrumental variables. To obtain a comprehensive conclusion, the other group where SNPs were smaller than the locus-wide significance level (1 × 10-5) were selected as instrumental variables. Based on the locus-wide significance level, the results indicated that there were causal effects of gut microbiome components on SLE risk. The inverse variance weighted (IVW) method suggested that Bacilli and Lactobacillales were positively correlated with the risk of SLE and Bacillales, Coprobacter and Lachnospira were negatively correlated with SLE risk. The results of weighted median method supported that Bacilli, Lactobacillales, and Eggerthella were risk factors for SLE and Bacillales and Coprobacter served as protective factors for SLE. The estimates of MR Egger suggested that genetically predicted Ruminiclostridium6 was negatively associated with SLE. Based on the genome-wide statistical significance threshold, the results showed that Actinobacteria might reduce the SLE risk. However, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) detected significant horizontal pleiotropy between the instrumental variables of Ruminiclostridium6 and outcome. This study support that there are beneficial or detrimental causal effects of gut microbiome components on SLE risk.

Download Full-text

Retirement and Cognitive Functioning

10.1093/oso/9780198808039.003.0004 ◽

2017 ◽

Author(s):

Raquel Fonseca ◽

Arie Kapteyn ◽

Gema Zamarro

Keyword(s):

Cognitive Functioning ◽

Instrumental Variables ◽

Fixed Effects ◽

Recent Literature ◽

Causal Effects ◽

Similar Data ◽

Skill Levels ◽

The World ◽

Country Differences

This chapter surveys recent literature on the effects of retirement on cognitive functioning at older ages around the world. Studies using similar data, definitions of cognition, and instruments to capture causal effects find that being retired leads to a decline of cognition, controlling for different specifications of age functions and other covariates. The size and significance of the estimated effects varied depending on specifications used, such as whether or not models included fixed effects, dynamic specifications, or alternative specifications of instrumental variables. The authors replicated several of these results using the same datasets. Factors that are likely causing the differences across specifications include endogeneity of right-hand side variables, and heterogeneity across gender, occupation, or skill levels. Results were especially sensitive to the inclusion of country fixed effects, to control for unobserved country differences, suggesting the key role of unobserved differences across countries, which both affect retirement ages and cognitive decline.

Download Full-text

Understanding the Assumptions Underlying Mendelian Randomization

10.20944/preprints202101.0035.v1 ◽

2021 ◽

Author(s):

Christiaan de Leeuw ◽

Jeanne Savage ◽

Ioan Gabriel Bucur ◽

Tom Heskes ◽

Danielle Posthuma

Keyword(s):

Instrumental Variables ◽

Experimental Research ◽

Genetic Variants ◽

Mendelian Randomization ◽

Secondary Analysis ◽

Genetic Data ◽

Causal Effects ◽

Data Sets ◽

Analysis Method

With the rapidly increasing availability of large genetic data sets in recent years, Mendelian Randomization (MR) has quickly gained popularity as a novel secondary analysis method. Leveraging genetic variants as instrumental variables, MR can be used to estimate the causal effects of one phenotype on another even when experimental research is not feasible, and therefore has the potential to be highly informative. It is dependent on strong assumptions however, often producing strongly biased results if these are not met. It is therefore imperative that these assumptions are well-understood by researchers aiming to use MR, in order to evaluate their validity in the context of their analyses and data. The aim of this perspective is therefore to further elucidate these assumptions and the role they play in MR, as well as how different kinds of data can be used to further support them.

Download Full-text