scholarly journals Causal Relationship between Plasma Adiponectin and Body Mass Index: One- and Two-Sample Bidirectional Mendelian Randomization Analyses in 460 397 Individuals

2020 ◽  
Vol 66 (12) ◽  
pp. 1548-1557
Author(s):  
Maria Booth Nielsen ◽  
Yunus Çolak ◽  
Marianne Benn ◽  
Børge Grønne Nordestgaard
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
High Plasma ◽  
General Population Study ◽  
Plasma Adiponectin ◽  
Genetically Determined ◽  
Randomization Analysis ◽  
High Bmi

Abstract Background Adiponectin is a protein hormone produced by adipocytes that may play an important role in obesity. However, the causal interrelation between plasma adiponectin and body mass index (BMI) is still uncertain. We tested the hypotheses that (a) plasma adiponectin and BMI are inversely associated observationally, (b) genetically high BMI is associated with lower plasma adiponectin, and (c) genetically high plasma adiponectin is associated with lower BMI. Methods Information on 108 896 individuals from the Copenhagen General Population Study was used in observational and bidirectional one-sample Mendelian randomization analyses, using 5 genetic variants for BMI and 3 for adiponectin. For independent confirmation, information on 322 154 individuals from the GIANT consortium, and 29 347 individuals from the ADIPOGen consortium was used in bidirectional two-sample Mendelian randomization analysis, using 68 genetic variants for BMI and 14 for adiponectin. Results In observational analyses, a 1 kg/m2 increase in BMI was associated with −0.44 µg/mL (95% confidence interval: −0.46, −0.42) in plasma adiponectin, whereas a 1 µg/mL increase in plasma adiponectin was associated with −0.11 kg/m2 (−0.12, −0.11) in BMI. In causal genetic analyses, no associations were observed between BMI and plasma adiponectin and vice versa. In one-sample Mendelian randomization analyses, a 1 kg/m2 genetically determined increase in BMI was associated with −0.13 µg/mL (−0.53, 0.28) in plasma adiponectin, whereas a 1 µg/mL genetically determined increase in plasma adiponectin was associated with 0.01 kg/m2 (−0.05, 0.07) in BMI. Corresponding estimates in the two-sample Mendelian randomization analyses were 0.03 µg/mL (−0.02, 0.07) and 0.03 kg/m2(−0.02, 0.07), respectively. Conclusions Observationally, plasma adiponectin and BMI are inversely associated. In contrast, genetically high plasma adiponectin is unlikely to influence BMI, and genetically high BMI is unlikely to influence plasma adiponectin.

scholarly journals Genetically determined body mass index mediates the effect of smoking on type 2 diabetes risk, but not coronary artery disease risk

2020 ◽  
Author(s):  
Christopher S Thom ◽  
Zhuoran Ding ◽  
Michael G Levin ◽  
Scott M Damrauer ◽  
Benjamin F Voight
Keyword(s):  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Body Mass ◽  
Mendelian Randomization ◽  
Smoking Behavior ◽  
Diabetes Risk ◽  
Artery Disease ◽  
Genetically Determined ◽  
Randomization Analysis

AbstractClinical observations have linked tobacco smoking with increased type 2 diabetes risk (1–5), a major public health concern (6). Mendelian randomization analysis has recently suggested smoking may be a causal risk factor for type 2 diabetes (7). However, this initial association could be mediated by additional causal risk factors correlated with smoking behavior, which have not been investigated to date. We hypothesized that body mass index (BMI) could explain the association between smoking and diabetes risk. First, we confirmed previous reports that genetically determined smoking behavior increased risk for both type 2 diabetes (OR=1.21, 95% CI: 1.15-1.27, P=1×10−12) and coronary artery disease (CAD; OR=1.21, 95% CI: 1.16-1.26, P=2×10−20). Additionally, a 2-fold increased smoking risk is positively associated with body mass index (BMI; ∼0.8 kg/m2, 95% CI: 0.54-0.98 kg/m2, P=1.8×10−11). In multivariable Mendelian randomization analysis, including BMI accounted for nearly all of the risk of smoking on type 2 diabetes (OR 1.06, 95% CI: 1.01-1.11, P=0.03). In contrast, the independent association between smoking and CAD persisted (OR 1.12, CI: 1.08-1.17, P=3×10−8) despite controlling for BMI. Causal mediation analyses agreed with these estimates. Our findings support a model whereby smoking initiation increases obesity, which in turn increases type 2 diabetes risk, with minimal if any direct effects from smoking on diabetes risk. Patients should be advised to stop smoking to limit both type 2 diabetes and CAD risk, and therapeutic efforts should consider pathophysiology relating smoking and obesity.

scholarly journals Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis

2014 ◽  
Vol 94 (2) ◽  
pp. 312 ◽  
Author(s):  
Michael V. Holmes ◽  
Leslie A. Lange ◽  
Tom Palmer ◽  
Matthew B. Lanktree ◽  
Kari E. North ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Mendelian Randomization ◽  
Causal Effects ◽  
Mendelian Randomization Analysis ◽  
Randomization Analysis ◽  
Cardiometabolic Traits

scholarly journals MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting for linkage disequilibrium and horizontal pleiotropy

2019 ◽  
Author(s):  
Qing Cheng ◽  
Yi Yang ◽  
Xingjie Shi ◽  
Kar-Fu Yeung ◽  
Can Yang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Linkage Disequilibrium ◽  
Body Mass ◽  
Genetic Variants ◽  
Mendelian Randomization ◽  
Alternative Methods ◽  
Summary Statistics ◽  
Causal Relationships ◽  
Disease Outcomes

AbstractThe proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IV) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we propose a probabilistic model for MR analysis to identify the casual effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP). MR-LDP utilizes a computationally efficient parameter-expanded variational Bayes expectation-maximization (PX-VBEM) algorithm to estimate the parameter of interest and further calibrates the evidence lower bound (ELBO) for a likelihood ratio test. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods in terms of both type-I error control and point estimates. Moreover, we used two real exposure-outcome pairs (CAD-CAD and Height-Height; CAD for coronary artery disease) to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol (HDL-C) on peripheral vascular disease (PVD), and a positive causal effect of body mass index (BMI) on hemorrhoids.

scholarly journals Effects of body mass index on relationship status, social contact, and socioeconomic position: Mendelian Randomization study in UK Biobank

2019 ◽  
Author(s):  
Laura D Howe ◽  
Roshni Kanayalal ◽  
Robin N Beaumont ◽  
Alisha R Davies ◽  
Timothy M Frayling ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Mendelian Randomization ◽  
Relationship Status ◽  
Social Activities ◽  
Uk Biobank ◽  
Non Linear ◽  
Low Bmi ◽  
Degree Level ◽  
High Bmi

AbstractObjectiveTo assess whether body mass index (BMI) has a causal effect on social and socioeconomic factors, including whether both high and low BMI can be detrimental.DesignMendelian Randomization, using genetic variants for BMI to obtain unconfounded estimates, and non-linear Mendelian Randomization.SettingUK Biobank.Participants378,244 men and women of European ancestry, mean age 57 (SD 8 years).Main outcome measuresTownsend deprivation index, income, age completed full time education, degree level education, job class, employment status, cohabiting relationship status, participation in leisure and social activities, visits from friends and family, and having someone to confide in.ResultsHigher BMI was causally associated with higher deprivation, lower income, fewer years of education, lower odds of degree-level education and skilled employment. For example, a 1 SD higher genetically-determined BMI (4.8kg/m2 in UK Biobank) was associated with £1,660 less income per annum [95%CI: £950, £2,380]. Non-linear Mendelian Randomization provided evidence that both low BMI (bottom decile, <22kg/m2) and high BMI (top seven deciles, >24.6kg/m2) can increase deprivation and reduce income. In men only, higher BMI was related to lower participation in leisure and social activities. There was no evidence of causal effects of BMI on visits from friends and family or in having someone to confide in. Non-linear Mendelian Randomization analysis showed that low BMI (bottom three deciles, <23.5kg/m2) reduces the odds of cohabiting with a partner or spouse for men, whereas high BMI (top two deciles, >30.7kg/m2) reduces the odds of cohabitation with a partner or spouse for women.ConclusionsBMI affects social and socioeconomic outcomes, with both high and low BMI being detrimental for some measures of SEP. This suggests that in addition to health benefits, maintaining healthy ranges of BMI across the population could have benefits both for individuals and society.

Plasma adiponectin and risk of asthma: observational analysis, genetic Mendelian randomisation and meta-analysis

Thorax ◽  
2021 ◽  
pp. thoraxjnl-2021-217675
Author(s):  
Maria Booth Nielsen ◽  
Børge G Nordestgaard ◽  
Marianne Benn ◽  
Yunus Çolak
Keyword(s):  
General Population ◽  
Population Study ◽  
Genetic Variants ◽  
Meta Analysis ◽  
High Plasma ◽  
Mendelian Randomisation ◽  
Uk Biobank ◽  
General Population Study ◽  
Plasma Adiponectin ◽  
The Uk

BackgroundAdiponectin, an adipocyte-secreted protein-hormone with inflammatory properties, has a potentially important role in the development and progression of asthma. Unravelling whether adiponectin is a causal risk factor for asthma is an important issue to clarify as adiponectin could be a potential novel drug target for the treatment of asthma.ObjectiveWe tested the hypothesis that plasma adiponectin is associated observationally and causally (using genetic variants as instrumental variables) with risk of asthma.MethodsIn the Copenhagen General Population Study, we did an observational analysis in 28 845 individuals (2278 asthma cases) with plasma adiponectin measurements, and a genetic one-sample Mendelian randomisation analysis in 94 868 individuals (7128 asthma cases) with 4 genetic variants. Furthermore, in the UK Biobank, we did a genetic two-sample Mendelian randomisation analysis in 462 933 individuals (53 598 asthma cases) with 12 genetic variants. Lastly, we meta-analysed the genetic findings.ResultsWhile a 1 unit log-transformed higher plasma adiponectin in the Copenhagen General Population Study was associated with an observational OR of 1.65 (95% CI 1.29 to 2.08) for asthma, the corresponding genetic causal OR was 1.03 (95% CI 0.75 to 1.42). The genetic causal OR for asthma in the UK Biobank was 1.00 (95% CI 0.99 to 1.00). Lastly, genetic meta-analysis confirmed lack of association between genetically high plasma adiponectin and causal OR for asthma.ConclusionObservationally, high plasma adiponectin is associated with increased risk of asthma; however, genetic evidence could not support a causal association between plasma adiponectin and asthma.

scholarly journals Dairy Consumption and Body Mass Index Among Adults: Mendelian Randomization Analysis of 184802 Individuals from 25 Studies

2018 ◽  
Vol 64 (1) ◽  
pp. 183-191 ◽  
Author(s):  
Tao Huang ◽  
Ming Ding ◽  
K M Bergholdt Helle ◽  
Tiange Wang ◽  
Yoriko Heianza ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Epidemiological Studies ◽  
Linear Regression Models ◽  
Dairy Intake ◽  
Dairy Consumption ◽  
Lactase Gene ◽  
Genetically Determined

Abstract BACKGROUND Associations between dairy intake and body mass index (BMI) have been inconsistently observed in epidemiological studies, and the causal relationship remains ill defined. METHODS We performed Mendelian randomization (MR) analysis using an established dairy intake-associated genetic polymorphism located upstream of the lactase gene (LCT-13910 C/T, rs4988235) as an instrumental variable (IV). Linear regression models were fitted to analyze associations between (a) dairy intake and BMI, (b) rs4988235 and dairy intake, and (c) rs4988235 and BMI in each study. The causal effect of dairy intake on BMI was quantified by IV estimators among 184802 participants from 25 studies. RESULTS Higher dairy intake was associated with higher BMI (β = 0.03 kg/m2 per serving/day; 95% CI, 0.00–0.06; P = 0.04), whereas the LCT genotype with 1 or 2 T allele was significantly associated with 0.20 (95% CI, 0.14–0.25) serving/day higher dairy intake (P = 3.15 × 10−12) and 0.12 (95% CI, 0.06–0.17) kg/m2 higher BMI (P = 2.11 × 10−5). MR analysis showed that the genetically determined higher dairy intake was significantly associated with higher BMI (β = 0.60 kg/m2 per serving/day; 95% CI, 0.27–0.92; P = 3.0 × 10−4). CONCLUSIONS The present study provides strong evidence to support a causal effect of higher dairy intake on increased BMI among adults.

scholarly journals Cannabis Use and the Risk of Cardiovascular Diseases: A Mendelian Randomization Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Jianqiang Zhao ◽  
Heng Chen ◽  
Chengui Zhuo ◽  
Shudong Xia
Keyword(s):  
Body Mass Index ◽  
Cardiovascular Diseases ◽  
Body Mass ◽  
Tobacco Use ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Cannabis Use ◽  
Sensitivity Analyses ◽  
Nucleotide Polymorphisms ◽  
Genetically Determined

Several observational studies have shown that cannabis use has negative effects on the cardiovascular system, but the causality of this relationship has not been confirmed. The aim of the current study was to estimate the effects of genetically determined cannabis use on risk of cardiovascular diseases. Ten single-nucleotide polymorphisms related to cannabis use were employed as instruments to estimate the association between genetically determined cannabis use and risk of cardiovascular diseases using a two-sample Mendelian randomization (MR) method. Summary statistics data on exposure and outcomes were obtained from different genome-wide association meta-analysis studies. The results of this MR analysis showed no causal effects of cannabis use on the risk of several common cardiovascular diseases, including coronary artery disease, myocardial infarction, stroke and ischemic stroke subtypes, atrial fibrillation (AF), and heart failure. Various sensitivity analyses yielded similar results, and no heterogeneity and directional pleiotropy were observed. After adjusting for tobacco use and body mass index, multivariable MR analysis suggested a causal effect of cannabis use on small vessel stroke (SVS) [odds ratio (OR) 1.17; 95% CI 1.02–1.35; p = 0.03] and AF (OR 1.06; 95% CI 1.01–1.10; p = 0.01), respectively. This two-sample MR study did not demonstrate a causal effect of genetic predisposition to cannabis use on several common cardiovascular outcomes. After adjusting for tobacco use and body mass index, the multivariable MR analysis suggested a detrimental effect of cannabis use on the risk of SVS and AF, respectively.

scholarly journals Causal Effects of Body Mass Index on Cardiometabolic Traits and Events: A Mendelian Randomization Analysis

2014 ◽  
Vol 94 (2) ◽  
pp. 198-208 ◽  
Author(s):  
Michael V. Holmes ◽  
Leslie A. Lange ◽  
Tom Palmer ◽  
Matthew B. Lanktree ◽  
Kari E. North ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Mendelian Randomization ◽  
Causal Effects ◽  
Mendelian Randomization Analysis ◽  
Randomization Analysis ◽  
Cardiometabolic Traits
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