Dataset Information

Using multivariable Mendelian randomization to disentangle the causal effects of lipid fractions.

ABSTRACT:

Background

Previous Mendelian randomization studies have suggested that, while low-density lipoprotein cholesterol (LDL-c) and triglycerides are causally implicated in coronary artery disease (CAD) risk, high-density lipoprotein cholesterol (HDL-c) may not be, with causal effect estimates compatible with the null.

Principal findings

The causal effects of these three lipid fractions can be better identified using the extended methods of 'multivariable Mendelian randomization'. We employ this approach using published data on 185 lipid-related genetic variants and their associations with lipid fractions in 188,578 participants, and with CAD risk in 22,233 cases and 64,762 controls. Our results suggest that HDL-c may be causally protective of CAD risk, independently of the effects of LDL-c and triglycerides. Estimated causal odds ratios per standard deviation increase, based on 162 variants not having pleiotropic associations with either blood pressure or body mass index, are 1.57 (95% credible interval 1.45 to 1.70) for LDL-c, 0.91 (0.83 to 0.99, p-value ?= 0.028) for HDL-c, and 1.29 (1.16 to 1.43) for triglycerides.

Significance

Some interventions on HDL-c concentrations may influence risk of CAD, but to a lesser extent than interventions on LDL-c. A causal interpretation of these estimates relies on the assumption that the genetic variants do not have pleiotropic associations with risk factors on other pathways to CAD. If they do, a weaker conclusion is that genetic predictors of LDL-c, HDL-c and triglycerides each have independent associations with CAD risk.

SUBMITTER: Burgess S

PROVIDER: S-EPMC4193746 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Using multivariable Mendelian randomization to disentangle the causal effects of lipid fractions.

Burgess Stephen S Freitag Daniel F DF Khan Hassan H Gorman Donal N DN Thompson Simon G SG

PloS one 20141010 10

<h4>Background</h4>Previous Mendelian randomization studies have suggested that, while low-density lipoprotein cholesterol (LDL-c) and triglycerides are causally implicated in coronary artery disease (CAD) risk, high-density lipoprotein cholesterol (HDL-c) may not be, with causal effect estimates compatible with the null.<h4>Principal findings</h4>The causal effects of these three lipid fractions can be better identified using the extended methods of 'multivariable Mendelian randomization'. We e ...[more]

PMID: 25302496

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Project description:IntroductionThe correlation between dyslipidemia and periodontitis is revealed through epidemiological studies. However, the results are affected by several confounding factors. This study aims to elucidate the genetic causal association between circulating lipid traits and periodontitis by two-sample Mendelian randomization (MR) analysis.MethodsAfter the different screening processes, two cohorts of circulating lipid traits from the UK Biobank were used as exposure data, including five circulating lipid traits. The Periodontitis cohort was selected from the GeneLifestyle Interactions in Dental Endpoints (GLIDE) consortium as outcome data. In univariable MR, the inverse variance weighted (IVW) was used in conjunction with six additional analytical methods to assess causality. The Cochran Q test, IGX 2 statistic, MR-PRESSO, and MR-Egger intercept were used to quantify heterogeneity and pleiotropy. The multivariable MR-IVW (MVMR-IVW) and MVMR-robust were mainly used as analytical methods in the multiple MR analyses.ResultsThe IVW estimates showed that genetically predicted Apolipoprotein A1 (apo A1) [odds ratio (OR)=1.158, 95% confidence interval (CI)=1.007-1.331, P-value=0.040] was potentially associated with the risk of periodontitis, but the statistical power of the results was low. Multivariable MR analysis did not reveal any significant causal relationship between apo A1 and periodontitis (OR=0.72, 95% CI=0.36-1.41, P-value=0.34). In the validation cohort, there was also no significant causal relationship between apo A1 and periodontitis (OR=1.079, 95% CI=0.903-1.290, P-value=0.401). Meanwhile, genetically predicted Apolipoprotein B (apo B), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and triglyceride (TG) (all P-values>0.05) were not significantly associated with the risk of periodontitis causal inference.ConclusionThis MR analysis was unable to provide genetic evidence for the influence of these five circulating lipid traits on periodontitis. However, a more extensive study with a more comprehensive circulating lipid profile and periodontitis data is needed due to study limitations.

Project description:BackgroundObservational studies have investigated the effect of serum lipids on kidney function, but these findings are limited by confounding, reverse causation and have reported conflicting results. Mendelian randomization (MR) studies address this confounding problem. However, they have been conducted mostly in European ancestry individuals. We, therefore, set out to investigate the effect of lipid traits on the estimated glomerular filtration rate (eGFR) based on serum creatinine in individuals of African ancestry.MethodsWe used the two-sample and multivariable Mendelian randomization (MVMR) approaches; in which instrument variables (IV's) for the predictor (lipid traits) were derived from summary-level data of a meta-analyzed African lipid GWAS (MALG, n = 24,215) from the African Partnership for Chronic Disease Research (APCDR) (n = 13,612) & the Africa Wits-IN-DEPTH partnership for Genomics studies (AWI-Gen) dataset (n = 10,603). The outcome IV's were computed from the eGFR summary-level data of African-ancestry individuals within the Million Veteran Program (n = 57,336). A random-effects inverse variance method was used in our primary analysis, and pleiotropy was adjusted for using robust and penalized sensitivity testing. The lipid predictors for the MVMR were high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, and triglycerides (TG).FindingsWe found a significant causal association between genetically predicted low-density lipoprotein (LDL) cholesterol and eGFR in African ancestry individuals β = 1.1 (95% CI [0.411-1.788]; p = 0.002). Similarly, total cholesterol (TC) showed a significant causal effect on eGFR β = 1.619 (95% CI [0.412-2.826]; p = 0.009). However, the IVW estimate showed that genetically predicted HDL-C β = -0.164, (95% CI = [-1.329 to 1.00]; p = 0.782), and TG β = -0.934 (CI = [-2.815 to 0.947]; p = 0.33) were not significantly causally associated with the risk of eGFR. In the multivariable analysis inverse-variance weighted (MVIVW) method, there was evidence for a causal association between LDL and eGFR β = 1.228 (CI = [0.477-1.979]; p = 0.001). A significant causal effect of Triglycerides (TG) on eGFR in the MVIVW analysis β = -1.3 ([-2.533 to -0.067]; p = 0.039) was observed as well. All the causal estimates reported reflect a unit change in the outcome per a 1 SD increase in the exposure. HDL showed no evidence of a significant causal association with eGFR in the MVIVW method (β = -0.117 (95% CI [-1.252 to 0.018]; p = 0.840)). We found no evidence of a reverse causal impact of eGFR on serum lipids. All our sensitivity analyses indicated no strong evidence of pleiotropy or heterogeneity between our instrumental variables for both the forward and reverse MR analysis.InterpretationIn this African ancestry population, genetically predicted higher LDL-C and TC are causally associated with higher eGFR levels, which may suggest that the relationship between LDL, TC and kidney function may be U-shaped. And as such, lowering LDL_C does not necessarily improve risk of kidney disease. This may also imply the reason why LDL_C is seen to be a poorer predictor of kidney function compared to HDL. In addition, this further supports that more work is warranted to confirm the potential association between lipid traits and risk of kidney disease in individuals of African Ancestry.FundingWellcome (220740/Z/20/Z).

Dataset Information

Using multivariable Mendelian randomization to disentangle the causal effects of lipid fractions.

Background

Principal findings

Significance

Publications

Using multivariable Mendelian randomization to disentangle the causal effects of lipid fractions.

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