Abstract
Apolipoprotein A-IV (apoA-IV) has been observed to be associated with lipids, kidney function, adiposity- and diabetes-related parameters. To assess the causal relationship of apoA-IV with these phenotypes, we conducted bidirectional Mendelian randomization (MR) analyses using publicly available summary-level datasets from GWAS consortia on apoA-IV concentrations (n = 13,813), kidney function (estimated glomerular filtration rate (eGFR), n = 133,413), lipid traits (HDL cholesterol, LDL cholesterol, triglycerides, n = 188,577), adiposity-related traits (body-mass-index (n = 322,206), waist-hip-ratio (n = 210,088)) and fasting glucose (n = 133,010). Main analyses consisted in inverse-variance weighted and multivariable MR, whereas MR-Egger regression and weighted median estimation were used as sensitivity analyses. We found that eGFR is likely to be causal on apoA-IV concentrations (53 SNPs; causal effect estimate per 1-SD increase in eGFR = −0.39; 95% CI = [−0.54, −0.24]; p-value = 2.4e-07). Triglyceride concentrations were also causally associated with apoA-IV concentrations (40 SNPs; causal effect estimate per 1-SD increase in triglycerides = −0.06; 95% CI = [−0.08, −0.04]; p-value = 4.8e-07), independently of HDL-C and LDL-C concentrations (causal effect estimate from multivariable MR = −0.06; 95% CI = [−0.10, −0.02]; p-value = 0.0014). Evaluating the inverse direction of causality revealed a possible causal association of apoA-IV on HDL-cholesterol (2 SNPs; causal effect estimate per one percent increase in apoA-IV = −0.40; 95% CI = [−0.60, −0.21]; p-value = 5.5e-05).
Highlights
Disease endpoints are often detected using continuous surrogate markers or are influenced by many other intermediate phenotypes, which might better represent the causal pathway leading to a disease than the disease state itself
In a recent genome-wide association study (GWAS) meta-analysis we have identified two variants within the APOA5-A4-C3-A1 cluster and one variant in the KLKB1 gene to be associated with Apolipoprotein A-IV (apoA-IV) concentrations[17]
To detect a causal effect of waist-hip-ratio adjusted for BMI (WHR) on apoA-IV, WHR would have to explain more than 15% of the phenotypic variance of apoA-IV
Summary
Disease endpoints are often detected using continuous surrogate markers (e.g. fasting glucose as marker for impaired glucose metabolism leading to diabetes) or are influenced by many other intermediate phenotypes, which might better represent the causal pathway leading to a disease than the disease state itself. On the other hand apoA-IV concentrations have been shown to change following food intake, gastric bypass surgery or weight loss[29, 30] Those findings warrant further investigation in causal associations and especially in the direction of the effects between apoA-IV and lipid levels, adiposity, diabetes-related parameters and kidney function. Recent developments in genetic epidemiology using genetic markers as proxies for a trait help estimate causal associations with a disease outcome through a Mendelian randomization approach[31]. For all of the already mentioned apoA-IV-associated and disease-related traits, large genome-wide association studies are available, in which dozens of genetic variants have been identified[18, 33,34,35,36]. A GWAS meta-analysis on apoA-IV concentrations has been published recently[17]
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