Association of Lipid Fractions With Risks for Coronary Artery Disease and Diabetes

Abstract

Importance: Low-density lipoprotein cholesterol (LDL-C) is causally related to coronary artery disease (CAD), but the relevance of high-density lipoprotein cholesterol (HDL-C) and triglycerides (TGs) is uncertain. Lowering of LDL-C levels by statin therapy modestly increases the risk of type 2 diabetes, but it is unknown whether this effect is specific to statins.

Objective: To investigate the associations of 3 routinely measured lipid fractions with CAD and diabetes through mendelian randomization (MR) using conventional MR and making use of newer approaches, such as multivariate MR and MR-Egger, that address the pleiotropy of genetic instruments where relevant.

Design, setting, and participants: Published data from genome-wide association studies were used to construct genetic instruments and then applied to investigate associations between lipid fractions and the risk of CAD and diabetes using MR approaches that took into account pleiotropy of genetic instruments. The study was conducted from March 12 to December 31, 2015.

Main outcomes and measures: Coronary artery disease and diabetes.

Results: Genetic instruments composed of 130 single-nucleotide polymorphisms (SNPs) were used for LDL-C (explaining 7.9% of its variance), 140 SNPs for HDL-C (6.6% of variance), and 140 SNPs for TGs (5.9% of variance). A 1-SD genetically instrumented elevation in LDL-C levels (equivalent to 38 mg/dL) and TG levels (equivalent to 89 mg/dL) was associated with higher CAD risk; odds ratios (ORs) were 1.68 (95% CI, 1.51-1.87) for LDL-C and 1.28 (95% CI, 1.13-1.45) for TGs. The corresponding OR for HDL-C (equivalent to a 16-mg/dL increase) was 0.95 (95% CI, 0.85-1.06). All 3 lipid traits were associated with a lower risk of type 2 diabetes. The ORs were 0.79 (95% CI, 0.71-0.88) for LDL-C and 0.83 (95% CI, 0.76-0.90) for HDL-C per 1-SD elevation. For TG, the MR estimates for diabetes were inconsistent, with MR-Egger giving an OR of 0.83 (95%CI, 0.72-0.95) per 1-SD elevation.

Conclusions and relevance: Routinely measured lipid fractions exhibit contrasting associations with the risk of CAD and diabetes. Increased LDL-C, HDL-C, and possibly TG levels are associated with a lower risk of diabetes. This information will be relevant to the design of clinical trials of lipid-modifying agents, which should carefully monitor participants for dysglycemia and the incidence of diabetes.

Figure 1. Pleiotropy and the validity of estimates derived from Mendelian randomization.

SNPs are used in a genetic instrument for an exposure to assess the association with risk of disease. For each exposure there is a ‘true relationship’, which we try to approximate from Mendelian randomization. For the purposes of simplicity, conventional MR is compared to MR-Egger. Vertical pleiotropy explains where the genetic instrument associates with biomarkers (other than the exposure) that are on the causal pathway from exposure through to disease. Horizontal pleiotropy is where the genetic instrument associates with additional traits not on the causal pathway of the exposure of interest. When horizontal pleiotropy is balanced, there should be no bias in the effect derived from MR. In this scenario, the estimate obtained from conventional MR is similar to that from MR-Egger. When horizontal pleiotropy is unbalanced (also termed ‘directional pleiotropy’), the pleiotropy systematically biases the estimate (which can be exaggerated or diminished) in a naïve analysis using conventional MR. In the example in Figure 1, the unbalanced pleiotropy exaggerates the magnitude of the association. Conventional MR will derive a biased estimate, whereas MR-Egger, correcting for unbalanced pleiotropy, should yield a valid estimate. An example of unbalanced horizontal pleiotropy is the relationship of HDL-C and risk of CAD; the association derived from conventional MR is different to that of MR-Egger with the latter indicating that, once unbalanced pleiotropy is accounted for, there is no effect of HDL-C on risk of CAD (see Figure 3).

Figure 2. Pipeline for derivation of the dataset used for Mendelian randomization analyses of lipid subtypes with risk of coronary artery disease and diabetes.

Figure 3. Associations of routinely measured lipids with risk of coronary artery disease (CAD) and type 2 diabetes (T2D) from Mendelian randomization analyses.

See Methods for description of the three Mendelian randomization (MR) models. Estimates for conventional MR are derived from two-sample MR that forces the slope through the origin, thereby not accounting for pleiotropy. Multivariate MR (MVMR) statistically adjusts for other lipid traits, and MR-Egger adjusts for unbalanced pleiotropy. R² refers to proportion of variance of lipid trait explained by the genetic instrument. 95% confidence intervals (CI) are Bonferroni-adjusted. To convert HDL-C and LDL-C to mmol/L, multiply by 0.0259; to convert triglycerides to mmol/L, multiply by 0.0113

Figure 4. Cross-hair plot of a one standard deviation increase in lipids and risk of CAD and T2D.

All estimates derived from MR-Egger. Error bars represent 95% confidence intervals (CI) that are Bonferroni-adjusted.