Genetically Mediated Lipid Metabolism and Risk of Insulin Resistance: Insights from Mendelian Randomization Studies

Previous studies provided evidence that abnormal levels of circulating lipids and intracellular lipids in peripheral tissues (e.g., liver, adipose tissue, and skeletal muscle) are related to insulin resistance and related complications, including type 2 diabetes mellitus (T2DM) and cardiovascular diseases (CVD).1, 2, 3, 4, 5 Because these metabolic diseases appear to be influenced by various metabolic, genetic, and environmental factors, the causality between lipid status and metabolic complication is confounded. Moreover, targeting the risk factors for diseases (e.g., CVD) showed no remarkable advantage in improving treatment outcomes for patients.6 The randomized controlled trial design overcomes the limitations of observational and epidemiological studies by random assignment of study subjects to the control or treatment group, avoiding influential bias through blinding of researchers and subjects.7, 8, 9, 10 Recently, Mendelian randomization (MR) studies coupling genetic data from population studies have revealed novel insights into the risk factors influencing the development of CVD and T2DM. Considering that the causality between risk factors and disease can help identify suitable targets for treatment, the MR approach can potentially advance our knowledge of causation or reverse causation between lipid metabolism and T2DM and can significantly improve treatment strategies, such as diet or behavioral modifications. To date, a limited number of MR study cases have investigated the relationship between lipid status and risk of T2DM, whereas most MR studies investigating CVD are relatively well reported and additionally analyze the indicators for insulin resistance (increased fasting glucose, insulin, and homeostatic model assessment for insulin resistance [HOMA-IR]) as the primary outcome of lipid status.11, 12, 13 Hence, this review incorporates data obtained from insulin resistance indicators from MR studies for both CVD and T2DM.

This review briefly conceptualizes MR and introduces the genetic traits related to lipid metabolism based on their consequences on insulin resistance in recent MR studies. The causality between lipid metabolism and risk of insulin resistance is discussed regarding 2 phases of lipid metabolism, namely, circulation of lipids and accumulation of lipids in peripheral tissues.

MR studies share similar study design elements with randomized controlled trial (RCT) studies.2, 14 An equal distribution of general features (e.g., age, sex, social factors, and disease history) in RCTs can reduce the probability of these variables affecting study results. Likewise, independent assortment of alleles according to Mendel's second law theoretically results in equal genetic background of individuals carrying the alleles of interest. Additionally, social and environmental factors should be equally distributed in the respective genotype groups if the cases and controls are selected from the same population. The biomarker modulated by the genetic variant can be extensively explored by analyzing its association with outcome or disease risk.9, 10 For the MR study design, once genotypes in the population are identified on a genome-wide level, genetic modulation of the biomarker can be simulated by statistical exploration of the dataset.

Enhanced genotyping technologies for analyzing single nucleotide polymorphisms (SNPs) in large population studies have enabled the accumulation of sufficient genetic data and the detection of weak genetic effects that contribute to various phenotypes with improved statistical power.7, 8 Consequently, genome-wide association (GWA) studies have successfully identified several variants affecting the incidence of diseases (e.g., CVD and T2DM) and related biochemical indicators. In GWA studies, most SNPs that show a significant signal quantitatively influence biomarker levels based on the inherited copy numbers of variants in an individual. MR design studies can then be performed to investigate the association between the exposure and the outcome, the association between genetic variants and the exposure, and the association between genetic variants and outcome measurements. Thus, recently obtained large-scale genetic data have laid the foundation for MR studies for T2DM and CVD and merge genetic information on both biomarkers and disease phenotypes (Fig. 1). The goal of MR is to determine whether a risk factor is causally involved in the disease pathogenesis.

Fig. 1 Schematic diagram of MR analysis to estimate expected association for genotypes (SNPs) with the trait of insulin resistance. The underlying assumption of MR analysis is that the genotype and phenotype association is independent of confounding factors. MR, Mendelian randomization; SNP, single nucleotide polymorphism; HOMA-IR, homeostatic model assessment for insulin resistance; T2DM, type 2 diabetes mellitus. Click for larger imageDownload as PowerPoint slide

However, MR studies have certain assumptions. First, the genetic variant of interest only affects the exposure and does not affect other variables that confound the association between the exposure and the outcome. The second assumption is that there are no unmeasured confounders of the associations between genetic variants and the outcome. Last, MR studies assume that genetic variants do not directly affect the outcome, other than mediating the risk factor of interest. To evaluate underlying assumptions, most MR studies investigating multiple genetic variants perform sensitivity analysis.8, 9, 10, 11

In addition to the endogenous lipid status discussed above, lipids derived from the diet (calories, carbohydrates, and fats) could act as risk factors for insulin resistance and related complications. Some GWA studies have been conducted to understand a genetic role in diet, including macronutrient intake45, 46 or alcohol consumption47; however, considering that diet is an exogenous factor, it can be difficult to use as an instrumental variable in the analysis. To date, few studies have attempted to use MR analyses for identifying genetic variants that mediate nutrient intake and their corresponding effects on the risk of developing insulin resistance or diabetes (Table 3).44, 48, 49

Table 3 Genotypes related to diet lipids or potential modulators for diet lipids and their effects on risk of insulin resistance or T2DM Click for larger imageClick for full tableDownload as Excel file

A recent study reported multiple SNPs that are strongly associated with nutrient intake and conducted MR analysis using those SNPs as instrumental variables.48 The study utilized data from the DIAGRAM (n=10,895) and MAGIC consortia (n=46,368) and found no significant association between SNPs related to increased carbohydrate intake (rs10163409, rs197273, and rs838145) and HOMA-IR or T2DM risk. In addition, genetically mediated increase in fat intake (rs838145) showed no significant effects on HOMA-IR. However, increased protein intake was causally associated with increased risk of insulin resistance (β=0.067, p=0.046) and T2DM incidence (β=0.806, p=0.002). In an MR study on Chinese participants (n=9,182), the genetic risk score for levels of 25-hydroxyvitamin D, a fat soluble vitamin, was not associated with fasting blood glucose or hemoglobin glycation levels.44

Although the study did not directly evaluate the effects of lipid intake on the risk of insulin resistance, results from other MR studies suggest the possibility of diet modification through genetic variation.21, 49 One of the MR studies that analyzed data from the MAGIC consortium49 reported that elevated serum concentrations of vitamin B12 based on the presence of genotype variant (FUT2) were inversely associated with HOMA-B (β=−0.033, p=1.81×10⁻³) without affecting circulating lipid levels or body fat accumulation. As noted above, a genetic variant for HMGCR, the target gene for statin, increased the risk of higher plasma insulin and fasting glucose levels in randomized CVD prevention trials.21 These results indicate that genotypes targeted by the intake of foods or nutrients with LDL-lowering properties or those that affect body fat warrant future MR studies.

Many MR studies show discrepancies between the observational analysis and the secondary instrumental analysis. In particular, circulating lipids show positive association with the indicators of insulin resistance, but this association was not detected in the analysis with instrumental variables.17, 20, 34 Such discrepancies could be partially attributed to bias from residual pleiotropic effects in the MR study, in which a genetic variant exerts effects beyond its specific effects on the biomarker of interest. In addition, it is possible that associations between lipid genotypes and indicators of insulin resistance were biased. First, carrying a larger number of LDL-C-raising alleles28 will increase the likelihood of an individual requiring medical therapy such as statins, which will reduce the effect of LDL-C. As a result, the effects of LDL-C-related SNPs on the outcomes of insulin resistance could be underestimated. Second, the effects of a SNP on patient outcomes (e.g., fasting glucose, fasting insulin, and T2DM incidence) can be diluted by the variability of biomarkers, which may only exert a small effect on an individual SNP and any risk factor acting in concert with multiple other factors; as a result, only a fraction of the inherited element could contribute to the disease. Investigating individual SNPs can limit the likelihood for unexpected pleiotropic effects. In particular, a study by Fall et al.19 showed that using more LDL-C-specific SNPs weakened the association between LDL-C levels and indicators of insulin resistance, suggesting that other SNPs can exert unknown pleiotropic effects. However, because of limited information on the function of SNPs, direct selection of the genes that play crucial roles in lipid metabolism is not practical. In addition, study populations should be sufficiently large to facilitate the detection of small effects to circumvent the dilution effect.

There are extremely limited data from Asian populations, including Koreans, in recent MR studies investigating T2DM and CVD. One Chinese study and one Japanese study were the only sources that could be referred to in this review. In most MR studies, a genetic variant explains only a small proportion of the total variance (usually less than 10%) in the biomarker levels. Therefore, if an SNP that could serve as a proxy for a certain type of lipids and a variant is highly ethnicity-specific (e.g., ADH1B), this will limit cross-comparisons of the same genetic variable across different ethnicities. Results should be interpreted with caution to estimate disease susceptibility and treatment response for Koreans or Asians. This current review also cites some studies that investigated the genetic variants related to nutrient intake that can modify lipid metabolism. However, genetic variants related to diet intake exert their effects indirectly and are highly pleiotropic, so that inference of the causal effect and key assumptions of MR may be biased. Considering that very limited data are available for the discovery of diet-related genetic variants, the association between diet-related genetic variants and insulin resistance in MR studies are challenging, and genome-wide and sequencing-based studies should be continued.

Identification of genetic variants of lipid-associated genes has provided useful information for stratification of disease risk as well as prediction of disease. Moreover, investigations into the genetic variants for metabolic diseases help to find novel therapeutic targets (e.g., PCSK9 and ApoB) and the development of modulatory factors for those targets. Hence many GWA studies have identified a number of loci as potential therapeutic targets for T2DM, and MR studies could provide validation of these loci. MR studies have revealed a strong association between lipid metabolism and insulin resistance and provided insights into the underlying disease mechanisms. MR studies also have confirmed the causal role of genetically altered lipid metabolism, particularly adiposity (e.g., FTO), in the loss of glycemic control and could support the utility of preventive interventions for public health. However, the relationship between circulating lipid levels and insulin resistance is not conclusive based on recent MR studies. Non-communicable diseases, such as T2DM and CVD, involve multiple genetic variants, and the complex nature of the biomarkers for these diseases raises methodological and practical challenges in conducting MR studies. The causal association between lipid levels and insulin resistance using MR should be interpreted with caution because of several limitations, including pleiotropic effects, lack of information on genotypes, and ethnic differences. Nevertheless, considering that metabolomics and proteomic technologies are more widely applied in population studies, MR will help advance knowledge on metabolic diseases and the development of suitable treatment strategies, including diet or behavioral modifications.