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Diabetes. Mar 2011; 60(3): 1008–1018.
Published online Feb 21, 2011. doi:  10.2337/db10-1317
PMCID: PMC3046819
EMSID: UKMS35724

Mendelian Randomization Studies Do Not Support a Role for Raised Circulating Triglyceride Levels Influencing Type 2 Diabetes, Glucose Levels, or Insulin Resistance

Abstract

OBJECTIVE

The causal nature of associations between circulating triglycerides, insulin resistance, and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes and raise normal fasting glucose levels and hepatic insulin resistance.

RESEARCH DESIGN AND METHODS

We tested 10 common genetic variants robustly associated with circulating triglyceride levels against the type 2 diabetes status in 5,637 case and 6,860 control subjects and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8,271 nondiabetic individuals from four studies.

RESULTS

Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (SD 0.59 [95% CI 0.52–0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that the carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio [OR] 0.99 [95% CI 0.97–1.01]; P = 0.26). In nondiabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (SD 0.00 per weighted allele [95% CI −0.01 to 0.02]; P = 0.72) or increased fasting glucose levels (0.00 [−0.01 to 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose, or fasting insulin and, for diabetes, showed a trend toward a protective association (OR per 1-SD increase in log10 triglycerides: 0.61 [95% CI 0.45–0.83]; P = 0.002).

CONCLUSIONS

Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes or raise fasting glucose or fasting insulin levels in nondiabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.

Raised circulating triglyceride levels are strongly correlated with insulin resistance, raised glucose levels, and type 2 diabetes (18), but the causal nature of these associations is unclear because of the complex interactions between fat, muscle, and liver insulin resistance, dyslipidemia, and insulin secretion by β-cells.

Several lines of evidence suggest that raised triglyceride levels could causally influence the risk of type 2 diabetes, high glucose levels, and insulin resistance. Accumulation of triglycerides in tissues other than adipose has been proposed to result in lipotoxicity, a process that may increase the risk of type 2 diabetes. For example, excess triglycerides in the liver causes fatty liver disease and is thought to impair hepatic insulin signaling, resulting in insulin resistance (reviewed in [9]), whereas exposure of the β-cell to free fatty acids (FFAs) is thought to impair insulin secretion (1013).

Epidemiological data support a possible etiological role for raised triglyceride levels in insulin resistance and type 2 diabetes. Raised serum triglycerides predict incident type 2 diabetes independently of BMI (14,6,1416), although prospective evidence does not rule out the possibility that early disease processes can influence such associations. Data from some trials show that individuals receiving lipid-lowering therapies are less likely to develop type 2 diabetes (14,1719). These findings have led to the proposal that therapies that lower circulating triglycerides could be used to improve insulin sensitivity and reduce the risk of type 2 diabetes (2022).

One useful method to help dissect the causal nature of the correlations between metabolic traits is Mendelian randomization (23). This approach uses the principle that the random assortment of genotypes in meiosis is independent of nongenetic factors, including environmental risk factors, confounding factors, or disease processes. There are good proof-of-principle examples of Mendelian randomization. These include the association between FTO genotypes, which are robustly associated with total fat mass, and type 2 diabetes and blood pressure, which confirmed the causal associations between adiposity and these outcomes (24,25), and the association between LDL cholesterol–associated variants and heart disease (26).

In this study, we extend the Mendelian randomization approach to test the hypothesis that raised circulating triglyceride levels have an etiological role in type 2 diabetes, raised fasting glucose levels, and fasting-based measures of insulin resistance.

RESEARCH DESIGN AND METHODS

Type 2 diabetes case-control study.

We studied 12,497 individuals (5,637 type 2 diabetic patients and 6,860 control subjects) from the Genetics of Diabetes Audit and Research in Tayside Scotland (Go-DARTS) study (27), a cross-sectional study that includes measures of circulating lipids, often with repeated measurements in the same individual (Table 1). Patients were excluded if their age at diagnosis was <35 or >70 years or if they needed insulin treatment within 1 year of diagnosis. For 2.1% of patients, age at diagnosis was not known, in which case those aged <45 years at the time of study were excluded. Control status was defined if individuals were between 35 and 80 years of age with an A1C <6.4% and/or fasting glucose <7 mmol/L. Analyses of associations involving triglyceride levels were limited to the 9,693 individuals (3,976 patients and 5,717 control subjects) that had triglyceride levels measured prior to taking any lipid-lowering medication. Of these individuals, 46.88% (74.72% of patients and 27.51% of control subjects) had more than one measure of triglycerides, in which case we used mean values.

TABLE 1
Clinical characteristics of individuals in four studies of continuous traits and case and control subjects of the Go-DARTS type 2 diabetes study

Fasting-based measures of insulin resistance and glucose levels.

For the study of continuous traits, we examined nondiabetic individuals from four studies. These studies were the Exeter Family Study of Childhood Health (EFSOCH) (28), the Go-DARTS study, the Fenland Study (29 Supplementary Information), and the British Women’s Heart and Health Study (BWHHS) (30) (Table 1). The EFSOCH study consisted of parents of babies born between 2000 and 2004 from Exeter, U.K. For EFSOCH mothers, we used fasting measures taken postpregnancy. The Go-DARTS subjects are a subset of those studied as control subjects in the type 2 diabetes study described above, who had fasting glucose, insulin, and nonfasting lipid measures available (fasting lipid measures were not taken). The Fenland Study is a population-based study in the East Cambridgeshire and Fenland areas of the U.K. The BWHHS is a prospective cohort study of women aged 60–79 years recruited from 23 towns across Britain from 1999 to 2000.

We only included individuals with fasting glucose values <7.0 mmol/L. None of the individuals in the EFSOCH study, 26 (<2%) in the Fenland Study, and 5% in the BWHHS were on lipid-lowering medications. We did not use triglyceride measures from individuals on lipid-lowering medications in the Go-DARTS study. Details of fasting glucose and fasting insulin measurement methods are given in Supplementary Table 1. We calculated additional fasting-based measures of insulin resistance and β-cell function using the homeostasis model assessment of β-cell function (HOMA-B) and HOMA of insulin resistance (HOMA-IR) using the HOMA calculator (available at http://www.dtu.ox.ac.uk).

Selection of single nucleotide polymorphisms, genotyping, and quality control.

We initially selected 12 independent single nucleotide polymorphisms (SNPs) that are associated with circulating triglyceride levels at genome-wide levels of significance (P < 5 × 10−8) (3135). We excluded two of these SNPs from our analyses (FADS1-rs174547 and GCKR-rs1260326) because they are strongly associated with several other quantitative traits relevant to diabetes (29,36,37).

We genotyped 10 selected SNPs in the four studies using either a modified Taqman assay, a KASPAR assay (http://www.kbioscience.co.uk), directly or imputed genotypes from the Affymetrix GeneChip Human Mapping 500 K array, or the Illumina Human CVD array (Supplementary Methods). The genotyping success rate for each SNP was >92% in all studies, and the concordance rate between duplicates (at least 7% of samples) was at least 97%. All 10 variants were in Hardy-Weinberg equilibrium in each of the four studies (P > 0.05).

Statistical analyses.

We used two approaches to assess the relationship between circulating triglyceride levels and diabetes-related outcomes: the triangulation approach outlined in Fig. 1and an instrumental variable approach (38). All statistical analyses were performed using Stata/SE version 10.1 for Windows (StataCorp, Brownsville, TX). Meta-analyses were performed using the inverse-variance weighted fixed-effects estimator implemented in the Stata command, “metan.”

FIG. 1.
Triangulation approach used to estimate the expected association for the SNP vs. type 2 diabetes or continuous trait (d) given the SNP versus triglyceride association (a) and the triglyceride versus type 2 diabetes or continuous trait associations (b ...

Observed association between triglyceride SNPs and triglyceride levels.

In each study, triglyceride levels (mmol/L) were log10 transformed before analysis. For the type 2 diabetes study, we generated age- and sex-corrected z scores of log10-transformed triglycerides, using all case and control subjects. To estimate the SNP versus triglyceride associations, we assumed a prevalence rate of 5% for type 2 diabetes in the U.K., and to be more representative of this general population we gave a weight of 95% to control subjects and 5% to case subjects. For continuous traits, we generated within-study z scores of log10-transformed triglycerides using the means and SD of the samples, where age, sex, triglyceride levels, and genotypes from at least eight of 10 SNPs were available.

Using both individual SNPs and a weighted allele score, we tested associations between genotypes and triglyceride levels. To create the weighted allele score, we used individuals with genotypes available from at least eight of 10 SNPs and accounted for the varying effect sizes of each SNP using equation 1, where w is the β-coefficient from the individual regressions of the SNP genotype against triglycerides.

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We rescaled the weighted score to reflect the number of available SNPs (ranging from 8 to 10) using equation 2, as described in Lin et al. (39). For all further tests, we used this allele score.

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We used this allele score as the independent variable and the log10-triglyceride z score as the dependent variable, and for the study of continuous traits we also used age and sex as covariates in linear regression analyses. In addition, we stratified individuals in each study into quintiles consisting of the 20% of individuals with increasing numbers of (weighted) triglyceride-raising alleles.

Observed association between triglycerides and outcomes.

Using 3,976 case and 5,717 control subjects from the Go-DARTS study, we estimated the odds ratio (OR) for type 2 diabetes per 1-SD increase in log10-triglyceride z score in a logistic regression analysis. For the four nondiabetes studies, we tested four continuous-outcome variables: fasting glucose, fasting insulin, HOMA-B, and HOMA-IR. We log10 transformed the outcome variables that were skewed and created z score within each study. We used the log10-triglyceride z score as the independent variable and each outcome z score as the dependent variable, with age and sex as covariates in linear regression analyses prior to meta-analysis.

Observed association between triglyceride SNPs and outcomes.

To test the association between triglyceride SNPs and type 2 diabetes, we used individual SNPs or the allele score as the independent variable and type 2 diabetes status as the dependent variable in logistic regression analyses, with age and sex as covariates. To test the association between triglyceride SNPs and continuous outcomes, we performed the same analyses but in linear regression models prior to meta-analysis.

Calculation of the approximate expected effect size of the association between triglyceride SNPs and outcomes.

If raised triglyceride levels are etiologically associated with the outcomes, then under certain assumptions we would expect the point estimate of the expected outcome (a per-allele OR for type 2 diabetes, or SD effect size for continuous traits; Fig. 1d) to be a function of 1) the SNP-triglyceride association and 2) the triglyceride-outcome association (i.e., d = SD effect size of a × SD effect size/OR of b in Fig. 1). SEs for the expected effect sizes were calculated using the Taylor series expansion of the ratio of two means (40).

Instrumental variable analysis.

To estimate the causal effect of triglycerides on outcomes, we performed instrumental variable analyses (Supplementary Methods and Supplementary Fig. 2). An instrumental variable analysis relates the variation in the potentially causal risk factor of interest (here, circulating triglyceride levels) that is influenced by the “instrument” (here, triglyceride genotypes) to the outcome (here, type 2 diabetes, fasting insulin, or fasting glucose levels). This method makes the assumption that the instrumental variable is not associated with measured or unmeasured confounders (likely to be true for genetic variants [38]) and is only related to the outcome via its effect on the risk factor. This produces an estimate of the causal effect in a similar way as an intention-to-treat analysis in a randomized controlled trial (38).

Instrumental variable analysis for type 2 diabetes case-control status.

We limited this analysis to the 8,335 individuals (3,090 case and 5,245 control subjects) who had triglyceride levels measured prior to taking any lipid-lowering medication and genotypes from at least eight of 10 triglyceride SNPs. Instrumental variable analysis was performed using a logistic control function estimator (41). The analysis was performed in two stages. In the first stage, we assessed the observational association between allele score and triglyceride z score, as described in Fig. 1a. We saved the predicted values and residuals from this regression model. In the second stage, we used the predicted values from stage 1 as the independent variable (reflecting an unconfounded estimate of triglyceride levels attributed to these genotypes) and diabetes status as the dependent variable in a logistic regression analysis. The residuals from stage 1 were included as a covariate, representing residual variation in triglyceride levels that is not attributed to these genotypes (41). We then used a Wald test to assess the evidence of a difference between the predicted-values coefficient (instrumental variable estimate of the causal effect of triglyceride levels on type 2 diabetes) and the residuals coefficient as test of endogeneity.

Instrumental variable analyses for fasting insulin, fasting glucose, HOMA-B, and HOMA-IR.

We performed the instrumental variable estimation for each outcome in each study using the two-stage least-squares estimator, implemented in the Stata command “ivreg2.” We tested for a difference between the instrumental variable and observational estimates using the Durbin-Wu-Hausman test of endogeneity. We meta-analyzed the instrumental variable estimates for each outcome from the individual studies.

Effects of triglyceride SNPs on HDL, LDL, and total cholesterol and effects when including GCKR and FADS1 SNPs.

We performed additional analyses, including tests on other lipid parameters to assess whether the results are predominantly driven by the variants’ effects on triglycerides. This was assessed by tests, including only the four SNPs with the weakest effects on HDL cholesterol relative to their effects on triglycerides (the SNPs in or near MLXIPL, ANGPTL3, NCAN, and TRIB1) in the allele score. We also assessed the effects when including the GCKR and FADS1 SNPs in the allele score and the effects when adjusting for BMI in addition to age and sex (Supplementary Methods).

RESULTS

Observed association between triglyceride SNPs and triglycerides.

Associations between individual SNPs and triglycerides, meta-analyzed across each of the four studies with nondiabetic individuals, and separately for the type 2 diabetes study, are shown in Table 2. The majority was highly significantly associated with circulating triglyceride levels, and all effects were consistent with those reported in genome-wide association studies. Individuals carrying greater numbers of (weighted) triglyceride-raising alleles had increased circulating triglyceride levels (Table 2, Fig. 2A, and Supplementary Fig. 1). For example, the group of individuals in the highest quintile of the weighted allele score had triglyceride levels that were 0.59 SDs (95% CI 0.52–0.65) higher than those in the lowest quintile. There was some evidence (I2 = 78.6%, P = 0.003) for heterogeneity between studies for the allele score–triglyceride association (Supplementary Fig. 1), but a random-effects meta-analysis resulted in a similar point estimate (data not shown).

TABLE 2
The association of individual SNPs and combinations of SNPs with circulating triglyceride levels from a meta-analysis of four studies of nondiabetic individuals and the Go-DARTS type 2 diabetes case-control study
FIG. 2.
The combined impact of the 10 triglyceride-associated SNPs on circulating triglyceride levels in the Go-DARTS study type 2 diabetic case and control subjects, with triangles representing the mean triglyceride z score within each triglyceride-increasing ...

Observed association between triglycerides and outcomes.

A 1-SD increase in log10-triglyceride levels was associated with an OR of 2.68 (95% CI 2.54–2.82) for type 2 diabetes in the Go-DARTS study. Triglyceride levels were associated with each of the four continuous outcomes across the four nondiabetes studies. A 1-SD increase in triglyceride levels was associated with 0.12 SDs (95% CI 0.1–0.15), 0.36 SDs (0.33–0.38), 0.41 SDs (0.38–0.43), and 0.40 SDs (0.38–0.42) higher fasting glucose, HOMA-B, fasting insulin, and HOMA-IR, respectively (Table 3 and Fig. 3A). There was some evidence for heterogeneity between the nondiabetic studies for the associations involving fasting insulin (I2 = 74.6%, P = 0.008), fasting glucose (I2 = 69.5%, P = 0.02), and HOMA-IR (I2 = 75.7%, P = 0.006). Random-effects meta-analyses resulted in similar point estimates (data not shown).

TABLE 3
Meta-analysis results of observed and instrumental variable analyses of triglyceride–continuous outcome associations
FIG. 3.
Meta-analysis of continuous traits. Triglyceride–outcome associations (A), weighted triglyceride allele score–outcome associations (B), and instrumental variable analyses of triglyceride–outcome associations (C), all corrected ...

Observed association between triglyceride SNPs and type 2 diabetes.

The details of the individual associations between triglyceride SNPs and type 2 diabetes are given in Table 4. None of the SNPs were associated with type 2 diabetes (P > 0.01). There was no evidence that individuals carrying greater numbers of (weighted) triglyceride-raising alleles were at increased risk for type 2 diabetes (Table 4 and Fig. 2B).

TABLE 4
The association of individual and combinations of SNPs with type 2 diabetes in the Go-DARTS type 2 diabetes case-control study

Observed association between triglyceride SNPs and fasting insulin, fasting glucose, HOMA-B, and HOMA-IR.

Associations between individual SNPs and each continuous outcome, meta-analyzed across the four nondiabetic studies, are given in Table 5 and Supplementary Table 2. None of the SNPs were associated with any of the four outcomes except for rs7819412 (XKR6-AMAC1L2 locus), where there was some evidence for a positive association with fasting insulin (P = 0.004) and HOMA-IR (P = 0.004). There was no evidence that carriers of greater numbers of (weighted) triglyceride-raising alleles were at risk for increased fasting glucose or fasting insulin levels (Table 5, Fig. 3B, and Supplementary Table 2). There was no heterogeneity between studies except for the allele score–glucose association (I2 = 80.9%, P = 0.001) and removing the one study influencing this heterogeneity, Go-DARTS, resulted in a nominal association between allele score and raised fasting glucose (P = 0.03).

TABLE 5
Associations of individual triglyceride SNPs and weighted allele score with fasting glucose and fasting insulin meta-analyzed across the four studies of nondiabetic individuals

Expected effect size of the association between triglyceride SNPs and type 2 diabetes.

Estimates of the expected ORs and 95% CIs for the allele score–type 2 diabetes association are shown in Table 4. For the allele score and each quintile comparison, the 95% CIs of the observed ORs excluded the expected point estimate ORs estimated from the function of the SNP–triglyceride and triglyceride–type 2 diabetes correlations and vice versa.

Expected effect size of the association between triglyceride SNPs and fasting insulin, fasting glucose, HOMA-B, and HOMA-IR.

Estimates of the expected effect sizes and the 95% CIs for the allele score–continuous outcome associations are given in Table 5 and Supplementary Table 2. For the allele score and each quintile comparison, the 95% CIs of the observed effect sizes excluded the approximate expected effect sizes estimated from the function of the SNP–triglyceride and triglyceride–outcome correlations and vice versa.

Instrumental variable estimate for type 2 diabetes.

Instrumental variable estimation provided strong evidence that raised circulating triglyceride levels do not causally result in an increased risk of type 2 diabetes. Instead, there was a suggestive protective association (a 1-SD increase in [genetically influenced] circulating triglycerides was associated with an OR for type 2 diabetes of 0.61 [95% CI 0.45–0.83]; P = 0.002). There was strong evidence that the instrumental variable OR (0.61 [0.45–0.83]) and standard OR (2.68 [2.53–2.84]) estimates were different from each other (P = 6 × 10−21).

Instrumental variable estimates for fasting insulin, fasting glucose, HOMA-B, and HOMA-IR.

Instrumental variable estimation gave strong evidence that genetically influenced circulating triglyceride levels do not have a causal effect on fasting insulin, fasting glucose, HOMA-B, or HOMA-IR (Table 3 and Fig. 3C). As found with the standard analyses described in the section “Observed association between triglyceride SNPs and fasting insulin, fasting glucose, HOMA-B, and HOMA-IR,” there was evidence of heterogeneity in the instrumental variable analysis with fasting glucose as an outcome (I2 = 79.4%, P = 0.002), and removing the Go-DARTS study control subjects, who caused this heterogeneity (Fig. 3C), resulted in nominal evidence of the association of increased triglycerides with increased glucose levels (P = 0.08). For all four outcomes, the instrumental variable estimates from the meta-analyses were inconsistent with estimates observed from standard regression analyses (Table 3).

Effects of triglyceride SNPs on HDL, LDL, and total cholesterol and effects when including GCKR and FADS1 SNPs.

We found very similar results in the series of sensitivity analyses with some possible exceptions. First, using the weighted allele score containing the four SNPs with disproportionately greater effects on triglycerides relative to HDL, we observed a possible stronger protective effect of higher triglycerides on type 2 diabetes (instrumental variable analysis: OR per 1-SD increase in log10-triglycerides: 0.34 [95% CI 0.19–0.59]; P = 0.0001) (Supplementary Table 4). Second, including the GCKR and FADS1 SNPs in the weighted allele model resulted in a possible protective association with fasting glucose levels compared with when these SNPs were not included (Supplementary Table 5). Third, adjusting for BMI resulted in a possible stronger protective effect of higher triglycerides on type 2 diabetes (0.35 [0.20–0.64]; P = 0.001), compared with when not adjusting for BMI (Supplementary Table 6).

DISCUSSION

Using a Mendelian randomization approach, our results show strong evidence that higher circulating triglyceride levels do not increase type 2 diabetes risk, fasting glucose, or fasting-based measures of insulin resistance. Our results are consistent with lifelong, raised circulating triglycerides conferring no net harm to the liver or β-cell. Our results suggest that alternative explanations are needed to explain the observational associations between raised triglyceride levels and diabetes and related traits. These explanations could include confounding factors or reverse-direction causal effects (i.e., type 2 diabetes and insulin resistance causing raised triglycerides). Other human genetic studies support the reverse-causation argument. For example, postreceptor defects in insulin resistance caused by AKT mutations result in increased hepatic lipogenesis and increased circulating triglycerides (8), and polymorphisms near the IRS1 gene that are robustly associated with insulin resistance (42) also result in raised triglycerides (43) (both associations at conventional levels of genome-wide significance, P < 5 × 10−8).

There are several strengths and limitations to our approach. The main strength is that we used genetic variants to test a complex relationship between metabolic traits. Because genetic variation is randomly sorted at meiosis, associations between SNPs and metabolic traits are unlikely to be biased, confounded, or influenced by disease processes. Furthermore, the effects of the genetic variants we have used are likely to reflect lifelong exposure to altered circulating triglycerides. In contrast, it is extremely difficult to disentangle likely causal directions between correlated human phenotypes using nongenetic approaches (38), and this is especially true for associations between metabolic factors such as lipid levels, diabetes, and insulin resistance (7). The second strength of our study is the statistical power. Because we used 10 common variants, our weighted allele score model compared large numbers of people with large differences in genetically influenced circulating triglyceride levels; for example, 20% of individuals carrying the most triglyceride-raising alleles had circulating levels 0.59 SDs higher than the 20% carrying the fewest. We therefore had very good power to see an effect of triglyceride variants on related metabolic traits if such a relationship existed (for example >80% power at P = 0.05 if circulating triglycerides 0.59 SDs higher than a baseline group resulted in a type 2 diabetes OR of 1.12). The third strength is that we used 10 variants that are likely to influence circulating triglyceride levels in a variety of ways. Although genome-wide association studies do not identify the causal gene involved, variants in or near LPL and ANGPTL3 are likely to influence lipoprotein lipase function, the key enzyme located in capillary surfaces that hydrolyses triglycerides to release fatty acids (44,45). Variants in APOA5 are among those with the strongest effects on circulating triglycerides and are likely to function through a variety of mechanisms, including reducing liver production of triglycerides (46,47). Variants near APOB are most likely to affect triglyceride clearance from the liver, and the variant at the PLTP locus is associated with altered PLTP expression in human liver samples, suggesting that it operates in the liver (31). Our data therefore suggest that the lack of association between circulating triglycerides, type 2 diabetes, and related outcomes is not dependent on the particular mechanism that alters triglyceride levels. A fourth strength of our study is that our results for continuous traits are consistent across four studies of different characteristics, including mean age ranges between 33.9 and 68.8 years, mean BMIs between 25.52 and 27.24 kg/m2, and different ratios of male and female subjects. The exception is fasting glucose, to which the Go-DARTS study contributes significant heterogeneity between studies, and the results are consistent with a small effect of triglycerides on fasting glucose levels in the remaining three studies.

There are several limitations to our study. Most importantly, we are testing circulating, not intracellular, triglycerides. We have not tested the role of triglycerides in the liver, and fasting insulin (and HOMA-IR) is primarily a measure of hepatic insulin resistance rather than muscle insulin resistance. Several of the gene variants are likely to operate in the liver by increasing the clearance of triglycerides into the circulation, which could be consistent with a lack of effect of these variants on hepatic-based measures of insulin resistance. A net effect of the triglyceride-raising alleles on increased clearance of triglycerides from the liver could also explain the suggestive protective association between increased (genetically influenced) circulating triglycerides and reduced risk of type 2 diabetes in the instrumental variable analysis. However, this association was not reflected by a protective association between triglyceride-raising alleles and hepatic measures of insulin resistance and could be attributed to chance. It will be important to test the association between triglyceride variants and oral glucose tolerance test–based or muscle-based measures of insulin resistance, such as those based on hyperinsulinemic-euglycemic clamps. Therefore, our results do not necessarily provide evidence against the lipotoxicity hypothesis, which states that raised triglyceride levels contribute to whole-body insulin resistance. In contrast, our results provide stronger evidence against the lipotoxicity hypothesis, in that raised circulating triglyceride levels contribute to altered β-cell function and type 2 diabetes. A second limitation is that we have not tested the effects of raised circulating triglyceride levels alone but rather a mixture of raised circulating triglycerides and, to a lesser extent, raised LDL and total cholesterol and lower HDL cholesterol. However, an analysis using just the four variants with disproportionate effects on circulating triglyceride levels relative to HDL cholesterol provided similar results. With the identification of an increasing number of genetic variants related to lipid fractions, it will be possible to produce multiple allele score instruments, which would allow a demonstration of a lack of pleiotropy in generating the observed findings. Finally, the 10 SNPs used only account for 3–5% of the phenotypic variation in circulating triglyceride levels. We have therefore not tested the full spectrum of genetically influenced triglyceride levels.

Further Mendelian randomization studies will be needed to test the role of circulating FFAs, which may be more critical to reduced β-cell function than triglycerides (48). We excluded from our main analysis the common variant near the FADS1 gene because this variant is most strongly associated with polyunsaturated fatty acids (49). The FADS1 variant is also associated with fasting-based measures of insulin secretion, such as fasting glucose and HOMA-B, and to a lesser extent type 2 diabetes (29), suggesting that FFAs could have a causal role in diabetes. Additional genetic studies are needed to assess the role of FFAs and different types of FFAs in insulin resistance and secretion.

In conclusion, we have performed a powerful Mendelian randomization analysis of circulating triglyceride levels. Our data provide evidence that genetically influenced raised circulating triglyceride levels do not increase the risk of type 2 diabetes and related metabolic traits.

Supplementary Material

Supplementary Data:

ACKNOWLEDGMENTS

This article is supported by a Medical Research Council (MRC) Project Grant, which provides salary support to R.M.H. (G0601625). R.M.F. is funded by a Sir Henry Wellcome Postdoctoral Fellowship (Wellcome Trust Grant 085541/Z/08/Z). B.S. and B.A.K. are employed as core members of the Peninsula National Institute for Health Research (NIHR) Clinical Research Facility. A.T.H. is a Wellcome Trust Research Leave Fellow. The EFSOCH was supported by the National Health Service Research and Development and the Wellcome Trust. The Go-DARTS study was supported by the Wellcome Trust (Biomedical Collections Grant GR072960). The BWHHS was funded by the Department of Health (England) Policy Research Programme and the British Heart Foundation. D.A.L., G.D.S., and T.M.P. work in a centre that receives core funding from the University of Bristol and the MRC (G0600705), the latter funds T.M.P.’s salary. The Fenland study is funded by the MRC and the Wellcome Trust. The InCHIANTI study was supported by contract funding from the National Institutes of Health National Institute on Aging (NIA), and the research was supported in part by the Intramural Research Program of the National Institutes of Health NIA. This work was partially funded by grants from the Wellcome Trust (083270/Z/07/Z) and MRC (G0601261).

No potential conflicts of interest relevant to this article were reported.

All authors contributed to the writing of the manuscript. N.M.G.D.S. and R.M.F. designed the study, performed analyses, and co-wrote the initial draft of the article. T.M.P., L.A.D., J.L., T.G., C.L., and M.N.W. performed genotyping and/or analyses in individual studies. B.S. and B.A.K. provided samples and data from individual studies and contributed to the design of the study. K.J.W., M.S.S., and R.M.H. performed genotyping and/or analyses in individual studies. M.I.M., G.D.S., S.E., A.T.H., N.W., D.A.L., A.D.M., and C.N.A.P. provided samples and data from individual studies and contributed to the design of the study. T.M.F. designed the study and co-wrote the manuscript.

Parts of this study were presented in poster form at the 70th Scientific Sessions of the American Diabetes Association, Orlando, Florida, 25–29 June 2010.

The authors thank David Savage (University of Cambridge) for helpful comments on the article. The authors acknowledge P.M. Clark (University Hospital Birmingham) for carrying out the insulin assays in the EFSOCH. The authors are grateful to all the volunteers for their time and help and to the general practitioners and practice staff for help with recruitment. The authors thank the Fenland Study coordination team and the field epidemiology team of the MRC Epidemiology Unit for recruitment and clinical testing. The authors also thank the NIHR Cambridge Biomedical Research Centre, Cambridge, U.K., for biochemical analyses.

Footnotes

This article contains Supplementary Data online at http://diabetes.diabetesjournals.org/lookup/suppl/doi:10.2337/db10-1317/-/DC1.

REFERENCES

1. Bonora E, Targher G, Alberiche M, et al. Predictors of insulin sensitivity in type 2 diabetes mellitus. Diabet Med 2002;19:535–542. [PubMed]
2. Wilson PW, Meigs JB, Sullivan L, Fox CS, Nathan DM, D’Agostino RB., Sr Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study. Arch Intern Med 2007;167:1068–1074. [PubMed]
3. Perry IJ, Wannamethee SG, Walker MK, Thomson AG, Whincup PH, Shaper AG. Prospective study of risk factors for development of non-insulin dependent diabetes in middle aged British men. BMJ 1995;310:560–564. [PMC free article] [PubMed]
4. Gupta AK, Dahlof B, Dobson J, Sever PS, Wedel H, Poulter NR, the Anglo-Scandinavian Cardiac Outcomes Trial Investigators Determinants of new-onset diabetes among 19,257 hypertensive patients randomized in the Anglo-Scandinavian Cardiac Outcomes Trial—Blood Pressure Lowering Arm and the relative influence of antihypertensive medication. Diabetes Care 2008;31:982–988. [PubMed]
5. Laakso M, Barrett-Connor E. Asymptomatic hyperglycemia is associated with lipid and lipoprotein changes favoring atherosclerosis. Arteriosclerosis 1989;9:665–672. [PubMed]
6. Schmidt MI, Duncan BB, Bang H, et al. Identifying individuals at high risk for diabetes: the Atherosclerosis Risk in Communities Study. Diabetes Care 2005;28:2013–2018. [PubMed]
7. Savage DB, Semple RK. Recent insights into fatty liver, metabolic dyslipidaemia and their links to insulin resistance. Curr Opin Lipidol 2010;21:329–336. [PubMed]
8. Semple RK, Sleigh A, Murgatroyd PR, et al. Postreceptor insulin resistance contributes to human dyslipidemia and hepatic steatosis. J Clin Invest 2009;119:315–322. [PMC free article] [PubMed]
9. Trauner M, Arrese M, Wagner M. Fatty liver and lipotoxicity. Biochim Biophys Acta 2010;1801:299–310. [PubMed]
10. Nolan CJ, Madiraju MS, Delghingaro-Augusto V, Peyot ML, Prentki M. Fatty acid signaling in the beta-cell and insulin secretion. Diabetes 2006;55(Suppl. 2):S16–S23. [PubMed]
11. Morgan NG, Dhayal S. G-protein coupled receptors mediating long chain fatty acid signalling in the pancreatic beta-cell. Biochem Pharmacol 2009;78:1419–1427. [PubMed]
12. Newsholme P, Keane D, Welters HJ, Morgan NG. Life and death decisions of the pancreatic beta-cell: the role of fatty acids. Clin Sci (Lond) 2007;112:27–42. [PubMed]
13. Haber EP, Procópio J, Carvalho CR, Carpinelli AR, Newsholme P, Curi R. New insights into fatty acid modulation of pancreatic beta-cell function. Int Rev Cytol 2006;248:1–41. [PubMed]
14. Freeman DJ, Norrie J, Sattar N, et al. Pravastatin and the development of diabetes mellitus: evidence for a protective treatment effect in the West of Scotland Coronary Prevention Study. Circulation 2001;103:357–362. [PubMed]
15. Dotevall A, Johansson S, Wilhelmsen L, Rosengren A. Increased levels of triglycerides, BMI and blood pressure and low physical activity increase the risk of diabetes in Swedish women: a prospective 18-year follow-up of the BEDA study. Diabet Med 2004;21:615–622. [PubMed]
16. McLaughlin T, Abbasi F, Cheal K, Chu J, Lamendola C, Reaven G. Use of metabolic markers to identify overweight individuals who are insulin resistant. Ann Intern Med 2003;139:802–809. [PubMed]
17. Tenenbaum A, Fisman EZ, Boyko V, et al. Attenuation of progression of insulin resistance in patients with coronary artery disease by bezafibrate. Arch Intern Med 2006;166:737–741. [PubMed]
18. Tenenbaum A, Motro M, Fisman EZ, et al. Effect of bezafibrate on incidence of type 2 diabetes mellitus in obese patients. Eur Heart J 2005;26:2032–2038. [PubMed]
19. Tenenbaum A, Motro M, Fisman EZ, et al. Peroxisome proliferator-activated receptor ligand bezafibrate for prevention of type 2 diabetes mellitus in patients with coronary artery disease. Circulation 2004;109:2197–2202. [PubMed]
20. Lee MK, Miles PD, Khoursheed M, Gao KM, Moossa AR, Olefsky JM. Metabolic effects of troglitazone on fructose-induced insulin resistance in the rat. Diabetes 1994;43:1435–1439. [PubMed]
21. Flordellis CS, Ilias I, Papavassiliou AG. New therapeutic options for the metabolic syndrome: what’s next? Trends Endocrinol Metab 2005;16:254–260. [PubMed]
22. Flory JH, Ellenberg S, Szapary PO, Strom BL, Hennessy S. Antidiabetic action of bezafibrate in a large observational database. Diabetes Care 2009;32:547–551. [PMC free article] [PubMed]
23. Davey Smith G, Ebrahim S. Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol 2003;32:1–22. [PubMed]
24. Timpson NJ, Harbord R, Davey Smith G, Zacho J, Tybjaerg-Hansen A, Nordestgaard BG. Does greater adiposity increase blood pressure and hypertension risk? Mendelian randomization using the FTO/MC4R genotype. Hypertension 2009;54:84–90. [PubMed]
25. Freathy RM, Timpson NJ, Lawlor DA, et al. Common variation in the FTO gene alters diabetes-related metabolic traits to the extent expected given its effect on BMI. Diabetes 2008;57:1419–1426. [PMC free article] [PubMed]
26. Linsel-Nitschke P, Götz A, Erdmann J, et al. Lifelong reduction of LDL-cholesterol related to a common variant in the LDL-receptor gene decreases the risk of coronary artery disease: a Mendelian randomisation study. PLoS ONE 2008;3:e2986. [PMC free article] [PubMed]
27. Morris AD, Boyle DI, MacAlpine R, et al. The diabetes audit and research in Tayside Scotland (DARTS) study: electronic record linkage to create a diabetes register. BMJ 1997;315:524–528. [PMC free article] [PubMed]
28. Knight B, Shields BM, Hattersley AT. The Exeter Family Study of Childhood Health (EFSOCH): study protocol and methodology. Paediatr Perinat Epidemiol 2006;20:172–179. [PubMed]
29. Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 2010;42:105–116. [PMC free article] [PubMed]
30. Lawlor DA, Bedford C, Taylor M, Ebrahim S. Geographical variation in cardiovascular disease, risk factors, and their control in older women: British Women’s Heart and Health Study. J Epidemiol Community Health 2003;57:134–140. [PMC free article] [PubMed]
31. Kathiresan S, Willer CJ, Peloso GM, et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat Genet 2009;41:56–65. [PMC free article] [PubMed]
32. Pennacchio LA, Olivier M, Hubacek JA, Krauss RM, Rubin EM, Cohen JC. Two independent apolipoprotein A5 haplotypes influence human plasma triglyceride levels. Hum Mol Genet 2002;11:3031–3038. [PubMed]
33. Rip J, Nierman MC, Ross CJ, et al. Lipoprotein lipase S447X: a naturally occurring gain-of-function mutation. Arterioscler Thromb Vasc Biol 2006;26:1236–1245. [PubMed]
34. Kathiresan S, Melander O, Guiducci C, et al. Six new loci associated with blood low-density lipoprotein cholesterol, high-density lipoprotein cholesterol or triglycerides in humans. Nat Genet 2008;40:189–197. [PMC free article] [PubMed]
35. Chen WM, Erdos MR, Jackson AU, et al. Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels. J Clin Invest 2008;118:2620–2628. [PMC free article] [PubMed]
36. Vaxillaire M, Cavalcanti-Proença C, Dechaume A, et al. The common P446L polymorphism in GCKR inversely modulates fasting glucose and triglyceride levels and reduces type 2 diabetes risk in the DESIR prospective general French population. Diabetes 2008;57:2253–2257. [PMC free article] [PubMed]
37. Orho-Melander M, Melander O, Guiducci C, et al. Common missense variant in the glucokinase regulatory protein gene is associated with increased plasma triglyceride and C-reactive protein but lower fasting glucose concentrations. Diabetes 2008;57:3112–3121. [PMC free article] [PubMed]
38. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 2008;27:1133–1163. [PubMed]
39. Lin X, Song K, Lim N, et al. Risk prediction of prevalent diabetes in a Swiss population using a weighted genetic score: the CoLaus Study. Diabetologia 2009;52:600–608. [PubMed]
40. Thomas DC, Lawlor DA, Thompson JR. Re: Estimation of bias in nongenetic observational studies using “Mendelian triangulation” by Bautista et al. Ann Epidemiol 2007;17:511–513. [PubMed]
41. Palmer TM, Thompson JR, Tobin MD, Sheehan NA, Burton PR. Adjusting for bias and unmeasured confounding in Mendelian randomization studies with binary responses. Int J Epidemiol 2008;37:1161–1168. [PubMed]
42. Rung J, Cauchi S, Albrechtsen A, et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat Genet 2009;41:1110–1115. [PubMed]
43. Teslovich TM, Musunuru K, Smith AV, et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 2010;466:707–713. [PMC free article] [PubMed]
44. Humphries SE, Nicaud V, Margalef J, Tiret L, Talmud PJ. Lipoprotein lipase gene variation is associated with a paternal history of premature coronary artery disease and fasting and postprandial plasma triglycerides: the European Atherosclerosis Research Study (EARS). Arterioscler Thromb Vasc Biol 1998;18:526–534. [PubMed]
45. Shimizugawa T, Ono M, Shimamura M, et al. ANGPTL3 decreases very low density lipoprotein triglyceride clearance by inhibition of lipoprotein lipase. J Biol Chem 2002;277:33742–33748. [PubMed]
46. Schaap FG, Rensen PC, Voshol PJ, et al. ApoAV reduces plasma triglycerides by inhibiting very low density lipoprotein-triglyceride (VLDL-TG) production and stimulating lipoprotein lipase-mediated VLDL-TG hydrolysis. J Biol Chem 2004;279:27941–27947. [PubMed]
47. Merkel M, Loeffler B, Kluger M, et al. Apolipoprotein AV accelerates plasma hydrolysis of triglyceride-rich lipoproteins by interaction with proteoglycan-bound lipoprotein lipase. J Biol Chem 2005;280:21553–21560. [PubMed]
48. El-Assaad W, Buteau J, Peyot ML, et al. Saturated fatty acids synergize with elevated glucose to cause pancreatic beta-cell death. Endocrinology 2003;144:4154–4163. [PubMed]
49. Schaeffer L, Gohlke H, Müller M, et al. Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids. Hum Mol Genet 2006;15:1745–1756. [PubMed]

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