Mendelian randomization (MR) provides us the opportunity to investigate the causal paths of metabolites in type 2 diabetes and glucose homeostasis. We developed and tested an MR approach based on genetic risk scoring for plasma metabolite levels, utilizing a pathway-based sensitivity analysis to control for nonspecific effects. We focused on 124 circulating metabolites that correlate with fasting glucose in the Erasmus Rucphen Family (ERF) study (n = 2,564) and tested the possible causal effect of each metabolite with glucose and type 2 diabetes and vice versa. We detected 14 paths with potential causal effects by MR, following pathway-based sensitivity analysis. Our results suggest that elevated plasma triglycerides might be partially responsible for increased glucose levels and type 2 diabetes risk, which is consistent with previous reports. Additionally, elevated HDL components, i.e., small HDL triglycerides, might have a causal role of elevating glucose levels. In contrast, large (L) and extra large (XL) HDL lipid components, i.e., XL-HDL cholesterol, XL-HDL–free cholesterol, XL-HDL phospholipids, L-HDL cholesterol, and L-HDL–free cholesterol, as well as HDL cholesterol seem to be protective against increasing fasting glucose but not against type 2 diabetes. Finally, we demonstrate that genetic predisposition to type 2 diabetes associates with increased levels of alanine and decreased levels of phosphatidylcholine alkyl-acyl C42:5 and phosphatidylcholine alkyl-acyl C44:4. Our MR results provide novel insight into promising causal paths to and from glucose and type 2 diabetes and underline the value of additional information from high-resolution metabolomics over classic biochemistry.

Type 2 diabetes is a progressive metabolic disease characterized by hyperglycemia, initially as a result of insulin resistance and in later stages also as a result of insulin insufficiency. Type 2 diabetes is also associated with dyslipidemia, including higher circulating concentrations of triglycerides and lower concentrations of HDL cholesterol. In addition, several circulating molecules have previously been shown to be dysregulated in type 2 diabetes, including phospholipids, branched-chain amino acids, keto-acid metabolites, and other metabolites such as acyl-carnitines (13). However, the causal paths between these metabolites and glucose/type 2 diabetes in humans remain unclear from observational studies and require randomized controlled trials that are difficult to conduct.

As an alternative, Mendelian randomization (MR) is an instrumental variable method that has gained popularity over the last decade to investigate causal effects of traits using genetic predictors. MR uses the principle that the allocation of genetic variants that affect a specific trait from parents to offspring is random and unrelated to factors other than the trait (4). Furthermore, associations between the genotype and the outcome will not be affected by reverse causation because disease will occur after the meiosis. MR has previously been used to determine whether metabolic markers such as classic blood lipids are causally involved in type 2 diabetes (511) and has yielded contradicting results. One reason for this could be that these studies are affected by the heterogeneous nature of the metabolic markers chosen, such as in the example of total HDL cholesterol, which in reality consists of a collection of different-sized HDL particles possibly with different functions. This may dilute the causal effects of single nucleotide polymorphisms (SNPs) when only combined (total) HDL is considered. However, false signals may also be due to pleiotropic effects of the chosen genetic variants leading to possibly invalid instrumental variables. As high-throughput analyses techniques improve, the quantification of circulating molecules is becoming ever more detailed and precise. For instance, instead of LDL cholesterol, HDL cholesterol, and total triglycerides determined by routine clinical biochemistry, lipoprotein particle size distribution and content as well as tens of biochemical components can now be measured using nuclear magnetic resonance (NMR) spectroscopy– and mass spectrometry (MS)-based approaches (12,13). These additional measures offer an opportunity to gain novel insight into the pathogenesis of diseases like type 2 diabetes. With the knowledge of genetic determinants of metabolites gained from genome-wide association studies (GWAS) (1416), one can use MR for causal inference given the specific conditions encoded in Fig. 1. In the current study, with the aim of unraveling potentially causal metabolic paths that underlie the observed associations, we used genetic predictors from published metabolite GWAS, guided by pathway-based evidence to select instrumental variables, and performed MR between selected metabolic markers and glucose/type 2 diabetes.

Figure 1

Overview of the MR process. T2DM, type 2 diabetes.

Figure 1

Overview of the MR process. T2DM, type 2 diabetes.

Study Population

The observational associations between metabolites and fasting glucose/type 2 diabetes were tested in the Erasmus Rucphen Family (ERF) study, which is a prospective family-based study with 3,465 individuals in the southwest of the Netherlands. The study protocol for ERF was approved by the medical ethics board of the Erasmus Medical Center, Rotterdam, the Netherlands (17). The baseline demographic data and measurements of the ERF participants were collected between 2002 and 2006. Venous blood samples were collected after at least 8 h fasting. The detailed description of the ERF study and related measurements were reported previously (17). Type 2 diabetes was defined according to a fasting plasma glucose ≥7.0 mmol/L and/or antidiabetes treatment. The analytical sample included 2,564 participants without diabetes and 212 participants with diabetes.

Metabolite Measurements

Metabolic markers were measured by five different metabolomics platforms using the methods that have been described in earlier publications (15,16,18,19). In total, 562 metabolic markers including subfractions of lipoproteins, triglycerides, phospholipids, ceramides, amino acids, acyl-carnitines, and small intermediate compounds, which throughout this article will be referred as “metabolites,” were measured either by NMR spectrometry or by MS. The platforms used in this research are the following: 1) liquid chromatography-MS (LC-MS) (116 positively charged lipids comprising 39 triglycerides, 47 phosphatidylcholines, 8 phosphatidylethanolamines, 20 sphingolipids, and 2 ceramides available in up to 2,638 participants) measured in the Netherlands Metabolomics Centre, Leiden, using the method described previously (18); 2) electrospray-ionization MS (ESI-MS) (in total, 148 phospholipids and sphingolipids comprising 16 plasmologens, 72 phosphatidylcholines, 27 phosphatidylethanolamines, 24 sphingolipids, and 9 ceramides available in up to 878 participants) measured in the Institute for Clinical Chemistry and Laboratory Medicine, University Hospital Regensburg, Regensburg, Germany, using the method described previously (14); 3) small molecular compounds window-based NMR spectroscopy (NMR-COMP) (41 molecules comprising 29 low–molecular weight molecules and 12 amino acids available in up to 2,639 participants) measured in the Center for Proteomics and Metabolomics, Leiden University Medical Center (19,20); 4) lipoprotein window-based NMR spectroscopy (NMR-LIPO) (104 lipoprotein particle subfractions comprising 28 VLDL components, 30 HDL components, 35 LDL components, 5 IDL components, and 6 plasma totals available in up to 2,609 participants) measured in the Center for Proteomics and Metabolomics, Leiden University Medical Center (lipoprotein subfraction concentrations were determined by the Bruker algorithm [Bruker BioSpin GmbH, Germany] [16]); and 5) AbsoluteIDQ p150 Kit of Biocrates Life Sciences AG (153 molecules comprising 14 amino acids, 91 phospholipids, 14 sphingolipids, 33 acyl-carnitines, and hexose available in up to 989 participants) measured with the experiments carried out at the Metabolomics Platform of the Genome Analysis Center at the Helmholtz Zentrum München, Germany, per manufacturer instructions (15). The laboratories had no access to phenotype information.

Statistical Methods

We assessed the pairwise partial correlation between each metabolite and each glycemic trait (i.e., fasting glucose, fasting insulin, HOMA of insulin resistance [HOMA-IR], BMI, and waist-to-hip ratio [WHR]) in the group of participants without diabetes. We included age, sex, and lipid-lowering medication as covariates. Bonferroni correction was applied based on the number of independent vectors in the data. By the matrix spectral decomposition method (21), we estimated 191 independent vectors using the pairwise bivariate correlation matrix of the 562 metabolites. A P value < 5.24 × 10−5 (0.05/191/5) adjusted by number of independent vectors and number of outcomes was used as the threshold for metabolome-wide significance. The metabolites associated with glucose in the ERF study were taken forward (n = 124) as candidates for MR. In this set of 124 metabolites, we also tested the association with type 2 diabetes using logistic regression.

MR

For each metabolite associated with glucose, we performed two-sample bidirectional MR. The same method on two-sample MR has been performed in the previous MR studies on type 2 diabetes (6,9,22). We tested whether genetically varying levels of a particular metabolite affect the risk for elevated glucose and type 2 diabetes (we call this the forward approach) and whether genetically increased risk of type 2 diabetes or elevated glucose is associated with circulating levels of a particular metabolite (we call this the backward approach). The associations between the instrumental variables and the exposure and the outcome are estimated from different studies, either the metabolite GWAS (1416,19) or fasting glucose/type 2 diabetes GWAS published by Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) and DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium (23,24), using the genetic risk score method. The effect of the genetic risk score was constructed by summing up the weighted effects of genome-wide significant SNPs on the exposure variable in relation to their effects on the outcome, as detailed in a previous publication (6). This was performed using summary statistics level data utilizing the method described by Dastani et al. (25) and implemented in the R package gtx. Figure 2 shows the overview of the instrumental variable construction. All SNPs were mapped to human genome build hg19. Given that MR assumes no pleiotropic effect beyond that on the risk factor of interest (i.e., exposure), we excluded the top SNPs from previously published BMI and WHR GWAS (26,27), as well as any SNPs within a 1-Mbp window distance of these, from the genetic score. We additionally excluded the genetic loci (1-Mbp window) of glucose, type 2 diabetes, insulin, and HOMA-IR extracted from previous publications (23,24,28) in the forward MR and the genetic loci (1-Mbp window) of the particular metabolite of interest, using the published GWAS information in the backward MR (see Supplementary Table 1 for a list of genetic loci excluded at this stage). We restricted the SNP lists to a set of independent SNPs in low linkage disequilibrium (pairwise R2 <0.05) for each test (29) based on the genotype data in ERF. SNPs with disproportionate effects in the risk score were excluded to reduce pleiotropy (see Supplementary Table 2 for a list of SNPs excluded at this stage). Genetic risk scores comprising >5 SNPs that explain >1% of variance in exposure were taken forward. This effort yielded 20 metabolite–glucose/type 2 diabetes sets in the forward MR and 76 glucose–metabolite sets and 79 type 2 diabetes–metabolite sets in the backward MR. A false discovery rate (FDR) of 0.05 was used as the significance threshold for the four series (i.e., metabolite–glucose, metabolite–type 2 diabetes, glucose–metabolite, and type 2 diabetes–metabolite series).

Figure 2

Data handling, quality checks, and exclusions during MR. *MAGIC and DIAGRAM sets are imputed based on HapMap2 and therefore do not include indels. MAF, minor allele frequency; T2DM, type 2 diabetes.

Figure 2

Data handling, quality checks, and exclusions during MR. *MAGIC and DIAGRAM sets are imputed based on HapMap2 and therefore do not include indels. MAF, minor allele frequency; T2DM, type 2 diabetes.

Pathway-Based Sensitivity Analysis

Although we applied several restrictions on the SNPs in the genetic risk scores as explained above, the instrumental variable assumption that the locus is associated with the outcome only via the association with the exposure (Fig. 1) is still hard to justify in practice. We harnessed the extensive background biological knowledge available to make the additional semiparametric assumption to get the MR estimates of the causal effect. That is, for each set, we evaluated whether we could identify the gene in proximity to the locus that could explain the change in exposure levels. If a gene codes for an enzyme that catalyzes the exposure or a related compound or if it is present in a signaling cascade that affects the exposure, we assumed that the link between the instrumental variable and the exposure was direct and not mediated by the outcome. For the forward approach, we checked the biological link between the locus and the target metabolite, and for the backward approach, we checked the link with glucose. As the pathway in the disease type 2 diabetes is complex, we did not check the biological link with type 2 diabetes in the backward approach. If the gene directly links to the exposure, the related SNPs are taken forward to calculate the genetic risk score. Next, MR is performed for any genetic risk score (comprising >5 SNPs) that explains >1% of variance in exposure. To explore potential mechanistic links between the locus and the exposure, we used an automated workflow that was developed in house to gather gene-specific knowledge of all genes in proximity to each locus. In detail, we downloaded a number of online databases from the respective FTP servers and integrated them offline in MATLAB. Subsequently, for each SNP we selected genes within a window of 100 kbp, with coordinates based on the dbSNP (30) and National Center for Biotechnology Information Gene (http://www.ncbi.nlm.nih.gov/gene; GRCh37), as well as genes whose expression is affected by the locus (GTEx-eQTL). Next, for each gene we gathered protein-related knowledge from UniProtKB (31) and affected pathways from ConsensusPathDB (32). Finally, for each protein we investigated metabolic activity by checking whether it concerned a transporter protein in TCDB (33) or enzyme in ExPASy (34) and, if so, checking what the catalyzed metabolic reaction was in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (35). For the KEGG database, the last freely available version was used. The database integration pipeline generated one HTML file for each locus, containing gene-specific knowledge and hyperlinks to the original database entries, which was then inspected to find a mechanistic link with the exposure. The strength of this approach is that it identifies loci for which the instrumental variable assumption can be validated using genetic and biochemical evidence from online databases. We have successfully applied this workflow in earlier studies (15,19,3637). Heemskerk et al. (37) give the best example of the power of our method, where we reanalyzed published results of a GWAS on metabolite levels (38) and confirmed the annotation by an in vitro experiment.

Observed Associations

Characteristics of the current study population are given in Table 1. Participants with type 2 diabetes were older and tended to be more often male and more likely to be on lipid-lowering medication. They had higher BMI, WHR, systolic blood pressure, triglycerides, fasting glucose, insulin, and HOMA-IR and lower HDL cholesterol, adiponectin, and LDL cholesterol.

Table 1

Characteristics of the study population

Control subjects, n = 2,564Case subjects, n = 212P valueP value*
Male 1,132 (44.1) 108 (50.9) 0.059 0.20 
Age (years) 48.2 ± 14.3 59.8 ± 11.8 6.4 × 10−32 2.1 × 10−12 
BMI (kg/m226.7 ± 4.6 30.0 ± 5.9 3.4 × 10−13 3.7 × 10−12 
WHR 0.86 ± 0.10 0.95 ± 0.10 9.5 × 10−27 2.6 × 10−17 
Systolic blood pressure (mmHg) 139 ± 20 154 ± 21 7.3 × 10−19 8.2 × 10−6 
Diastolic blood pressure (mmHg) 80.3 ± 10.0 82.9 ± 9.9 4.5 × 10−4 0.11 
LDL cholesterol (mmol/L) 3.8 ± 1.0 3.2 ± 1.0 4.8 × 10−15 1.0 × 10−9 
Triglycerides (mmol/L) 1.2 (0.8, 1.6) 1.6 (1.1, 1.9) 2.0 × 10−10 5.1 × 10−6 
HDL cholesterol (mmol/L) 1.3 ± 0.4 1.1 ± 0.3 2.7 × 10−11 5.6 × 10−8 
Fasting glucose (mmol/L) 4.5 ± 0.7 7.4 ± 2.2 9.4 × 10−44 1.5 × 10−54 
Fasting insulin (mU/L) 11 (8, 15) 16 (11, 22) 1.2 × 10−7 9.0 × 10−7 
HOMA-IR 2.3 (1.6, 3.1) 5.0 (3.7, 7.4) 1.5 × 10−23 2.5 × 10−24 
Lipid-lowering medication  265 (10.3) 99 (46.7) 7.2 × 10−20 1.5 × 10−22 
Control subjects, n = 2,564Case subjects, n = 212P valueP value*
Male 1,132 (44.1) 108 (50.9) 0.059 0.20 
Age (years) 48.2 ± 14.3 59.8 ± 11.8 6.4 × 10−32 2.1 × 10−12 
BMI (kg/m226.7 ± 4.6 30.0 ± 5.9 3.4 × 10−13 3.7 × 10−12 
WHR 0.86 ± 0.10 0.95 ± 0.10 9.5 × 10−27 2.6 × 10−17 
Systolic blood pressure (mmHg) 139 ± 20 154 ± 21 7.3 × 10−19 8.2 × 10−6 
Diastolic blood pressure (mmHg) 80.3 ± 10.0 82.9 ± 9.9 4.5 × 10−4 0.11 
LDL cholesterol (mmol/L) 3.8 ± 1.0 3.2 ± 1.0 4.8 × 10−15 1.0 × 10−9 
Triglycerides (mmol/L) 1.2 (0.8, 1.6) 1.6 (1.1, 1.9) 2.0 × 10−10 5.1 × 10−6 
HDL cholesterol (mmol/L) 1.3 ± 0.4 1.1 ± 0.3 2.7 × 10−11 5.6 × 10−8 
Fasting glucose (mmol/L) 4.5 ± 0.7 7.4 ± 2.2 9.4 × 10−44 1.5 × 10−54 
Fasting insulin (mU/L) 11 (8, 15) 16 (11, 22) 1.2 × 10−7 9.0 × 10−7 
HOMA-IR 2.3 (1.6, 3.1) 5.0 (3.7, 7.4) 1.5 × 10−23 2.5 × 10−24 
Lipid-lowering medication  265 (10.3) 99 (46.7) 7.2 × 10−20 1.5 × 10−22 

Data are mean ± SD, median (interquartile range), or n (%). Triglycerides, fasting insulin, adiponectin, and HOMA-IR were natural logarithm transformed prior to analysis. P value: t test and χ2 test were used in continuous variables and categorical variables, respectively.

*Logistic regression was used with adjusting age, sex, and lipid-lowering medication.

We identified 124 metabolites that observationally associate with fasting glucose in the control population with P value < 5.24 × 10−5 (Fig. 3). These consisted of 36 phospholipids (Fig. 3A), 20 triglycerides (Fig. 3B), 24 small molecular compounds (Fig. 3C), and 44 lipoprotein particle subfractions (Fig. 3D). Correlation coefficients and P values as well as the overlap with previous research for all 124 metabolites are given in Supplementary Table 3. A clustered heatmap of correlation structure in between the 124 selected metabolites is shown in Supplementary Fig. 1. Among the 124, 112 of them also associated with type 2 diabetes (P < 0.05) and their associations with type 2 diabetes and glucose were in the same direction. In addition, their associations with BMI, WHR, fasting insulin, and HOMA-IR were in line with the direction of their associations with glucose. Out of the 124 metabolites, 90 correlated positively and 34 correlated negatively with fasting glucose. We observed negative correlation between glucose and alkyl-acyl and diacyl phosphatidylcholines (mostly of the polyunsaturated type), lysophosphatidylcholines (mostly of the saturated type), and parts of the lipoprotein subfractions from LDL and HDL. These lipoprotein subfractions particularly consisted of lipid components of extra large (XL) and large (L) LDL particles and XL-HDL and L-HDL particles, as well as total HDL measurements. The second cluster of metabolites that we observed to correlate positively with glucose included several phospholipids, phosphotidylcholines, phosphatidylethanolamines, and lysophosphatidylcholines. Amino acids and low–molecular weight compounds also correlated positively with glucose, in addition to lipid side groups and triglycerides. Finally, from the lipoprotein subfractions, small (S), extra small (XS), medium (M), and XL-VLDL particles, as well as the total VLDL components, followed by IDL and LDL triglycerides, XS-LDL to M-LDL particle components, and the ApoA1 and triglyceride content of S-HDL particles were correlated positively with fasting glucose in the population without diabetes.

Figure 3

Metabolites correlated with markers of type 2 diabetes (T2DM) and anthropometric risk factors. A: Phosphatidylcholines. B: Triglycerides. C: Small molecules and amino acids. D: Lipoproteins. The associations between metabolites and continuous variables were performed by partial correlation in the population without diabetes. The color in the figure displays the value of correlation coefficient. The associations between metabolites and type 2 diabetes status were performed by logistic regression. The color in the figure displays the standardized effect of metabolites on type 2 diabetes. Age, sex, and lipid-lowering medication are considered as covariates. *0.05 < P value < 5.24 × 10−5 (0.05/191/5). •P value < 0.05 and P value ≥ 5.24 × 10−5. (B): selected measurement is from the Biocrates platform when the same metabolite is also captured by the LC-MS/NMR-COMP/NMR-LIPO platform. (E): selected measurement is from the ESI-MS platform when the same metabolite is also captured by the LC-MS platform.

Figure 3

Metabolites correlated with markers of type 2 diabetes (T2DM) and anthropometric risk factors. A: Phosphatidylcholines. B: Triglycerides. C: Small molecules and amino acids. D: Lipoproteins. The associations between metabolites and continuous variables were performed by partial correlation in the population without diabetes. The color in the figure displays the value of correlation coefficient. The associations between metabolites and type 2 diabetes status were performed by logistic regression. The color in the figure displays the standardized effect of metabolites on type 2 diabetes. Age, sex, and lipid-lowering medication are considered as covariates. *0.05 < P value < 5.24 × 10−5 (0.05/191/5). •P value < 0.05 and P value ≥ 5.24 × 10−5. (B): selected measurement is from the Biocrates platform when the same metabolite is also captured by the LC-MS/NMR-COMP/NMR-LIPO platform. (E): selected measurement is from the ESI-MS platform when the same metabolite is also captured by the LC-MS platform.

MR

Table 2 shows the significant results from the association of the relevant metabolites with fasting glucose using MR. Among the 20 eligible metabolite–glucose/type 2 diabetes sets, genetically decreased levels of 8 metabolites associated significantly with fasting glucose (FDR <0.05). These include XL-HDL cholesterol (FDR = 0.03), XL-HDL phospholipids (FDR = 2.76 × 10−3), XS-VLDL phospholipids (FDR = 0.04), XL-HDL–free cholesterol (FDR = 0.01), L-HDL cholesterol (FDR = 0.01), L-HDL–free cholesterol (FDR = 2.76 × 10−3), HDL cholesterol (FDR = 0.04), and IDL phospholipids (FDR = 0.04). After the pathway-based subset analysis, a causal role for IDL phospholipids was not supported (FDR = 0.17). At the same time, pathway-based sensitivity analysis revealed possibly causal roles for three additional metabolic markers, including S-VLDL triglycerides (FDR = 0.04), S-HDL triglycerides (FDR = 0.04), and plasma triglycerides (FDR = 0.04). Table 3 shows the suggested causal effects of metabolites on type 2 diabetes, i.e., XS-VLDL phospholipids, IDL phospholipids, and plasma triglycerides. Interestingly, the statistical significance for both XS-VLDL phospholipids and IDL phospholipids in the initial results is filtered out after the sensitivity analysis (FDR: XS-VLDL phospholipids 0.03 vs. 0.31; IDL phospholipids 0.01 vs. 0.24), while plasma triglycerides shift to being borderline significant (FDR = 0.07 vs. 0.046). The results from the full lists of performed forward MR tests are given in Supplementary Table 4, and the SNPs included in all the genetic risk scores are given in Supplementary Table 5.

Table 2

MR of metabolites (exposure) on fasting glucose (outcome)

ExposureOutcome
Fasting glucose
Fasting glucose*
R2 (%)nβFDRR2 (%)nβFDR
S-VLDL triglycerides
 
4.80
 
13
 
0.06
 
0.08
 
3.92
 
10
 
0.08
 
0.04
 
XS-VLDL phospholipids
 
7.97
 
23
 
−0.06
 
0.04
 
6.30
 
15
 
−0.07
 
0.04
 
IDL phospholipids
 
7.16
 
26
 
−0.06
 
0.04
 
4.84
 
15
 
−0.05
 
0.17
 
XL-HDL cholesterol
 
4.25
 
10
 
−0.09
 
0.03
 
4.25
 
10
 
−0.09
 
0.03
 
XL-HDL–free cholesterol
 
6.48
 
16
 
−0.09
 
0.01†
 
6.48
 
16
 
−0.09
 
0.01†
 
XL-HDL phospholipids
 
10.21
 
22
 
−0.08
 
2.76 × 10−3
 
9.61
 
19
 
−0.09
 
1.72 × 10−3
 
L-HDL cholesterol
 
7.58
 
17
 
−0.08
 
0.01†
 
7.41
 
16
 
−0.08
 
0.01†
 
L-HDL–free cholesterol
 
7.58
 
18
 
−0.09
 
2.76 × 10−3
 
7.27
 
16
 
−0.10
 
1.72 × 10−3
 
HDL cholesterol
 
4.84
 
10
 
−0.07
 
0.04
 
4.67
 
9
 
−0.07
 
0.04
 
S-HDL triglycerides
 
3.97
 
11
 
0.07
 
0.08
 
3.52
 
9
 
0.09
 
0.04
 
Plasma triglycerides 3.93 11 0.07 0.08 2.78 0.10 0.04 
ExposureOutcome
Fasting glucose
Fasting glucose*
R2 (%)nβFDRR2 (%)nβFDR
S-VLDL triglycerides
 
4.80
 
13
 
0.06
 
0.08
 
3.92
 
10
 
0.08
 
0.04
 
XS-VLDL phospholipids
 
7.97
 
23
 
−0.06
 
0.04
 
6.30
 
15
 
−0.07
 
0.04
 
IDL phospholipids
 
7.16
 
26
 
−0.06
 
0.04
 
4.84
 
15
 
−0.05
 
0.17
 
XL-HDL cholesterol
 
4.25
 
10
 
−0.09
 
0.03
 
4.25
 
10
 
−0.09
 
0.03
 
XL-HDL–free cholesterol
 
6.48
 
16
 
−0.09
 
0.01†
 
6.48
 
16
 
−0.09
 
0.01†
 
XL-HDL phospholipids
 
10.21
 
22
 
−0.08
 
2.76 × 10−3
 
9.61
 
19
 
−0.09
 
1.72 × 10−3
 
L-HDL cholesterol
 
7.58
 
17
 
−0.08
 
0.01†
 
7.41
 
16
 
−0.08
 
0.01†
 
L-HDL–free cholesterol
 
7.58
 
18
 
−0.09
 
2.76 × 10−3
 
7.27
 
16
 
−0.10
 
1.72 × 10−3
 
HDL cholesterol
 
4.84
 
10
 
−0.07
 
0.04
 
4.67
 
9
 
−0.07
 
0.04
 
S-HDL triglycerides
 
3.97
 
11
 
0.07
 
0.08
 
3.52
 
9
 
0.09
 
0.04
 
Plasma triglycerides 3.93 11 0.07 0.08 2.78 0.10 0.04 

The MR sets with FDR <0.05 with respect to either outcome are shown. R2 (%): the percentage of explained variance in the exposure by genetic risk score. n: the number of SNPs in the genetic risk score. β: the weighted effect of the genetic risk score of exposure on outcome. FDR: an FDR on the number of MR sets adjusted P value.

*Results of pathway-based analysis.

†The MR sets with P value < Bonferroni P value 2.5 × 10−3 (0.05/20).

Table 3

MR of metabolites (exposure) on type 2 diabetes (outcome)

ExposureOutcome
Type 2 diabetes
Type 2 diabetes*
R2 (%)nβFDRR2 (%)nβFDR
XS-VLDL phospholipids
 
8.02
 
23
 
−0.08
 
0.03
 
6.34
 
15
 
−0.06
 
0.31
 
IDL phospholipids
 
7.18
 
26
 
−0.09
 
0.01
 
4.86
 
15
 
−0.07
 
0.24
 
Plasma triglycerides 4.21 12 0.08 0.07 3.16 0.12 0.046 
ExposureOutcome
Type 2 diabetes
Type 2 diabetes*
R2 (%)nβFDRR2 (%)nβFDR
XS-VLDL phospholipids
 
8.02
 
23
 
−0.08
 
0.03
 
6.34
 
15
 
−0.06
 
0.31
 
IDL phospholipids
 
7.18
 
26
 
−0.09
 
0.01
 
4.86
 
15
 
−0.07
 
0.24
 
Plasma triglycerides 4.21 12 0.08 0.07 3.16 0.12 0.046 

The MR sets with either FDR <0.05 are shown. R2 (%): the percentage of explained variance in the exposure by genetic risk score. n: the number of SNPs in the genetic risk score. β: the weighted effect of the genetic risk score of exposure on outcome. FDR: an FDR on the number of MR sets adjusted P value.

*Results of pathway-based analysis.

†The MR sets with P value < Bonferroni P value 2.5 × 10−3 (0.05/20).

The significant results of the backward MR are shown in Table 4. We found that genetic predisposition to type 2 diabetes is associated with lower levels of phosphatidylcholine alkyl-acyl 42:5 (FDR = 0.02) and phosphatidylcholine alkyl-acyl 44:4 (FDR = 0.02) and higher levels of alanine (FDR = 0.02). The details of all the tested SNP sets are shown in Supplementary Table 6 and Supplementary Table 7. No possible causal role for glucose was supported. As the genetic risk scores of the glucose explained <1% of variance, the backward MR with pathway analysis was not performed. Figure 4 displays the suggested paths discovered by the MR approach after the pathway-based sensitivity analysis. Overall, the associations estimated by MR were in the consistent direction with the observed associations in the ERF study.

Table 4

MR of fasting glucose/type 2 diabetes (exposure) on metabolites (outcome)

OutcomeExposure
Fasting glucose
Type 2 diabetes
R2 (%)nβFDRR2 (%)nβFDR
PC alkyl-acyl C42:5
 
0.83
 
13
 
NP
 
NP
 
1.51
 
32
 
−0.08
 
0.02
 
PC alkyl-acyl C44:4
 
1.10
 
15
 
0.02
 
0.95
 
1.51
 
32
 
−0.08
 
0.02
 
Alanine 1.06 14 0.06 0.48 1.48 31 0.08 0.02 
OutcomeExposure
Fasting glucose
Type 2 diabetes
R2 (%)nβFDRR2 (%)nβFDR
PC alkyl-acyl C42:5
 
0.83
 
13
 
NP
 
NP
 
1.51
 
32
 
−0.08
 
0.02
 
PC alkyl-acyl C44:4
 
1.10
 
15
 
0.02
 
0.95
 
1.51
 
32
 
−0.08
 
0.02
 
Alanine 1.06 14 0.06 0.48 1.48 31 0.08 0.02 

The MR sets with either FDR <0.05 are shown. R2 (%): the percentage of explained variance in the exposure by genetic risk score. n: the number of SNPs in the genetic risk score. β: the weighted effect of the genetic risk score of exposure on outcome. FDR: an FDR on the number of MR sets adjusted P value. NP, not performed; PC, phosphatidylcholine.

†The MR sets with P value < Bonferroni P value 6.33 × 10−4 (0.05/79).

Figure 4

Suggested causal paths for glucose homeostasis and type 2 diabetes (T2DM) after pathway-based sensitivity analysis. C, cholesterol; FC, free cholesterol; FG, fasting glucose; P, phospholipids; PCae, phosphatidylcholine alkyl-acyl; TG, triglycerides. The gene names above the metabolite names indicate the loci where the SNPs used in the genetic risk score are located.

Figure 4

Suggested causal paths for glucose homeostasis and type 2 diabetes (T2DM) after pathway-based sensitivity analysis. C, cholesterol; FC, free cholesterol; FG, fasting glucose; P, phospholipids; PCae, phosphatidylcholine alkyl-acyl; TG, triglycerides. The gene names above the metabolite names indicate the loci where the SNPs used in the genetic risk score are located.

We selected 124 metabolites that are correlated with glucose in the population without diabetes, and using MR, we tested whether this metabolic profile points to any causal paths involved in glucose level or type 2 diabetes. Combining metabolomics and MR, we detected 14 candidate causal associations: 10 metabolites influencing fasting glucose, 1 influencing type 2 diabetes, and 3 influenced by type 2 diabetes.

Our initial observational association tests yielded correlation estimates within the expected range of power calculations for the 124 glucose-associated metabolites. To our knowledge, 35 of these metabolites were previously shown to be associated with glucose or type 2 diabetes, including 31 concordant and 4 discordant results (Supplementary Table 3) in studies with very limited sample size (39,40). Our significant results on subfractions of lipoproteins yielded resolution on the established association of dyslipidemia, especially for the HDL subfractions.

One of our main findings is that genetically increased cholesterol and free cholesterol contents of circulating XL-HDL and L-HDL particles, XL-HDL phospholipids, and HDL cholesterol, together with XS-VLDL phospholipids, associate with decreased glucose levels. Our second finding is that triglyceride contents of S-HDL and S-VLDL particles, as well as total plasma triglycerides, seem to have a glucose-increasing causal effect and, considering the total triglycerides, this effect has been extended to the outcome type 2 diabetes. Finally, we showed that genetic predisposition to type 2 diabetes associates with lower levels of two alkyl-acyl phosphatidylcholines and a higher level of alanine. Our report is the first using higher-resolution (metabolomics-driven), lipoprotein-based exposure variables. Hence, no other study exists for comparison, although HDL cholesterol, LDL cholesterol, and total triglycerides (from routine biochemistry) have been previously studied as exposure variables for MR in order to understand their causal effects on type 2 diabetes and glucose (an overview is given in Table 5). Our method is similar to the method of White et al. (9) and Fall et al. (6) in terms of the application of the genetic risk score function utilizing the DIAGRAM/MAGIC data sets. White et al. (9) showed that high levels of all three blood lipids (HDL cholesterol, LDL cholesterol, and plasma triglycerides) were genetically associated with a lower risk of diabetes, although the results for triglycerides were inconsistent. However, the study did not consider the genetic variants that might be involved in the confounding phenotypes such as BMI or WHR, nor did it exclude the SNPs that are involved in type 2 diabetes directly. Fall et al. (6) showed that the association between total HDL cholesterol risk score and low fasting glucose was attenuated when adjusted for the effects of SNPs on LDL cholesterol, triglycerides, and surrogates of adiposity. Different from the two studies mentioned above, the MR in our study was done in a broad spectrum of metabolites and included a detailed subclassification of lipoproteins that have not been tested before. Using such high-resolution phenotypes, we demonstrate that the decreasing effect of HDL cholesterol on fasting glucose is more specific to the L-HDL or XL-HDL subclasses, whereas for S-HDL triglycerides, an increasing effect exists. These results advocate that a higher resolution of high density in lipoproteins may reveal the observed epidemiological associations or biological functions of HDL cholesterol more accurately and will uncover the mystery of complex lipids such as HDL. Certain HDL subfractions and characteristics of these subfractions may have independent associations with glucose, particularly for the small versus large size particles. Such a different role for HDL triglycerides and HDL large fractions occurred upon sleeve gastrectomy of obese patients and was associated with reduced insulin resistance and HDL remodeling (41). In addition, as experimentally shown, HDL indeed may mediate glucose regulation in the pathophysiology of type 2 diabetes (42). Suggested mechanisms include the following (43): 1) insulin secretion from pancreatic β-cells combating cellular lipid accumulation and lipotoxicity (44) and endoplasmic reticulum stress and apoptosis (45,46), 2) insulin-independent glucose uptake by muscle via the AMP-activated protein kinase (47) and calcium/calmodulin activated protein kinase (48), and 3) insulin sensitivity (49). The Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events (ILLUMINATE) trial (50) demonstrated that in a subgroup of participants with diabetes, statin treatment led to increased glucose levels, while this effect was not observed in participants treated with combination of statin and CETP inhibitor torcetrapib, suggesting that CETP inhibition and consequent HDL cholesterol elevation may improve glycemic control in patients with diabetes. It is of note that the CETP gene is a major determinant of XL-HDL and was included in our MR experiment.

Table 5

Review of the previous MR in metabolites or lipids and type 2 diabetes or glucose

StudyMethodsExposureOutcomeOR/β (95% CI)P valueInstrumental variables and pleiotropy control
Lotta et al., 2016 (22)
 
Two-sample MR
 
Isoleucine
 
T2DM (n = 315,571)
 
1.44 (1.22, 1.17)
 
2.0 × 10−5
 
1) Independent SNPs from GWAS meta-analysis.
2) Control for pleiotropy.
 
Leucine
 
1.73 (1.28, 2.34)
 
3.4 × 10−4
 
Valine
 
1.45 (1.18, 1.77)
 
3.4 × 10−5
 
Marott et al., 2016 (8)
 
Two-stage least-squares regression
 
HDL-C
 
T2DM (n = 93,097)
 
0.86 (0.43, 1.72)
 
0.68
 
3 variants from ABCA1, CETP.
 
TG
 
T2DM (n = 97,199)
 
1.05 (0.88, 1.24)
 
0.60
 
4 variants from TRIB1, APOA5, LPL.
 
White et al., 2016 (9)
 
Conventional two-sample MR; multivariate MR; MR-Egger
 
LDL-C
 
T2DM (DIAGRAM)
 
0.79 (0.71, 0.88)
 
P < 0.05
 
1) Independent SNPs from GLGC GWAS.
2) gtx package with pleiotropic control.
 
HDL-C
 
0.83 (0.76, 0.90)
 
P < 0.05
 
TG
 
0.83 (0.72, 0.95)*
 
P < 0.05
 
Haase et al., 2015 (5)
 
Two-stage least-squares regression
 
HDL-C
 
T2DM (n = 47,627)
 
0.93 (0.78, 1.11)
 
0.42
 
9 variants from ABCA1, CETP, LCAT, LIPC, APOA1.
 
Fall et al., 2015 (6)
 
Two-sample MR
 
LDL-C
 
T2DM (DIAGRAM)
 
−0.03 (−0.19, 0.12)*
 
0.67
 
1) Independent SNPs with large effect on the lipid and smaller effect on other lipid fractions from GLGC GWAS.
2) gtx package with pleiotropic control.
 
FG (MAGIC)
 
0 (−0.03, 0.03)*
 
0.85
 
HDL-C
 
T2DM (DIAGRAM)
 
−0.19 (−0.38, −0.01)*
 
0.04
 
FG (MAGIC)
 
−0.02 (−0.06, 0.01)*
 
0.24
 
Andersson et al., 2015 (10)
 
Two-stage least-squares regression
 
LDL-C
 
Incident T2DM
 
0.85 (0.76, 0.96)
 
0.009
 
GRS from 37 LDL-C–related SNPs without any pleiotropic control.
 
Islam et al., 2012 (11)
 
Two-stage least-squares regression
 
TG
 
T2DM (n = 2,111)
 
0.04 (0.014, 0.072)*
 
0.004
 
Included 10 independent SNPs from previous studies (excluded FADS1 and GCKR).
 
De Silva et al., 2011 (7Two-stage least-squares regression TG T2DM (n = 8,335)
 
0.99 (0.97, 1.01)
 
0.26
 
Included 10 independent SNPs from previous studies (excluded FADS1 and GCKR). 
FG (n = 8,271) 0 (−0.01, 0.01)* 0.88 
StudyMethodsExposureOutcomeOR/β (95% CI)P valueInstrumental variables and pleiotropy control
Lotta et al., 2016 (22)
 
Two-sample MR
 
Isoleucine
 
T2DM (n = 315,571)
 
1.44 (1.22, 1.17)
 
2.0 × 10−5
 
1) Independent SNPs from GWAS meta-analysis.
2) Control for pleiotropy.
 
Leucine
 
1.73 (1.28, 2.34)
 
3.4 × 10−4
 
Valine
 
1.45 (1.18, 1.77)
 
3.4 × 10−5
 
Marott et al., 2016 (8)
 
Two-stage least-squares regression
 
HDL-C
 
T2DM (n = 93,097)
 
0.86 (0.43, 1.72)
 
0.68
 
3 variants from ABCA1, CETP.
 
TG
 
T2DM (n = 97,199)
 
1.05 (0.88, 1.24)
 
0.60
 
4 variants from TRIB1, APOA5, LPL.
 
White et al., 2016 (9)
 
Conventional two-sample MR; multivariate MR; MR-Egger
 
LDL-C
 
T2DM (DIAGRAM)
 
0.79 (0.71, 0.88)
 
P < 0.05
 
1) Independent SNPs from GLGC GWAS.
2) gtx package with pleiotropic control.
 
HDL-C
 
0.83 (0.76, 0.90)
 
P < 0.05
 
TG
 
0.83 (0.72, 0.95)*
 
P < 0.05
 
Haase et al., 2015 (5)
 
Two-stage least-squares regression
 
HDL-C
 
T2DM (n = 47,627)
 
0.93 (0.78, 1.11)
 
0.42
 
9 variants from ABCA1, CETP, LCAT, LIPC, APOA1.
 
Fall et al., 2015 (6)
 
Two-sample MR
 
LDL-C
 
T2DM (DIAGRAM)
 
−0.03 (−0.19, 0.12)*
 
0.67
 
1) Independent SNPs with large effect on the lipid and smaller effect on other lipid fractions from GLGC GWAS.
2) gtx package with pleiotropic control.
 
FG (MAGIC)
 
0 (−0.03, 0.03)*
 
0.85
 
HDL-C
 
T2DM (DIAGRAM)
 
−0.19 (−0.38, −0.01)*
 
0.04
 
FG (MAGIC)
 
−0.02 (−0.06, 0.01)*
 
0.24
 
Andersson et al., 2015 (10)
 
Two-stage least-squares regression
 
LDL-C
 
Incident T2DM
 
0.85 (0.76, 0.96)
 
0.009
 
GRS from 37 LDL-C–related SNPs without any pleiotropic control.
 
Islam et al., 2012 (11)
 
Two-stage least-squares regression
 
TG
 
T2DM (n = 2,111)
 
0.04 (0.014, 0.072)*
 
0.004
 
Included 10 independent SNPs from previous studies (excluded FADS1 and GCKR).
 
De Silva et al., 2011 (7Two-stage least-squares regression TG T2DM (n = 8,335)
 
0.99 (0.97, 1.01)
 
0.26
 
Included 10 independent SNPs from previous studies (excluded FADS1 and GCKR). 
FG (n = 8,271) 0 (−0.01, 0.01)* 0.88 

FG, fasting glucose; HDL-C, HDL cholesterol; LDL-C, LDL cholesterol; TG, triglycerides; T2DM, type 2 diabetes.

*β (95% CI).

We have detected three associations potentially pointing out an influence of type 2 diabetes over the metabolome. The first two are long-chain polyunsaturated alkyl-acyl phosphatidylcholines that are decreased in type 2 diabetes. This is interesting considering our previous report, which showed that three shorter-chain polyunsaturated alkyl-acyl phosphatidylcholines are increased in patients with type 2 diabetes and decreased in patients using the glucose-lowering drug metformin (51). The other molecule affected by diabetes was alanine, which is a nonessential amino acid and can be synthesized in the body from pyruvate and branched-chain amino acids such as valine, leucine, and isoleucine. Alanine has been previously implicated in glucose response (52). The enzyme alanine aminotransferase (ALT) catalyzes the conversion of alanine to pyruvate and glutamate, and high levels of ALT indicate liver damage.

Our study differs from previous reports in three ways. First, we used a bidirectional approach and included a wide range of molecular markers to be tested, using high-resolution phenotypes, measured by MS or NMR. Second, we exploited pathway knowledge that was gathered through an automated workflow to perform subset analysis in MR. Statistical methods that deal with pleiotropy in MR analyses, such as the Egger regression method (9,53,54), exist but are still being refined. They all rely on additional strong assumptions about the unobserved pleiotropy, such as the InSIDE assumption, and are sensitive to violations of these assumptions. They also suffer from a lack of power. However, one can harness the available genetic and biological knowledge in online databases in order to maximize the uniqueness of the genetic risk score for the exposure variable for this purpose and to validate the chosen instruments. It has to be mentioned that although powerful for most metabolites, our approach with the genetic and biological knowledge is also conservative because it ultimately relies on the comprehensiveness of the content of the databases that are included. As a consequence, all loci for which no strong evidence is present that a nearby gene directly affects the exposure, e.g., because the involved gene is affected through a yet-unknown regulatory mechanism, are excluded. Considering glucose for which the instrumental strength was initially lower compared with the others, the pathway approach yielded lower explained variance in exposure (R2 <1%). While one can argue that this would lead to lack of power, it may also reflect the fact that polygenic traits such as glucose may not be the most suitable exposure variables for an MR analysis. To limit this, we utilized the large population–based GWAS of broad-spectrum metabolites and fasting glucose/type 2 diabetes with the combined-instrument MR approach (25). We want to point out that although we controlled the pleiotropic effects between the outcome and exposure by 1) excluding the known predictors, 2) heterogeneity tests, and finally 3) pathway analysis, we cannot exclude a correlation between the genetic instruments tested, especially for the HDL subfractions, for which the coding genes overlap. While effect alleles were weighted by their original effects estimates from each GWAS (of exposure variables), there was strong overlap in the SNPs used for different lipid subfractions, meaning the genetic instruments were not highly specific to these subfractions.

In conclusion, using MR, the current study provides evidence for potentially causal metabolic paths of glucose homeostasis and type 2 diabetes. Our results indicate that an increase of large HDL particles might have a decreasing effect on glucose, while an increase of small HDL particles might have an increasing effect, refining earlier MR findings suggesting a possible causal effect of HDL on glucose levels as well as pointing out these particles as targets for glucose management. We further found evidence that type 2 diabetes may alter levels of alkyl-acyl phosphatidylcholines and alanine, which also here can be translated into prevention of disease complications and prognosis.

Acknowledgments. The authors are grateful to all study participants and their relatives, general practitioners, and neurologists for their contributions to the ERF study; P. Veraart (Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands) for her help in genealogy; J. Vergeer (Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands) for the supervision of the laboratory work; and P. Snijders (Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands) for his help in data collection.

Funding. The ERF study was supported by the Netherlands Consortium for Systems Biology, within the framework of the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research. The ERF study as a part of the European Special Populations Research Network was supported by European Commission Sixth Framework Programme STRP grant no. 018947 (LSHG-CT-2006-01947) and also received funding from the European Community Seventh Framework Programme (FP7/2007-2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission under the program “Quality of Life and Management of the Living Resources” of Fifth Framework Programme (no. QLG2-CT-2002-01254), as well as FP7 project EUROHEADPAIN (no. 602633). High-throughput genetic analysis of the ERF data was supported by a joint grant from the Netherlands Organisation for Scientific Research and the Russian Foundation for Basic Research (NWO-RFBR 047.017.043). High-throughput metabolomics measurements of the ERF study have been supported by Biobanking and BioMolecular resources Research Infrastructure the Netherlands (BBMRI-NL). Biocrates platform measurements were supported by the European Community's Seventh Framework Programme (FP7/2007–2013), ENGAGE Consortium, grant agreement HEALTH-F4-2007-201413. Lipidomics analysis was supported by the European Commission FP7 grant LipidomicNet (2007-202272). J.L., C.M.v.D., and A.D. have used exchange grants from the Personalized pREvention of Chronic DIseases consortium (PRECeDI). S.S. has been awarded the Erasmus Mundus–Western Balkans (ERAWEB) mobility program academic scholarship. A.D. is supported by a Veni grant (2015) from ZonMw.

The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Author Contributions. J.L. and J.B.v.K. analyzed the data. J.L., J.B.v.K., S.S., K.W.v.D., A.V., T.H., A.C.H., E.S., N.A.S., C.M.v.D., and A.D. explained the results and wrote the manuscript. A.V., T.H., and A.C.H. generated the metabolomics data. C.M.v.D. and A.D. designed the study. J.L. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

1.
Floegel
A
,
Stefan
N
,
Yu
Z
, et al
.
Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach
.
Diabetes
2013
;
62
:
639
648
[PubMed]
2.
Kotronen
A
,
Velagapudi
VR
,
Yetukuri
L
, et al
.
Serum saturated fatty acids containing triacylglycerols are better markers of insulin resistance than total serum triacylglycerol concentrations
.
Diabetologia
2009
;
52
:
684
690
[PubMed]
3.
Roberts
LD
,
Koulman
A
,
Griffin
JL
.
Towards metabolic biomarkers of insulin resistance and type 2 diabetes: progress from the metabolome
.
Lancet Diabetes Endocrinol
2014
;
2
:
65
75
[PubMed]
4.
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]
5.
Haase
CL
,
Tybjærg-Hansen
A
,
Nordestgaard
BG
,
Frikke-Schmidt
R
.
HDL cholesterol and risk of type 2 diabetes: a Mendelian randomization study
.
Diabetes
2015
;
64
:
3328
3333
[PubMed]
6.
Fall
T
,
Xie
W
,
Poon
W
, et al.;
GENESIS Consortium
.
Using genetic variants to assess the relationship between circulating lipids and type 2 diabetes
.
Diabetes
2015
;
64
:
2676
2684
[PubMed]
7.
De Silva
NM
,
Freathy
RM
,
Palmer
TM
, et al
.
Mendelian randomization studies do not support a role for raised circulating triglyceride levels influencing type 2 diabetes, glucose levels, or insulin resistance
.
Diabetes
2011
;
60
:
1008
1018
[PubMed]
8.
Marott
SC
,
Nordestgaard
BG
,
Tybjærg-Hansen
A
,
Benn
M
.
Components of the metabolic syndrome and risk of type 2 diabetes
.
J Clin Endocrinol Metab
2016
;
101
:
3212
3221
[PubMed]
9.
White J, Swerdlow DI, Preiss D, et al. Association of lipid fractions with risks for coronary artery disease and diabetes. JAMA Cardiol 2016;1:692–699
10.
Andersson
C
,
Lyass
A
,
Larson
MG
,
Robins
SJ
,
Vasan
RS
.
Low-density-lipoprotein cholesterol concentrations and risk of incident diabetes: epidemiological and genetic insights from the Framingham Heart Study
.
Diabetologia
2015
;
58
:
2774
2780
[PubMed]
11.
Islam
M
,
Jafar
TH
,
Wood
AR
, et al
.
Multiple genetic variants explain measurable variance in type 2 diabetes-related traits in Pakistanis
.
Diabetologia
2012
;
55
:
2193
2204
[PubMed]
12.
Moreno-Gordaliza E, van der Lee SJ, Demirkan A, et al.
A novel method for serum lipoprotein profiling using high performance capillary isotachophoresis
.
Anal Chim Acta
2016
;
944
:
57
59
13.
Suna
T
,
Salminen
A
,
Soininen
P
, et al
.
1H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self-organising maps
.
NMR Biomed
2007
;
20
:
658
672
[PubMed]
14.
Demirkan
A
,
van Duijn
CM
,
Ugocsai
P
, et al.;
DIAGRAM Consortium
;
CARDIoGRAM Consortium
;
CHARGE Consortium
;
EUROSPAN consortium
.
Genome-wide association study identifies novel loci associated with circulating phospho- and sphingolipid concentrations
.
PLoS Genet
2012
;
8
:
e1002490
[PubMed]
15.
Draisma
HHM
,
Pool
R
,
Kobl
M
, et al
.
Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels
.
Nat Commun
2015
;
6
:
7208
[PubMed]
16.
Kettunen
J
,
Demirkan
A
,
Würtz
P
, et al
.
Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA
.
Nat Commun
2016
;
7
:
11122
[PubMed]
17.
Santos
RL
,
Zillikens
MC
,
Rivadeneira
FR
, et al
.
Heritability of fasting glucose levels in a young genetically isolated population
.
Diabetologia
2006
;
49
:
667
672
[PubMed]
18.
Gonzalez-Covarrubias
V
,
Beekman
M
,
Uh
HW
, et al
.
Lipidomics of familial longevity
.
Aging Cell
2013
;
12
:
426
434
[PubMed]
19.
Demirkan
A
,
Henneman
P
,
Verhoeven
A
, et al
.
Insight in genome-wide association of metabolite quantitative traits by exome sequence analyses
.
PLoS Genet
2015
;
11
:
e1004835
[PubMed]
20.
Verhoeven
A
,
Slagboom
E
,
Wuhrer
M
,
Giera
M
,
Mayboroda
OA
.
Automated quantification of metabolites in blood-derived samples by NMR
.
Anal Chim Acta
2017
;
976
:
52
62
[PubMed]
21.
Li
J
,
Ji
L
.
Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix
.
Heredity (Edinb)
2005
;
95
:
221
227
[PubMed]
22.
Lotta
LA
,
Scott
RA
,
Sharp
SJ
, et al
.
Genetic predisposition to an impaired metabolism of the branched-chain amino acids and risk of type 2 diabetes: a Mendelian randomisation analysis
.
PLoS Med
2016
;
13
:
e1002179
[PubMed]
23.
Scott
RA
,
Lagou
V
,
Welch
RP
, et al.;
DIAbetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium
.
Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways
.
Nat Genet
2012
;
44
:
991
1005
[PubMed]
24.
Morris
AP
,
Voight
BF
,
Teslovich
TM
, et al.;
Wellcome Trust Case Control Consortium
;
Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) Investigators
;
Genetic Investigation of ANthropometric Traits (GIANT) Consortium
;
Asian Genetic Epidemiology Network–Type 2 Diabetes (AGEN-T2D) Consortium
;
South Asian Type 2 Diabetes (SAT2D) Consortium
;
DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium
.
Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes
.
Nat Genet
2012
;
44
:
981
990
[PubMed]
25.
Dastani
Z
,
Hivert
MF
,
Timpson
N
, et al.;
DIAGRAM+ Consortium
;
MAGIC Consortium
;
GLGC Investigators
;
MuTHER Consortium
;
DIAGRAM Consortium
;
GIANT Consortium
;
Global B Pgen Consortium
;
Procardis Consortium
;
MAGIC investigators
;
GLGC Consortium
.
Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals
.
PLoS Genet
2012
;
8
:
e1002607
[PubMed]
26.
Locke
AE
,
Kahali
B
,
Berndt
SI
, et al.;
LifeLines Cohort Study
;
ADIPOGen Consortium
;
AGEN-BMI Working Group
;
CARDIOGRAMplusC4D Consortium
;
CKDGen Consortium
;
GLGC
;
ICBP
;
MAGIC Investigators
;
MuTHER Consortium
;
MIGen Consortium
;
PAGE Consortium
;
ReproGen Consortium
;
GENIE Consortium
;
International Endogene Consortium
.
Genetic studies of body mass index yield new insights for obesity biology
.
Nature
2015
;
518
:
197
206
[PubMed]
27.
Shungin
D
,
Winkler
TW
,
Croteau-Chonka
DC
, et al.;
ADIPOGen Consortium
;
CARDIOGRAMplusC4D Consortium
;
CKDGen Consortium
;
GEFOS Consortium
;
GENIE Consortium
;
GLGC
;
ICBP
;
International Endogene Consortium
;
LifeLines Cohort Study
;
MAGIC Investigators
;
MuTHER Consortium
;
PAGE Consortium
;
ReproGen Consortium
.
New genetic loci link adipose and insulin biology to body fat distribution
.
Nature
2015
;
518
:
187
196
[PubMed]
28.
Prasad
RB
,
Groop
L
.
Genetics of type 2 diabetes-pitfalls and possibilities
.
Genes (Basel)
2015
;
6
:
87
123
[PubMed]
29.
Purcell
S
,
Neale
B
,
Todd-Brown
K
, et al
.
PLINK: a tool set for whole-genome association and population-based linkage analyses
.
Am J Hum Genet
2007
;
81
:
559
575
[PubMed]
30.
Sherry
ST
,
Ward
MH
,
Kholodov
M
, et al
.
dbSNP: the NCBI database of genetic variation
.
Nucleic Acids Res
2001
;
29
:
308
311
[PubMed]
31.
Magrane M, UniProt Consortium DIPOGen Consortium. UniProt Knowledgebase: a hub of integrated protein data. Database (Oxford) 2011;2011
32.
Kamburov
A
,
Pentchev
K
,
Galicka
H
,
Wierling
C
,
Lehrach
H
,
Herwig
R
.
ConsensusPathDB: toward a more complete picture of cell biology
.
Nucleic Acids Res
2011
;
39
:
D712
D717
[PubMed]
33.
Saier
MH
 Jr
,
Tran
CV
,
Barabote
RD
.
TCDB: the Transporter Classification Database for membrane transport protein analyses and information
.
Nucleic Acids Res
2006
;
34
:
D181
D186
[PubMed]
34.
Gasteiger
E
,
Gattiker
A
,
Hoogland
C
,
Ivanyi
I
,
Appel
RD
,
Bairoch
A
.
ExPASy: the proteomics server for in-depth protein knowledge and analysis
.
Nucleic Acids Res
2003
;
31
:
3784
3788
[PubMed]
35.
Kanehisa
M
,
Goto
S
.
KEGG: Kyoto Encyclopedia of Genes and Genomes
.
Nucleic Acids Res
2000
;
28
:
27
30
[PubMed]
36.
van Leeuwen
EM
,
Sabo
A
,
Bis
JC
, et al.;
LifeLines Cohort Study
;
CHARGE Lipids Working Group
.
Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels
.
J Med Genet
2016
;
53
:
441
449
[PubMed]
37.
Heemskerk
MM
,
van Harmelen
VJ
,
van Dijk
KW
,
van Klinken
JB
.
Reanalysis of mGWAS results and in vitro validation show that lactate dehydrogenase interacts with branched-chain amino acid metabolism
.
Eur J Hum Genet
2016
;
24
:
142
145
[PubMed]
38.
Suhre
K
,
Shin
SY
,
Petersen
AK
, et al.;
CARDIoGRAM
.
Human metabolic individuality in biomedical and pharmaceutical research
.
Nature
2011
;
477
:
54
60
[PubMed]
39.
Suhre
K
,
Meisinger
C
,
Döring
A
, et al
.
Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting
.
PLoS One
2010
;
5
:
e13953
[PubMed]
40.
Cheng
S
,
Rhee
EP
,
Larson
MG
, et al
.
Metabolite profiling identifies pathways associated with metabolic risk in humans
.
Circulation
2012
;
125
:
2222
2231
[PubMed]
41.
Eickhoff
H
,
Guimarães
A
,
Louro
TM
,
Seiça
RM
,
Castro E Sousa
F
.
Insulin resistance and beta cell function before and after sleeve gastrectomy in obese patients with impaired fasting glucose or type 2 diabetes
.
Surg Endosc
2015
;
29
:
438
443
[PubMed]
42.
Drew
BG
,
Rye
KA
,
Duffy
SJ
,
Barter
P
,
Kingwell
BA
.
The emerging role of HDL in glucose metabolism
.
Nat Rev Endocrinol
2012
;
8
:
237
245
[PubMed]
43.
Siebel
AL
,
Heywood
SE
,
Kingwell
BA
.
HDL and glucose metabolism: current evidence and therapeutic potential
.
Front Pharmacol
2015
;
6
:
258
[PubMed]
44.
Hao
M
,
Head
WS
,
Gunawardana
SC
,
Hasty
AH
,
Piston
DW
.
Direct effect of cholesterol on insulin secretion: a novel mechanism for pancreatic beta-cell dysfunction
.
Diabetes
2007
;
56
:
2328
2338
[PubMed]
45.
Pétremand
J
,
Bulat
N
,
Butty
AC
, et al
.
Involvement of 4E-BP1 in the protection induced by HDLs on pancreatic beta-cells
.
Mol Endocrinol
2009
;
23
:
1572
1586
[PubMed]
46.
Rütti
S
,
Ehses
JA
,
Sibler
RA
, et al
.
Low- and high-density lipoproteins modulate function, apoptosis, and proliferation of primary human and murine pancreatic beta-cells
.
Endocrinology
2009
;
150
:
4521
4530
[PubMed]
47.
Dalla-Riva
J
,
Stenkula
KG
,
Petrlova
J
,
Lagerstedt
JO
.
Discoidal HDL and apoA-I-derived peptides improve glucose uptake in skeletal muscle
.
J Lipid Res
2013
;
54
:
1275
1282
[PubMed]
48.
Drew
BG
,
Duffy
SJ
,
Formosa
MF
, et al
.
High-density lipoprotein modulates glucose metabolism in patients with type 2 diabetes mellitus
.
Circulation
2009
;
119
:
2103
2111
[PubMed]
49.
Briand
F
,
Prunet-Marcassus
B
,
Thieblemont
Q
, et al
.
Raising HDL with CETP inhibitor torcetrapib improves glucose homeostasis in dyslipidemic and insulin resistant hamsters
.
Atherosclerosis
2014
;
233
:
359
362
[PubMed]
50.
Barter
PJ
,
Rye
KA
,
Tardif
JC
, et al
.
Effect of torcetrapib on glucose, insulin, and hemoglobin A1c in subjects in the Investigation of Lipid Level Management to Understand its Impact in Atherosclerotic Events (ILLUMINATE) trial
.
Circulation
2011
;
124
:
555
562
[PubMed]
51.
Xu
T
,
Brandmaier
S
,
Messias
AC
, et al
.
Effects of metformin on metabolite profiles and LDL cholesterol in patients with type 2 diabetes
.
Diabetes Care
2015
;
38
:
1858
1867
[PubMed]
52.
Würtz
P
,
Tiainen
M
,
Mäkinen
VP
, et al
.
Circulating metabolite predictors of glycemia in middle-aged men and women
.
Diabetes Care
2012
;
35
:
1749
1756
[PubMed]
53.
Tyrrell
J
,
Jones
SE
,
Beaumont
R
, et al
.
Height, body mass index, and socioeconomic status: mendelian randomisation study in UK Biobank
.
BMJ
2016
;
352
:
i582
[PubMed]
54.
Burgess
S
,
Thompson
SG
.
Interpreting findings from Mendelian randomization using the MR-Egger method
.
Eur J Epidemiol
2017
;
32
:
377
389
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