Diabetes Volume 63, September 2014
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Naishi Li,1,2 Marijke R. van der Sijde,3 LifeLines Cohort Study Group,4* Stephan J.L. Bakker,5
Robin P.F. Dullaart,6 Pim van der Harst,7 Ron T. Gansevoort,5 Clara C. Elbers,8,9,10 Cisca Wijmenga,3
Harold Snieder,11 Marten H. Hofker,1 and Jingyuan Fu3
Pleiotropic Effects of Lipid
Genes on Plasma Glucose,
HbA1c, and HOMA-IR Levels
Diabetes 2014;63:3149–3158 | DOI: 10.2337/db13-1800
single nucleotide polymorphism level, 15 lipid loci showed
a pleiotropic association with glucose traits (P < 0.01),
of which eight (CETP, MLXIPL, PLTP, GCKR, APOB,
APOE-C1-C2, CYP7A1, and TIMD4) had opposite allelic directions of effect on dyslipidemia and glucose
levels. Our findings suggest a complex genetic regulation and metabolic interplay between lipids and
glucose.
1Department of Molecular Genetics, University of Groningen, University Medical
Center Groningen, Groningen, the Netherlands
2Department of Endocrinology, Key Laboratory of Endocrinology of Ministry of
Health, Peking Union Medical College Hospital, Peking Union Medical College,
Chinese Academy of Medical Science, Beijing, China
3Department of Genetics, University of Groningen, University Medical Center
Groningen, Groningen, the Netherlands
4The LifeLines Cohort Study, University of Groningen, University Medical Center
Groningen, Groningen, the Netherlands
5Department of Internal Medicine, Division of Nephrology, University of Groningen,
University Medical Center Groningen, Groningen, the Netherlands
6Department of Endocrinology, University of Groningen, University Medical Center
Groningen, Groningen, the Netherlands
7Department of Cardiology, University of Groningen, University Medical Center
Groningen, Groningen, the Netherlands
8 Department of Genetics, University of Pennsylvania, School of Medicine,
Philadelphia, PA
9 Department of Medical Genetics, Biomedical Genetics, University Medical
Center, Utrecht, the Netherlands
10 Julius
Dyslipidemia is known to be strongly associated with
elevated levels of fasting plasma glucose (FPG), insulin
resistance (IR), and type 2 diabetes (T2D). It is characterized by increased circulating concentrations of triglyceride
(TG), total cholesterol (TC), LDL-cholesterol (LDL-C),
and/or decreased circulating HDL-cholesterol (HDL-C)
levels. Hypertriglyceridemia and decreased HDL-C levels
are important components in the metabolic syndrome.
Moreover, circulating HDL-C levels have been shown to
be a predictor of future IR or T2D (1,2), whereas some
lipid-lowering therapy can reduce the incidence of T2D
Center for Health Sciences and Primary Care, University Medical
Center Utrecht, Utrecht, the Netherlands
11Department of Epidemiology, Genetic Epidemiology and Bioinformatics Unit,
University of Groningen, University Medical Center Groningen, Groningen, the
Netherlands
Corresponding authors: Marten H. Hofker, [email protected], and Jingyuan
Fu, [email protected].
Received 25 November 2013 and accepted 4 April 2014.
This article contains Supplementary Data online at http://diabetes
.diabetesjournals.org/lookup/suppl/doi:10.2337/db13-1800/-/DC1.
N.L., M.R.v.d.S., H.S., M.H.H., and J.F. contributed equally to this study.
*A list of the members of the LifeLines Cohort Study Group can be found in the
APPENDIX.
© 2014 by the American Diabetes Association. Readers may use this article as
long as the work is properly cited, the use is educational and not for profit, and
the work is not altered.
GENETICS/GENOMES/PROTEOMICS/METABOLOMICS
Dyslipidemia is strongly associated with raised plasma
glucose levels and insulin resistance (IR), and genomewide association studies have identified 95 loci that
explain a substantial proportion of the variance in blood
lipids. However, the loci’s effects on glucose-related
traits are largely unknown. We have studied these lipid
loci and tested their association collectively and individually with fasting plasma glucose (FPG), glycated hemoglobin (HbA1c), and IR in two independent cohorts:
10,995 subjects from LifeLines Cohort Study and 2,438
subjects from Prevention of Renal and Vascular Endstage Disease (PREVEND) study. In contrast to the
positive relationship between dyslipidemia and glucose traits, the genetic predisposition to dyslipidemia
showed a pleiotropic lowering effect on glucose traits.
Specifically, the genetic risk score related to higher
triglyceride level was correlated with lower levels of
FPG (P = 9.6 3 10210 and P = 0.03 in LifeLines and
PREVEND, respectively), HbA1c (P = 4.2 3 1027 in LifeLines), and HOMA of estimated IR (P = 6.2 3 1024 in
PREVEND), after adjusting for blood lipid levels. At the
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Pleiotropic Effects of Lipid Genes
(3,4). However, the nature of the relationship between
dyslipidemia and plasma glucose levels and IR is still not
well understood at the genetic and molecular level. In
the last 5–10 years, genome-wide association studies
(GWAS) have revealed a large number of genetic loci
underlying the susceptibility to human diseases or the
variation in complex traits. So far, single nucleotide polymorphisms (SNPs) at 95 loci have been robustly associated with blood lipids and explain 10–12% of the total
variance (5). The most recent large-scale GWAS metaanalysis has discovered an additional 62 novel loci with
smaller effect. These loci could collectively explain 1.6–
2.4% additional variation in lipids (6). In total, more
than 70 loci have been associated with T2D, IR, FPG
levels, and other glucose-related traits (7–14). In contrast to the strong correlations at the clinical level, the
number of shared genetic components between dyslipidemia and glucose-related traits is surprisingly low. Genetic variants at only five lipid loci (GCKR, FADS1, IRS1,
KLF14, and HFE) have been associated with T2D or
glucose-related traits at the genome-wide significance
level. Several studies have investigated the combined effect of multiple lipid loci on T2D and glucose-related
traits to unravel their causal relationship using the Mendelian randomization approach (15). De Silva et al. (16)
focused on the TG loci and observed no association between a TG genetic risk score and FPG levels, IR, or risk of
T2D. In contrast, a study by Qi et al. (17) included more
lipid SNPs and observed that the genetic predisposition to
low HDL-C or high TG was related to elevated T2D risk.
However, they did not address glucose-related continuous
traits.
We therefore aimed to examine the impact of lipidassociated SNPs on glucose-related traits, including FPG,
HbA1c, and HOMA of estimated IR (HOMA-IR), in more
than 13,000 subjects from two independent cohorts. We
investigated two possible effect-paths to answer the questions of 1) whether lipid-associated SNPs affect glucoserelated traits by mediating the effect of blood lipids (path
A) or 2) whether lipid-associated SNPs have pleiotropic
effects on glucose-related traits independent of blood lipids
level (path B) (Fig. 1).
Figure 1—Two possible effect-paths of lipid genes on glucoserelated trait. The effect-path A indicates that lipid-associated
SNPs affect blood lipid levels that could predispose to variation in
glucose-related traits. In this path, lipid-associated SNPs are associated with glucose-related traits, but this association depends on
blood lipid levels (no pleiotropic effect). After regressing out the
effects of blood lipids, the association should disappear. The
effect-path B indicates the pleiotropic effect of lipid-associated
SNPs on glucose-related traits, which is independent of blood
lipid levels. After regressing out the effects of blood lipids, the
association between lipid SNPs and glucose-related traits should
remain significant.
Diabetes Volume 63, September 2014
RESEARCH DESIGN AND METHODS
Study Cohort
The study was approved by the Ethics Committee of the
University Medical Centre Groningen and included two
independent prospective cohorts.
LifeLines Cohort
We used 13,105 unrelated, genotyped subjects from the
LifeLines cohort, which is representative of Caucasian
residents in three provinces of the northern Netherlands.
The cohort was started in 2006 and now includes 165,000
participants (18). All participants had a medical examination at baseline and will be followed for 30 years. In this
study, we used the clinical measurements at baseline. TC
was measured with an enzymatic colorimetric method,
HDL-C with a colorimetric method, and TG with a colorimetric UV method (Modular P analyzer; Roche Diagnostics, Burgdorf, Switzerland). FPG was measured with
a hexokinase method (Modular P analyzer). The HbA1c
level was measured using a turbidimetric inhibition immunoassay (COBAS INTEGRA 800 CTS analyzer; Roche
Diagnostics, Almere, the Netherlands), but standardized
against the reference method of the International Federation of Clinical Chemistry and Laboratory Medicine
(IFCC). The LDL-C concentration was calculated using
the Friedewald equation. More details have been given
previously (19,20). Fasting insulin was not measured in
the LifeLines cohort, preventing calculation of HOMA-IR.
Prevention of Renal and Vascular Endstage Disease
(PREVEND) Cohort
We used a subset of 3,649 unrelated, genotyped subjects
from the PREVEND study, which is an ongoing prospective study started in 1997 to investigate the natural
course of increased levels of urinary albumin excretion
and its relation to renal and cardiovascular disease. The
initial cohort consisted of 8,592 subjects. We used the
clinical measurements at baseline. HDL-C was measured
with a homogeneous method (direct HDL, Aeroset System;
Abbott Laboratories, Abbott Park, IL). TG levels were measured on a mega-multianalyzer after enzymatic splitting
with lipoprotein lipase (GPO-PAP; Merck, Darmstadt,
Germany). TC and FPG were assessed using Kodak
Ektachem dry chemistry (Eastman Kodak, Rochester,
NY). Insulin was measured with an AxSYM auto-analyzer
(Abbott Diagnostics, Amstelveen, the Netherlands). The
details of the protocol for measuring HDL-C, TC, TG,
plasma glucose, and insulin levels have been described
earlier (21,22). LDL-C concentration was calculated using
the Friedewald equation. HOMA-IR was calculated by
(glucose 3 insulin)/22.5. The HbA1c level was not measured in the PREVEND cohort.
Selection of Subjects
In this study, we only included the clinic measurements of
fasting blood samples from nondiabetic individuals. Diabetes was defined by any one of the following criteria, if
applicable: 1) self-reported diabetes, 2) FPG $7.0 mmol/L,
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Li and Associates
3) HbA1c $6.5% (47.5 mmol/mol), or 4) taking antidiabetes medication. We also excluded individuals taking
lipid-lowering medicines and participants who did not fast
for 8 h before clinic measurement. We did not exclude
individuals with anemia, as we did not observe any significant impact of anemia on HbA1c levels (Student t test
P value = 0.44). Our final study population comprised
10,995 LifeLines subjects and 2,438 PREVEND subjects.
The clinical characteristics of our cohort are summarized
in Table 1.
Genotyping, Imputation, and Quality Control
Both the LifeLines and PREVEND cohorts were genotyped
using Illumina HumanCytoSNP-12 BeadChip (Illumina,
San Diego, CA). The ungenotyped SNPs were imputed using
the software program BEAGLE (23). We used the Northern
Europeans from Utah (CEU) population, HapMap release
24, as our reference panel. We used the best-guessed genotype for the imputed SNPs. Our quality control was described
earlier (19).
Selection of Lipid-Associated SNPs
In this study, we focused on the 95 lipid loci that explain
the most variation in lipids (5). Different lipids can have
different best SNPs in the same locus. The risk alleles and
their effect size were extracted for each SNP and each
lipid type. We excluded three SNPs for which genotypes
could not be imputed in LifeLines and PREVEND. These
were rs13238203 at the TYW1B locus, rs2412710 at
CAPN3, and rs1800961 at HNF4A. Thus, our study had
129 lipid-associated SNPs, including 46 SNPs for HDL-C,
37 SNPs for LDL-C, 30 SNPs for TG, and 51 SNPs for TC
(Supplementary Tables 1–4).
Risk Score Calculation
For each lipid and for each individual, the unweighted risk
score and weighted risk score were calculated, respectively. The unweighted risk score was calculated as the
total number of risk alleles per individual. For the weighted
risk score, the risk alleles were weighted by their estimated
Table 1—Clinical characteristics of the two cohorts
Number of individuals
LifeLines
PREVEND
10,995
2,438
Covariates
Sex (male/female)
Age (mean 6 SD)
BMI (mean 6 SD)
4,441/6,554
47.5 6 10.7
26.1 6 4.1
1,158/1,280
49.8 612.5
26.1 6 4.2
Lipids (mean 6 SD)
HDL-C (mmol/L)
LDL-C (mmol/L)
TC (mmol/L)
Log10(TG)
1.46
3.34
5.16
0.03
6
6
6
6
0.39
0.88
0.98
0.22
Glucose-related traits (mean 6 SD)
FPG (mmol/L)
4.96 6 0.48
HbA1c
%
5.50 6 0.31
mmol/mol
36.6 6 3.4
Log10(HOMA-IR)
NA
1.33
4.06
5.67
0.08
6
6
6
6
0.40
1.09
1.07
0.23
4.77 6 0.62
NA
NA
0.23 6 0.28
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effect size. We further rescaled the risk scores between
0 and 1 by dividing the risk scores by the maximum risk.
Thus the unweighted risk model can be described as
.
j¼n
(1)
Si ¼ ∑j¼1 gij 2n
where gij is the number of risk alleles for the jth SNP in the
ith individual, coding as 0 for homozygous nonrisk alleles,
1 for heterozygous genotype, or 2 for homozygous risk
alleles, and n is the total number of associated SNPs per
lipid trait. The weighted risk score is described as
.
j¼n
WSi ¼ ∑j¼n
(2)
j¼1 bj ∗gij ∑
j¼1 2∗bj
where bj refers to the estimated effect size for the jth SNP.
Association Between the Lipid SNPs and the Levels of
Blood Lipids
The TG level was log10 transformed. The residual lipid
levels were obtained using linear regression model adjusting for the covariates age, age2, and sex. The model is
described as
yi ¼ agei þ age2i þ sexi þ ei
(3)
where yi refers to an observed lipid level (TG, HDL-C,
LDL-C, or TC) for the ith individual and ei is the remaining
residual. The residuals were used as a phenotypic trait
and subjected to a Spearman correlation analysis with
the lipid risk scores in the LifeLines and PREVEND
cohorts, respectively. The association with individual
SNPs was tested using a linear regression between the
SNP genotype and the lipid residuals. We performed
a meta-analysis to combine the effect of the LifeLines
and PREVEND data using a weighted z-score approach.
As all the tested SNPs are well-established lipid loci, we
felt no need to control the false discovery rate. Nominal
associations at P , 0.01 were reported to have higher
confidence in the effect direction of the association.
Association Between the Lipid Risk Scores and the
Levels of Glucose-Related Traits
The HOMA-IR level was log10 transformed. The residual
glucose-related traits were obtained using Eq. 3, adjusting
for the covariates age, age2, and sex. The residuals were
used as a phenotypic trait and subjected to a Spearman
correlation analysis with the lipid risk scores.
Pleiotropic Association Between the Lipid Risk Scores
and the Levels of Glucose-Related Traits, After
Adjusting for Blood Lipids
As shown in Fig. 1, both paths A and B can result in an
association between lipid SNPs and glucose-related traits.
To make a distinction between the two effect paths, we
included blood lipid levels as covariates and regressed out
their effect on glucose traits using a linear regression
model
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Pleiotropic Effects of Lipid Genes
yi ¼ agei þ age2i þ sexi þ HDLi þ LDLi
þ TGi þ TCi þ e;i
Diabetes Volume 63, September 2014
(4)
where yi refers to the glucose-related trait (FPG, HbA1c, or
HOMA-IR) of the ith individual and ei’ refers to the residuals for glucose traits that are independent of lipid levels.
The residuals represent the proportion of variation in
glucose traits that cannot be explained by blood lipid
levels. Thus, the residuals were used as lipid-independent
glucose traits and subjected to Spearman correlation analysis with lipid risk scores. The association identified between lipid loci and the residuals is independent from
blood lipid levels, referred to as pleiotropic association.
Lipid levels and glucose-related traits often show strong
correlation at biological and metabolic levels. In this way,
the model can remove not only the genetic variation in
glucose-related traits that propagates through blood lipids
in path A, but also additional variation explained by blood
lipids through other (biological or metabolic) sources. As
a result, the model will have more power to detect pleiotropic genetic associations.
Pleiotropic Association Between Individual Lipid SNPs
and Glucose-Related Traits
To test the pleiotropic association between individual lipid
SNPs and glucose-related traits, we performed linear regression analysis between the SNP genotype and glucoserelated traits after adjusting the blood lipids using Eq. 4.
We performed a meta-analysis to combine the effect of the
LifeLines and PREVEND data using a weighted z-score approach. Nominal associations at P , 0.01 were reported.
RESULTS
Observed Association Between Lipid SNPs and Lipid
Levels in Both Cohorts
We observed a significant positive correlation between lipid
risk scores and the lipid levels in our cohorts. The weighted
risk scores outperformed the unweighted risk scores
(Supplementary Table 5) and explained a substantial proportion of the variation in lipid levels: 4.95% for HDL-C,
3.61% for LDL-C, 3.61% for TG, and 4.41% for TC in LifeLines (Fig. 2); 3.69% for HDL-C, 1.90% LDL-C; 4.37% TG,
and 2.07% for TC in PREVEND. As the weighted risk score
was superior to the unweighted risk score, we used the
former in all further analysis. The single SNP association
per lipid and the meta-analysis across the LifeLines and
PREVEND cohorts are shown in Supplementary Tables
1–4. Based on this meta-analysis at a P value of 0.01, we
replicated the association for 21 HDL-C SNPs, 17 LDL-C
SNPs, 12 TG SNPs, and 21 TC SNPs (Supplementary Tables
1–4). All of these SNPs showed the same allelic direction as
that reported by Teslovich et al. (5).
Significant Correlation at Phenotypic Level but No
Association Observed Between Lipid Risk Scores
and Glucose-Related Traits
We assessed three different glucose-related traits in two
independent cohorts: FPG from both the LifeLines and
PREVEND cohorts, HbA1c from LifeLines, and HOMA-IR
from PREVEND. After adjusting for covariates (age, age2,
and sex), we observed a significant correlation between
glucose-related traits and the lipid levels. Consistent with
epidemiological observations, individuals who have higher
TG, TC, and LDL-C levels or lower HDL-C levels tend to
have higher levels of glucose-related traits (Table 2). Most
of the strongest correlations were observed between TG
and glucose-related traits. Despite strong correlations at
the phenotypic level, we only observed a weak correlation
between the TG risk score and HbA1c level in the LifeLines
cohort (Supplementary Table 6). However, genetic predisposition for higher TG levels was associated with lower
HbA1c levels in the LifeLines cohort (r = 20.025, P =
0.01), in contrast to their positive correlation at the phenotypic level (r = 0.19, P = 1.7 3 10290 ).
Pleiotropic Association Between Lipid Risk Scores and
Glucose-Related Traits
We further regressed out the phenotypic correlation
structure between lipids and glucose-related traits and
obtained lipid-independent glucose traits. At the genetic
level, we observed an opposite genetic effect between
dyslipidemia and glucose-related traits in the two independent cohorts: higher TG, TC, and LDL-C risk scores
or lower HDL-C risk scores were correlated with lower
glucose-related traits (Table 3). Specifically, TG risk scores
were positively correlated with TG level (P = 4.2 3 10291
in LifeLines, P = 4.5 3 10225 in PREVEND), but negatively correlated with FPG (P = 9.6 3 10210 in LifeLines,
P = 0.03 in PREVEND), HbA1c (P = 4.2 3 1027 in LifeLines), and HOMA-IR (P = 6.2 3 1024 in PREVEND). In
LifeLines, we also observed a similar negative correlation
between glucose-related traits and the risk scores of LDLC and TC. Contrary to other lipid types, HDL-C risk scores
were positively correlated with HDL-C level and FPG level
(P = 1.8 3 1024 in LifeLines, P = 1.9 3 1023 in PREVEND),
HbA1c (P = 0.003 in LifeLines), and HOMA-IR (P = 3.2 3
1028 in PREVEND).
Pleiotropic Association of Single Lipid SNPs With
Glucose Traits
The lipid risk scores showed pleiotropic associations with
glucose-related traits. However, this does not necessarily
mean that all lipid SNPs have pleiotropic effects on
glucose-related traits. Thus, we further tested the pleiotropic association for individual SNPs. We detected
a pleiotropic association for 18 lipid SNPs at 15 unique
loci that were associated at P , 0.01: 11 SNPs for FPG
level, 8 lipid SNPs for HbA1c, and 9 SNPs for HOMA-IR
(Fig. 3, Supplementary Table 7). For 8 out of 15 loci, the
genetic predisposition for dyslipidemia (higher levels of
TG, TC, and LDL-C and lower HDL-C level) was associated
with lower levels of glucose-related traits (lower FPG,
HbA1c, or HOMA-IR level) (Fig. 3). These eight loci are
CETP, MLXIPL, PLTP, GCKR, APOB, APOE-C1-C2, CYP7A1,
and TIMD4 (Fig. 3). For instance, the rs7205804-G allele at
the CETP locus has been reported to be associated with
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Figure 2—The relationship between lipids and lipid risk scores in LifeLine cohort. The histograms represent the distribution of the risk
scores in the LifeLines cohort. The x-axis shows the genetic risk score and the y-axis on the left represents the number of individuals per
group. The mean and SD of HDL-C, LDL-C, TG, and TC per risk score group are shown. The y-axis on the right indicates the levels of lipid
residuals, adjusted for age, age2, and sex.
higher levels of TG (5). In our cohorts, this allele was
consistently associated with higher levels of TG (P =
0.014) and LDL-C (P = 3.1 3 1026) and with lower
HDL-C (P = 6.2 3 10270). It was also significantly associated with lower levels of FPG (P = 9.2 3 1024), HbA1c
(P = 9.2 3 1023), and HOMA-IR (P = 3.2 3 1023).
Hence, an individual with the G allele will have a higher
probability of developing dyslipidemia, while being genetically predisposed to lower levels of FPG, HbA 1c,
and HOMA-IR. The same phenomenon was observed for
Table 2—Phenotypic correlation between lipids and glucose-related traits
LifeLines
FPG
HbA1c
PREVEND
FPG
Log10(HOMA-IR)
HDL-C
LDL-C
Log10(TG)
TC
r = 20.159
P = 5.1 3 10263
r = 20.10
P = 6.1 3 10225
r = 0.10
P = 2.4 3 10226
r = 0.10
P = 2.4 3 10226
r = 0.19
P = 1.7 3 10290
r = 0.19
P = 1.7 3 10290
r = 0.08
P = 4.7 3 10216
r = 0.11
P = 8.1 3 10233
r = 20.17
P = 6.0 3 10217
r = 20.34
P = 7.5 3 10266
r = 0.12
P = 5.8 3 1029
r = 0.21
P = 5.8 3 10225
r = 0.15
P = 5.6 3 10213
r = 0.43
P = 1.2 3 102109
r = 0.09
P = 1.8 3 1025
r = 0.15
P = 8.0 3 10214
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Pleiotropic Effects of Lipid Genes
Diabetes Volume 63, September 2014
Table 3—Spearman correlation between lipid risk scores
and glucose-related traits, after adjusting circulating lipids
level
LifeLines risk scores
HDL-C
LDL-C
TG
TC
PREVEND risk scores
HDL-C
LDL-C
TG
TC
FPG
HbA1c
r = 0.036
P = 1.8 3 1024
r = 20.029
P = 2.6 3 1023
r = 20.058
P = 9.6 3 10210
r = 20.022
P = 0.019
r = 0.028
P = 0.003
r = 20.035
P = 2.2 3 1024
r = 20.048
P = 4.2 3 1027
r = 20.038
P = 5.7 3 1025
FPG
HOMA-IR
r = 0.063
P = 1.9 3 1023
r = 0.028
P = 0.17
r = 20.044
P = 0.030
r = 0.018
P = 0.38
r = 0.113
P = 3.2 3 1028
r = 20.003
P = 0.88
r = 20.07
P = 6.2 3 1024
r = 0.001
P = 0.96
another SNP, rs3764261, at the same locus, which is
strongly linked with rs7205804 (Fig. 3). We further compared the allelic directions of SNPs on glucose-related
traits before and after adjusting for lipids. The allelic
directions were consistent, but the associations became
stronger after adjusting for lipid levels (Supplementary
Table 8). This suggests that regressing out the genetic
and other variation that could be explained by blood lipids
did indeed increase the power for detecting pleiotropic
effects.
DISCUSSION
In this study, we investigated the impact of lipidassociated SNPs on three glucose-related traits in more
than 13,000 subjects from two independent cohorts. We
investigated two possible effect-paths (Fig. 1) to answer
the questions of 1) whether lipid-associated SNPs affect
glucose-related traits by mediating blood lipids (path A) or
2) whether lipid-associated SNPs have pleiotropic effects
on glucose-related traits independent of blood lipids level
(path B). We first observed no significant correlation between the genetic risk scores for dyslipidemia and
glucose-related traits, except for a weak inverse correlation between the TG genetic risk score and HbA1c. Second, after adjusting for circulating lipid levels, we observed
significant associations between genetic risk scores for
each type of lipid and the glucose-related traits. Strikingly,
the genetic predisposition for dyslipidemia (increased TG,
TC, or LDL-C and decreased HDL-C) showed a protective
effect on glucose-related traits (decreasing the levels of
FPG, HbA1c, and HOMA-IR). These observations are in
contrast with the metabolic link between hyperglycemia
and dyslipidemia. For the first time, we are able to report
the pleiotropic effect of lipid genes on glucose-related
traits and confirm the relevance of path B. However, this
does not completely rule out path A as the assumption of
no pleiotropic effect no longer holds. It seems likely that
both effect-paths are relevant. Thus, to identify whether
lipid-associated SNPs affect glucose-related traits as mediated by the effect of blood lipids, an advanced mathematical model is required to cope with pleiotropic effects. On
the one hand, a genetic predisposition for dyslipidemia can
result in unfavorable lipid profile, which can increase the
levels of FPG, HbA1c, or HOMA-IR. On the other hand,
these lipid loci can lower the levels of glucose-related traits
through other processes. This balancing of two opposite
effects may explain the low power seen in trying to detect
the association between lipid loci with glucose-related
traits, despite their strong phenotypic correlation.
Out of the 95 established lipid loci that we investigated, 13 and 16 loci were reported to associate with
fasting glucose or diabetes, respectively, at a nominal P ,
0.05 level, by the most recent article from the Global
Lipids Genetics Consortium (6). However, it is still not
clear whether these associations are pleiotropic or mediated by blood lipid levels. In our study, we observed that
six of the previously reported loci were pleiotropically
associated with glucose-related traits at P , 0.01 (Fig.
4). In total, we detected such pleiotropic associations
for 15 loci. They are APOB, GCKR, TIMD4, LPA, HLA-B,
MLXIPL, NPC1L1, CYP7A1, FADS1–2-3, LRP1, LACTB,
CETP, APOE-C1-C2, TOP1, and PLTP. Although the association of these loci with plasma lipid levels has been
well established, their functions in either lipid or glucose
metabolism are not known. Based on current knowledge
about molecular functions, we have categorized these genes
into four subgroups. First, some of these genes encode
plasma proteins closely associated with lipoproteins, including APOB and APOE-C1-C2. Second, some genes encode
key enzymes or other functional proteins in lipid metabolism, including MLXIPL, FADS1–2-3, NPC1L1, CYP7A1,
CETP, LRP1, and PLTP. MLXIPL encodes for carbohydrateresponsive element–binding protein (ChREBP) that binds
to the promoter of several glycolytic and lipogenic genes
and has been identified as a key regulator of both de novo
lipogenesis and the glucose metabolism (24–27). The
third subgroup includes GCKR, which encodes glucokinase
regulatory protein (GKRP), which inhibits the activity of
glucokinase, the first enzyme of glycolysis catalyzing the
phosphorylation of glucose into glucose-6-phosphate. The
activity of glucokinase will influence the amount of substrate for de novo lipogenesis in liver, thereby affecting the
blood lipid profile (28). Other genes, including TIMD4,
HLA-B, LACTB, LPA, and TOP1, constitute the fourth
subgroup and are involved in a variety of physiological
processes. TIMD4 and HLA-B are both associated with
immune-related disorders. LACTB encodes a protein component of ribosome, whereas TOP1 encodes a DNA topoisomerase for transcription. The pleiotropic effects of
those genes may be due to more common physiological
processes.
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Figure 3—Allelic direction comparisons of 18 pleiotropic SNPs. A total of 18 SNPs at 15 unique loci were associated with glucose-related
traits at P < 0.01, adjusted for age, age2, sex, and circulating lipid levels. The allelic direction and strength of association for each SNP is
shown separately. The x-axis indicates the strength of association in terms of the z-score, or the weighted z-score of the meta-analysis if
applicable. The y-axis indicates seven different traits. Each diamond represents the association between the SNP with one of the traits and
the colors represent the significance. The black diamonds indicate a significant association at the P < 0.01 level with allelic directions
consistent with those reported in the literature. The gray diamonds indicate 1) established association as reported in the literature, 2) not
significant in our study at P < 0.01, 3) but allelic direction in our cohort was consistent with reports in the literature. The white diamonds
indicate nonsignificant associations. The vertical line indicates a z-score of zero. If the diamonds are located on the left side of the vertical line,
this indicates the left allele was associated with higher levels of the trait; otherwise, the right allele is associated with higher levels of the trait.
Eleven SNPs at eight unique loci were marked in boldface type because of their opposite allelic directions on dyslipidemia and glucose traits.
Our most surprising results are that the higher genetic
risk scores for dyslipidemia were associated with lower
levels of FPG, HbA1c, and HOMA-IR. Out of the 15 loci
that are associated with both lipids and glucose-related
traits independently, 8 (CETP, MLXIPL, PLTP, GCKR,
APOB, APOE-C1-C2, CYP7A1, and TIMD4) exerted an opposite allelic effect on dyslipidemia and glucose traits.
That the genetic effect was opposite to the observed
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Pleiotropic Effects of Lipid Genes
Figure 4—Venn diagram illustrating the overlap of lipid loci that
showed pleiotropic associations with glucose-related traits in our
study with lipid loci previously reported to be associated with FPG
and/or T2D. The previously reported associations between lipid loci
and FPG and T2D at a nominal P < 0.05 level were extracted from
Supplementary Table 12 of ref. 6.
phenotypic relationship suggests there is a complex genetic regulation and metabolic interplay between lipids
and glucose metabolism. We only examined the levels of
lipids and glucose in blood. It is therefore important to
recognize that the underlying mechanisms are likely active in the tissues where lipid or glucose are primarily
synthesized, used, or stored, which include liver, adipose
tissue, muscle, and others. For example, hepatic lipogenesis and lipid retention can be important mechanisms.
Previous studies have reported a few genes with a similar
opposite effect on lipid metabolism and glucose traits,
including GCKR (29). When the glucose-lowering allele
reduces the activity of its encoding protein GKRP, the
activity of glucokinase will increase, as it is negatively
regulated by GKRP. De novo lipogenesis will then speed
up, which eventually causes increased synthesis of TG.
Furthermore, TG in hepatocytes will induce lipotoxicity
and hypertriglyceridemia, which may induce IR and downregulate the activity of glucokinase, thus acting as a “negative feedback system.” Interestingly, GKRP influences
the formation of glucose-6-phosphate, thus affecting the
activation of ChREBP, the product of MLXIPL. A mouse
experiment has shown that MLXIPL overexpression in
liver can dissociate hepatic steatosis from glucose intolerance, depending on obese background or diet (30),
whereas knockdown of MLXIPL in mice can improve
hypertriglyceridemia (31). This suggests that the interaction of glucose and lipids metabolism is much more complex than we anticipated. Another mechanism could be
hepatic secretion of VLDL. Another mouse experiment
has shown that the reduced export of VLDL can result
Diabetes Volume 63, September 2014
in reduced plasma VLDL and TG levels but increased IR
and steatosis (32). The lipoproteins (APOE and APOB) are
very likely to exert an opposite effect on lipids and glucose
traits through this mechanism. Many other loci have been
found to show an opposite effect on lipid versus glucose
traits: the LDL-C and TG-lowering allele at GATAD2A/
CILP2/PBX4 has been associated with an increased risk
of T2D (33), the TG-lowering allele at the PNPLA3 locus
has also been associated with a higher risk of T2D in
obese individuals (34), and the waist-to-hip ratio–decreasing
allele at the GRB14 locus has been associated with higher
FPG levels (35). This evidence is in line with our observation that genetic predisposition to dyslipidemia is associated with lower level of FPG, HbA1c, and HOMA-IR.
However, it remains poorly understood how these genes
affect both glucose and lipids or the mechanisms involved. Glucose utilization and lipid synthesis are highly
intertwined in many tissues, including liver, adipose tissues, and muscle, and different mechanisms can be involved. Thus, a systems biology approach is crucial to
mechanically illustrate the complex interplay between lipogenesis and glycolysis.
Our analysis has presented a model for the detection of
genetic pleiotropy. With expanding work on ever larger
GWASs on a wide variety of phenotypes, it is increasingly
being observed that some risk loci show associations
with multiple phenotypes. Understanding pleiotropic
effects on complex traits has important clinical implications, e.g., understanding the comorbidity of multiple diseases and reducing unexpected side effects of
therapeutic interventions. As recently discussed, systematic detection of such effects remains challenging and
requires new methodological frameworks (36). Phenotypes that share more genetic background typically show
higher correlations at the phenotypic level. Our study
clearly showed that regressing out the genetic and other
variation explained by blood lipids substantially increased
the power to detect genetic pleiotropy.
In conclusion, for the first time, we have systematically
assessed the pleiotropic effect of lipid-associated SNPs on
glucose-related traits. Although, at a clinical level, dyslipidemia is associated with elevated plasma glucose levels and IR,
we observed that the genetic predisposition to dyslipidemia
is related to lower levels of FPG, HbA1c, and HOMA-IR. Our
findings suggest there is a complex genetic regulation and
metabolic interplay between lipid and glucose metabolism.
The positive metabolic relationship but opposite genetic effect may explain the low power seen in studies trying to
detect the genetic association of lipid loci with glucose traits.
Further studies investigating the potential evolutionary
implications (37,38) and underlying functional mechanisms
using a system-based approach are warranted.
Acknowledgments. The authors thank D.J. Reijngoud (Department of
Laboratory Medicine, University Medical Center Groningen) for discussion and
J.L. Senior (Department of Genetics, University Medical Center Groningen) for
editing the text.
diabetes.diabetesjournals.org
Funding. This work was performed within the framework of the Center for
Translational Molecular Medicine (www.ctmm.nl) and project PREDICCt (grant
01C-104), and supported by a Netherlands Organisation for Scientific Research
(NWO) VENI grant 863.09.007 (J.F.) and the Netherlands Heart Foundation, Dutch
Diabetes Research Foundation, Dutch Kidney Foundation, and Systems Biology
Centre for Energy Metabolism and Ageing, Groningen. N.L. was financially supported by the Graduate School for Drug Exploration, University of Groningen. The
LifeLines Cohort Study was supported by the NWO (grant 175.010.2007.006);
Economic Structure Enhancing Fund of the Dutch government; Ministry of Economic
Affairs; Ministry of Education, Culture and Science; Ministry for Health, Welfare and
Sports; Northern Netherlands Provinces Alliance, Province of Groningen; University
Medical Center Groningen, University of Groningen; Dutch Kidney Foundation; and
the Dutch Diabetes Research Foundation. The PREVEND study was funded by
grants from the Dutch Kidney Foundation (Bussum, the Netherlands).
Duality of Interest. No potential conflicts of interest relevant to this article
were reported.
Author Contributions. N.L., H.S., M.H.H., and J.F. researched data and
wrote the manuscript. M.R.v.d.S. and J.F. performed data analysis. LifeLines
group authors contributed data from the LifeLines cohort. S.J.L.B., R.P.F.D.,
P.v.d.H., and R.T.G. contributed data from the PREVEND cohort. C.C.E. and C.W.
contributed to the discussion and reviewed the manuscript. H.S., M.H.H., and J.F.
designed the study. M.H.H. and J.F. are the guarantors of this work and, as such,
had full access to all the data in the study and take responsibility for the integrity
of the data and the accuracy of the data analysis.
Appendix
LifeLines Cohort Study Group authors are Behrooz Z. Alizadeh (Department of
Epidemiology), Rudolf A. de Boer (Department of Cardiology), H. Marike Boezen
(Department of Epidemiology), Marcel Bruinenberg (LifeLines Cohort Study),
Lude Franke (Department of Genetics), P.v.d.H., Hans L. Hillege (Department
of Epidemiology, Department of Cardiology), Melanie M. van der Klauw
(Department of Endocrinology), Gerjan Navis (Department of Internal Medicine,
Division of Nephrology), Johan Ormel (Interdisciplinary Center of Psychopathology of Emotion Regulation, Department of Psychiatry), Dirkje S. Postma
(Department of Pulmonology), Judith G.M. Rosmalen (Interdisciplinary Center of
Psychopathology of Emotion Regulation, Department of Psychiatry), Joris P.
Slaets (University Center for Geriatric Medicine), H.S., Ronald P. Stolk
(Department of Epidemiology), Bruce H.R. Wolffenbuttel (Department of
Endocrinology), and C.W. All affiliations are part of the University of Groningen,
University Medical Center Groningen, Groningen, the Netherlands.
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