Impact of HDL genetic risk scores on coronary artery calcified plaque and mortality in individuals with type 2 diabetes from the Diabetes Heart Study
Impact of HDL genetic risk scores on coronary artery calcified plaque and mortality in individuals with type 2 diabetes from the Diabetes Heart Study
Laura M Raffield 0 1 2
Amanda J Cox 0 1 6
Fang-Chi Hsu 5
Maggie C-Y Ng 0 1
Carl D Langefeld 5
J Jeffrey Carr 4
Barry I Freedman 3
Donald W Bowden 0 1 6
0 Center for Diabetes Research, Wake Forest School of Medicine , Winston-Salem, NC , USA
1 Center for Human Genomics, Wake Forest School of Medicine , Winston-Salem, NC , USA
2 Molecular Genetics and Genomics Program, Wake Forest School of Medicine , Winston-Salem, NC , USA
3 Department of Internal Medicine - Nephrology, Wake Forest School of Medicine , Winston-Salem, NC , USA
4 Department of Radiologic Sciences, Wake Forest School of Medicine , Winston-Salem, NC , USA
5 Department of Biostatistical Sciences, Wake Forest School of Medicine , Winston-Salem, NC , USA
6 Department of Biochemistry, Wake Forest School of Medicine , Winston-Salem, NC , USA
Background: Patients with type 2 diabetes (T2D) are at elevated risk for cardiovascular disease (CVD) events and mortality. Recent studies have assessed the impact of genetic variants affecting high-density lipoprotein cholesterol (HDL) concentrations on CVD risk in the general population. This study examined the utility of HDL-associated single nucleotide polymorphisms (SNPs) for CVD risk prediction in European Americans with T2D enrolled in the Diabetes Heart Study (DHS). Methods: Genetic risk scores (GRS) of HDL-associated SNPs were constructed and evaluated for potential associations with mortality and with coronary artery calcified atherosclerotic plaque (CAC), a measure of subclinical CVD strongly associated with CVD events and mortality. Two sets of SNPs were used to construct GRS; while all SNPs were selected primarily for their impacts on HDL, one set of SNPs had pleiotropic effects on other lipid parameters, while the other set lacked effects on low-density lipoprotein cholesterol (LDL) or triglyceride concentrations. Results: The GRS were specifically associated with HDL concentrations (4.90 10-7 < p < 0.02) in models adjusted for age, sex, and body mass index (BMI), but were not associated with LDL or triglycerides. Cox proportional hazards regression analysis suggested the HDL-associated GRS had no impact on risk of CVD-mortality (0.48 < p < 0.99) in models adjusted for other known CVD risk factors. However, associations between several of the GRS and CAC were observed (3.85 10-4 < p < 0.03) in models adjusted for other known CVD risk factors. Conclusions: The GRS analyzed in this study provide a tool for assessment of HDL-associated SNPs and their impact on CVD risk in T2D. The observed associations between several of the GRS and CAC suggest a potential role for HDL-associated SNPs on subclinical CVD risk in patients with T2D.
High-density lipoprotein cholesterol; Type 2 diabetes; Coronary artery calcified plaque; Mortality; Genetic risk score
Patients with T2D have significantly increased risk for
CVD, with mortality rates from heart disease at least
twofold higher than in adults without diabetes . In addition,
patients with T2D have higher rates of dyslipidemia, an
important CVD risk factor. In particular, individuals with
T2D tend to have increased triglyceride concentrations
and decreased HDL cholesterol concentrations . An
inverse relationship between HDL concentrations and
CVD-mortality has long been observed ; however, it
is not clear whether HDL is a significant contributing
factor in development of CVD and whether
interventions to specifically alter HDL concentrations markedly
impact CVD risk.
Voight et al.  used Mendelian randomization to
assess the impact of HDL concentrations on myocardial
infarction (MI) risk in over 12,000 MI cases and over
40,000 controls. Neither a coding variant in LIPG nor a
genetic risk score (GRS) derived from 14 common SNPs,
both of which were robustly associated with HDL and
not with other lipid parameters, was associated with MI.
A one standard deviation increase in HDL cholesterol
due to GRS was not associated with a significant change
in MI risk, though epidemiological data suggested a
change in HDL concentration of this magnitude would
be associated with an approximate 38% reduction in risk
for MI. These data do not support a major role for
HDL-associated SNPs in MI.
Given that CVD accounts for more than 65% of
allcause mortality in individuals with T2D, determining the
effects of HDL concentrations on CVD in this high risk
group is of particular interest , as low HDL
concentrations have been epidemiologically associated with
CVD risk in T2D . Previous large meta-analyses have
not specifically analyzed the effect of HDL associated
GRS in clinically relevant, community-based cohorts of
patients at high risk for CVD, such as patients with T2D.
In the current study we constructed GRS containing
SNPs associated solely with HDL, SNPs associated with
HDL which have apparent pleiotropic effects on other
lipid parameters, and both sets of SNPs for analysis in
combined scores. We analyzed these GRS for
associations with mortality and a measure of subclinical CVD
burden, coronary artery calcified atherosclerotic plaque
(CAC) [6-8], in a cohort of patients with T2D from the
Diabetes Heart Study (DHS).
Study design and sample
DHS participants were recruited from outpatient
internal medicine and endocrinology clinics and from the
community from 1998 through 2005 in western North
Carolina. Siblings concordant for T2D without advanced
renal insufficiency were recruited, with additional
nondiabetic siblings enrolled whenever possible. Recruitment
was based upon family structure, and there were no
inclusions/exclusions based on evidence of prevalent CVD at
the time of recruitment. Ascertainment and recruitment
have been described in detail previously [9-12]. T2D was
defined as diabetes developing after the age of 35 years
treated with insulin and/or oral agents, in the absence of
historical evidence of ketoacidosis. Diabetes diagnosis was
confirmed by measurement of fasting glucose and glycated
hemoglobin (HbA1C) at the exam visit. Analyses
completed for the current investigation included 983
selfdescribed European American individuals with T2D from
466 DHS families.
Study protocols were approved by the Institutional
Review Board at Wake Forest School of Medicine, and all
participants provided written informed consent.
Participant examinations were conducted in the General
Clinical Research Center of the Wake Forest Baptist Medical
Center. Examinations included interviews for medical
history and health behaviors, anthropometric measures,
resting blood pressure, electrocardiography, fasting blood
sampling for laboratory analyses, and spot urine
collection. Individuals were considered hypertensive if they were
prescribed anti-hypertensive medication or had blood
pressure measurements exceeding 140 mmHg (systolic) or
90 mmHg (diastolic). Standard laboratory analyses
included fasting glucose, HbA1C, total cholesterol, HDL, and
triglycerides. Low-density lipoprotein cholesterol (LDL)
concentration was calculated using the Friedewald
equation, and LDL concentrations were considered valid
for subjects whose triglycerides were less than 796 mg/
dL. CAC was measured using fast-gated helical
computed tomography (CT) scanners, and calcium scores
were calculated as previously described and reported as
an Agatston score [13,14].
Vital status was determined for all subjects from the
National Social Security Death Index maintained by the
United States Social Security Administration. For
participants confirmed as deceased, length of follow-up was
determined from the date of initial study visit to date of
death. For all other participants, the length of follow-up
was determined from the date of the initial study visit to
the end of 2011. For deceased participants, copies of death
certificates were obtained from relevant county Vital
Records Offices to determine cause of death. Cause of death
was categorized based on information contained in death
certificates as CVD-mortality (MI, congestive heart failure,
cardiac arrhythmia, sudden cardiac death, peripheral
vascular disease, and stroke) or either cancer, infection,
end-stage renal disease, accidental, or other (including
obstructive pulmonary disease, pulmonary fibrosis, liver
failure and Alzheimers dementia). Association with mortality
was assessed for both CVD-mortality and all-cause
mortality, i.e. death from any cause.
Total genomic DNA was purified from whole blood
samples using the PUREGENE DNA isolation kit (Gentra Inc.,
Minneapolis, MN). DNA concentration was quantified
using standardized fluorometric readings on a Hoefer
DyNA Quant 200 fluorometer (Hoefer Pharmacia Biotech
Inc., San Francisco, CA). Genotype data for specific SNPs
was derived from: (i) the MassARRAY SNP Genotyping
System (Sequenom Inc., San Diego, CA) (n = 4 SNPs),
(ii) a genome wide association study (GWAS) using the
Affymetrix Genome-Wide Human SNP Array 5.0
(Affymetrix Inc., Santa Clara, CA) (n = 2 SNPs), (iii)
Illumina HumanExome BeadChips (Illumina Inc., San
Diego, CA) (n = 18 SNPs), and (iv) GWAS imputed data
(n = 4 SNPs).
Genotyping using the MassARRAY SNP Genotyping
System was completed as described previously .
Primers for PCR amplification and extension reactions
were designed using the MassARRAY Assay Design
Software (Sequenom). Samples were diluted to a final
concentration of 5 ng/l, and single-base extension
reaction products were separated and scored using a
matrix-assisted laser desorption ionization/time of flight
mass spectrometer. To evaluate genotyping accuracy, 39
quality control samples were included as blind
duplicates. The concordance rate for these blind duplicates
For the DHS GWAS data, genotype calling was
completed using the BRLLM-P algorithm in Genotyping
Console v4.0 (Affymetrix). Samples failing to meet an
intensity quality control threshold (n = 4) were not
included for genotype calling and those failing to meet a
minimum acceptable call rate of 95% (n = 3) were
excluded from further analyses. An additional 39 samples
were included as blind duplicates within the genotyping
set to serve as quality controls; the concordance rate for
these blind duplicates was 99.0 0.72% (mean standard
For the DHS Exome Chip data, genotype calling was
completed using Genome Studio Software v1.9.4 (Illumina).
Samples failing to meet a minimum acceptable call rate of
98% (n = 3) were excluded from further analyses. An
additional 58 samples were included as blind duplicates within
the genotyping set to serve as quality controls; the
concordance rate for blind duplicates was 99.9 0.0001%
(mean SD). Additional quality control of GWAS and
Exome Chip data sets was completed to exclude
samples with poor quality genotype calls, gender errors, or
unclear/unexpected sibling relationships.
For SNPs where direct genotyping data was not
available, genotype data was obtained from GWAS imputed
data. Imputation of 1,000 Genomes Project SNPs was
completed using the program IMPUTE2 and the Phase I
v2, cosmopolitan (integrated) reference panel, build 37
[16,17]. SNPs that were used for imputation were required
to have low missingness and show no significant departure
from Hardy-Weinberg expectations (p > 1 10-4). To
maximize the quality of imputation, the samples were
not pre-phased. Only imputed SNPs with a
confidence score > 0.90 and information score > 0.50 were
used. A total of ~4.5 million SNPs passed imputation
For all SNPs used to derive the GRS, the minimum
acceptable call rate was 95%; the average SNP call rate was
99.4% 1.2% (mean SD), and the average sample call
rate was 99.4% 1.4%. Allele and genotype frequencies
were calculated from unrelated individuals and tested
for departures from Hardy-Weinberg equilibrium. No
SNPs showed significant departure from Hardy-Weinberg
equilibrium (p > 0.05). One SNP (rs386000) included by
Voight et al.  in their HDL GRS failed genotyping and
was not included in the current analysis.
Both unweighted GRS and GRS weighted by SNP effect
size were derived for two sets of SNPs previously
reported to be associated with HDL . One set of 14
SNPs had documented effects on HDL concentrations
only and were used by Voight et al.  for construction
of a GRS. We created GRS from 13 of these SNPS with
good quality genotyping data (rs386000 excluded) (Score
1; 1a = unweighted, 1b = weighted).
In addition, Voight et al.  also reported an
additional set of 15 SNPs, including a coding variant in
LIPG, as associated with HDL concentrations, with some
of these SNPs also reported to have pleiotropic effects
on LDL cholesterol and triglyceride concentrations. All
SNPs were primarily selected for their impact on HDL.
Weighted and unweighted GRS were derived from this
additional set of 15 SNPs (Score 2; 2a = unweighted, 2b =
weighted). SNPs (n = 26) from both sets were also
combined to derive unweighted and weighted combined
GRS; two pairs of SNPs (rs2338104 and rs7134594;
rs2271293 and rs16942887) are in strong linkage
disequilibrium (r2 > 0.90), and as such two SNPs (rs7134594 and
rs2271293) were excluded from the combined scores.
SNPs in the combined GRS were weighted by their effect
sizes in mmol/L. The effect sizes used were drawn from
the Voight et al. paper or the Global Lipid Genetic
Consortium GWAS for Lipids paper that Voight et al. cited
[4,18]. All derived GRS (1a, 1b, 2a, 2b, Combined
Unweighted, Combined Weighted) were tested for
association with HDL, LDL and triglycerides to evaluate
whether the GRS were a measure of genetic contributions
to either HDL only or to global lipid levels.
For all GRS, the effect allele was assigned as the allele
associated with an increase in plasma HDL
concentrations, i.e., an increase in GRS can be interpreted as an
increase in genetic predisposition for elevated plasma
HDL, as seen in the GRS used by Voight et al. .
Unweighted scores were derived by adding the number
of effect alleles across each SNP. The SNPs were also
weighted by their previously reported effect sizes . For
the weighted scores, the number of effect alleles
possessed by an individual at a particular SNP locus was
multiplied by a weight derived from that SNPs effect
size contribution to the total effect size for all SNPs
included in the GRS. For individuals missing genotype
data for a particular SNP, the mean genotype calculated
in the DHS for that given SNP was assigned .
For statistical analyses, continuous variables were
transformed as necessary to approximate normality. Single SNP
association analyses were performed using variance
components methods implemented in Sequential Oligogenic
Linkage Analysis Routines (SOLAR) version 6.4.1 (Texas
Biomedical Research Institute, San Antonio, TX) to
account for relatedness between subjects . Association
was examined assuming an additive model of inheritance.
Age and sex were included as covariates in single SNP
association analyses for HDL.
GRS were considered as both ordinal (three tertiles:
T1, T2, T3 derived from increasing tertile ranges) and
continuous variables. Relationships between the GRS and
HDL, LDL, triglycerides, CAC, and prior history of CVD
and MI were examined using marginal models with
generalized estimating equations. The models account for
familial correlation using a sandwich estimator of the
variance under exchangeable correlation. Relationships
between GRS and both all-cause and CVD-mortality were
examined using Cox proportional hazards models with
sandwich-based variance estimation due to the inclusion
of related individuals in this study. Associations were
adjusted for covariates including age, sex, BMI, smoking
status (history of current or prior smoking), hypertension,
cholesterol medication use, prior CVD, oral T2D
medication use, and insulin use as indicated. All analyses were
performed in SAS 9.3 (SAS Institute, Cary, NC). Statistical
significance was accepted at p < 0.05.
The goal of this study was to assess the impact of
HDLassociated SNPs on CVD risk in subjects from the DHS
of families enriched for T2D. We assessed the
relationships between GRS based on HDL-associated SNPs and
both subclinical CVD and mortality risk. The clinical
characteristics of the 983 European American individuals
with T2D included in the study are summarized in Table 1.
Subjects exhibited a variety of known CVD risk factors,
including hypertension, high body mass, and dyslipidemia.
Subclinical CVD was prevalent, demonstrated by the large
number of subjects who had detectable CAC.
A total of 28 SNPs were genotyped (Table 2), including
13 SNPs solely associated with HDL and 15 SNPs
associated with HDL that may also have pleiotropic effects on
other lipid parameters . We first performed single
SNP association analyses for these 28 SNPs included in
our GRS. Four SNPs (rs3764261, rs1800588, rs3890182,
rs4759375) were associated (7.00 10-5 < p < 0.02) with
HDL concentrations, with the estimated effects in the
same direction and of similar magnitude as those
previously reported by Voight et al. . The coding variant in
LIPG (rs61755018) reported by Voight et al. was not
significantly associated with HDL concentrations in our
Next, weighted and unweighted GRS were analyzed.
One GRS was derived from 13 SNPs (rs386000 excluded)
reported to have effects solely on HDL (Score 1; 1a =
unweighted, 1b = weighted; Table 2); scores ranged from
8.0 to 21.0 (14.7 2.2, mean SD) for GRS 1a and from
7.8 to 21.0 (13.4 2.1) for GRS 1b. The second GRS was
derived from an additional set of 15 SNPs associated with
HDL concentrations, some of which also have pleiotropic
effects on LDL cholesterol and triglycerides (Score 2; 2a =
unweighted, 2b = weighted; Table 2); scores ranged from
7.0 to 20.0 (13.3 2.0) for GRS 2a and from 5.4 to 17.8
(11.0 2.1) for GRS 2b. Additional GRS were constructed
from a combined set of 26 SNPs, with two SNPs not
included in the combined scores due to strong linkage
disequilibrium with other included SNPs (Combined
Unweighted and Combined Weighted scores; Table 2);
scores ranged from 17.0 to 36.0 (26.8 2.9) for the
Combined Unweighted GRS and from 12.2 to 31.8 (21.2 3.2)
for the Combined Weighted GRS.
An initial analysis of tertiles of GRS 1 and 2 (i.e. upper,
middle, and lower third of GRS distribution) as an
ordinal variable was performed to test for a linear trend of
association with plasma HDL concentrations. As GRS 1
and 2 were linearly associated with HDL concentrations
(6.18 10-6 < p < 0.007 in models adjusted for additional
CVD risk factors), we next examined the GRS as
continuous variables, including GRS 1 and 2 and the
Combined GRS. When analyses were adjusted for age, sex,
and BMI, all weighted and unweighted GRS were
associated with increased HDL (Table 3) but were not
associated with LDL cholesterol or triglyceride concentrations
(p > 0.05). In addition, significant associations were
observed between several of the GRS and CAC (Table 3).
This suggests a potential impact of a genetic
predisposition to increased HDL on reduced risk for subclinical
CVD in patients with T2D. Further analyses indicated
that the GRS were not significantly associated with
CVD-mortality (Table 4), nor were any significant
associations observed between the GRS and history of prior
T2D Duration (years)
Smoking (current or past) (%)
Deceased (from CVD) (%)
Cholesterol Medications (%)
Oral T2D Medications (%)
Systolic BP (mmHg)
Diastolic BP (mmHg)
Hemoglobin A1C (%)
Total Cholesterol (mg/dL)
HDL cholesterol (mg/dL)
LDL cholesterol (mg/dL)
Coronary Artery Calcified
Table 1 Demographic and clinical characteristics of 983
individuals with type 2 diabetes from the Diabetes Heart
Mean SD or %
CVD or history of prior MI (0.06 < p < 0.87). The GRS
were also not associated with all-cause mortality, with
the exception of the nominal associations of GRS 2b,
the weighted GRS which includes some SNPs with
pleiotropic effects on lipid parameters other than HDL,
and the Combined Weighted GRS (Table 4). Analyses
were also performed with further adjustment for
diabetic medication use; results were essentially unchanged
(Additional files 1 and 2).
In an effort to further quantify these findings, tertiles
of GRS 1 and 2 were also analyzed in reference to the
lowest GRS tertile. The highest tertile was consistently
associated with increased HDL concentrations (Additional
file 3), with GRS 2b displaying the strongest association
(p = 4.31 10-5 for comparison of the highest in reference
to the lowest GRS tertile). Further quantification of risk
for all-cause mortality suggests individuals in the highest
tertile for GRS 2b experience an approximate 40%
reduction in risk of mortality compared to those in the lowest
tertile (HR: 0.57; 95% CI: 0.39- 0.83; p = 0.003).
The present study evaluated HDL-associated SNPs in
patients with T2D using weighted and unweighted GRS.
It is important to assess the impact of HDL-associated
SNPs in patients with T2D, as decreased HDL
concentrations are epidemiologically associated with higher
CVDmortality and are often observed in patients with T2D
[2,3]. In addition to conventional lipid parameters,
measures of prevalent disease, and mortality, we evaluated
associations of these GRS with CAC. CAC has been shown
in the DHS and other cohorts to be a strong independent
predictor of CVD events and mortality [6,8]. Compared to
individuals with low or nonexistent CAC (CAC 09),
individuals in the DHS with very high CAC ( 1,000)
experienced >6-fold increased risk for all-cause mortality
and >11-fold increased risk for CVD-mortality [8,21].
CAC has not previously been examined in studies of the
impact of HDL-associated variants on CVD risk. Hence,
analysis of our HDL GRS with this subclinical measure of
CVD was unique and of clinical relevance.
For the weighted and unweighted GRS assessed, higher
GRS values were clearly associated with increased plasma
HDL. The GRS were not associated with LDL cholesterol
and triglyceride concentrations, allowing assessment of the
effects of genetic predisposition to higher or lower HDL
without confounding effects from associations with other
lipid parameters. No evidence of association between the
GRS and CVD-mortality was observed; however,
associations between the GRS and CAC were observed. These
associations point to a potential reduction in risk for
subclinical CVD in patients with T2D with a genetic
predisposition to increased HDL.
Prior analyses of HDL-associated SNPs
Recently, Voight et al.  evaluated two sets of SNPs
associated with higher HDL to determine whether
HDLassociated SNPs would confer protection from MI in the
general population. They concluded that a genetic
predisposition to higher HDL did not detectably influence
MI risk. However, Voight et al. did not address the
association of their GRS in high CVD risk groups such as
patients with T2D . Moreover, it is important to test
the applicability of results from extremely large
metaanalyses in community-based cohorts. This is a necessary
step in assessing the value of these GRS for introduction
into clinical medicine. We hypothesized that GRS of
HDL-associated SNPs could provide a tool for evaluating
the effect of HDL concentrations on CVD risk in patients
Prior studies in the general population have generally
shown little impact of genetic variants affecting HDL
concentrations on CVD risk. In two large Danish
studies, variants associated with a decrease in HDL
cholesterol did not increase CVD risk [22,23]. In another study
with 8,473 Caucasian participants, GRS were strongly
associated with HDL concentrations, but were not
associated with CVD . However, few previous studies have
focused on patients with T2D, so it is unclear whether
HDL-associated SNPs impact CVD risk in this group, a
gap that we addressed in this study.
Analysis of GRS for associations with lipid parameters
We did not detect many of the single SNP associations
with HDL concentrations reported by Voight et al. .
This is not surprising, as the current study has lower
power to detect associations given the modest effect
sizes previously reported. However, we observed
significant associations between all of the GRS assessed and
HDL, allowing us to consider the impact of genetic
determinants of HDL concentrations on CVD risk. Some
Coronary Artery Calcified Plaque (CAC)
0.027 (-0.001- 0.055)
0.033 (0.003- 0.063)
0.046 (0.016- 0.076)
0.061 (0.034- 0.089)
0.035 (0.015- 0.055)
0.043 (0.025- 0.061)
0.317 (-0.678- 1.311)
0.243 (-0.782- 1.268)
0.823 (-1.978- 0.332)
0.507 (-1.614- 0.600)
0.243 (-1.018- 0.533)
0.260 (-0.988- 0.468)
0.002 (-0.019- 0.015)
0.005 (-0.022- 0.013)
0.013 (-0.034- 0.008)
0.009 (-0.028- 0.010)
0.010 (-0.023- 0.004)
0.007 (-0.020- 0.005)
0.177 (-0.854- 1.207)
0.838 (-1.987- 0.312)
0.604 (-1.713- 0.506)
0.261 (-1.033- 0.512)
0.325 (-1.054- 0.404)
0.005 (-0.021- 0.012)
0.007 (-0.024- 0.010)
0.016 (-0.036- 0.004)
0.013 (-0.031- 0.005)
0.012 (-0.025- 0.001)
0.010 (-0.022- 0.002)
1.42 10-5 0.062 (0.036- 0.088)
2.60 10-6 0.063 (0.037- 0.088)
3.63 10-5 0.040 (0.021- 0.058)
4.65 10-6 0.044 (0.027- 0.061)
4.90 10-7 0.044 (0.027- 0.061)
3.85 10-4 0.052 (0.024- 0.080)
0.245 (-0.759- 1.248)
0.140 (-0.887- 1.167)
0.805 (-1.955- 0.346)
0.585 (-1.698- 0.529)
0.266 (-1.038- 0.507)
0.327 (-1.060- 0.406)
0.006 (-0.022- 0.010)
0.008 (-0.024- 0.009)
0.015 (-0.036- 0.005)
0.013 (-0.031- 0.005)
0.012 (-0.025- 0.001)
0.010 (-0.022- 0.002)
Table 3 Associations between HDL genetic risk scores and HDL, LDL, triglycerides, and coronary artery calcified plaque
0.079 (-0.154- -0.003) 0.042
0.065 (-0.128- -0.001) 0.046
0.067 (-0.146- 0.012)
0.072 (-0.154- 0.011)
0.117 (-0.197- -0.036) 0.005
0.083 (-0.140- -0.026) 0.004
0.087 (-0.141- -0.033) 0.002
0.053 (-0.119- 0.014)
0.063 (-0.134- 0.009)
0.088 (-0.156- -0.020) 0.011
0.077 (-0.123- -0.031) 0.001
0.069 (-0.114- -0.024) 0.002
0.068 (-0.128- -0.008) 0.027
0.059 (-0.123- 0.004)
0.082 (-0.149- -0.014) 0.017
0.101 (-0.163- -0.038) 0.002
0.080 (-0.124- -0.037) 3.18 10-4
0.075 (-0.117- -0.034) 3.85 10-4
Analysis was performed using marginal models with generalized estimating equations. Model 1 is unadjusted; Model 2 is adjusted for age, sex, and body mass
index (BMI); Model 3 is adjusted for age, sex, BMI, smoking, hypertension, and prior cardiovascular disease. Associations are reported as the estimate and its
95% confidence interval (CI).
past studies of SNPs associated with HDL in patients
with T2D have examined SNPs with pleiotropic effects
on other lipid parameters or other traits, such as obesity,
which could also influence CVD risk [25-28]. An
important strength of this study is that the GRS were not
associated with LDL cholesterol and triglyceride
concentrations, indicating the GRS are mainly informative for
an individuals HDL concentration, as opposed to more
global lipid concentrations.
In our study, consistent with the GRS used by Voight
et al., a higher GRS indicates a predisposition to higher
HDL . While we refer to all GRS as risk scores by
convention, epidemiological data would in fact indicate that
higher GRS values should be associated with increased
protection from CVD, not increased risk, as higher plasma
HDL is protective . However, constructing the GRS so
that higher GRS values would be associated with decreased
HDL would not have changed our results; p-values would
remain unchanged and effect estimates would be of the
same magnitude in the opposite direction.
Analysis of GRS for associations with mortality and
Association of the GRS with HDL allowed us to
subsequently consider the impact of genetic predisposition to
higher or lower HDL on all-cause mortality and
CVDmortality in T2D. While the HDL GRS were not associated
with CVD-mortality, association with all-cause mortality
Table 4 Association between HDL genetic risk scores analyzed as a continuous variable and all-cause and
Analysis was performed using Cox proportional hazards regression. Model 1 is unadjusted; Model 2 is adjusted for age and sex; Model 3 is adjusted for age, sex,
body mass index, smoking, hypertension, cholesterol medication use, and prior cardiovascular disease. The hazard ratio (HR) and its 95% confidence interval (CI)
was observed for GRS 2b, the weighted GRS which
includes SNPs with pleiotropic effects on LDL cholesterol
and triglycerides, and the Combined Weighted GRS. It is
possible that these associations are due to the reported
pleiotropic effects on LDL and triglycerides of some of the
included SNPs, although the GRS overall were not
associated with LDL and triglyceride concentrations.
Alternatively this observation may be due to impacts of HDL on
risk for mortality through pathways other than CVD. A
recent study by Qi et al.  examined HDL, LDL, and
triglyceride GRS and demonstrated a modest increase in
T2D risk with increased HDL GRS. While the current
study consists entirely of T2D affected individuals, the
study by Qi et al.  suggests a role for HDL in diabetes
pathogenesis, perhaps impacting mortality. Apart from
reverse cholesterol transport, the cardinal function of HDL
in ameliorating CVD risk, HDL particles also demonstrate
anti-inflammatory, antioxidant, and anti-apoptotic effects
which may be important mechanisms contributing to risk
for mortality . Further, while the GRS assessed in this
study are associated with HDL concentrations, their impact
on HDL particle composition and function is unclear and
may further contribute to mechanisms underpinning the
observed associations with all-cause mortality.
While we did not observed significant associations
between the HDL GRS and CVD-mortality, we did observe
interesting associations between the GRS and CAC, a
measure of subclinical CVD risk. We observed an
association between GRS 1a, the unweighted GRS which
contains SNPs which affect HDL concentrations only, and
CAC. Likewise associations with CAC were observed for
GRS 2a and 2b (SNPs with pleiotropic effects on lipid
parameters other than HDL), as well as the Combined
Unweighted and Weighted GRS. This points to a potential
role of HDL-associated SNPs in subclinical CVD risk; it is
possible we are not observing the impact of
HDLassociated SNPs on CVD-mortality due to a low number
of events, with association with CAC observed due to
increased power for analyses of this continuous trait. This
may point to a differing impact of HDL-associated SNPs
in individuals with T2D than is seen in most studies of the
general population. However, little work on associations
between CAC and HDL-associated SNPs has been done
in the general population, so these SNPs may be
associated with CAC in the general population as well.
SNPs included in our GRS were selected for the strength
of their association with HDL concentrations in previous
studies [4,18]; other SNPs more modestly associated
with HDL were not the focus of the current study but
may, in some cases, have a greater impact on disease risk
[27,31]. The impact of genetic variants associated with
HDL may also be modulated by environmental factors,
which was not assessed here [32,33]. The disparate
treatments prescribed to individuals in the DHS, including
oral T2D medications and insulin, may also impact
CVD outcomes; however, inclusion of these covariates
in our models did not substantially change our results
(Additional files 1 and 2). For analyses of CVD
mortality, our power to detect a modest effect (HR ~ 1.1)
associated with our GRS was limited (<60% power); however,
our power to detect a larger effect (HR ~ 1.2) was high
(>95% power). As a result, we cannot exclude a modest
impact of our HDL GRS on CVD mortality that was not
detected in this study. In contrast, limited power was
not a concern for analyses of all-cause mortality (>85%
power to detect a HR of ~1.1) due to the increased
number of events, nor for analyses of continuous
measures such as CAC and HDL (>85% power for a modest
effect size of 0.02).
In summary, the GRS analyzed in this paper provide a
useful tool for assessing the impact of genetic
predisposition to higher or lower HDL concentrations on risk for
CVD in patients with T2D. As has been observed in
studies in the general population, there was no
association between the HDL GRS and risk of CVD-mortality.
However, associations between some of the GRS and
CAC were observed, pointing to a potential role of
genetic variants affecting HDL concentrations in risk for
subclinical CVD in patients with T2D. Further study of
HDL-associated genetic variants will be needed to
further clarify whether these variants are important
determinants of CVD risk in T2D affected individuals.
Additional file 1: Associations between HDL genetic risk scores and
HDL, LDL, triglycerides, and coronary artery calcified plaque (CAC),
with and without adjustment for diabetic medication use.
Additional file 2: Association between HDL genetic risk scores
analyzed as a continuous variable and all-cause and CVD-mortality,
with and without adjustment for diabetic medication use.
Additional file 3: Association between HDL genetic risk score
tertiles and all-cause and CVD- mortality using unadjusted
proportional hazards regression models and between risk scores
and HDL levels using unadjusted marginal models incorporating
generalized estimating equations. Hazard ratios (HR) or estimates
(as appropriate) and 95% confidence intervals (CI) are reported
relative to the lowest tertile.
BMI: Body mass index; CAC: Coronary artery calcified plaque; CT: Computed
tomography; CVD: Cardiovascular disease; DHS: Diabetes Heart Study;
HDL: High-density lipoprotein cholesterol; GRS: Genetic risk score;
GWAS: Genome wide association study; HbA1C: Glycated hemoglobin;
LDL: Low-density lipoprotein cholesterol; MI: Myocardial infarction;
SD: Standard deviation; SNP: Single nucleotide polymorphism;
SOLAR: Sequential Oligogenic Linkage Analysis Routines; T2D: Type 2
The authors declare that they have no competing interests.
LMR performed SNP genotyping, perfomed statistical analysis, and prepared
the manuscript; AJC assisted with SNP genotyping, contributed to the
statistical analysis, and assisted with the manuscript preparation; FCH
contributed to the statistical analysis and reviewed and edited the
manuscript; MCYN contributed to the management of the genetic data and
reviewed the manuscript; CDL performed the SNP imputation and reviewed
the manuscript; JJC was involved in the initial design of the Diabetes Heart
Study, contributed to patient ascertainment and clinical evaluation, and
reviewed the manuscript; BIF was involved in the initial design of the
Diabetes Heart Study, contributed to patient ascertainment and clinical
evaluation, and reviewed and edited the manuscript; DWB leads the
Diabetes Heart Study and assisted with the manuscript preparation. All
authors read and approved the final manuscript.
This study was supported by R01 HL092301 to D.W.B. The authors thank the
Wake Forest School of Medicine investigators and staff and the participants
of the DHS study for their valuable contributions.
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