International Journal of Obesity (2003) 27, 491–497
& 2003 Nature Publishing Group All rights reserved 0307-0565/03 $25.00
www.nature.com/ijo
PAPER
Evidence for joint action of genes on diabetes status
and CVD risk factors in American Indians: the Strong
Heart Family Study
KE North1*, JT Williams2, TK Welty3, LG Best4, ET Lee5, RR Fabsitz6, BV Howard7 and JW MacCluer2
1
Department of Epidemiology, University of North Carolina, Chapel Hill, NC, USA; 2Department of Genetics, Southwest
Foundation for Biomedical Research, San Antonio, TX, USA; 3Aberdeen Area Tribal Chairmen’s Health Board, Rapid City, SD,
USA; 4Missouri Breaks Industries Research, Inc., Timber Lake, SD, USA; 5Center for American Indian Health Research, School of
Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA; 6Epidemiology and Biometry Program,
National Heart, Lung, and Blood Institute, Bethesda, MD, USA; and 7MedStar Research Institute, Washington, DC, USA
OBJECTIVES: Previous research among American Indians of the strong heart family study (SHFS) has demonstrated significant
heritabilities for CVD risk factors and implicated diabetes as an important predictor of several of the phenotypes. Moreover, we
recently demonstrated that genetic effects on CVD risk factors differed in diabetic and nondiabetic individuals. In this paper, we
investigated whether a significant genetic influence on diabetes status could be identified, and whether there is evidence for
joint action of genes on diabetes status and related CVD risk factors.
METHODS AND RESULTS: Approximately 950 men and women, age 18 or older, in 32 extended families, were examined
between 1997 and 1999. We estimated the effects of genes and environmental covariates on diabetes status using a threshold
model and a maximum likelihood variance component approach. Diabetes status exhibited a residual heritability of 22%
(h2 ¼ 0.22). We also estimated the genetic and environmental correlations between diabetes susceptibility and eight risk factors
for CVD. All eight CVD risk factors displayed significant genetic correlations with diabetes status (BMI (rG ¼ 0.55), fibrinogen
(rG ¼ 0.40), HDL-C (rG ¼ 0.37), ln triglycerides (rG ¼ 0.65), FAT (rG ¼ 0.38 ), PAI-1 (rG ¼ 0.67), SBP (rG ¼ 0.57), and WHR
(rG ¼ 0.58)). Three of eight traits (HDL-C (rE ¼ 0.32), ln triglycerides (rE ¼ 0.33), and fibrinogen (rE ¼ 0.20)) displayed
significant environmental correlations with diabetes status.
CONCLUSIONS: These findings suggest that in the context of a high prevalence of diabetes, still unidentified diabetes genes
may play an important role in influencing variation in CVD risk factors.
International Journal of Obesity (2003) 27, 491–497. doi:10.1038/sj.ijo.0802261
Keywords: pleiotropy; diabetes status; CVD risk factors; American Indians; strong heart family study
Introduction
Cardiovascular disease (CVD) is the most common macrovascular complication of diabetes mellitus, accounting for
80% of deaths among diabetic individuals.1,2 The relation of
diabetes to CVD and associated CVD risk factors has been the
focus of recent research among American Indians of the
Strong Heart Study (SHS). This longitudinal population-based
study was initiated in 1988 to determine the prevalence and
*Correspondence: Dr KE North, Department of Epidemiology, University
of North Carolina at Chapel Hill, Bank of America Center, 137 E. Franklin
Street, Suite 306, Chapel Hill, NC 27514-3628, USA.
E-mail: [email protected]
Received 5 June 2002; revised 5 November 2002;
accepted 9 December 2002
incidence of CVD in 13 American-Indian communities in
three geographic regions (Arizona (AZ), Oklahoma (OK), and
North and South Dakota (DK)), and to identify CVDassociated risk factors. A pilot family study was added in the
third phase, the Strong Heart Family Study (SHFS), which was
designed to localize genes influencing CVD risk factors in 30
large families (approximately 950 participants).
Among American Indians of the SHS the prevalence of
diabetes has reached epidemic proportions3 and is believed
to contribute to the increasing rates of CVD morbidity and
mortality.4,5 Additionally, alarming increases in CVD risk
factors across time have been reported.3 Many CVD risk
factor phenotypes are significantly heritable, and diabetes
status has been identified as an important correlate of these
risk factors in American Indians participating in the SHFS.6
Action of genes on diabetes status and CVD risk factors
KE North et al
492
Moreover, we recently demonstrated that genetic effects on
obesity and lipid-related CVD risk factors in the SHFS
differed in diabetic and nondiabetic individuals.7
The heritability of diabetes susceptibility has not been
examined in American Indians of the SHFS. However, many
studies have implicated a strong genetic component to
variation in diabetes status.8 Other evidence that diabetes
has strong genetic determinants includes marked diseaserate differences between populations9,10 and a close correspondence between genetic admixture rates and disease
prevalence in hybrid populations.11–13 Twin studies have
demonstrated a high rate of concordance among monozygotic (MZ) twins.14–17 Additionally, family studies have
established the contribution of a positive family history in
the development of diabetes18,19 and a higher prevalence of
diabetes in first, second, and third degree relatives in
comparison to controls20,21. All these studies are suggestive
of a significant genetic component to diabetes susceptibility.
In this study, we wished to determine if a significant
genetic influence on diabetes status could be identified and
whether there is evidence for the joint action of genes on
diabetes status and related quantitative CVD risk factors in
the American Indians of the SHFS. To accomplish these
objectives, we first estimated the effects of genes and
environmental covariates on diabetes susceptibility, using a
threshold model and a maximum likelihood variance
component approach.22 We then used an extension of
variance component methods to determine the genetic and
environmental correlations between quantitative risk factors
for CVD and discrete disease outcomes (diabetes status).23
Genetic and environmental correlations with diabetes
susceptibility were assessed for eight CVD risk factors (body
fat (FAT), body mass index (BMI), HDL cholesterol (HDL-C),
ln fibrinogen, ln triglycerides, plasminogen activator inhibitor 1 (PAI-1), systolic blood pressure (SBP), and waist-to-hip
ratio (WHR)).
Materials and methods
Strong heart study
The study design and methods of the SHS have been
described previously.24 The SHS is a longitudinal study with
three clinical examinations and mortality and morbidity
surveillance of resident tribal members aged 45–74 y (the
cohort). During the baseline examination, conducted between 1989 and 1991, 4549 tribal members were examined.
The second examination, conducted between 1993 and
1995, included 89% of the surviving members of the original
cohort and the third examination, conducted in 1998–1999,
included 88% of surviving members of the cohort. A pilot
family study, the SHFS, was added in 1998 in which 8–12
extended families (more than 300 family members at least
18 y of age) were recruited and examined at each center. An
extension of the pilot family study is currently in progress
and will involve recruitment of approximately 90 additional
families, for a total of 2700 new participants.
International Journal of Obesity
Strong heart family study
The design and methods of the SHFS have been described
elsewhere.7 Approximately 950 men and women, age 18 y or
older, in 32 extended families, were examined between 1997
and 1999 in the three field centers: Arizona, Oklahoma,
and North and South Dakota. The Arizona Field center is
located in Phoenix and has enrolled primarily Pima Indians,
but there are also representatives of the closely related
Maricopa and Tohono O’odham tribes.24 The Oklahoma
center encompasses 11 tribes, primarily Plains and Southeastern American Indians living in the Lawton-Anadarko,
Oklahoma area (Apache, Caddo, Comanche, Delaware, Fort
Sill Apache, Kiowa, and Wichita tribes). The Dakota Center
has examined participants primarily from the Cheyenne
River Sioux tribe of the Cheyenne River reservation in South
Dakota.
Phenotypic, demographic, and lifestyle data
The SHFS exam consisted of a personal interview, physical
examination, laboratory tests, and a carotid ultrasound.
Standard protocols were used for the collection of all data
and are described in detail in previous publications24,25.
Body fat mass was measured using an RJL bioelectric
impedance meter and estimated by the RJL formula based
on total body water.26
Blood pressure was measured three times and the mean of
the last two measurements was used for analysis.
Fasting blood samples were obtained during the physical
examination for the measurement of lipids, lipoproteins,
apolipoproteins, insulin, glucose, plasma creatinine, plasma
fibrinogen, and PAI-124,27. All phenotypes were assayed at
MedStar Research Institute, Washington, DC, using standard
laboratory methods as previously described24,27. The bquantification procedure for lipoprotein quantification, as
described by the Lipid Research Clinics, was applied to these
data for family study participants. For cohort participants,
the b-estimation procedure for lipoprotein quantification
was applied.28 Triglycerides were measured using the Triglyceride-GB system pack for Hitachi 717. Fibrinogen was
measured in an automated clot-rate assay based upon the
original method of Clauss.29 The ST4 Instrument
(Diagnostica Stago), with standardization with the CAP
reference material, was used to measure fibrinogen levels.
PAI-1 was measured using a double antibody ELISA, originally developed by DeClerck et al.30 This assay is sensitive to
free PAI-1 (latent and active) and not to PAI-1 in complex
with t-PA.
The glucose tolerance test was performed only if the
participant was not on insulin or any oral hypoglycemic
medications for known diabetes and if the participant had a
fasting glucose value less than 225 mg/dl (as determined by
Acucek II, Baxter Healthcare, Grand Prairie, TX, USA).
Diabetes (DM), impaired glucose tolerance (IGT), and
normal glucose tolerance (NGT) status were determined
according to the World Health Organization criteria.31
Action of genes on diabetes status and CVD risk factors
KE North et al
493
All participants gave informed consent for this study,
which was approved by the Institutional Review Boards of all
of the participating institutions.
Statistical genetic methods
The phenotypic variance in CVD risk factors was partitioned
into additive genetic and environmental variance components,32 using maximum likelihood variance decomposition
methods.33–35 These univariate quantitative genetic analyses
were performed using the computer package SOLAR.36
The heritability of diabetes status was estimated using a
pedigree-based maximum likelihood method that models
affection status by a liability threshold model.32,22 Although
disease status is a qualitative trait, with individuals scored as
either affected or unaffected, the threshold model assumes
there is an underlying quantitative liability to disease. If an
individual’s liability exceeds a specified threshold, the
individual becomes affected. If an individual’s liability is
below the threshold, the individual is unaffected. Liabilities
and the threshold value itself are estimated using age- and
sex-specific parameters, and the population prevalence of
the disease trait.
We next used a mixed discrete-continuous trait variance
component method for mixed traits to examine the
genetic relation between diabetes susceptibility and quantitative variation in CVD risk factors.23 Specifically, we
estimated the genetic and environmental correlations between diabetes susceptibility and eight quantitative risk
factors for CVD.
P-values for estimated effects are obtained using likelihood
ratio tests, where the likelihood of a given model is estimated
and compared to the likelihood of the model in which the
effect is absent. Twice the difference in the natural
logarithmic likelihoods is asymptotically distributed as a
1/2 : 1/2 mixture of a w2 variable with one degree of freedom
and a point mass at zero.37
Evidence consistent with pleiotropy (in which the same
gene influences several traits) is indicated by additive genetic
correlations that are significantly different from zero. The
detection of significant shared genetic effects on multiple
phenotypes can lead to hypotheses regarding the genetic
regulation of complex phenotypes, identify possible constellations of genes underlying suites of complex phenotypes, and when major locus effects have been detected,
generate tentative linkage hypotheses.
Application to SHFS data
To maximize the power to detect genetic correlations, our
analyses used combined data from all three centers. We used
the covariates sex, age, age-by-sex interaction, age2, age2-bysex interaction, and center, because these effects are
common to both diabetes susceptibility and the CVD risk
factors examined here. No additional covariate adjustments
were made as previous studies have demonstrated a minimal
reduction of the genetic signal for the traits examined here,
when additionally adjusted for BMI and smoking status.6 All
individuals with impaired glucose tolerance (n ¼ 119) were
excluded from these analyses. The analysis of each phenotype was restricted to those individuals for whom complete
covariate data were available. Lipid phenotypes were not
analyzed for individuals currently taking lipid-lowering
medications (n ¼ 13), and blood pressure measures were not
analyzed for individuals currently taking antihypertensive
medications (n ¼ 143). For each quantitative trait, all outliers
(here defined as any value more than 3.5 s.d. from the mean)
were removed prior to analysis to minimize the effect of trait
kurtosis.38
Results
Information on diabetes status was available for 887
participants. The prevalence of diabetes was 30% (nearly all
type II diabetes), with a higher proportion of diabetic female
(168/518, 32%) than male individuals (100/369, 27%). The
median age of the sample is 39.2 y and the high rate of
diabetes in this young sample is a clear indication of the
continuing problem of diabetes among American-Indian
populations.
In total, 31 of 32 of the SHFS families had at least one
affected participant, with a mean of 8.6 diabetics per family
group (range between 4 and 17). The diabetic participants in
these 31 families included a large number of relative pair
types (Table 1). A total of 4079 relation pairs included at least
one diabetic participant with many instances of first degree
(eg, siblings or parents and offspring), second degree (eg,
avuncular pairs, grandparent–grandchild pairs), and third
degree or greater relative pairs (eg, great-avuncular pairs, first
cousin pairs, and second cousins).
Heritability of diabetes status
Using the threshold model, diabetes status exhibited a
residual heritability of 41% (h2 ¼ 0.4170.09, Po0.00001).
Therefore, after correction for age, sex, age-by-sex interaction, age2, and age2-by-sex interaction, 41% of the
Table 1 Strong Heart Family Study: numbers of examined relative pairs
including at least one diabetic participant
Relationship
Number of
relative pairs
Parent–offspring
Siblings
Half-siblings
Grandparent–grandchild
Avuncular
Grand avuncular
First cousins
First cousins once removed
Second cousins
Other
429
350
58
154
1139
416
542
663
68
260
Total relative pairs
4079
International Journal of Obesity
Action of genes on diabetes status and CVD risk factors
KE North et al
494
remaining variation in liability to diabetes in this population
can be attributed to additive genetic factors. We also
corrected for BMI and the heritability was reduced to 31%
(h2 ¼ 0.3170.10, Po0.0001). Recognizing that there are
twice as many diabetic participants in Arizona as compared
to Dakota and Oklahoma, we speculated that including the
center effect might remove some of the genetic component
in diabetes status. When center was included as a covariate,
diabetes status exhibited a residual heritability of 22%
(h2 ¼ 0.2270.10, P ¼ 0.002). Nonetheless, it was important
to include center as a covariate in all analyses to obtain an
estimate of between-center differences. When larger sample
sizes become available, independent analyses will be conducted in each center.
lipid-, clotting-, and blood pressure-related phenotypes and
have implicated diabetes status as an important correlate.6
Moreover, we recently demonstrated that genetic effects on
obesity- and lipid-related CVD risk factors in the SHFS
differed in diabetic and nondiabetic individuals.7 Motivated
by these findings, we were interested to determine if a
significant genetic influence on diabetes status itself could be
identified, and whether there is evidence for the joint action
of genes on diabetes status and related quantitative CVD risk
factors in the American Indians of the SHFS. In our previous
analyses, only five traits (BMI, FAT, WHR, HDL-C, and ln
triglycerides) showed evidence for distinct genetic effects in
diabetic and nondiabetic individuals. However, high standard errors of parameter estimates were obtained for the
three additional phenotypes, indicating low power of the
sample.
In this study, after the effects of age, sex, age-by-sex
interaction, and center have been accounted for, we have
demonstrated a moderate additive genetic effect on diabetes
status. To our knowledge, there are few published actual
heritability estimates for diabetes status8. Nonetheless, there
is strong evidence that diabetes has strong genetic determinants9–21. The identification of moderate genetic influences
on diabetes status in the American Indians of the SHFS is
important, not only in its own right, but also because
diabetes status has been implicated as the best predictor of
CVD.5
We also found evidence for common genetic effects on
diabetes status and eight obesity, lipid, clotting, and blood
pressure traits. In fact, such pleiotropic action of genes is
predicted in highly coordinated systems, such as in CVD risk
factors (eg, HDL-C and BMI). Although no prior studies have
reported genetic correlations between diabetes status and
these CVD traits, studies have consistently demonstrated an
effect of diabetes on obesity,39,40 lipids,41–44 clotting
traits45,46, and blood pressure measures.47 In the American
Indians of the SHFS, there is a high prevalence of diabetes. In
Correlations between diabetes status and CVD risk
factors
The descriptive statistics for cardiovascular disease-risk
factors in diabetics and nondiabetics are reported in
Table 2. The genetic and environmental correlations of
diabetes status with CVD risk factors are given in Table 3.
These correlations vary widely, but all CVD risk factors
display a significant genetic correlation with diabetes status.
In total, three of eight CVD risk factors (HDL-C, triglycerides,
and fibrinogen) also show a significant environmental
correlation with diabetes status.
As all eight CVD risk factors displayed significant genetic
correlations with diabetes status, we briefly examined
whether other CVD risk factors would also show significant
genetic correlations with diabetes status. For apolipoprotein
B and LDL-C, no significant genetic correlation was detected
(data not shown).
Discussion
Our previous analyses of American Indians from the SHFS
have demonstrated significant heritabilities for obesity-,
Table 2
A summary of descriptive statistics of cardiovascular disease risk factors presented by diabetes status: The Strong Heart Family Study
Diabetic
Phenotype
Obesity
BMI (kg/m2)
FAT (%)
WHR (ratio)
Lipidsa
Mean
s.d.
Nondiabetic
Sample size
Mean
s.d.
Sample size
33.48
38.35
0.96
6.32
8.76
0.06
271
263
270
30.31
34.82
0.91
6.61
10.19
0.08
499
499
495
HDL-C (mg/dl)
Triglyceride (mg/d1)
39.23
170.04
11.04
107.44
266
265
44.73
115.83
12.93
62.27
492
499
Clotting
Fibrinogen (mg/d1)
PAI-1 (ng/ml)
368.83
58.91
80.92
39.18
249
254
307.46
49.66
67.78
35.97
491
493
Hypertensionb
SBP (mmHg)
128.42
16.71
178
119.34
12.62
479
a
Individuals taking cholesterol-lowering medications were not included in these analyses.
Individuals taking antihypertensive medications were not included in these analyses. For all phenotypes, all outliers were removed prior to analysis.
b
International Journal of Obesity
Action of genes on diabetes status and CVD risk factors
KE North et al
495
Table 3 Genetic (rG) and environmental (rE) correlations between diabetes
status and CVD risk factors among American Indians of the SHFS
CVD risk factor
BMI (kg/m2)
FAT (%)
WHR (ratio)
HDL-C (mg/dl)a
ln Triglyceride (mg/dl)a
ln Fibrinogen (mg/dl)
PAI-1 (ng/ml)
SBP (mmHg)b
rE
rG
0.55
0.38
0.58
0.37
0.65
0.40
0.67
0.57
(0.14)***
(0.14)*
(0.15)***
(0.27)**
(0.21)***
(0.11)**
(0.17)***
(0.21)**
0.10
0.11
0.13
0.32
0.33
0.20
0.18
0.11
(0.13)
(0.14)
(0.14)
(0.18)**
(0.13)**
(0.11)***
(0.10)
(0.11)
a
Individuals taking cholesterol-lowering medications were not included in
these analyses.
b
Individuals taking antihypertensive medications were not included in these
analyses. For all phenotypes, all outliers were removed prior to analysis.
E, Environmental; G, Genetic. Numerical values in parentheses are standard
errors.
Asterisks after numerical values of individual coefficients indicate significance
as follows: *(Pr0.05), **(Pr0.01), ***(Pr0.001).
the context of this genetic, metabolic, and neuroendocrine
background, it may be that a common genetic effect in
diabetics, or a diabetes susceptibility gene, is an important
influence on variation in CVD risk factors.
Interestingly, six of the CVD risk factors that are indicative
of gene-by-gene interaction with diabetes status are components of the metabolic syndrome, a cluster of metabolic
abnormalities including central obesity, abnormal glucose
tolerance, elevated insulin, and triglycerides, depressed HDLC, and hypertension.48–50 Previous studies have implicated a
common underlying genetic factor influencing the development of the syndrome50–55, and the suggestion of pleiotropy
in diabetes status and some metabolic syndrome traits, is
consistent with those findings. Future research will explore
this relationship more closely by examining the clustering of
the metabolic syndrome traits, and using these clusters in
the calculation of heritabilities and in linkage analysis.
Three traits, HDL-C, triglycerides, and fibrinogen, displayed significant environmental correlations with diabetes
status. These findings are suggestive of common unmeasured
residual effects (for example, diet and/or physical activity)
on diabetes status and lipid and clotting levels. Although no
other studies have reported an environmental correlation
between diabetes status and lipid or clotting measures,
relations between diabetes and lipid levels56–58 and between
diabetes and clotting variables59,60 have been consistently
demonstrated. Lifestyle factors such as smoking, alcohol
consumption, diet, and physical activity may also be
implicated in these findings, as the residual variance can
encompass any unmeasured environmental effect. Additionally, studies have consistently demonstrated an effect of
smoking, alcohol consumption, diet, and physical activity
on lipid levels58,61–67 and on fibrinogen levels.68–74 Within
the SHS populations, elevated triglyceride concentrations
were significantly associated with the development of
diabetes in men with normal glucose tolerance.75 In
addition, lifestyle factors such as smoking and physical
activity have been shown to affect lipid levels.25,76
To maximize the number of individuals entering the
analyses, our models included only age, age2, sex, and center
as covariates. It would have been of interest to estimate the
effect of diabetes duration; however, many individuals did
not provide information on diabetes duration and we were
unable to consider this effect.
Given the multiple comparisons presented in Table 3, it
might be argued that a correction for multiple tests was
needed. Using a Bonferroni correction for eight comparisons,
a P-value of 0.05/8 ¼ 0.00625 would be required to ensure the
conventional 5% level of statistical significance over all
traits77). Using such an approach, only four of the eight
genetic correlations would have been considered statistically
significant (BMI, WHR, ln triglyceride, and PAI-1). However,
this correction assumes that all of the comparisons are
independent, which they are not in our study (eg, ln
triglyceride and HDL-C).
In summary, a significant heritability of diabetes status,
and several significant genetic and environmental interactions between diabetes status and eight CVD risk factors were
demonstrated. We believe that this represents an important
step in the understanding of the determinants of CVD risk
factors. These findings will be important for future research,
as statistical genetic models incorporating these interactions
should better approximate the biological reality of the traits,
and make it easier to detect, localize, and identify genes
contributing to variation and covariation of CVD risk
factors, and to measure their effects. These results are a first
step in the search for CVD risk factor genes in American
Indians. Future research will determine the chromosomal
location of CVD risk factors genes and ultimately, functional
polymorphisms associated with the variability will be
identified.
Acknowledgements
We thank the Strong Heart Family Study participants.
Without their participation, this project would not have
been possible. In addition, the cooperation of the Indian
Health Service hospitals and clinics, and the directors of the
Strong Heart Study clinics, Betty Jarvis, Marcia O’Leary, Dr
Tauqeer Ali, Alan Crawford, and the many collaborators and
staff of the Strong Heart Study have made this project
possible. We also thank Drs John Blangero, Laura Almasy,
Tony Comuzzie, and Lisa Martin for their generous contribution to this research. This research was conducted while
author (KEN) was a postdoctoral fellow at the Southwest
Foundation for Biomedical Research and was funded by a
cooperative agreement that includes Grants U01 HL65520,
U01 HL41642, U01 HL41652, U01 HL41654, U01 HL65521
from the National Heart, Lung, and Blood Institute. The
views expressed in this paper are those of the authors and do
not necessarily reflect those of the Indian Health Service.
International Journal of Obesity
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