(Circulation. 2001;103:78.)
© 2001 American Heart Association, Inc.
Clinical Investigation and Reports |
Evidence for Joint Genetic Control of Insulin Sensitivity and Systolic Blood Pressure in Hispanic Families With a Hypertensive Proband
From the Department of Preventive Medicine (A.H.X., S.P.A., S.T.), the Atherosclerosis Research Unit (S.P.A., H.N.H.), and the Department of Medicine (J.D., E.T., P.C.H., H.N.H., T.A.B.), University of Southern California Keck School of Medicine, Los Angeles, Calif; the Division of Medical Genetics (L.J.R., L.S.-C.C., J.I.R.), Departments of Medicine and Pediatrics, Cedars-Sinai Medical Center, Los Angeles, Calif; and the Departments of Medicine (W.A.H., J.I.R.), Pediatrics (L.J.R., J.I.R.), and Human Genetics (J.I.R.), University of California at Los Angeles Medical School.
Correspondence to Thomas A. Buchanan, MD, Room 6602, General Hospital, 1200 N State St, Los Angeles, CA 90089-9317. E-mail buchanan{at}hsc.usc.edu
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Abstract |
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Background—The clustering of hypertension, insulin resistance, and obesity remains unexplained. We tested for genetic and nongenetic influences on the association among these traits in Hispanic families with hypertension.
Methods and Results—Blood pressure and body mass index (BMI) were measured in 331 members of 73 Hispanic families in which an index case (proband) had hypertension. Insulin sensitivity (SI) was measured by euglycemic clamp in 287 probands and their spouses (parents’ generation) or their adult offspring. Correlation analysis examined relationships among traits within and between generations. Path analysis estimated genetic and nongenetic contributions to variability in systolic blood pressure (SBP), SI, and the correlation between them. In the offspring, there was a significant correlation between individuals for each trait, as well as significant correlations within and between individuals for all possible pairs of traits. Between generations, SBP, SI, and BMI in parents correlated with the same traits in their offspring; BMI in parents correlated with SI and SBP in offspring; and SI in parents correlated with SBP in offspring. Path analysis estimated that among offspring, genetic effects unrelated to BMI accounted for 60.8% of the variation in SBP, 36.8% of the variation in SI, and 31.5% of the correlation between SBP and SI after adjustment for age and sex. Heritable effects related to BMI accounted for an additional 14.0% of variation in SBP, 26.8% of variation in SI, and 56.3% of variation in their correlation.
Conclusions—Clustering of hypertension and insulin resistance in Hispanic Americans is accounted for in part by heritable factors both associated with and independent of BMI.
Key Words: blood pressure • insulin • obesity • genetics • risk factors
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Introduction |
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Hypertension, insulin resistance, and obesity coexist more frequently than can be explained by their individual prevalence rates.1 2 3 4 5 6 7 8 Physiological studies suggest mechanisms by which insulin resistance and hyperinsulinemia could change blood pressure (BP),9 10 11 12 variations in vascular tone and regional blood flow could alter insulin action,13 14 15 16 and obesity could lead to insulin resistance and elevated BP.17 18 However, these physiological studies do not address the potential for shared genetic regulation of BP, insulin resistance, and obesity. The present study was conducted to look for such shared genetic influences in Hispanic Americans, an ethnic group in which hyperinsulinemia predicts the development of hypertension.19
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Methods |
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Subjects
Subjects are from a family-based genetic study of hypertension and insulin resistance. Probands were recruited from hypertension clinics and by advertisements in the community. Probands (n=73) had hypertension (BP
140/90 mm Hg or pharmacological
therapy for hypertension) with onset before age 65 years and no
evidence for secondary causes and at least 2 adult offspring willing to
participate in phenotyping. Spouses of probands (n=43) were 18 to 65
years old, and offspring of probands (n=220) were at least 16 years
old. Hypertensive individuals were studied after 2 weeks without
antihypertensive medications. All participants were of Mexican,
Salvadoran, or Guatemalan descent, as defined by the origin of both
parents and 3 of 4 grandparents. They gave written, informed consent
for participation in the study, which was approved by the Institutional
Review Board of LAC+USC Medical Center.
Phenotyping
Height was measured with a stadiometer, and weight
was measured on a beam balance. BP was measured by Dinamap (Critikon,
Inc) after subjects had been sitting with legs dangling for >5
minutes. Width of the BP cuff was 80% of the arm circumference in
each subject.
Glucose clamps were performed on individuals with fasting
serum glucose concentrations <140 mg/dL. Ninety-five percent of
subjects had clamps within 8 weeks after the BP determinations.
Subjects rested supine, and intravenous lines were placed
in 1 antecubital vein for infusions and the ipsilateral dorsal hand for
sampling of arterialized (60°C) venous blood. A primed
infusion (60 mU/m2 surface area/min) of
human insulin (Novolin R, Novo Nordisk) was administered for 120
minutes. Blood was sampled at 5-minute intervals, and dextrose was
infused to maintain plasma glucose concentrations, measured by glucose
oxidase (Beckman Glucose Analyzer, Beckman Instruments), at
100 mg/dL. Potassium chloride was infused at 5 mEq/h to prevent
hypokalemia. Blood samples drawn at -30, -20, -10, 160, 170, and
180 minutes were centrifuged within 20 minutes, and plasma was
placed at -80°C until measurement of insulin (Linco
Research).
Data Analysis
Means of triplicate BP measurements were used for
data analysis. Body mass index (BMI) was calculated as (weight
in kilograms)/(height in meters)2. Insulin
sensitivity (SI) was assessed as the mean
glucose infusion rate during the final 30 minutes of the 2-hour insulin
infusion, expressed relative to the body surface area.
Means of age, BP, BMI, SI, fasting insulin, and steady-state glucose and insulin concentrations during clamps were compared among probands, spouses of probands, and offspring by repeated ANOVA. When an ANOVA F test was significant, pairwise comparisons were made with a Bonferroni multiple comparison adjustment. BP, SI, and BMI were log-transformed to induce normality before other analyses were done. Multiple regression procedures were used, with up to cubic terms in the model, to adjust each trait for significant effects of age, sex, and interactions between these 2 variables. Residuals from the multiple regression analyses were standardized to means of 0 and SDs of 1 before subsequent analysis.
Familial correlations were calculated to look for evidence that BP and SI were related through familial factors. Heritability of each trait was estimated by variance components analysis by SOLAR (Sequential Oligogenic Linkage Analysis Routines) software.20 Because heritability was greater for systolic BP (SBP) than for diastolic BP (DBP) (0.57 versus 0.35), only SBP was used in the subsequent analyses. Correlations between pairs of traits within individuals (parents or offspring) and between single traits and pairs of traits in pairs of individuals (parent-parent, parent-offspring, and offspring-offspring) were estimated simultaneously by maximum likelihood methods.
Path analysis was used to model the joint
transmission of SBP and SI and to test whether
the transmission could be accounted for by genetic or environmental
patterns.21 The path model
(Figure 1
and Table 6) assumed SBP and SI
to be functions of linear additive effects of their respective
genotypes, G1 and G2; of a heritable component of BMI (genetic
and environmental combined); and of other nonfamilial environmental
influences.22 The model also
allows for generational differences in the effects of genetic and
environmental influences on SBP and SI. The
correlation of SBP and SI was modeled in 3
components: (1) 2 separate genetic components (G1, G2), which are
correlated according to the parameter
G; (2) 2 separate residual components, which
are correlated according to the parameters
R in offspring and
R in parents; and (3) a single familial
component of BMI that is common to both. Maximum likelihood methods
were used to estimate the parameters of the path model when
all 3 components of variability were free to change. Modeling was
repeated separately for SBP and SI with the
heritable BMI effect set to zero and, subsequently, with the non-BMI
genetic effect set to zero. Likelihood ratio tests were used to compare
the 3 models. Significant contributions of the BMI-related and
non–BMI-related effects were accepted if the first model was
significantly better than the second and third models. An analogous
approach was taken to assess contributions to the variation in the
correlation between SBP and SI. However, the
non-BMI genetic effects and residual effects were set to zero in the
second and third models because there was no single
parameter describing the influence of BMI on both SBP and
SI (see parameters
c1y1 and
c2y2 in
Figure 1) that could be set to zero.
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SEGPATH was used for correlational and path analyses.23 Analyses were done with and without correction for ascertainment of families by probands with hypertension. The results were similar, and the unadjusted results are presented.
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Results |
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Characteristics of the Cohort
Three probands declined phenotyping, and it was not medically safe to stop antihypertensive medications in 2 others; thus, phenotypic data were obtained from 68 probands (31% of whom were male), 43 of their spouses (53% male), and 220 of their offspring (42% male). Probands had 1 to 9 offspring; 89% had 2 to 4 offspring. Data on BP and BMI were available for all individuals. All of the probands, 10 (23.3%) of the spouses of probands, and 25 (11.4%) of the offspring had SBP >140 mm Hg and/or DBP >90 mm Hg. Eighty-five percent of probands, 79% of spouses, and 54% of offspring had a BMI >27 kg/m2. Forty-nine probands, 28 spouses, and 210 offspring had euglycemic clamps. Others either refused the clamp (2 probands, 3 spouses, and 6 offspring) or had fasting hyperglycemia >140 mg/dL.
SBP, DBP, BMI, and insulin resistance were greatest in
probands, intermediate in their spouses, and lowest in the offspring
(Table 1). By contrast, there were no important
differences among the 3 groups in steady-state plasma glucose or
insulin concentrations during clamps.
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Correlations Within Individuals
Within individuals in the offspring generation, there
were significant correlations (Table 2) between SBP and
SI (inverse), SI and BMI
(inverse), and SBP and BMI (direct). In the parents’ generation (Table 2), the within-individual correlation for SI and
BMI was of the same magnitude and direction as in the offspring.
Within-individual correlations between SBP and
SI and between SBP and BMI were weaker in
parents than in offspring when the correlations were calculated for all
parents
combined,
for probands only (SBP versus SI,
r=-0.20; SBP versus BMI,
r=0.20), or for spouses only
(SBP versus SI,
r=-0.32; SBP versus BMI,
r=0.13).
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Correlations Between Individuals in the
Same Generation
Between siblings in the offspring generation (Table 3),
each of the 3 traits of interest was directly correlated to itself,
yielding strong evidence for heritability. In addition, SBP in 1
sibling was inversely correlated with SI in
another, SI in 1 was inversely correlated with
BMI in another, and SBP in 1 was directly correlated with BMI in
another. In the parents’ generation (Table 3), none of the possible
correlations between the same trait in spousal pairs were statistically
significant.
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Correlations Between Individuals in
Different Generations
Each of SBP, SI, and BMI in
parents was significantly and directly correlated to the same trait in
their offspring (Table 4). Three of the possible cross-correlations
between 1 trait in parents and another trait in offspring were
statistically significant: SI in parents was
inversely correlated with SBP in offspring, BMI in parents was
inversely correlated with SI in offspring, and
BMI in parents was directly correlated with SBP in offspring
(Table 4.)
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Path Analysis
The most parsimonious model derived from path
analysis (P=0.35 for
difference from observed correlations) provided evidence for inherited
influences on both SBP and SI in the offspring
(Table 5). The effects of age and sex explained 14.4% of
interindividual variance in SBP. After adjustment for those effects,
60.8% of the remaining variation was explained by a genetic effect
that was not associated with BMI and 14.0% was explained by a
transmissible effect associated with BMI. Thus, heritable or
transmissible effects explained 74.8% of the age- and sex-adjusted
variance in SBP. For SI, age and sex explained
only 2.4% of the individual variation among offspring. Of the
remaining variation, 36.8% was explained by a non-BMI genetic effect
and 26.8% was explained by a transmissible effect associated with BMI.
Path analysis also revealed evidence for an inherited influence
on the correlation between SBP and SI in the
offspring (Table 5). After age and sex adjustment, 31.5% of that
correlation was accounted for by a genetic effect that was unrelated to
BMI and 56.2% was accounted for by a transmissible effect associated
with
BMI.
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Discussion |
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Among Hispanic American families identified by probands with essential hypertension, we found 2 types of evidence for shared inherited influences on SBP, SI, and obesity. The first type came from correlational analysis within and between parent and offspring generations. When single traits were considered in pairs of family members, significant correlations were found for SBP, SI, and BMI in sibling pairs and in parent-offspring pairs. The correlations were stronger for the sibling pairs, and those correlations gave strong evidence for heritability of each trait. No significant correlations were found for any single trait between pairs of parents. When different traits were considered in the same individual, the correlation between SI and BMI was equally strong in parent and offspring generations, but correlations between SBP and SI and between SBP and BMI were stronger in the offspring. When different traits were compared between family members, significant correlations were observed for all pairs of traits in the offspring generation but for no pairs of traits in the parent generation. This lack of correlations between traits in parents, who share many environmental exposures with each other and with their offspring, suggests that the intertrait correlations observed in sibling pairs and in parent-sibling pairs did not result entirely from shared environmental influences.
The importance of genetic influences was confirmed by path analysis. After adjustment for the effects of age and sex, a large portion of the residual variance for SBP and for SI was explained by genetic factors not related to BMI. Additional variance was explained by transmissible factors that were correlated with BMI. Hanis et al24 reported a similar finding for SBP and body weight in children. Perhaps most importantly, heritable factors explained some of the correlation between SBP and SI. One component of that correlation was explained by a genetic influence that was independent of BMI. A second component was explained by a transmissible factor that was correlated with BMI. Although the latter factor may include environmental and genetic components, the high heritability of BMI in siblings argues that a considerable portion of the BMI effect may be genetic. Taken together, our findings provide evidence that the well-known association between hypertension and insulin resistance has a substantial genetic component at its roots.
The genetic influences identified by path analysis warrant some clarification. No analysis was conducted for linkage or association of phenotypes with specific genes or genetic markers. Rather, the pattern of correlations between individuals within families was used to assess genetic and environmental influences on trait variability. Compared with variance components modeling, which is commonly used in these types of studies, path analysis provides a practical computational approach in the situation of multiple quantitative traits. Genetic influences on SBP, SI, and the correlation between them were hypothesized as parent-offspring gene-gene correlations of 0.5 for full sibs. Separate, transmissible influences of BMI were quantified from correlations between parental BMI and either SBP, SI, or their correlation in the offspring, after accounting for the effects of age and sex. Remaining variability was attributed to nonmeasured factors, presumably random environmental effects. The approach assumed additive effects and therefore could neither detect nor account for epistasis. Nonetheless, the good fit of the model provides evidence for the existence of both genetic and nongenetic influences on SBP, SI, and their correlation. Defining the precise magnitude of the effects will await identification of the underlying genes.
Like all modeling approaches, path analysis was based on some fundamental assumptions. First, transmissible environmental effects were included in the transmissible effects of BMI. If transmissible environmental effects operated through mechanisms other than BMI, then those effects would have been attributed to genetic influences on SBP or SI. Because the data were very well fit by parent-offspring gene-gene correlations of 0.5, this possibility is unlikely. However, we did not measure environmental factors such as nutrient intake or physical activity. In the absence of such measures, BMI was used as an indirect measure of obesity and related environmental effects. The second assumption was that genetic and heritable components of the effects of BMI on SBP and SI were independent. This assumption simplified the modeling process but could have resulted in some overestimation of the genetic influences.25 The third assumption was that data were multivariately normally distributed within families. Data were log-transformed before analysis, and no deviations from univariate normality were detected in the residuals after adjustment for effects of age and sex. Even if normality were not achieved, Rao et al26 have shown that relatively large departures from a normal distribution produce reasonably unbiased parameter estimates.
The relative weakness of correlations between pairs of traits in individual parents compared with individual offspring suggests that over time, environmental factors may obscure the relationship between genetic influences and phenotypic expression of traits. This phenomenon was not evident in the relationship between SI and obesity, which was equally strong in parents and offspring. By contrast, relationships between BP and SI and between BP and BMI in individual offspring were approximately twice as strong as the analogous relationships in their parents. One possible explanation is that alterations in one or the other phenotype (eg, changes in BP with age for reasons unrelated to obesity or insulin resistance) confounded the shared heritable influences on these phenotypes in the parents. Such an occurrence would make genotype-phenotype relationships more readily detectable in young individuals. Accordingly, the major focus of the project from which the present report is derived is on quantitative trait linkage analysis for genes regulating SI, BP, and obesity in the offspring generation.
Our findings of correlations among BP, SI, and obesity are consistent with prior reports.1 2 17 19 27 28 29 Those reports have created much speculation about physiological mechanisms underlying various features of the insulin resistance syndrome. To date, it has been extremely difficult to unravel precise physiological mechanisms that actually explain the clustering of metabolic and cardiovascular abnormalities. Our findings suggest that some of the clustering may be mediated through obesity, perhaps by alteration of renal sodium handling,17 sympathetic activity,9 10 or vascular insulin action.13 14 Our findings also provide important new information that shared genetic determinants of SI and BP play a role in their clustering. We speculate that a genetic approach will be useful in dissecting out the complex relationships of these common abnormalities in the population.
In summary, we found that SBP, SI, and BMI were correlated in individual members of Hispanic hypertensive families. More importantly, the traits were correlated among siblings and between parents and siblings in a pattern that suggests shared genetic influences on BP and SI. Some of the influence was inherited in a pattern that was independent of obesity, whereas another component was associated with heritable aspects of obesity. The correlations between SI and BP were stronger in the offspring than in the parental generation. Taken together, these findings provide a strong rationale for family-based studies focused on quantitative assessment of phenotypic traits related to hypertension in young, at-risk individuals to identify genes that contribute to the insulin resistance syndrome in Hispanic Americans.
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Acknowledgments |
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This study was supported by grant No. P50-HL-55005 from NHLBI, NIH; grant Nos. M01-RR-43 and M01-RR-425 from the GCRC Program, NCRR, NIH; and the Cedars-Sinai Board of Governors’ Chair in Human Genetics. We thank the following for help in patient studies and laboratory and data analysis: Susie Nakao and the nurses of the General Clinical Research Center, Blanca Jaurez, Jose Esparago, Lilit Zeberians, Mike Salce, Ann Buley, and George Martinez.
Received May 18, 2000; revision received August 7, 2000; accepted August 9, 2000.
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