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Original Studies |
Genotypes with Body Mass Index in Normal Healthy Postmenopausal Caucasian Women1
Osteoporosis Research Center and Department of Biomedical Sciences, Creighton University (H.-W.D., J.L., R.D., K.M.D., M.J., G.G., H.D., R.R.R.), Omaha, Nebraska 68131; and Laboratory of Molecular and Statistical Genetics, HuNan Normal University College of Life Sciences (H.-W.D.), ChangSha 41800, People’s Republic of China
Address all correspondence and requests for reprints to: Hong-Wen Deng, Ph.D., Osteoporosis Research Center, Creighton University, 601 North 30th Street, Omaha, Nebraska 68131. E-mail: deng{at}creighton.edu
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Abstract |
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 Top Abstract IntroductionSubjects and MethodsResultsReferences |
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gene locus are associated with
BMI variation. Data on BMI, age, and ER- genotypes were obtained
from 108 healthy midwestern U.S. postmenopausal Caucasian women. The
study subjects were unrelated and aged 65 yr and over (mean age ±
SD, 73.4 ± 5.1 yr), with an average BMI of 25.25
(SD, 4.04). The ER- genotypes were obtained by PCR
followed by restriction enzyme PvuII digestion. We found
that in our study subjects the ER- genotypes are significantly
associated with BMI (by ANOVA, P = 0.04),
explaining about 6.2% of the BMI variation in our study sample. The
allelic effects of this locus on BMI are approximately additive. In our
sample, individuals of the PP and Pp genotypes have, respectively,
11.4% and 4.8% higher BMI than individuals of the pp genotype. There
is a significant ER- genotype by age interaction, so that in our
sample PP individuals tend to gain weight with age, whereas Pp and pp
individuals tend to lose weight with age. Therefore, the ER-
polymorphisms are associated with BMI variation in healthy
postmenopausal Caucasian women aged 65 yr and over. Our result is
consistent with some recent findings suggesting the potential effects
of the ER on BMI. The importance of the ER- genotypes in other
populations and other age groups needs to be demonstrated. Although the
results of the ER- genotype by age interaction are obtained here
from cross-sectional data, direct confirmation may come from
longitudinal studies in which individuals are measured multiple times
over several years. The importance of the ER- genotypes on BMI
should be confirmed by further studies using methods robust to the
potential problem of population substructuring that may confound the
conclusions of population association studies. |
Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsReferences |
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The polymorphisms of the gene for the -type estrogen receptor
(ER-) are reportedly associated with ER expression, altered ER
function, some disorders (e.g. breast cancer, hypertension,
and spontaneous abortion), and bone mass density variation (for
references, see Refs. 5, 6, 7). Several lines of recent evidence suggest a
potential role for the ER in the determination of BMI: 1) a subtype of
ER may have a role in ER-mediated responses in human adipose tissue
(8); 2) a potent estrogen agonist/antagonist decreases fat body mass in
aged female rats (9); 3) a selective ER modulator increases body mass
(10); 4) the progesterone/estrogen receptor ratio correlates positively
with BMI, which may reflect a correlation between body mass and serum
estrogens (11); and 5) BMI is associated with ER level in breast cancer
patients (12).
In this study we report an association of BMI with genotypes at the
ER- gene locus, quantify the magnitude of the ER- genotypic
effects on BMI variation, and reveal an interaction effect between the
ER- genotypes and age in determining BMI variation. To the best of
our knowledge, this is the first study reporting genotypic effects of
the ER- locus on BMI variation.
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Subjects and Methods |
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The 108 subjects in this study came from the elderly women in a study of low dose, continuous, estrogen/progestin (LDE) recently completed in our center (5, 6, 13). They were unrelated, healthy, postmenopausal women from midwestern U.S. The data employed for this study were those measured at the time of study entry, i.e. before any subject had taken any medication. Their ages ranged from 65.0–87.4 yr, with a mean age ± SD of 73.4 ± 5.1 yr. Their height ranged from 1.430–1.717 m, with a mean height ± SD of 1.580 ± 0.060 m. Their weight ranged from 42.0–100.8 kg, with a mean ± SD of 63.4 ± 11.2 kg. Their BMI ranged from 17.15–39.04 kg/m2, with a mean ± SD of 25.25 ± 4.04 kg/m2. They were a relatively homogenous ethnic group, all Caucasians of European origin.
The subjects were chosen randomly to enter the LDE study according to the following entry and exclusion criteria. The principal entry criterion was a spinal bone mineral density (BMD) less than 0.90 g/cm2 measured by dual energy x-ray absorptiometry with a Norland XR-26 scanner (software version 2.2.1; Norland Corp., Ft. Atkinson, WI). The average BMD z-score for all study subjects was -0.182 (SD, 0.834). The BMD z-scores are standard normal values obtained by adjusting BMD measurements for age with general referent population data provided by the scanner manufacturer. Low BMD values were selected for the study subjects with an aim to investigate the ability of low dose, continuous, estrogen/progestin to increase or preserve BMD in elderly women with relatively low BMD. Exclusion criteria were previous hip fracture, estrogen replacement or treatment with calcitonin in the previous 6 months, any treatment with bisphosphonates or fluoride, treatment with corticosteroids for more than 6 months duration, any corticosteroid treatment within the previous 6 months, and excessive cigarette smoking, defined as more than 10 cigarettes/day. Treatment with thyroid hormone was accepted provided the subject was euthyroid, and serum TSH was normal. Good health and absence of major organ system disease were documented by clinical examination and blood chemistry profile. The study was reviewed and approved by the Creighton University institutional review board. Each subject provided written informed consent before entering the study.
Of the 108 study subjects who were analyzed for BMI, 21 did not have body scans at the baseline before entering the LDE study. Of the remaining 87 subjects who did have body scans at the baseline, 8 had prosthetics, such as hip or knee replacements, which render body measurements invalid. Whole body scans by the Norland XR-26 scanner can give results for body mass composition as bone mass, fat mass, and lean mass. Therefore, there were 79 individuals who had fat mass measurements.
Genotyping
DNA was isolated from whole blood using the Puregene DNA
isolation kit (Gentra Systems, Inc., Minneapolis, MN). The
oligonucleotide primers used to determine the PvuII
polymorphisms within the ER- gene were those used by Yaich (14)
(forward, 5'-CTGCCACCCTATCTGTATCTTTTCCTATTCTCC-3'; reverse,
5'-TCTTTCTCTGCCACCC-TGGCGTCGATTATCTGA-3'). After an initial denaturing
at 94 C for 5 min, PCR was conducted through 30 cycles of the following
steps: denaturation at 94 C for 30 s, annealing at 62 C for
20 s, and polymerase extension at 72 C for 90 s. After
cycling, a final extension at 72 C for 10 min was performed. After
amplification, the PCR product of approximately 1.3 kb was digested
with restriction endonuclease PvuII, and electrophoresed in
a 1.5% agarose gel. This PCR product contains a part for intron 1 and
exon 2 of the ER- gene. The PvuII restriction fragment
length polymorphism is a commonly employed marker for genetic analyses
of the ER gene, and it is located in intro I of the ER- gene. Gels
were stained with ethidium bromide, visualized under UV light, and
photographed. The absence of the PvuII site was designated
the P allele, and the presence of this restriction enzyme cutting site
was designated the p allele.
Statistical analyses
All statistical analyses were conducted using SAS (15). The
genotype frequencies were tested against Hardy-Weinberg ratios by 
2
tests. Normality of the BMI data within each of the three ER-
genotypes was tested by graphic methods (16). Bartlett’s test (16) was
performed to test the homogeneity of variances in BMI for the
individuals of the three ER- genotypes. One-way ANOVA was performed
to test the association between BMI variation and the ER- genotype
(Fig. 1
). Multiple regression analysis
was performed for BMI. The regressor variables were age, ER-
genotype, and the interaction between them modeled as the product of
these two factors. Outlier diagnosis for the regression analysis was
performed by the R-Student statistic (15). The assumptions of the
regression analysis were inspected with residual plot analyses. In
multiple regression analyses, the three ER- genotypes were coded
numerically, with 0 for PP, 1 for Pp, and 2 for pp, respectively. This
coding reflects the number and the amount of additive effects of the p
allele in an individual. This coding can be justified by the finding
(Fig. 1) that the within-locus allelic effects at the ER- locus are
approximately additive, so that pp has the smallest BMI, Pp has an
intermediate BMI, and PP has the highest BMI. The proportion of the
total BMI variance that is attributable to the ER- genotype is
obtained with the VARCOMP procedure in SAS (15). As a significant
ER- genotype by age interaction was found, separate regression
analyses were performed for BMI within each of the three ER-
genotypes, with age as the regressor variable. The three regression
lines, the BMI and age data for each individual within each of the
three genotypes, were plotted in Fig. 2
to demonstrate intuitively the interaction between age and ER-
genotypic effects. Among the 79 subjects who had fat mass measurements,
we performed statistical analyses for body fat mass similar to the
analyses we performed for BMI.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsReferences |
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genotype frequencies were in close agreement with
Hardy-Weinberg ratios (by 2
test, P = 0.997). There were 26 individuals with the PP
genotype, 54 with the Pp genotype, and 28 with the pp genotype. The P
allele frequency was 0.49, and the p allele frequency was 0.51.
The analyses did not reveal any significant violation of the
assumptions of ANOVA and regression analyses. For example, the
p value was 0.22 for Bartlett’s test for homogeneity of
variances of BMI for the three ER- genotypes. In our study sample of
108 elderly Caucasian women, ANOVA revealed significant ER-
genotypic effects (P = 0.04) for BMI, explaining about
6.2% of the BMI variation. In our study subjects, on the average,
individuals of the pp genotype had the lowest BMI [24.11 ± 0.63
(±SE) kg/m2], individuals
of the Pp genotype had intermediate BMI (25.26 ± 0.53
kg/m2), and individuals of the PP genotype had
the highest BMI (26.85 ± 0.91 kg/m2). Thus,
ER- genotypic effects on BMI are approximately additive (Fig. 1
). In
our study subjects, individuals with Pp and PP genotypes had,
respectively, approximately 4.8% and 11.4% higher BMI than those with
the pp genotype. There was a significant (P = 0.01)
ER- genotype by age interaction, as revealed by the partial
regression coefficient for the interaction term in the multiple
regression analysis (Table 1). This
interaction signals that the dynamics of BMI with aging were not the
same for the three ER- genotypes and that the differences among
genotypes depends on the specific age or age ranges of the subjects.
With the caveat of cohort effects, Fig. 2 clearly shows that
individuals of the PP genotype tend to gain weight with age, whereas
those of Pp and pp genotypes tend to lose weight with age. It is also
clear (Fig. 2) that, due to the ER- genotype by age interaction,
individuals of the PP genotype tended to have higher BMI than
individuals of Pp and pp genotypes beyond the age of about 70 yr,
whereas below the age of about 70 yr, the Pp and pp genotypes tended to
have higher BMI than PP individuals. However, overall in our sample, PP
individuals had, on the average, the highest BMI despite the ER-
genotype by age interaction. This is because most of the study subjects
were elderly women over 70 yr old (Fig. 2).
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), consistent with the
findings in Fig. 1 for BMI, the test result was not significant
(P = 0.50). It is not clear whether the lack of
significance is due to sample size differences or other factors. Other
analyses of fat mass also showed patterns of results similar to those
for BMI, but none achieved statistically significant results. Larger
samples will be needed to examine genotypic effects of the ER- locus
on fat mass.
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To the best of our knowledge, this is the first report on the
association of the ER- genotypes with BMI variation that suggests a
potential role for the ER- genotypic effects on BMI for women. The
PvuII restriction fragment length polymorphism is located in
intro I of the ER- gene. The mutation at the PvuII
restriction site itself is thus unlikely to be of functional
importance. However, our results suggest that there is a functional
mutation in the ER gene that is significant for BMI variation.
Therefore, if our results are robust upon further studies in different
populations, further detailed molecular genetic studies of the ER gene
are warranted to identify the functional mutation.
The findings from this study were consistent with previous
evidence suggesting a potential role of ER- in determining BMI. The
genomic location of the ER- gene is on 6q25.1 in the human genome
(http://www3.ncbi.nlm.nih.gov/Omim). Interestingly, this genomic
location coincides with the syntenic location in the human genome
(6q25-q27) that harbors an important quantitative trait locus (QTL) for
BMI, as suggested by QTL mapping in mice (4, 17). As stated in the
introduction, gene-searching results of different studies using various
approaches and even those using the same approach (including QTL
mapping studies) are often inconsistent. Therefore, our results here
serve at least as a valuable confirmatory study for the importance of
the genomic region 6q25-q27. Most importantly, genomic regions
identified in QTL mapping studies are generally large (e.g.
10–30 cM in length). There are usually dozens of genes in such a
genomic region and which one may be important is usually not clear
without extensive further studies. Therefore, our results are important
in explicitly suggesting that the ER- gene may be the gene important
for BMI variation in the genomic region 6q25-q27 identified in the
previous QTL mapping study.
The significant ER- genotype by age interaction is detected here
from cross-sectional data; direct confirmation may come from
longitudinal studies in which individuals are measured multiple times
over several years. The finding of an ER- genotype by age
interaction suggests that appropriate sampling and/or statistical
analysis to control for the age of the study subjects may be necessary
to detect ER- genotypic effects. The ER- genotype by age
interaction implies that the magnitude and the direction of the ER-
genotypic effect depends on the age of the subjects. Therefore, in
future studies to substantiate the ER- genotypic effects on BMI in
other populations and/or other age groups, care should be taken to
control for the age of the study subjects. Either study subjects within
a narrow age range should be collected and analyzed, or BMI should be
adjusted for the ages of the study subjects by statistical methods such
as multiple regression for each genotype respectively. Although
significant associations of the ER- genotypes with BMI were detected
in our sample, the magnitudes of the ER- genotypic effects on BMI
were relatively small. Nevertheless, the genetic effects were
significant, so that different genotypes can differ by up to
about 11.4% of the average BMI, a magnitude that can potentially
have significant clinical implications and applications.
Significant genetic results generated by regular population association
studies that are not family based may be plagued by the problem of
population admixture (18, 19, 20, 21). Therefore, future research should be
conducted to eliminate this possibility using approaches such as the
transmission disequilibrium test (22). As various genotypes at the
ER- locus may have different effects at different ages, the ages of
the study subjects should be considered and properly controlled or
adjusted in future studies.
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Acknowledgments |
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Footnotes |
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Received November 10, 1999.
Revised April 20, 2000.
Accepted May 2, 2000.
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References |
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