International Journal of Obesity (1999) 23, 449±455
ß 1999 Stockton Press All rights reserved 0307±0565/99 $12.00
http://www.stockton-press.co.uk/ijo
Genetic and environmental in¯uences on waistto-hip ratio and waist circumference in an older
Swedish twin population
TL Nelson1*, GP Vogler1, NL Pedersen2,3 and TP Miles4
1
Department of Biobehavioral Health and Center for Developmental and Health Genetics, The Pennsylvania State University, 101 Amy
Gardner House, University Park, PA 16802, USA; 2Division of Genetic Epidemiology, Institute of Environmental Medicine, Karolinska
Institute, Box 210, S 171 77 Stockholm, Sweden; 3Department of Psychology, University of Southern California, Los Angeles, CA, USA
and 4Department of Family Practice, Division of Geriatrics, University of Texas Health Sciences Center-San Antonio, 7703 Floyd Curl
Drive, San Antonio, TX 78284, USA
OBJECTIVE: To investigate the genetic and environmental in¯uences on waist-to-hip ratio (WHR) and waist
circumference (WC) measurements in males and females.
DESIGN: Measurements taken from 1989 ± 1991 as part of The Swedish Adoption=Twin Study of Aging (SATSA) were
used for analysis. The SATSA sample contains both twins reared together as well as twins reared apart.
SUBJECTS: 322 pairs of twins (50 identical, 82 fraternal male pairs and 67 identical, 123 fraternal female pairs); age
range: 45 ± 85 y (average age, 65 y).
MEASUREMENTS: Waist-to-hip ratio (WHR), waist circumference (WC) and body mass index (BMI).
RESULTS: In males, additive genetic effects were found to account for 28% of the variance in WHR and 46% of the
variance in WC. In females, additive genetic effects were found to account for 48% of the variance in WHR and 66% of
the variance in WC. The remaining variance in males was attributed to unique environmental effects (WHR, 72%; WC,
54%) and in females the remaining variance was attributed to unique environmental effects (WHR, 46%; WC, 34%) and
age (WHR, 6%). When BMI was added into these models it accounted for a portion of the genetic and environmental
variance in WHR, and over half of the genetic and environmental variance in WC.
CONCLUSION: There are both genetic and environmental in¯uences on WHR and WC, independent of BMI in both
males and females, and the differences between the sexes are signi®cantly different.
Keywords: genetic; environmental; waist-to-hip ratio; waist circumference
Introduction
The adverse metabolic consequences of adipose tissue
located in the abdominal region has been documented
extensively. Regional body fat distribution has been
associated with non insulin-dependent diabetes mellitus (NIDDM), hypercholesterolaemia, hyperinsulinaemia, insulin resistance, hypertriglyceridaemia,
atherosclerosis and hypertension.1 ± 5
There are several ways to measure body fat distribution including anthropometric measures, computerized
tomography (CT), magnetic resonance imaging (MRI)
or dual energy X-ray absorptiometry (DEXA). CT scans
and MRIs and DEXA are the ideal measures of central
abdominal fat, however, their use in large-scale studies
is limited due to the expense of these procedures.
Anthropometric measures are most commonly used in
such large-scale epidemiological studies, although they
only provide an indirect estimate of abdominal body fat.
*Correspondence: Tracy L. Nelson, Department of
Epidemiology, Cardiovascular Disease Program, 137 E. Franklin
St, NationsBank Plaza, Suite 306, The University of North
Carolina at Chapel Hill, Chapel Hill, NC 27514, USA.
E-mail: [email protected]
Received 21 April 1998; revised 12 November 1998; accepted
2 December 1998
The waist-to-hip ratio (WHR) has been the most commonly used anthropometric measure, however, recent
work suggests that using the waist circumference (WC)
is more closely associated with measures of abdominal
visceral fat, that is, the fat most associated with the
metabolic problems listed above.6 ± 8
There are known to be both genetic and environmental in¯uences on regional body fat distribution,
however, it is still unclear how these in¯uences may
vary among different age groups and in each gender.
It is important to obtain estimates for age effects and
gender effects, because regional body fat distribution
is known to increase with age and average values tend
to be quite different for males and females.9,10 Genetic
in¯uences have been estimated in several studies for
WHR. In a family study of Mexican Americans,
genetic in¯uences were found to account for 17.3%
of the variance in WHR.11 Other estimates have found
WHR to be 28 ± 50% transmissible; such estimates
include both genetic and cultural factors (that is,
shared environment).12,13 There are three twin studies
we are aware of that estimated WHR and=or WC in
adults. Selby et al, 14 using The National Heart, Lung
and Blood Institute's Twin Study (NHLBI) found
heritability estimates for WHR (h2 ˆ 0.31, P ˆ 0.07)
and WC (h2 ˆ 0.46, P ˆ 0.02) after controlling for
Genetic in¯uences on WHR and waist circumference
TL Nelson et al
450
body mass index (BMI) Cardon et al 15 also used the
NHLBI Twin Study data and found a heritability of
46% (12% of this variance was shared with BMI) for
WHR. They used maximum likelihood methods
which provide more power than the methods used
by Selby et al.14 Rose et al 16 using the Kaiser
Permanente Twin Study found a heritability of 61%
for WHR and 76% for WC in females, after controlling for BMI and age.
This is the ®rst analysis we are aware of that uses a
twin=adoption design to estimate the heritability of
WHR and WC. Family studies, as with twin studies,
may overestimate genetic effects due to confounding
genetic and environmental factors, since relatives
living together share environments and genes. Twin
studies may overestimate genetic effects if monozygotic (MZ) twins share more environmental in¯uences
than dizygotic (DZ) twins. A design that reduces this
kind of bias is one that combines twin studies with
adoption studies. The Swedish Adoption=Twin Study
of Aging (SATSA) uses this design by comparing MZ
and DZ twins reared together (MZT and DZT, respectively) and twins reared apart (MZA and DZA,
respectively).
The purpose of this analysis was to determine the
genetic and environmental components of variance in
body fat distribution, measured by WHR and WC, in
Swedish men and Swedish women with an average
age of 65 y.
Methods
Data for this study came from the Swedish Adoption=Twin Study of Aging (SATSA). The SATSA
sample was identi®ed through the Swedish Twin
Registry, which includes almost 25 000 pairs of
same-sexed twins born in Sweden during 1886 ±
1958.17 The SATSA subregistry was formed in 1984
by contacting twin pairs identi®ed in the Swedish
Twin Registry as having been reared apart, along
with a matched sample of twins reared together.
Those pairs reared apart were identi®ed based on
their responses to the item: `How long did you and
your twin partner live together in the same home?' All
pairs in which one or both members indicated that
they were separated from each other prior to their 11th
birthday were included for further study. The identi®cation and characterization of the SATSA sample
has been described in detail elsewhere.18 Measurements of WHR and WC were obtained from a subset
of 322 pairs of twins which includes pairs where both
twins may not have information on WHR and WC,
who underwent physical examinations between
1989 ± 1991. The average age of twins used in this
analysis was 65 y (range 45 ± 85 y) with 60% female
and 40% male.
Measures
WC measurements were obtained at the circumference around the smallest part of the waist, and hip
measurements as the circumference around the widest
point between hips and buttocks. WHR was then
determined by dividing the waist measurement by
the hip measurement. Age and gender were determined by self-reported information during screening
in 1984. BMI was calculated as weight (kg) divided
by height squared (m2). Height (m) and weight (kg)
were obtained from subjects dressed in lightweight
clothes with their shoes removed.
Statistical analysis
Analyses included descriptive statistics and genetic
analyses. Descriptive analyses were performed using
SAS,19 and included means and standard deviations as
well as correlations of WHR, WC and BMI.
Genetic analyses were performed using Mx20 to
evaluate quantitative contributions of genetic (additive and dominant) and environmental (shared and
unique) components. The assumptions using this
model-®tting analysis are that MZ twins share 100%
of their additive and dominant genetic effects and DZ
twins share 50% of their additive genetic effects and
25% of their dominant genetic effects. Twins reared
together share similar rearing environmental effects
and those reared apart do not share rearing environmental effects. This sample allowed us to partition out
dominant, as well as additive, genetic effects, because
the SATSA sample contains twins reared together in
addition to twins reared apart (see Figure 1). In
traditional twin studies, which only contain twins
reared together, shared rearing environmental and
dominant effects confound each other and therefore
cannot be estimated separately.
In large multivariate samples, there is often missing
data. One twin may not have data for the variable
being studied. To avoid having to discard all data
from such pairs for analysis, we used Mx,20 a model®tting program that allows missing data to be considered in the analysis. Since Mx uses raw data instead
of variance-covariance matrices, the program does not
give an actual ®t statistic (that is, w2) for the overall
model but gives instead a maximum likelihood statistic (minus two times the log-likelihood), based on the
multivariate normal function. Relative ®t of nested
models can be evaluated by ®rst determining the
maximum likelihood statistic for a general model
and then comparing to a more constrained model.
The difference between minus twice the log-likelihood of each model is distributed as a w2 with degrees
of freedom being the difference in the number of
parameters estimated in the two different models. So
Genetic in¯uences on WHR and waist circumference
TL Nelson et al
Table 1 Mean values and standard deviations (s.d.) for waistto-hip ratio (WHR), waist circumference (WC) and body mass
index (BMI) in 322 pairs of twins by gender, zygosity and rearing
status
Males
Variable
Figure 1 Univariate genetic model. Path diagram shows genetic
and environmental effects on Trait 1. MZT ˆ monozygotic twins
reared together; MZA ˆ monozygotic twins reared apart;
DZT ˆ dizygotic twins reared together; DZA ˆ dizygotic twins
reared apart; G ˆ genetic factor; D ˆ dominant genetic factor;
ES ˆ shared environmental factor; E ˆ unique environmental
factor; Trait 1 indicates trait in twin 1 and trait in twin 2; and
Age indicates age in years for twin 1 and twin 2.
for example, if shared environmental effects were set
to zero and compared to the general model (difference
between minus twice the log-likelihoods), a statistically signi®cant w2 would mean shared environmental
effects were a signi®cant component of the variance
for the variable under consideration. The relative
importance of genetic and environmental in¯uences
on WHR and WC can be calculated by squaring and
summing the parameter estimates of components of
variance for each measure and dividing each squared
parameter estimate by the sum of all the squared
estimates of components of variance.
Since both age and gender are known to affect WHR
and WC, the analyses considered age as a covariate in
the model and gender differences were assessed. Since
BMI was highly correlated with both WHR and WC
(males BMI ± WHR ˆ 0.53 P < 0.0001, BMI ±
WC ˆ 0.84 P < 0.0001; females BMI ± WHR ˆ 0.40
P < 0.0001, BMI ± WC ˆ 0.87 P < 0.0001), we also ran
bivariate analyses with BMI in the model, to determine
genetic and environmental variance in WHR and WC,
independent of BMI.
Results
Sample characteristics
Female subjects were aged, on average, 67 9 y and
male subjects were aged, on average, 63 8 y. Table 1
lists mean levels of WHR, WC and BMI by gender
and rearing status. The mean values are about average
for this age range in the Swedish population.
Table 2 shows the correlations by gender, zygosity
and rearing status for WHR, WC and BMI. In general,
intra-pair correlations for MZ twins were higher than
Females
Mean
WHR
MZT
MZA
DZT
DZA
WC
MZT
MZA
DZT
DZA
BMI
MZT
MZA
DZT
DZA
s.d.
Mean
s.d.
0.93
0.95
0.91
0.93
(27)
(23)
(43)
(39)
0.05
0.06
0.05
0.05
0.81
0.81
0.81
0.81
(43)
(24)
(49)
(74)
0.06
0.05
0.06
0.06
96.28
98.21
92.57
97.03
(27)
(23)
(43)
(39)
9.80
8.54
8.23
8.31
82.20
83.05
82.79
84.46
(43)
(24)
(49)
(74)
10.22
8.73
9.00
12.53
26.13
26.07
24.61
26.16
(27)
(23)
(43)
(39)
3.38
3.13
2.64
3.10
24.96
25.05
25.41
26.20
(43)
(24)
(49)
(74)
3.72
3.80
4.00
5.34
The number of pairs are in parenthesis. Note: these are not all
full pairs (that is, variables are included where we only have
information for one twin).
MZT ˆ monozygotic twins reared together; MZA ˆ monozygotic
twins reared apart; DZT ˆ dizygotic twins reared together;
DZA ˆ dizygotic twins reared apart.
Table 2 Intra-pair correlations and 95% con®dence intervals
(CI) for waist-to-hip ratio (WHR), waist circumference (WC) and
body mass index (BMI) in males and females by zygosity and
rearing status
Variable
WHR
MZT
MZA
DZT
DZA
WC
MZT
MZA
DZT
DZA
BMI
MZT
MZA
DZT
DZA
Males
N
95% CI
Females
N
95% CI
0.48
0.38
7 0.12
7 0.07
27 (0.12, 0.73)
0.65
19 ( 7 0.09, 0.71) 7 0.002
36 ( 7 0.43, 0.22)
0.30
32 ( 7 0.41, 0.28)
0.29
30 (0.38, 0.82)
19 ( 7 0.45, 0.45)
37 ( 7 0.03, 0.57)
61
0.04, 0.51)
0.52
0.64
0.06
0.08
27 (0.17, 0.75)
19 (0.26, 0.84)
36 ( 7 0.28, 0.38)
32 ( 7 0.27, 0.42)
0.64
0.48
0.43
0.32
30
19
37
61
(0.35,
(0.03,
(0.12,
(0.07,
0.81)
0.77)
0.66)
0.68)
0.67
0.55
0.33
0.22
27 (0.39, 0.84)
19 (0.13, 0.80)
36 (0.00, 0.59)
32 ( 7 0.14, 0.53)
0.63
0.65
0.50
0.38
30
19
37
61
(0.34,
(0.28,
(0.21,
(0.14,
0.80)
0.86)
0.70)
0.57)
Note: these are all full pairs (pairs where one twin had missing
data were deleted to obtain the correlations).
MZT ˆ monozygotic twins reared together; MZA ˆ monozygotic
twins reared apart; DZT ˆ dizygotic twins reared together;
DZA ˆ dizygotic twins reared apart.
those for DZ twins, indicating genetic in¯uences.
There were differences in correlations between
males and females indicating possible gender differences in heritability for abdominal fatness. The negative correlations for WHR in the male DZA and DZT
and female MZA twins might suggest the twins'
WHR scores are in opposite directions, however
these correlations were not signi®cantly different
from zero. In traditional genetic analysis, such correlations would limit our ability to assess genetic
in¯uences, because all the information contained in
all groups, regarding genetic in¯uences, is not used
451
Genetic in¯uences on WHR and waist circumference
TL Nelson et al
452
simultaneously. However, we used model-®tting analyses, which permits analysis of groups of twins
simultaneously. This method is more powerful at
detecting genetic effects because it uses all of the
information in all of the groups in a single comprehensive analysis.
Quantitative genetic analysis
To assess whether males and females should be
considered separately in this genetic analysis, a constrained model was tested, where the parameter estimates were set equal for males and females (see Table
3 and Table 4). These models were signi®cantly worse
than the full models (WHR: w2 ˆ 18.05, df ˆ 6,
P < 0.01; WC: w2 ˆ 27.18, df ˆ 6, P < 0.001) indicating males and females had signi®cantly different
Table 3
Test of models for genetic and environmental
in¯uences on waist-to-hip ratio (WHR)
WHR
1. Full model
2. Constrained model
(males ˆ females)
3. Constrained model
(no D)
4. Constrained model
(no A)
5. Constrained model
(no Es)
6. Constrained model
(no D, A or Es)
7. Constrained model
(no Es or D)
8. Constrained model
(no A or D)
7 2 loglikelihood df
Difference
w2
df P-value
AIC
621.978
895
640.027
901 18.05 6
0.01
6.05
624.461
897 2.48
2
NS
7 1.52
622.558
897 0.58
2
NS
7 3.42
622.238
897 0.26
2
NS
7 3.74
645.068
901 23.09 6
624.744
899 2.77
4
NS
7 5.23
634.634
899 12.66 4
0.05
4.66
0.001
11.06
AIC ˆ Akaike's information criterion; NS ˆ not statistically
signi®cant; A ˆ additive genetic effects; D ˆ dominant genetic
effects; Es ˆ shared environmental effects.
Table 4
Test of models for genetic and environmental
in¯uences on waist circumference (WC)
WC
1. Full model
2 Constrained model
(males ˆ females)
3. Constrained model
(no D)
4. Constrained model
(no A)
5. Constrained model
(no Es)
6. Constrained model
(no D, A or Es)
7. Constrained model
(no Es or D)
8. Constrained model
(no A or D)
7 2 loglikelihood df
Difference
w2
df P-value
AIC
6570.301
893
6597.476
899 27.18 6
6572.270
895 1.97
2
NS
7 2.03
6573.292
895 2.99
2
NS
7 1.01
6571.677
895 1.38
2
NS
7 2.62
6618.562
899 48.26 6
6573.712
897 3.41
6593.754
897 23.45 4
4
0.001
0.001
NS
0.001
15.18
36.26
parameter estimates for measures of WHR and WC.
A reduced model, where dominant genetic loadings
were set to zero in both males and females, had no
signi®cant loss of ®t compared to the full model, nor
did setting additive genetic effects to zero or shared
environmental effects to zero. However, setting dominant and additive genetic effects and shared rearing
environmental effects to zero, in males and females,
resulted in a signi®cantly worse ®t of the model. To
further test this model, we set only shared rearing
environmental effects and dominant genetic effects to
zero, and this did not change the ®t of the model.
However, setting additive genetic effects and dominant genetic effects to zero resulted in a signi®cantly
worse model indicating the importance of genetic
effects on WHR and WC. To determine which
model was the most parsimonious, we computed the
Akaike's Information Criterion (AIC) (w2-2df). The
model with the lowest value of this index ®ts best,
according to the AIC. As can be seen in Table 3 and
Table 4, model 7 was the most parsimonious
(AIC ˆ 7 5.23 and 7 4.59). Model 7 was also the
most parsimonious for univariate genetic analysis of
BMI (not presented).
Table 5 presents the percentage of genetic and
environmental in¯uences for WHR, WC and BMI.
In males, additive genetic effects were found to
account for 28% of the variance in WHR and 46%
of the variance in WC. In females, additive genetic
effects were found to account for 48% of the variance
in WHR and 66% of the variance in WC. The
remaining variance in males was attributed to
unique environmental effects (WHR, 72%; WC,
54%) and in females the remaining variance was
attributed to unique environment (WHR, 46%; WC,
34%) and age (WHR, 6%). When BMI was added into
the WHR and WC models (Figures 2 and 3), it
accounted for a portion of the genetic and environmental variance in WHR and over half of the genetic
and environmental variance in WC.
To present the nature of the genetic and environmental covariances in another way, in Table 6, we
have reported phenotypic correlations for WHR and
BMI, as well as WC and BMI, in both males and
females. We then broke down the phenotypic correlations into a component due to genetic effects and a
component due to environmental effects. See Appendix for a description of the phenotypic correlation
calculations.
Table 5
Percent genetic and environmental in¯uences for
waist-to-hip ratio (WHR), waist circumference (WC) and body
mass index (BMI)
Males
7 4.59
15.45
AIC ˆ Akaike's information criterion; NS ˆ not statistically
signi®cant; A ˆ additive genetic effects; D ˆ dominant genetic
effects; Es ˆ shared environmental effects.
Females
Genetic Environmental Age Genetic Environmental Age
WHR
WC
BMI
28%
46%
57%
72%
54%
43%
-
48%
66%
72%
46%
34%
28%
6%
-
Genetic in¯uences on WHR and waist circumference
TL Nelson et al
Table 6
Genetic (G) and environmental (E) components of
phenotypic correlations for waist-to-hip ratio (WHR) and body
mass index (BMI) as well as for waist circumference (WC) and
BMI in males and females
WHR
WC
Males BMI
Females BMI
0.49(G ˆ 0.13; E ˆ 0.36)
0.82(G ˆ 0.41; E ˆ 0.41)
0.35(G ˆ 0.20; E ˆ 0.15)
0.86(G ˆ 0.60; E ˆ 0.26)
was unique to WHR and 8% was contributed from
genetic effects common to BMI, while 14% of the
genetic in¯uence was unique to WC and 52% was
contributed from BMI. These results also suggest
there are environmental effects unique to WHR, WC
and BMI, and that both WHR and BMI, as well as WC
and BMI, also have environmental effects in common.
Discussion
Figure 2 Multivariate genetic model for waist-to-hip ratio
(WHR) and body mass index (BMI) in males and females. Path
diagram shows genetic (G) and environmental (E) effects on
WHR and BMI. G1 is genetic factor 1 which loads on BMI and
WHR; G2 is genetic factor 2 which only loads on WHR. E1 is
unique environmental factor 1 which loads on BMI and WHR and
E2 is unique environmental factor 2 which only loads on WHR.
Age indicates age in years for twins 1 and twin 2.
Figure 3 Multivariate genetic model for waist circumference
(WC) and body mass index (BMI) in males and females. Path
diagram shows genetic (G) and environmental (E) effects on WC
and BMI. G1 is genetic factor 1 which loads on BMI and WHR; G2
is genetic factor 2 which only loads on WC. E1 is unique
environmental factor 1 which loads on BMI and WC and E2 is
unique environmental factor 2 which only loads on WC.
Overall, there are genetic effects unique to BMI,
WHR and WC, and there are also genetic effects in
common to BMI and WHR, as well as BMI and WC.
In males, 25% of the genetic in¯uence was unique to
WHR and 3% was contributed from genetic effects in
common with BMI, while 17% of the genetic
in¯uence was unique to WC and 29% contributed
from BMI. In females, 40% of the genetic in¯uence
The purpose of this study, was to estimate the effects
of genetic and environmental in¯uences on regional
body fat distribution, as measured by WHR and WC.
We found an additive genetic in¯uence on WHR and
WC, in both males and females. This genetic effect
was signi®cantly different for males and females, and
age was a covariate for females. The heritability
estimates of WHR and WC are hard to compare to
those found in other studies, because of the different
methods used. For example, some of the studies used
families which in most cases meant they could not
separate out genetic effects from cultural or shared
environmental effects. The Canadian Fitness Survey
showed WHR to be about 28% transmittable (this
estimate included genetic and cultural effects).12 Data
from the San Antonio Family Heart Study showed
WHR to be 17.3% heritable, but this was with men
and women combined,11 and among women in the
Iowa Women's Health Study13 it was found that the
WHR was 40 ± 50% transmittable, but once again they
could not separate genes from the shared environment.
The NHLBI study by Selby et al 14 suggested a nonsigni®cant heritability of 0.31 (P ˆ 0.07) for WHR,
but a signi®cant heritability for WC (0.46, P ˆ 0.02),
however they used the classical estimates based on the
intraclass correlations, which have low power and do
not use all the information simultaneously. Rose et
al 16 found among female twins, in the Kaiser Permanente Twin Study, that WHR was 61% heritable while
WC was 76% heritable, after adjusting for BMI
and age. The study by Cardon et al 15 is the most
comparable to our data, because they used quantitative genetic analysis, however, they only used males.
Their results showed that genetic effects explained
46% of the variance in WHR, while ours showed 28%
of the variance was explained by genetic effects in
males. They also looked at what percentage of this
variance was shared with BMI and found that 12% of
the 46% genetic variance in WHR was explained by
BMI, while our study found that 3% of the 28%
genetic variance was explained by BMI. The differences in these ®ndings may be due to the potential for
shared environmental effects to confound genetic
effects and thus over-estimate heritabilities when
quantitative analyses are made with only twins
reared together. A twin study by Carey et al 21 used
DEXA scans to measure central abdominal fat and
453
Genetic in¯uences on WHR and waist circumference
TL Nelson et al
454
they have reported a heritability estimate of 70%
among females, after controlling for age and total
body fat. DEXA scans provide a direct measure of
abdominal fat and this estimate may be the most
accurate of the above.
Based on these studies, it is clear there are genetic
and environmental in¯uences on regional fat distribution, independent of overall body fat. The present
study gives more insight into the genetic effects on
measures of WHR and WC, as model-®tting analyses
permit the analysis of groups of twins simultaneously.
We were able to separate shared environmental effects
from genetic effects and estimates were obtained for
males and females separately. This study also provides estimates of effects in common with BMI. After
adding BMI to the model we found some genetic
effects in common, indicating that some of the same
genetic effects that are in¯uencing BMI are also
in¯uencing WHR and WC. Of the genetic variance
in WHR, < 25% is in common with BMI in males and
females, and over half of the genetic variance in WC
is in common with BMI in males and females. The
differences may be due to the nature of the measures,
as we found WC to be more highly correlated to BMI
than WHR (males, BMI-WC ˆ 0.84, P < 0.0001,
BMI-WHR ˆ 0.53, P < 0.0001; females, BMIWC ˆ 0.87 P < 0.0001, BMI-WHR ˆ 0.40, P <
0.0001). Other research has found some effects in
common for abdominal fatness and overall fatness, as
well as a single genetic effect that may be unique to
abdominal fatness.15,22,23 There also appears to be a
percentage of environmental effects in common for
BMI and WHR, and over half of the environmental
effects are in common between BMI and WC in males
and females. Such common environmental in¯uences
may include overeating, lack of physical activity
and=or alcohol consumption, as all of these have
been found to be associated with indicators of abdominal fat and BMI.24 ± 27
Conclusion
Based on these ®ndings, it is important to look at
genetic and environmental effects of regional body fat
distribution, in each gender separately, as well as to
look speci®cally at age effects. Males tend to have
more abdominal fat than women. However, as women
become postmenopausal, it is thought that these differences become less pronounced. We did ®nd age
effects accounted for a small portion of the variance in
WHR for females, however, we did not have the
power to observe these age effects by cohort (that
is, 50 ± 60 y; 60 ± 70 y etc.) nor did we have measures
of menopausal status. Future studies may wish to look
at whether the length of time women are postmenopausal may be used as an indirect way of assessing
how much visceral or abdominal fat they may have.
Age is also known to be associated with increases in
abdominal fat, as people age, body composition
changes, more fat becomes deposited in the abdominal
fat deposits and less on the periphery. More work is
needed to see if genetic effects are different among
different cohorts (that is, 40 ± 50 y 50 ± 60 y, 70 ± 80 y
and > 80 y), as well as in each gender among these
varying cohorts. If there are different genetic effects at
different ages, there may also be different environmental in¯uences acting through the adulthood and
into old age, possibly through gene-environment interactions. Bouchard et al 23 has found a major gene,
accounting for 51% of the variance in abdominal fat,
as measured by CT scans. Once these genes are
localized, genotyping can occur, and these potential
genetic and environmental interactions can be studied.
Acknowledgements
This project has been supported by grants from the
National Institute on Aging (AG-04563, AG-10175),
the National Heart, Lung and Blood Institute (HL55976), the MacArthur Foundation Research Network
on Successful Aging and the Swedish Council for
Social Research. T. Nelson has been supported by the
National Institutes on Aging (AG-10430) and
National Institute of Child Health and Human Development (HD-07454).
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Appendix
for variable x, and hy is the square root of the
heritability for variable y; rGxy is the genetic correlation between variables x and y. This is calculated as:
p
Cov…Gx†…Gy† VGx VGy
Calculation of phenotypic, genetic and environmental correlations
The following is the equation used to calculate the
phenotypic correlations, as well as the genetic and
environmental components of this phenotypic correlation.
rP ˆ hx hy rGxy ‡ ex ey rExy
Where rP ˆ phenotypic correlation between variable x
and variable y; hx is the square root of the heritability
which is the genetic covariance of x and y divided by
the product of the square root of the genetic variance
of x and the square root of the genetic variance of y.
The environmental correlation is calculated the same
way.
455