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1 Center for Human Genetics and 2 Center for Physical Development Research, Katholieke Universiteit Leuven, B-3000 Leuven, Belgium; and 3 Department of Human Genetics, Medical College of Virginia, Richmond, Virginia 23298
ABSTRACT
INTRODUCTION
METHODS
RESULTS
DISCUSSION
APPENDIX
ACKNOWLEDGEMENTS
FOOTNOTES
REFERENCES
ABSTRACT 
Loos, R., M. Thomis, H. H. Maes, G. Beunen, A. L. Claessens,
C. Derom, E. Legius, R. Derom, and R. Vlietinck. Gender-specific regional changes in genetic structure of muscularity in early adolescence. J. Appl. Physiol. 82(6):
1802-1810, 1997.
Genetic and environmental influences on muscle
circumference measurements of the extremities were estimated in 105 pairs of twins between 10 and 14 yr of age. Four circumferences,
extended upper arm (EAC), forearm (FC), thigh (TC), and calf (CC), were
measured. Univariate model fitting revealed that the largest part
(87-95%) of the variance for all circumferences at most ages was
explained by additive genetic factors. Sex differences were observed
for some age categories. Multivariate analyses showed a different
pattern evolving according to age and gender. In boys from 10 to 12 yr
of age, one general genetic factor influenced all four circumferences.
With increasing age, an arm-leg model emerged, one genetic factor
influencing the arm and another genetic factor the leg circumferences.
In young girls one genetic factor loaded on the proximal (EAC,TC) and
another on the distal (FC,CC) circumferences. With subjects at age 14 yr, an arm-leg model was observed. High genetic correlations indicated
that genetic factors related to EAC, FC, TC, and CC did not act
independently. The age- and gender-specific changes in the genetic
structure suggest pubertal influences. This study shows that muscle
circumferences are highly heritable characteristics and are therefore a
promising starting point at which to locate their genes. Gene mapping
could validate the gender-specific change of the genetic structure with
age and region.
skeletal muscle circumferences; twins; genetic model fitting; univariate and multivariate genetic analysis; gender and age effects
INTRODUCTION SKELETAL MUSCLE SIZE is an indicator for
undernutrition, aging, and muscular and neuromuscular diseases and is
important in a variety of sports disciplines (9, 10, 19).
Circumferences of extended upper arm (EAC) and calf (CC) are the most
commonly used sites to estimate muscularity, followed by the
circumferences of the forearm (FC) and thigh (TC). Most often, they are
investigated by means of anthropometric or radiographic measurement.
Muscle is the major tissue composing the circumferences.
Genetic influence on extremity circumferences is suggested by family
studies, most often by comparing the correlations between parents and
offspring and between full and half-siblings, controlling for age,
gender, and rearing conditions (1, 13-15, 20, 27). It needs to be
emphasized that the comparison of results of these studies is
complicated by differences in sample characteristics (type of
relationship, age, gender, size) and differences in the methods used to
estimate heritability.
Little et al. (15) found evidence for different genetic influence
depending on body region. However most studies (13, 14, 20, 27) found
only small differences between the correlations of upper and lower
extremity circumferences. Gender differences were observed
by Kaur and Singh (14), Mueller and Malina (20), and Little et al.
(15).
Comparison of monozygotic (MZ) and dizygotic (DZ) twins also provides
insight into the possible genetic control of the trait. Twin studies
indicate strong evidence for a genetic component of circumference
measurements, with a heritability generally ranging between 0.53 and
0.75 (2, 4, 5, 8). Heritability coefficients are higher for CC than for
upper arm circumference in adults (4), whereas only little difference
was found in schoolchildren (2). Gender differences were found by some
authors (5) but not by others (8).
All these studies investigated the genetic influence of each
circumference separately. Until now, apparently no study ever examined
the covariation between circumferences in a multivariate way nor
assessed whether the genetic structure was similar in either gender or
changed over time.
The purpose of this study was to estimate the genetic and environmental
contributions to the variation of skeletal muscle characteristics, as
assessed by four anthropometric circumferences of the extremities. The
specific questions to be answered were 1) how large are the genetic and
environmental influences on each of the four circumferences?;
2) are the same genes influencing the circumference in different regions?;
3) how stable is this pattern over
time?; and 4) is the heritability
the same in men and women and are the same genes expressed in men and
women?
These questions were answered by univariate and multivariate model
fitting.
METHODS
Table 1.
Number of twin pairs by zygostiy, gender, and age
Age, yr
MZ
DZ
Total
Male
Female
Male
Female
Opposite sex
10
21
22
21
20
21
105
11
21
22
20
20
20
103
12
20
22
21
20
20
103
13
20
22
21
20
20
103
14
19
21
20
18
20
98
MZ, monozygotic; DZ, dizygotic.
2
goodness-of-fit statistic: the lower the
2, the better the fit. The
significance of each latent factor (A, D, C, and E) was tested by
comparing the full model to the model leaving out this factor. A
significantly worse fit of this submodel compared with the full model
indicated the significance of this factor. The second criterion was the
parsimony, as measured by Akaike's Information Criterion (AIC) (22) of
the models, which combines the
2 with the degrees of freedom
(df) (22)
|
2 values
equal to zero, but the most parsimonious model (with the most
df) will have the lowest AIC. Alternative univariate and
multivariate models were compared by the AIC (22).
Because 2 does not provide an
adequate assessment of the goodness of fit for multivariate models, the
Tucker-Lewis Index (TLI) (22) was calculated, which takes parsimony
into account. For this purpose, the traditional "null" model was
fitted; this assumes no covariance at all but estimates only the
variances of each variable of each individual
(TLInull). Because of the
special character of twin data, an additional null model,
the diagonal model (TLIdiag),
was fitted. This model estimates the within-variable across-twin covariances, in addi- tion to the variances of
the variables. Parsimony is accounted for by dividing the
2 values of the tested model
and the null model by their df: the higher the index the better the
fit. The goodness of fit was further evaluated by fitting a saturated
model, i.e., a double Cholesky model (22) (triangular decomposition for
A and for E factors). Because this model is fully saturated, it
provides an indication for the lowest limit of
2 obtainable with the data.
Because multivariate models can be complex, alternative models were
also compared by the AIC.
The statistical modeling package Mx (21) was used to evaluate the
fitting of univariate and multivariate models. Results with a
probability level <0.05 were considered as statistically significant.
RESULTS
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2 and the standardized
parameter estimates are summarized in Table 3. The AE-model that
incorporated additive genes and environment, unique to the individual
members of a twin pair, provided the best explanation of the variation.
Additive genes explained 87-95% of the variation of all
circumferences, except for CC at age 14 yr, where a C factor explained
26%, and only 64% was left to be explained by genes. E factors
accounted for 4-14%. Incorporation of differences due to gender
(gAE) improved the fit in several cases, especially at younger ages.
Between 10 and 12 yr of age, the variance in girls was ~1.2 times
greater than in boys (gender heterogeneity).
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2 difference test. In girls
10-13 yr of age, two correlating common genetic factors were
required, one for the proximal (EAC and TC) and one for the distal (FC
and CC) limb circumferences. The
2 of the proximal-distal model
was significantly (P < 0.05) lower than the 2 of the general model
with only one common genetic factor. The correlation between the two
common genetic factors, however, was high (0.95-0.97). At an older
age, the best fitting model was the same in both genders. The arm-leg
model contained two genetic factors, one common to the arm
circumferences (EAC and FC) and one to the leg circumferences (TC and
CC). Although the correlation between those two common genetic factors
was high (0.93 and 0.92 in boys, 0.94 in girls), the fit was
significantly (P < 0.05) better than
for the general model with only one common genetic factor. At all age
levels and for both genders, there was only one environmental factor
common to all four variables.
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DISCUSSION
The main findings of this longitudinal study are the high genetic determination of the circumferences and the gender-specific change of the multivariate genetic structure with age.
The striking similarity of all circumferences in MZ twins, indicated by the correlations ranging between 0.72 and 0.95, approachs the test-retest reliability of 0.97-0.99 we obtained. MZ twins resemble each other nearly as much as one individual measured on different occasions. This suggested a very strong genetic determination. The correlations in DZ twins, ranging from 0.30 to 0.76, showed also a familial relatedness. Because some of these DZ correlations exceeded one-half of the corresponding MZ correlations, environmental influences, common to both twin members, could be suspected. The presence of a strong genetic influence (87-95%), suggested by comparison of the MZ and DZ correlations, was confirmed by the model fitting. This genetic influence remained stable over age in both genders. A suspected common environmental factor, however, could not be demonstrated, except for CC at the age of 14 yr. In structural equation modeling, common environmental factors can only be identified when the effect or the sample size is relatively large.
The comparison of heritability estimates from previous studies with the present ones is difficult because of differences in sample composition and in the method of heritability calculation.
The high genetic contribution confirms the results found in most earlier twin studies (2, 4, 5). Heritabilities obtained from the family studies are known to be often lower than those obtained from twin studies. Because DZ twins can be considered to be similar to other first-degree relatives, the DZ correlations were used to compare our results with the sibling-sibling and parent-offspring correlations of previous family studies.
The parent-offspring correlations (13, 14, 27) of circumference measurements ranged between 0.17 and 0.42, which are lower than our DZ correlations. This might be due to a difference in generational and in environmental conditions between parents and offspring.
The DZ correlations in this study, ranging between 0.30 and 0.76, are similar but somewhat higher than the sibling-sibling correlations of schoolchildren (15, 20) and young adults (13, 14), ranging between 0.35 and 0.45.
The narrow heritability for the EAC (0.30) in 10-yr-old siblings estimated by Bouchard et al. (1), on the basis of path analysis, is much lower than the genetic contribution (0.87) in this study and in other twin studies (2, 4). Susanne (27) found also a low heritability coefficient (estimated with the method of Fisher) of only 0.47 for arm circumferences and CC. The genetic component was of the same magnitude for the circumferences of the upper and lower extremities. This confirmed the findings of most investigators (2, 13, 14, 20, 27). Only Clark (4) and Little et al. (15) found a higher genetic component for the lower extremity circumferences compared with the upper extremity circumferences. They did not mention, however, whether this difference was statistically significant. Gender differences were mainly apparent at younger ages. Additive genes contributed equally to the variance in both boys and girls, but because girls had a larger total variance, the genetic variance was higher in girls than in boys. This may be due to the difference in individual and gender-related timing of puberty. While the girls were already in puberty, most boys were still in preadolescence. This might result in a larger variance in girls than in boys. The gender difference may also be caused by the marked increase of subcutaneous fat in the extremities in girls (28). This influences the utility of limb circumference as a measure of muscularity for girls. A higher genetic influence for women on CC and EAC is also reported in adolescents (5) and in schoolchildren (15, 20). Little et al. (15) ascribe the gender difference to the environmental impact, which might be larger in boys than in girls. Because no common environmental factor was identified in this study, this hypothesized environmental impact could not be confirmed. Hewitt (11) and Hoshi et al. (12) did not observe any gender difference in calf muscularity by radiographic analysis, either.
Because of the method used, none of the earlier studies could identify a shared environmental component, although many of them (1, 8, 13-15, 20, 25, 27) assume that environment contributes to the variance of these variables. In the present study, the presence of a shared environmental factor was only apparent at age 14 yr.
To our knowledge, no comparable multivariate path analysis of the covariation between limb circumferences has been reported. This multivariate analysis revealed the importance of genetic factors, common to different regions. With increasing age, there is evidence that the genetic architecture that underlies the variation and covariation in limb circumferences changes. This is not identical in both genders.
In boys between 10 and 12 yr of age, covariation and variation of the variables were explained by the general model, assuming one common genetic factor influencing the circumferences taken at the four regions. The genetic architecture changes with age. This change could already be suspected because the arm-leg model, assuming two correlated common genetic factors, one for the arm circumferences and one for the leg circumferences, fitted the data equally well but was less parsimonious. At ages 13 and 14 yr, this tendency was confirmed because the fit of the arm-leg model was statistically significantly better. This might suggest that a different set of genes influences arm vs. leg circumferences. The strong correlation between the two common genetic factors indicated, however, that the same genes had a different influence on arms than on legs. The fact that the presence of two common genetic factors was more pronounced at ages 13 and 14 yr than at earlier ages, possibly reflected an age-associated effect of genes at different maturational stages, as suggested by Mueller and Malina (20) and Little et al. (15). Phenotypic observation might indicate this genetic structure because boys gain relatively more muscle mass in the arms compared with the legs (28).
In girls of all ages, two common genetic factors were observed, although the loading of these factors at ages 10-13 yr differed significantly from the loading at age 14 yr. At ages 10-13 yr, the proximal-distal model fitted data best; one factor loaded on the proximal limb circumferences and another on the distal limb circumferences, whereas at age 14 yr, as in boys at this age, there was evidence for the arm-leg model; one factor loaded on the two arm circumferences and one on the two leg circumferences. This switch in girls was rather unexpected because the proximal-distal model in the younger subjects did not suggest a strong covariation between arm and leg circumferences. The accumulation of fat in the extremities may influence the circumference as an indicator for muscularity (28), which complicates the interpretation. Like in boys, the two factors correlated highly at all age levels. This again suggested that nearly the same set of genes was responsible for variation and covariation of the four variables but that they acted differently depending on the body region.
These age-associated changes and gender differences in genetic control could be influenced by several factors. First, the validity of circumference measurements as indicators of muscle tissue may result in gender differences; fat accumulation may be a confounding factor in girls. However, the pattern of age- and gender-associated variation in anthropometric estimates of limb musculature is similar to that for radiographic measurements (17). Second, genders may differ because of gender-specific changes in muscle mass and fat accumulation during puberty. Boys experience a large increase in muscle mass, more so in the upper arm than in the calf, resulting in a maximum increase in muscle mass ~3-6 mo after peak height velocity (PHV). Girls accumulate relatively more fat on the extremities and boys more on the trunk, especially during puberal years, when the typical gender-specific subcutaneous fat is accentuated (17, 18, 28). Furthermore, the difference in biological maturation of boys and girls at the same ages may resort to age-associated and gender-specific differences. On the average, girls experience PHV at ~12 yr of age and boys at ~14 yr of age (18). Consequently, girls are biologically two years more advanced than boys at the same age.
Given the timing of the changes in genetic architecture, especially in boys, it is tempting to associate these changes with hormonal secretions and the concomitant changes in muscle tissue. To demonstrate the common genetic architecture, multivariate analyses need to be carried out in which both hormonal secretion and muscle dimensions are incorporated (18).
Because circumferences are mainly determined by muscle (17, 29), our findings could be explained by the biology of this tissue. Two questions arise: 1) can the regional difference in genetic structure be explained by genes known from the literature to influence muscle tissue in different regions?; and 2) what factors could explain the temporal changes of the genetic structure in either gender?
Heritable neuromuscular diseases in childhood (7) show that the action of genes controlling skeletal muscle development is neither stable in time nor do these genes influence different regions in the same way. Some of these genes affect predominantly the muscles in certain body regions. Autosomal dominant myotonic dystrophy and different types of hereditary motor and sensory neuropathies have a more severe effect on the distal musculature than on the proximal. Spinal muscular atrophy, the Duchenne-type of muscular dystrophies, and limb-girdle muscular dystrophies affect the proximal musculature and, only in a later stage, the distal musculature. The facioscapulohumeral muscular dystrophy affects predominantly the muscles of the face, neck, and shoulder girdle. To some extent, this is what the proximal-distal model assumes. Furthermore, several genes on the X-chromosome have an impact on neuromuscular function, i.e., genes for Duchenne muscular dystrophy, spinobulbar muscular atrophy, and myotubular myopathy. Abnormalities or polymorphisms in these genes will have a different effect in male vs. female children (7). There is thus ample evidence from genetic diseases that several genes influence muscles in different regions of the body.
In both boys and girls, additive genetic factor(s), common to several regions, explained the largest part of the phenotypic variation. The genetic influence, specific for each region, was low, especially in girls. This suggested that there was no clear evidence for "circumference"-specific genes and even strengthened the assumption that genes coding for muscle circumferences did not act completely independently. Total genetic contribution (common and specific) to the variance of each phenotype accounted for up to 94% of the total variance, which confirmed the univariate results and suggested a strong genetic basis for the phenotypic variation in different muscle areas. The contribution of unique environment was low.
In summary, this study confirmed that muscle circumferences are some of the most heritable characteristics in humans and are therefore a promising starting point to locate their genes. The environmental influence, shared by both twin members, was too small to be demonstrable. Multivariate analysis of the covariance between different muscle regions showed that additive genetic factors influenced the muscle circumferences differently according to the region in boys and girls at different ages. The high correlation between the genetic factors suggested that muscle circumferences might be mediated to some extent by the same genes that act not completely independently. Gene mapping should validate this gender-specific change of the genetic structure with age and region.
ACKNOWLEDGEMENTS
We are very greatful to E. Feys for the anthropometric measurements and to Dr. R. Lysens for medical examinations.
FOOTNOTES
Address for reprint requests: R. Loos, Center of Human Genetics, Faculty of Medicine, Katholieke Universiteit Leuven, Herestraat 49, O&N6, B-3000 Leuven, Belgium (E-mail: ruth.loos{at}med.kuleuven.ac.be).
Received 5 November 1996; accepted in final form 12 February 1997.
APPENDIX
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W. Huygens, M. A. I. Thomis, M. W. Peeters, J. Aerssens, R. Vlietinck, and G. P. Beunen Quantitative trait loci for human muscle strength: linkage analysis of myostatin pathway genes Physiol Genomics, August 11, 2005; 22(3): 390 - 397. [Abstract] [Full Text] [PDF]
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W. Huygens, M. A. Thomis, M. W. Peeters, J. Aerssens, R. Janssen, R. F. Vlietinck, and G. Beunen Linkage of myostatin pathway genes with knee strength in humans Physiol Genomics, May 19, 2004; 17(3): 264 - 270. [Abstract] [Full Text] [PDF]
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