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Genetics of Pubertal Timing and Its Associations With Relative Weight in Childhood and Adult Height: The Swedish Young Male Twins Study

^a Department of Public Health, University of Helsinki, Helsinki, Finland
^b National Public Health Institute, Helsinki, Finland
^c Department of Pediatrics, Kuopio University Hospital, Kuopio, Finland
^d Division of Epidemiology, Stockholm Centre of Public Health, Stockholm, Sweden
^e Department of Public Health Sciences, Karolinska Institute, Stockholm, Sweden

	ABSTRACT

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OBJECTIVE. Previous studies have suggested that the timing of puberty is associated with BMI in childhood and adult stature. Because the genetic background of these associations is not thoroughly investigated, we aimed to analyze it in a longitudinal twin cohort.

METHODS. We studied a Swedish cohort of 99 monozygotic and 76 dizygotic twin pairs born between 1973 and 1979 with weight and length or height measured annually from birth to age 18 years. Age at onset of pubertal growth spurt, age at peak height velocity, and final height were estimated by a parametric JPA2 growth model. The genetic architecture and mutual associations of these traits and childhood BMI were analyzed by linear structural equation modeling.

RESULTS. The heritability estimate was 0.91 for age at onset of pubertal growth spurt, 0.93 for age at peak height velocity, and 0.97 for adult height. Age at onset of pubertal growth spurt was negatively associated with BMI from 1 to 10 years of age and stature in early adulthood. For age at peak height velocity, we found similar associations with childhood BMI and stature in early adulthood. These associations were explained by common genetic factors.

CONCLUSION. Growth during puberty is strictly genetically regulated. These genetic factors also explain why boys who matured early had higher BMI through childhood and taller stature in early adulthood.

Key Words: genetics • puberty • stature • BMI

Abbreviations: r_A—additive genetic correlation • r_E—unique environmental correlation • CI—confidence interval

Puberty is an important part of human growth. Hormonal changes in puberty trigger the onset of adolescent growth spurt and finally lead to the fusion of growth plates in the long bones, consequently leading to the permanent cessation of growth.¹ The period of rapid growth during puberty is a unique feature of the human growth pattern not known in other primates and may have a background in human evolution.² From 50% to 80% of the variation in the timing of puberty has been found to be because of genetic differences between individuals.³ However, environmental factors also modify the onset of puberty as seen in a trend of earlier onset of puberty during the 20th century in many industrialized countries.⁴ Most previous studies on the timing of puberty have been performed with girls and, most often, the age at menarche has been used as a marker for the timing, because it is easy to measure, and self-reported data are relatively reliable. Studies using timing of the pubertal height growth as a marker are more rare, because they need longitudinal measurements of height.

Early twin studies showed that pubertal growth is under strong genetic control.^5,6 Two previous twin studies based on mathematical growth models found that pubertal growth characteristics were strongly genetically regulated, and heritability estimates for them varied from 0.49 to 0.96.^7,8 There is also evidence that a new set of genes affecting height may be turned on during the pubertal growth spurt and then turned off.^5,9 However, these studies were based on a small number of twin pairs and covered only a part of the growth period.

Timing of puberty is also associated with human anthropometrics. High BMI in childhood has been found to be associated with earlier timing of puberty both in boys and girls.^10,11 In girls undergoing puberty within normal limits, shorter adult stature has been reported for those who start puberty early.¹² However, such an effect of timing has not been reported for boys.¹³ Knowledge is scarce about how genetic factors contribute to these mutual associations among BMI in childhood, timing of puberty, and final adult height. In this study, we aimed to analyze these questions in a sample of Swedish male twins having annual height measurements from birth to early adulthood.

	METHODS

TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
Data Sources
The data were derived from the Swedish Young Male Twins Study described in detail elsewhere and approved by the ethics committee at the Karolinska Institute.¹⁴ All of the male twins born in Sweden from 1973 to 1979 were identified in the Swedish Medical Birth Register covering >99% of all births in Sweden. For most twin pairs, zygosity was based on validated questions of physical similarity in childhood asked in postal questionnaires sent to all identified twins in 1998 and 2002 (response rates: 79% and 62%, respectively).^15,16 In 2003, twins belonging to complete pairs of undetermined zygosity were offered a DNA test,¹⁷ and 114 additional pairs were classified as monozygotic or dizygotic.

Information on growth was derived from several registers linked together by using the unique personal identification number assigned to all Swedish citizens a few days after birth, as described in detail elsewhere.^14,18 Birth order, birth length and weight, and gestational age were derived from the Swedish Medical Birth Register and the questionnaire of 1998. Growth data were recorded routinely as part of health checkups at public child-health centers from birth to 6 years of age and after that in annual health care examinations at schools to age 17 years. Because of the universal school system in Sweden, nearly all of the children and adolescents attended the public school health services during the study period. For all of the twins who responded to the questionnaire in 2002 and gave written permission, growth data were retrieved manually from municipal and county council archives all over Sweden. We were able to locate archived records from child health centers and/or school health services for 50% of the twins who participated in the survey of 2002. Data on measured height in early adulthood were derived from the Military Service Conscription Register. The conscription examination, which precedes active military service, was mandatory in Sweden by law for all Swedish male citizens born between 1973 and 1979, and only men with a severe handicap or chronic disease were exempted. During the time of the conscription examination, twins were 17.5 to 20.0 years old (mean: 18.2 years; SD: 0.3 years); 8 subjects who were older or younger were excluded from the data. We used ponderal index (kilograms per meter cubed) at birth and BMI (kilograms per meter squared) from ages 1 to 10 years as the indicator of relative weight. Ponderal index at birth was adjusted for gestational age and BMI at other ages for the exact age at the time of measurement. This was done by computing regression residuals of ponderal index or BMI with age as an independent variable in a regression model. Logarithmic transformation was used to normalize the distributions of ponderal index and BMI.

For the data analyses, we selected those participants who had 10 measures of height during the whole growth period, and 5 of them were between 10 and 20 years of age; the average number of measurements from birth to 20 years of age in this subsample was 18. The final data included 610 twin individuals, including 99 monozygotic and 76 dizygotic complete twin pairs. For 107 twin individuals we could not determinate zygosity, and they were removed from all of the genetic analyses.

Statistical Methods
We analyzed the data using a parametric JPA2 growth model developed by Jolicoeur et al.¹⁹ In a previous evaluation study, this model was found to capture the complexity of growth well during the whole growth period and to explain growth data better than previous growth models.²⁰ The formula for the JPA2 model is as follows:

As seen in the formula, the model includes 7 parameters to be estimated: A is the final height; D₁, D₂, and D₃ are positive time scale factors; and C₁, C₂, and C₃ are positive exponents. In addition, t is age measured from the time of fertilization and X is measured height at age t. Although the JPA2 model is a descriptive model without a solid biological base, its 3 logistic components match well with the 3 phases of human growth, that is, infancy, early childhood, and puberty, which may be under the control of different hormonal systems.²¹ The model parameters were estimated by using the R statistical software (www.r-project.org).

We estimated the parameters of the JPA2 growth curve by a nonlinear regression model (the nlme function of the R software). We included all 7 of the parameters in the model as fixed effects and all of the parameters except C₃ also as individual-level random effects. Using estimated parameters, we calculated predicted growth, growth velocity, and growth acceleration curves for each individual (predicted and observed growth and growth velocity curves are available from Dr Rasmussen on request). The age at onset of growth spurt was defined to be the age when growth acceleration first changed sign from negative to positive after the age of 9 years, and the age at peak height velocity was the age when the sign next changed from positive to negative.²² The model explained 93% of the variation in height in the data. The average prediction error for the whole sample was –0.015 (variance: 2.077), and average prediction errors for individuals varied from –0.507 (variance: 0.171) to 0.149 (variance: 11.190).

After fitting the JPA2 model, we analyzed the genetic architecture of age at onset of growth spurt, age at peak height velocity, and final height. Classic twin analysis based on linear structural equation modeling was used.²³ Although monozygotic twins are genetically identical, dizygotic twins share, on average, 50% of their segregating genes. Genetic variation can be divided into additive genetic variation, which is the sum of the effects of all of the alleles affecting the trait, and dominance genetic variation caused by interaction between alleles in the same locus. Epistatic effects, that is, interaction between alleles in different loci, are assumed to be absent. Additive and dominance genetic effects have 1 correlation within monozygotic pairs and 0.50 and 0.25 within dizygotic pairs, respectively. Both monozygotic and dizygotic twins are assumed to share the same amount of environmental variation, which is partly shared by a twin pair (common environment) and partly unique to each twin individual (unique environment), including any random measurement error.

Based on the above assumptions, 4 sources of variation interpreted as latent and standardized variance components in a structural equation model can be identified: additive genetic, genetic dominance, common environment, and unique environment. Our data include only twins reared together and, therefore, do not allow modeling of genetic dominance and common environmental effects simultaneously. In addition, we needed to make the assumptions of random mating and lack of gene-environment interaction.

The genetic models were conducted by using the Mx statistical package (Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA), and the raw data analysis option was used to allow the inclusion of incomplete twin pairs.²⁴ We simultaneously modeled age at onset of growth spurt, age at peak height velocity, and final height as estimated by the JPA2 growth model using the trivariate Cholesky decomposition (Fig 1). A Cholesky decomposition does not make any assumptions on the underlying genetic architecture but simply decomposes the variation and covariation in the data into a series of uncorrelated genetic and environmental factors. The first factor influences all of the traits, the second factor influences only the second and possibly third trait, and the third factor influences only the third trait. The assumptions of twin modeling, for example, equal variances and means for monozygotic and dizygotic twins, as well as for first-born and second-born cotwins, were tested by comparing twin models with saturated models, which do not make these assumptions. The fit of models was tested comparing ² goodness-of-fit statistics and degrees of freedom between nested models; a large change in the ² values compared with the change in degrees of freedom between 2 nested models indicates that the more simple model does not describe the data well, and the eliminated parameters are important in the model.

View larger version (16K): [in this window] [in a new window]   FIGURE 1 Schematic representation of the bivariate and trivariate Cholesky decomposition for additive genetic (A1, A2, and A3) and unique environmental (E1, E2, and E3) influences. PHV indicates peak height velocity.

	RESULTS

TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
We started the analysis by fitting the JPA2 growth model to the data. Based on the parameters of the model shown in the footnote, we computed ages at onset of growth spurt and peak height velocity and final height (Table 1). The growth spurt started, on average, at age of 11.6 years, and height velocity peaked at age 14.1 years. The final adult height estimated from the model was 179.1 cm. No significant differences in means or variances were found between monozygotic or dizygotic twins. However, twins with unknown zygosity were, on average, somewhat shorter than those with valid zygosity determination.

Table 2 presents intraclass correlations and parameter estimates of additive genetic and unique environmental factors. The effect of genetic factors was seen in higher correlations within monozygotic rather than dizygotic twin pairs. Heritability estimates were high both for ages at onset of growth spurt (a² = 0.91) and peak height velocity (a² = 0.93). The highest heritability was found for model estimated final height (a² = 0.97). The rest of the variation was explained by unshared environmental factors.

View larger version (16K): [in this window] [in a new window]   FIGURE 2 Additive genetic (rA) and specific environmental (rE) correlations for age at onset of growth spurt, age at peak height velocity (PHV), and final adult height on the basis of reparametrization of the Cholesky decomposition.

	DISCUSSION

TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
The heritability was 0.91 for age at onset of growth spurt and 0.93 for age at peak height velocity in this Swedish cohort of male twin pairs. Of interest is that these heritability estimates are almost the same as those reported from a Belgian twin study; that is, 0.93 and 0.92, respectively.⁸ Lower heritability estimates, 0.49 and 0.74, respectively, were found in a Polish twin study.⁷ It is possible that there are real differences in the relative contributions of genetic and environmental factors to the timing of puberty between populations, for example, because of differences in environmental stress. However, these comparisons should be interpreted with caution, because the Belgian and Polish studies included only 86 and 99 twin pairs, respectively, and did not report CIs for the heritability estimates. In addition, these studies used a different growth model, namely, the Preece-Baines model 1.²⁵

Other previous studies reporting heritability estimates for onset of puberty include only girls and were based mainly on age at menarche. In most of these studies, the heritability estimates varied from 0.50 to 0.68,^26–29 and only 1 study reported a lower estimate, that is, 0.30³⁰; this low heritability estimate was because this study used a different model than the other studies. Thus, in these previous studies, the heritability estimates for age at menarche in girls were systematically lower than we found for onset of growth spurt in boys. It is difficult to make direct comparisons between puberty and adolescence growth, even within and especially between genders. In a cohort of black girls in the United States, the correlation between age at peak height velocity based on the Preece-Baines model 1 and at onset of puberty was relatively modest (r = 0.37), showing that many factors other than onset of puberty affect the timing of peak height velocity.³¹ A longitudinal study of Polish boys using a large number of maturity indicators found that these indicators loaded on 1 factor, explaining 77% of the sample variance.³² This suggests that there is a common biological background behind different indicators of maturation. A previous Finnish study found that part of the genetic factors affecting growth during puberty and puberty-related body changes are the same but are partly specific for each characteristic.³³ This study also reported that genetic factors affecting puberty were largely the same for boys and girls.

Our results showed that timing of puberty was correlated both with adult height and childhood BMI. As also reported previously,^10,11 those with high BMI in childhood had earlier onset of puberty than those with lower BMI. Our genetic analyses revealed a strong genetic factor behind the associations between childhood BMI and timing of puberty. This genetic effect may operate through endocrinologic factors if, for example, the same hormonal factors stimulate higher BMI in childhood and early puberty. Those with earlier age at puberty had taller stature than boys who matured later. Previous studies have suggested that, in girls, early puberty is associated with shorter adult stature,¹² whereas in boys no association was found.¹³ It is noteworthy that onset of puberty in these 2 previous studies was measured by using age at menarche and genital development, whereas in our study timing of puberty was based on acceleration of growth. More importantly, however, the last measurement of height in our data was done at 17.5 to 20.0 years of age. In these data, we found that growth between 16 and 18 years of age was only 0.93 cm/year, suggesting that, in general, growth is likely to be slow after 18 years of age. However, especially in boys who matured later, height may have increased slightly after the final measurement. In a Belgian study, it was found that late-maturing boys were shorter at 17 to 18 years of age, but this difference disappeared by 30 years of age.³⁴ It is, thus, possible that the correlation between stature and the timing of maturation would disappear if we had later measures of height available.

In this study, we used the JPA2 model to estimate ages at onset of growth spurt and peak height velocity.¹⁹ The first parametric growth model was proposed as early as 1937, and during the last 7 decades, several parametric growth models have been developed covering part or all of the human growth period (for a review of the growth models see Hauspie and Molinari³⁵). The JPA2 model fits well to our purposes, because it covers the whole human growth period and has been shown to describe growth well compared with previous models.²⁰ It also worked well with our data as seen in the high portion (93%) of variance of height explained by the model.

An important question is how it is possible to generalize our results based on twin data to the total Swedish population. It is well known that twin pregnancies are different from singleton pregnancies and are characterized by earlier gestational age, lower birth weight, and rapid catch-up growth.³⁶ However, the difference in height between twins and singletons has been found to disappear during childhood.³⁷ In this study we did not find any differences in the timing of puberty between monozygotic and dizygotic twins, suggesting that intrauterine conditions do not affect puberty; monochorionic monozygotic twins have been found to be more prone to birth defects than other monozygotic and dizygotic twins, and, thus, if neonatal factors have a major effect on puberty, this should have been seen in differences between monozygotic and dizygotic twins.³⁸ In addition, the mean height in monozygotic and dizygotic twins at conscription, 179.4 cm and 179.7 cm, respectively, was almost the same as among all of the conscripts in these birth cohorts (179.6 cm; N = 333 512 men). Therefore, it seems unlikely that the special features of twin pregnancies have major effects on growth in adolescence. However, as expected, mean birth weight was lower in twins than in singletons in these birth cohorts. The lack of association between ponderal index at birth and timing of puberty may be because of special features of twin pregnancies and should be interpreted with caution.

Our study has important strengths but also limitations. The main strength is measured data on weight and length or height from birth to early adulthood in twins. We were able to find the growth records for only half of our study subjects. However, all of the children in Sweden are covered by preventive child health services and a universal school system, and conscription examination was mandatory by law for all boys born between 1973 and 1979. Therefore, it seems unlikely that our twin cohort was selected with respect to height, health status, or socioeconomic position. The inability to find growth data on twins in archives all over Sweden is most likely because of administrative circumstances unrelated to these factors. However 1 factor that may have caused some selection bias is the high mobility of families during childhood, making it more difficult to track the growth records of such individuals.

A limitation of our study was that zygosity determination for most twin pairs was based on self-reported information. However, determination of zygosity by DNA analysis was offered to all of the complete pairs of undetermined zygosity on the basis of their answers to the same classic questions in 1998 and 2002. Because in the case of any disagreement in responses to these questions in the 2 surveys twins were coded to have undetermined zygosity, it is likely that very few twin pairs were misclassified with respect to zygosity. Another limitation is that conclusions could only be drawn for boys, because we had no information about girls. However, there are markedly fewer previous studies on the genetic architecture of puberty in boys than in girls.

	CONCLUSIONS

TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 
Growth during puberty is under strong genetic control. Early timing of adolescent growth spurt is associated with higher BMI in childhood and taller stature in early adulthood. These associations have a common genetic background, which may influence timing of growth through hormonal pathways. Additional longitudinal studies, including measured genes and hormonal factors, are needed to better understand the biological mechanisms behind these associations.

	ACKNOWLEDGMENTS

 
This work was partially funded by a grant from the Swedish Research Council to Dr Rasmussen (No. 40186801). Dr Silventoinen was supported by the Academy of Finland (grant 108297) and works in the Academy of Finland Centre of Excellence in Complex Disease Genetics.

	FOOTNOTES

 
Accepted Sep 18, 2007.

Address correspondence to Finn Rasmussen, MD, PhD, MPH, Department of Public Health Sciences, Child and Adolescent Public Health Epidemiology Group, Karolinska Institute, Norrbacka, SE-171 76 Stockholm, Sweden. E-mail: finn.rasmussen{at}ki.se

The authors have indicated they have no financial relationships relevant to this article to disclose.

What's Known on This Subject Timing of puberty is genetically regulated, and early puberty is associated with high BMI in childhood, at least in girls using age at menarche as an indicator of puberty.

 

What This Study Adds Ages at onset of pubertal growth spurt and at peak height velocity are strictly genetically regulated in boys. The association between high childhood BMI from age 1 to 10 and early puberty was because of common genetic factors.

 

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TOP ABSTRACT METHODS RESULTS DISCUSSION CONCLUSIONS REFERENCES

 

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