Startseite Growth in achondroplasia, from birth to adulthood, analysed by the JPA-2 model
Artikel Open Access

Growth in achondroplasia, from birth to adulthood, analysed by the JPA-2 model

  • Mariana del Pino EMAIL logo , Virginia Fano und Paula Adamo
Veröffentlicht/Copyright: 12. November 2020

Abstract

Objectives

In general population, there are three phases in the human growth curve: infancy, childhood and puberty, with different main factors involved in their regulation and mathematical models to fit them. Achondroplasia children experience a fast decreasing growth during infancy and an “adolescent growth spurt”; however, there are no longitudinal studies that cover the analysis of the whole post-natal growth. Here we analyse the whole growth curve from infancy to adulthood applying the JPA-2 mathematical model.

Methods

Twenty-seven patients, 17 girls and 10 boys with achondroplasia, who reached adult size, were included. Height growth data was collected from birth until adulthood. Individual growth curves were estimated by fitting the JPA-2 model to each individual’s height for age data.

Results

Height growth velocity curves show that after a period of fast decreasing growth velocity since birth, with a mean of 9.7 cm/year at 1 year old, the growth velocity is stable in late preschool years, with a mean of 4.2 cm/year. In boys, age and peak height velocity in puberty were 13.75 years and 5.08 cm/year and reach a mean adult height of 130.52 cm. In girls, the age and peak height velocity in puberty were 11.1 years and 4.32 cm/year and reach a mean adult height of 119.2 cm.

Conclusions

The study of individual growth curves in achondroplasia children by the JPA-2 model shows the three periods, infancy, childhood and puberty, with a similar shape but lesser in magnitude than general population.

Introduction

Achondroplasia (ACH), an autosomal-dominant disorder, is the most common form of inherited severe and disproportionate short stature, occurring with birth prevalence between 1 in 10,000 and 1 in 30,000 live births [1]. It is caused by a gain of function mutation in the type 3 fibroblast growth factor receptor gene (FGFR3), located on chromosome 4p16.3 that leads to abnormal endochondral ossification [2], [, 3].

In the general population, the pattern of human linear growth is very well documented [4], [, 5]. There are three phases in the growth curve: infancy, childhood and puberty. These periods are the additive effect of various biological processes with different main factors involved in their regulation, as well as different mathematical models to fit them [4], [, 5]. From birth, the rapidly decelerating growth in the first 2 years of life is a prolongation of foetal growth. This is the infancy component, which appears to be mainly nutritionally determined and guided by thyroid hormones. Until the age of 3 years, growth is an additive combination of the infancy and childhood components. The childhood component is a period with a lower and slowly decreasing velocity, which lasts up to puberty and is under the additional strong influence of growth hormones. The final component of the human growth curve is puberty, in which sexual hormones initiate the pubertal spurt and subsequently lead to the end of growth, through the closure of the epiphysis [4], [, 5].

In the ACH population, Horton et al. published the first growth charts from birth until adulthood, in 1978 [6]. They observed a period of fast decreasing growth, experienced during infancy and childhood, which was also later described by other authors [6], [7], [8], [9], [10]. Unfortunately, beyond 10 years of age, there were limited measurements, and Horton [1978] could not be certain if there was a pubertal growth spurt [6]. Hoover Fong [2017] and Merker [2018] did not see a pubertal growth spurt when they analysed the average curves in cross-sectional design studies [9], [, 11]. Nevertheless, the inspection of some individual growth patterns by Merker showed a clear acceleration in growth velocity during early pubertal ages [9]. However, as a result of longitudinal design studies, del Pino et al. [2018] showed that ACH adolescents experience a period of rapid increase of height growth velocity with a later slowdown, the “adolescent growth spurt” [12]. In all periods, the ACH growth curve was similar in shape but lesser in magnitude in comparison to the general population [4], [5], [10], [12]. Nevertheless, none of these studies included a longitudinal analysis of the whole post-natal growth curve from birth until adulthood.

Analysis of the whole post-natal growth curve would help us to understand the growth dynamics in a condition with severe and disproportionate short stature and to analyse the effect of emerging potential therapeutic strategies on the different phases of growth [13]. Some of the potential treatments are aimed at reducing the excessive activation of FGFR3, which has a negative influence on the epiphyseal growth plates of the limbs, inhibiting endochondral ossification and disturbing their growth [14].

This investigation is an observational, descriptive, cohort study. In this study, the longitudinal growth in ACH children is described, applying a mathematical model to include the whole post-natal growth.

Patients and methods

Sample

Growth was analysed in children with a confirmed diagnosis of ACH, attending our growth clinic at Garrahan Hospital, with measurements of height from birth until adulthood. A total of 27 out of 385 children, 17 girls and 10 boys, provided sufficient data to analyse growth. Some anthropometric data of these patients were published previously [10], [, 12].

Inclusion criteria were children who had a birth length record, a first length measurement in our growth clinic between 0.42 and 0.59 years old, and at least one measurement annually until adulthood. The median (interquartile range) number of measurements per child was 22.5 (18.75, 24).

Exclusions criteria were pre-term children (born before 37 weeks of gestational age); a presence of any other chronic disease or co-morbidities that could affect growth; and patients who underwent surgical leg lengthening or spinal arthrodesis. In this way, eight children were excluded because they underwent surgical leg lengthening and four because of spinal arthrodesis.

Diagnosis of the disease was made based on clinical examination and X-ray specific signs, according to Spranger [15]. Molecular testing was carried out in 22 out of 27 children, and all were heterozygous.

The following information was collected: date of birth, gestational age, length and weight at birth, age, sex, height and information about any other disease which may affect growth. Anthropometric data at birth were obtained from perinatal records provided by parents.

Methods

All children’s measurements were initially taken and followed up by the same trained observer (AP) during routine visits, with standardized techniques [16]. The supine length was measured until 4 years of age and, from then onwards, the standing height was measured with Harpenden instruments.

The individual’s growth records were plotted. At the time of the last measurement, all patients had reached their adult height with the apparent indication of an upper plateau. To obtain comparable data, individual growth curves were estimated by fitting the JPA-2 model to each individual’s height for age data [17].

JPA-2 model

The JPA-2 model [17] was designed to fit post-natal growth and includes three components that match with natural periods of human growth – infancy, childhood and puberty – utilizing post-natal age because this model has been proven to fit post-natal data very well from birth [17], [, 18]. It includes eight parameters and has the following mathematical expression:

y=a{11/[1+[(t+e)/b1]c1+[(t+e)/b2]c2+[(t+e)/b3]c3]}

In this function, y = height reached at age t; t = post-natal age; a = adult height, b1b2,and b3 = time-scale factors; c1,c2and c3 = dimensionless exponents; and e = estimated pre-natal duration of growth [17].

The following biological parameters were obtained from JPA-2 fits on each individual’s serial growth data: age, size (height) and velocity at 1 and 5 years of age, at take-off (the point of minimal pre-pubertal growth velocity), at peak velocity in puberty and at adulthood.

The goodness of fit was assessed by the residual standard deviation (RSD). In this study, we considered a fit to be acceptable if the RSD was no higher than 0.5 cm. The pooled RSD in the sample was 0.31 cm, with a range of 0.24–0.49 cm.

The mean-constant curve was obtained by feeding the mean values of the function parameters into the model [18].

The software used was KaleidaGraph 4.5.3.

The project was approved by the research review committee and the ethics review committee from Garrahan Paediatric Hospital.

Results

Twenty-seven children, 17 girls and 10 boys, provided sufficient data to analyse growth from birth until adulthood.

Figures 1 and 2 show the distance and velocity mean-constant curves for height for girls and boys, respectively. The shapes of the growth curves are similar and have the three periods described in children from the general population [4], [, 5]: infancy, childhood and puberty. The velocity curves show that, after a period of fast decreasing growth velocity from birth, with a mean growth velocity of approximately 9.7 cm/year at 1 year of age, the mean growth velocity is 4.2 cm/year in the late preschool years, in both sexes. After a period of slightly decreasing growth velocity, the pubertal spurt is initiated and growth velocity increases, achieving its maximum at age of peak height velocity. In girls, the age and the peak velocity in puberty are 11.1 years and 4.32 cm/year, and they reach a mean adult height of 119.2 cm. In boys, the age and the peak velocity in puberty are 13.75 years and 5.08 cm/year, and they reach a mean adult height of 130.52 cm. After the age of peak height velocity, growth velocity decreases until they stop growing.

Figure 1: Distance and velocity mean-constant growth curves for height of JPA-2 model fitted to 17 girls. The growth curve shows the three periods of the human growth curve: infancy, childhood and puberty [4], [, 5].
Figure 1:

Distance and velocity mean-constant growth curves for height of JPA-2 model fitted to 17 girls. The growth curve shows the three periods of the human growth curve: infancy, childhood and puberty [4], [, 5].

Figure 2: Distance and velocity mean-constant growth curves for height of JPA-2 model fitted to 10 boys. The growth curve shows the three periods of the human growth curve: infancy, childhood and puberty [4], [, 5].
Figure 2:

Distance and velocity mean-constant growth curves for height of JPA-2 model fitted to 10 boys. The growth curve shows the three periods of the human growth curve: infancy, childhood and puberty [4], [, 5].

Table 1 shows the mean and standard deviations of the derived biological variables, obtained by fitting JPA-2 to the growth, in height. Growth velocity during infancy and childhood are similar in both sexes. During adolescence, ACH boys entered puberty 2 years later than girls, and the peak height velocity is slightly higher in boys. The difference in adult height between boys and girls (11.3 cm) comes from the later onset of the pubertal growth spurt (or longer childhood growth) in boys.

Table 1:

Mean and standard deviations of the JPA-2 model derived biological variables in achondroplasia children.

Boys N=10Girls N=17
Decimal age (years)Height (SD), cmGrowth velocity (SD), cm/yearDecimal age (years)Height (SD), cmGrowth velocity (SD), cm/year
167.3 (2.1)9.73 (1.08)163.2 (2.5)9.62 (1.4)
588.9(3.2)4.16 (0.6)585.6 (3.6)4.35 (0.6)
Take off10.90 (1.25)110.98 (7.15)3.07 (0.57)8.90 (1.5)101.36 (6.15)3.68 (0.4)
Peak velocity13.75 (1.12)123.86 (5.7)5.08 (0.49)11.1 (0.88)110.0 (3.46)4.32 (0.7)
Adult height17.7130.5 (5.0)0.01 (0.02)16.4119.2 (5.4)0.003 (0.02)

The curves of Figures 3, 4, and 5 represent the JPA-2 model fitted to the longitudinal data of three children, including all measurements since birth. A girl with puberty close to the mean (Figure 1), a boy with early puberty (Figure 2) and a boy with late puberty (Figure 3).

Figure 3: A plot of the JPA 2 model fitted to the longitudinal data of an ACH girl (No 15) including all measurements since birth. The upper curve represents the distance curve. The middle curve represents the first derivate, depicting the speed of growth in cm/year, while solid dots represent observed speeds in the individual child considered. The lower curve represents the second derivate: the acceleration of the growth in cm/year/year. The arrows A and B indicate the ages at which the acceleration curve vanishes (zero acceleration), that is to say, the age of the onset (take-off) and the peak of the pubertal growth spurt: for this girl at 9.0 (A) and 11.4 years of age (B). This girl had a take-off and peak height velocity close to the mean age and reached an adult height of 118.22 cm. The growth curve shows the three periods of the human growth curve [4], [, 5]: infancy, childhood and puberty. The curve has a similar shape but lesser magnitude, with a growth velocity of 3.89 cm/year vs. 7.7 cm/year of a girl from the general population [21], [22], [23].
Figure 3:

A plot of the JPA 2 model fitted to the longitudinal data of an ACH girl (No 15) including all measurements since birth. The upper curve represents the distance curve. The middle curve represents the first derivate, depicting the speed of growth in cm/year, while solid dots represent observed speeds in the individual child considered. The lower curve represents the second derivate: the acceleration of the growth in cm/year/year. The arrows A and B indicate the ages at which the acceleration curve vanishes (zero acceleration), that is to say, the age of the onset (take-off) and the peak of the pubertal growth spurt: for this girl at 9.0 (A) and 11.4 years of age (B). This girl had a take-off and peak height velocity close to the mean age and reached an adult height of 118.22 cm. The growth curve shows the three periods of the human growth curve [4], [, 5]: infancy, childhood and puberty. The curve has a similar shape but lesser magnitude, with a growth velocity of 3.89 cm/year vs. 7.7 cm/year of a girl from the general population [21], [22], [23].

Figure 4: A plot of the JPA 2 model fitted to the longitudinal data of an ACH boy (No 10) including all measurements since birth. The upper curve represents the distance curve. The middle curve represents the first derivate, depicting the speed of growth in cm/year, while solid dots represent observed speeds in the individual child considered. The lower curve represents the second derivate: the acceleration of the growth in cm/year/year. The arrows A and B indicate the ages at which the acceleration curve vanishes (zero acceleration), that is to say, the age of the onset (take-off) and the peak of the pubertal growth spurt: for this, boy at 9.5 (A) and 11.7 years of age (B). This boy had the take-off and the peak height velocity before mean age, approximately 2 years earlier (early puberty) and reached an adult height of 133.9 cm. The growth curve shows the three periods described by Karlberg [4]: infancy, childhood and puberty. The curve has a similar shape but lesser magnitude with a growth velocity of 5.08 cm/year vs. 9.3 cm/year of a boy from the general population [22], [23].
Figure 4:

A plot of the JPA 2 model fitted to the longitudinal data of an ACH boy (No 10) including all measurements since birth. The upper curve represents the distance curve. The middle curve represents the first derivate, depicting the speed of growth in cm/year, while solid dots represent observed speeds in the individual child considered. The lower curve represents the second derivate: the acceleration of the growth in cm/year/year. The arrows A and B indicate the ages at which the acceleration curve vanishes (zero acceleration), that is to say, the age of the onset (take-off) and the peak of the pubertal growth spurt: for this, boy at 9.5 (A) and 11.7 years of age (B). This boy had the take-off and the peak height velocity before mean age, approximately 2 years earlier (early puberty) and reached an adult height of 133.9 cm. The growth curve shows the three periods described by Karlberg [4]: infancy, childhood and puberty. The curve has a similar shape but lesser magnitude with a growth velocity of 5.08 cm/year vs. 9.3 cm/year of a boy from the general population [22], [23].

Figure 5: A plot of the JPA 2 model fitted to the longitudinal data of an ACH boy (No 10) including all measurements since birth. The upper curve represents the distance curve. The middle curve represents the first derivate, depicting the speed of growth in cm/year, while solid dots represent observed speeds in the individual child considered. The lower curve represents the second derivate: the acceleration of the growth in cm/year/year. The arrows A and B indicate the ages at which the acceleration curve vanishes (zero acceleration), that is to say, the age of the onset (take- off) and the peak of the pubertal growth spurt: for this, boy at 12.6 (A) and 15.15 years of age (B). This boy had the take-off and the peak height velocity after the mean age, approximately 1.75 years later (late puberty) and reached an adult height of 131.37 cm.The growth curve shows the three periods of the human growth curve [4], [, 5]: infancy, childhood and puberty. The curve has a similar shape but lesser magnitude with a growth velocity of 4.60 cm/year vs. 9.3 cm/year of a boy from the general population [22], [23].
Figure 5:

A plot of the JPA 2 model fitted to the longitudinal data of an ACH boy (No 10) including all measurements since birth. The upper curve represents the distance curve. The middle curve represents the first derivate, depicting the speed of growth in cm/year, while solid dots represent observed speeds in the individual child considered. The lower curve represents the second derivate: the acceleration of the growth in cm/year/year. The arrows A and B indicate the ages at which the acceleration curve vanishes (zero acceleration), that is to say, the age of the onset (take- off) and the peak of the pubertal growth spurt: for this, boy at 12.6 (A) and 15.15 years of age (B). This boy had the take-off and the peak height velocity after the mean age, approximately 1.75 years later (late puberty) and reached an adult height of 131.37 cm.The growth curve shows the three periods of the human growth curve [4], [, 5]: infancy, childhood and puberty. The curve has a similar shape but lesser magnitude with a growth velocity of 4.60 cm/year vs. 9.3 cm/year of a boy from the general population [22], [23].

Discussion

This is the first study on growth in the ACH population, from birth to adulthood, applying a parametric model, the JPA-2, to analyse growth longitudinally [17]. This analysis helps us to understand the growth process in ACH children.

In the general population, the growth pattern in height, which is constituted by the growth as a total of the legs and trunk length, is characterized by a gradually decreasing velocity during infancy and childhood, followed by a substantial acceleration that marks the beginning of the pubertal growth spurt in the early teens. This growth velocity increases and achieves its maximum at the age of peak height velocity [5]. The pubertal growth spurt is a persistent feature of the normal growth curve; however, sometimes differences in tempo among adolescents are very pronounced. The range of the peak height velocity is about 3 years for boys and girls. In early adolescence, some children grow at peak height velocity while others still have to start their growth spurt. In mid-adolescence, early maturing children will approach their final height while others grow at maximum velocity. As a consequence of these variations in tempo, the pubertal growth spurt can be observed when measurements are taken at reasonable intervals and when longitudinal growth data is analysed with appropriate mathematical models [4], [5], [18]

The gain of function of the FGFR3 mutation plays an important role in pre-natal skeletal development, with a greater negative influence in the epiphyseal growth plates of the limbs, inhibiting endochondral ossification and disturbing limb growth more severely than the trunk [14]. Consequently, ACH children have a severe disproportionate short stature [6], [8], [9], [10], [11], [19], [20], with short legs and arms, and a total growth velocity reduced in comparison to the growth velocity in the general population [4], [5], [10], [12]. Therefore, only a study of growth with a longitudinal design and suitable mathematical models can help us describe the growth patterns and analyse the dynamics of the growth process in ACH.

Applying the JPA-2 model to the growth data, we identified the three phases of growth (infancy, childhood and puberty) with a similar shape but lesser magnitude than the general population [4], [, 5]. If we compare our results with general population growth data, analysed with JPA-2, the growth velocity of ACH children is lesser in magnitude. The mean growth velocities are 6.2 and 4.2 cm/year vs. 10 and 7 cm/year at 2 and 5 years old, respectively [21]. During puberty, the age at take-off is similar in comparison to Polish girls and Finn children [21], [, 22]. The age at peak height velocity in ACH is similar to Polish girls and Chinese and Finn children and is half the magnitude: approximately 5.08 and 4.32 cm/year vs. 9.3 and 7.7 cm/year for average stature boys and girls, respectively [21], [22], [23].

The comparison of height data between ACH boys and girls shows that the difference (11 cm at adulthood) arises mainly during adolescence, as in the general population [24]. Differences in adult height between sexes in ACH have previously been described by different authors [6], [8], [9], [11], [19].

There are differences between heights in the present study and the results of our previous report [8]. This can be explained by the difference in sample size: only 17 girls and 10 boys were included in the present study.

In ACH children, the presence of the three phases of the growth curve with a similar shape to the general population shows that the negative influence of the FGFR3 mutation in the epiphyseal growth plates does not completely nullify nutritional, endocrinological and other factors involved in the growth process. On the other hand, the present study reinforces the existence of the pubertal spurt in ACH adolescents, which is still under discussion in the scientific literature [9], [11], [25]. The reason why the pubertal spurt is often not observed in growth studies may be because time intervals between measurements are too long or the result of measurement errors. In this report, the same trained observer was used (AP), and they have been carrying out anthropometry since 1991.

Regarding growth-promoting treatments in ACH children, several therapies are being developed [13]. These potential new treatments propose to stimulate linear growth, improving stature, decreasing disproportion and preventing further complications [13]. Understanding the growth process in ACH would help us to analyse the effect of these potential treatments in the different phases of growth.

There are manifold reasons for studying human growth. One motivation is mainly scientific and relates to an understanding of the growth process [18]. The traditional approach is to identify a suitable parametric model for the given measurement and age range, in order to analyse growth longitudinally. Thus, the growth curve is characterized and summarized by the fitted parameters of the model, which can be used instead of the raw data. The JPA-2 is a parametric model that was originally designed to fit post-natal growth in the general population and covers the whole growth process: infancy, childhood and puberty [17], [, 18]. We used the eight-parameter JPA-2 model because it is robust, extensively tested and provides an excellent fit from birth onwards [17], [21], [22], [23]. We focused on studying height from birth until adulthood in ACH children where the JPA-2 curve, with its eight estimated parameters, fits individual growth curves very well, with an RSD of 0.24–0.49 cm. Thus, this model could be applied in other growth disorders in which size and growth dynamics are affected in a general way. However, applying the JPA-2 model involves fitting many curves individually, and the ideal approach would be to fit a form of the curve to all subjects simultaneously. This could be done with the alternative SuperImposition by Translation And Rotation (SITAR) model, a shape-invariant growth curve model [26]. The SITAR model summarizes individual growth curves with a single curve and subject-specific random effects. The random effects reflect each patient´s size, growth tempo and growth velocity, all of them biologically meaningful, and explains the heterogeneity in growth between individuals [26]. Analysing growth data with the most recently available SITAR method will be our next goal for studying growth in ACH.

Conclusion

This is the first study on growth in the ACH population, from birth to adulthood, applying a parametric model to analyse growth longitudinally. The height growth curve in ACH children shows the three periods described in the general population (infancy, childhood and puberty) with a similar shape but lesser magnitude than the general population. Our results reaffirm previous reports that ACH children have a “pubertal growth spurt” during adolescence.


Corresponding author: Mariana del Pino, Growth and Development, Garrahan Hospital, Combate de los Pozos 1881 (1245), Buenos Aires, Argentina, Telephone: +0054 9 11 4122 6221, Fax: +0054 9 11 43085325, E-mail:

Acknowledgement

We would like to thank patients and their families. We would also like to thank our reviewers for their feedback, which improved the overall manuscript considerably.

  1. Research funding: None declared.

  2. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  3. Competing interests: No funding organizations played a role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.

  4. Ethical statement: The project was approved by the research review committee and the ethics review committee from Garrahan Paediatric Hospital.

References

1. Horton, WA, Hall, JG, Hecht, JT. Achondroplasia. Lancet 2007;370:162–72. https://doi.org/10.1016/s0140-6736(07)61090-3.Suche in Google Scholar

2. Shiang, R, Thompson, LM, Zhu, YZ, Church, DM, Fielder, TJ, Bocian, M, et al.. Mutations in the transmembrane domain of FGFR3 cause the most common genetic form of dwarfism, Achondroplasia. Cell 1994;78:335–42. https://doi.org/10.1016/0092-8674(94)90302-6.Suche in Google Scholar

3. Bellus, GA, Hefferon, TW, Ortiz de Luna, RI, Hecht, JT, Horton, WA, Machado, M, et al.. Achondroplasia is defined by recurrent G380R mutations of FGFR3. Am J Hum Genet 1995;56:368–73.Suche in Google Scholar

4. Karlberg, JA. Biologically-oriented mathematical model (ICP) for human growth. Acta Paediatr Scand Suppl 1989;350:70–94. https://doi.org/10.1111/j.1651-2227.1989.tb11199.x.Suche in Google Scholar

5. Falker, F, Tanner, JM, editors. Human growth: a comprehensive treatise 2nd ed. New York: Plenum Press; 1986.10.1007/978-1-4613-2101-9Suche in Google Scholar

6. Horton, WA, Rotter, JI, Rimoin, DL, Scott, CI, Hall, JG. Standard growth curves for achondroplasia. J Pediatr 1978;93:435–8. https://doi.org/10.1016/s0022-3476(78)81152-4.Suche in Google Scholar

7. Hoover-Fong, JE, Schulze, KJ, McGready, J, Barnes, H, Scott, CI. Age-appropriate body mass index in children with achondroplasia: interpretation in relation to indexes of height. Am J Clin Nutr 2008;88:364–71. https://doi.org/10.1093/ajcn/88.2.364.Suche in Google Scholar

8. del Pino, M, Fano, V, Lejarraga, H. Growth references for height, weight, and head circumference for Argentine children with achondroplasia. Eur J Pediatr 2011;170:453–9. https://doi.org/10.1007/s00431-010-1302-8.Suche in Google Scholar

9. Merker, A, Neumeyer, L, Hertel, NT, Grigelioniene, G, Mäkitie, O, Mohnike, K, et al.. Growth in achondroplasia: development of height, weight, head circumference, and body mass index in a European cohort. Am J Med Genet 2018;176:1723–34. https://doi.org/10.1002/ajmg.a.38853.Suche in Google Scholar

10. del Pino, M, Fano, V, Adamo, P. Height growth velocity during infancy and childhood in achondroplasia. Am J Med Genet 2019;179:1001–9. https://doi.org/10.1002/ajmg.a.61120.Suche in Google Scholar

11. Hoover-Fong, J, McGready, J, Schulze, K, Alade, AY, Scott, CI. A height-for-age growth reference for children with achondroplasia: expanded applications and comparison with original reference data. Am J Med Genet 2017;173:1226–30. https://doi.org/10.1002/ajmg.a.38150.Suche in Google Scholar

12. del Pino, M, Fano, V, Adamo, P. Growth velocity and biological variables during puberty in achondroplasia. J Pediatr Endocrinol Metab 2018;31:421–8. https://doi.org/10.1515/jpem-2017-0471.Suche in Google Scholar

13. Ornitz, DM, Legeai-Mallet, L. Achondroplasia: development, pathogenesis, and therapy. Dev Dynam 2017;246:291–309. https://doi.org/10.1002/dvdy.24479.Suche in Google Scholar

14. Mugniery, E, Dacquin, R, Marty, C, Benoist-Lasselin, C, de Vernejoul, MC, Jurdic, P, et al.. An activating FGFR3 mutation affects trabecular bone formation via a paracrine mechanism during growth. Hum Mol Genet 2012;21:2503–13. https://doi.org/10.1093/hmg/dds065.Suche in Google Scholar

15. Spranger, JW. Achondroplasia. In: Spranger JW, Brill, PW, Poznanski, A, editors. Bone Dysplasia: an atlas of genetic disorders of skeletal development, 2e. New York: Oxford University Press; 2012:83–9 p.10.1093/med/9780195396089.003.0019Suche in Google Scholar

16. Lejarraga, H, Heinrich, JJ, Rodríguez, A. Rules and techniques of anthropometric measurements. Revista del Hospital de Niños 1975;17:165–71.Suche in Google Scholar

17. Jolicoeur, P, Pontier, J, Abidi, H. Asymptotic models for the longitudinal growth of human stature. Am J Hum Biol 1992;4:461–8. https://doi.org/10.1002/ajhb.1310040405.Suche in Google Scholar

18. Hauspie, RC, Cameron, N, Molinari, L, editors. Methods in human growth research. 1st ed. United Kingdom: Cambridge University Press; 2004:205–56 p.10.1017/CBO9780511542411.009Suche in Google Scholar

19. Tofts, L, Das, S, Collins, F, Burton, KLO. Growth charts for Australian children with achondroplasia. Am J Med Genet 2017;173:2189–200. https://doi.org/10.1002/ajmg.a.38312.Suche in Google Scholar

20. Merker, A, Neumeyer, L, Hertel, NT, Grigelioniene, G, Mohnike, K, Hagenäs, L. Development of body proportions in achondroplasia: sitting height, leg length, arm span, and foot length. Am J Med Genet 2018;176:1819–29. https://doi.org/10.1002/ajmg.a.40356.Suche in Google Scholar

21. Durda-Masny, M, Hanć, T, Czapla, Z, Szwed, A. BMI at menarche and timing of growth spurt and puberty in Polish girls - longitudinal study. Anthropol Anzeiger 2019;76:37–47. https://doi.org/10.1127/anthranz/2019/0920.Suche in Google Scholar

22. Sovio, U, Bennett, AJ, Millwood, IY, Molitor, J, O’Reilly, PF, Timpson, NJ, et al.. Genetic determinants of height growth assessed longitudinally from infancy to adulthood in the northern Finland birth cohort 1966. PLoS Genet 2009;5:e1000409. https://doi.org/10.1371/journal.pgen.1000409.Suche in Google Scholar

23. Li, H, Leung, SS, Lam, PK, Zhang, X, Chen, XX, Wang, SL. Height and weight percentile curves of Beijing children and adolescents 0-18 years, 1995. Ann Hum Biol 1999;26:457–71. https://doi.org/10.1080/030144699282570.Suche in Google Scholar

24. Hauspie, R, Das, SR, Preece, MA, Tanner, JM, Susanne, C. Decomposition of sexual dimorphism in adult size of height, sitting height, shoulder width and hip width in a British and West Bengal sample. In: Ghesquire, J, Martin, RD, Newcombe, F, editors. Human Sexual dimorphism. London: Taylor & Francis; 1985:207–15 p.Suche in Google Scholar

25. Pauli, RM. Achondroplasia: a comprehensive clinical review. Orphanet J Rare Dis 2019;14:1–49. Review. https://doi.org/10.1186/s13023-018-0972-6.Suche in Google Scholar

26. Cole, TJ, Donaldson, MD, Ben-Shlomo, Y. SITAR--a useful instrument for growth curve analysis. Int J Epidemiol 2010;39:1558–66. https://doi.org/10.1093/ije/dyq115.Suche in Google Scholar

Received: 2020-05-21
Accepted: 2020-08-31
Published Online: 2020-11-12
Published in Print: 2020-12-16

© 2020 Mariana del Pino et al., published by De Gruyter, Berlin/Boston

This work is licensed under the Creative Commons Attribution 4.0 International License.

Artikel in diesem Heft

  1. Frontmatter
  2. Review Article
  3. Papillary thyroid carcinoma in children with Hashimoto’s thyroiditis – a review of the literature between 2000 and 2020
  4. Original Articles
  5. Tyrosine metabolism in health and disease: slow-release amino acids therapy improves tyrosine homeostasis in phenylketonuria
  6. Evolution of Hashimoto thyroiditis in children with type 1 diabetes mellitus (TIDM)
  7. Glycated hemoglobin variability and microvascular complications in patients with type 1 diabetes mellitus
  8. Delineation of the genetic and clinical spectrum, including candidate genes, of monogenic diabetes: a multicenter study in South Korea
  9. Can we use copeptin as a biomarker for masked hypertension or metabolic syndrome in obese children and adolescents?
  10. Relationship of acanthosis nigricans with metabolic syndrome in obese children
  11. Daily intake of macronutrients and energy in childhood and its association with cardiometabolic risk factors in Colombians
  12. The effect of treatment with recombinant human growth hormone (rhGH) on linear growth and adult height in children with idiopathic short stature (ISS): a systematic review and meta-analysis
  13. Growth in achondroplasia, from birth to adulthood, analysed by the JPA-2 model
  14. Short Communication
  15. Assessing disparities in barriers to attending pediatric diabetes camp
  16. Letter to the Editor
  17. Severity in pediatric type 1 diabetes mellitus debut during the COVID-19 pandemic
  18. Case Reports
  19. The use of glimepiride for the treatment of neonatal diabetes mellitus caused by a novel mutation of the ABCC8 gene
  20. Effect of recombinant human insulin-like growth factor 1 therapy in a child with 3-M syndrome-1 with CUL7 gene mutation
  21. A nonsense variant in FGFR1: a rare cause of combined pituitary hormone deficiency
  22. Treatment response to long term antiresorptive therapy in osteogenesis imperfecta type VI: does genotype matter?
Heruntergeladen am 10.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/jpem-2020-0298/html
Button zum nach oben scrollen