uxology studying human growth and development
TRANSCRIPT
This book is a comprehensive description of human physical growth and development (Auxology) with contributions by 56 interna-tionally reputed experts. The entire spectrum of basic and advanced in-formation on growth tracking, growth predic-tion, short-term-, catch-up- and rapid growth, nutritional and social factors influencing hu-man growth, and issues related to preventive health care, growth in ethnic minorities and migrants, and growth in developing countries is presented. The text is generously illustrated (283 color figures and 89 comprehensive tables). It also introduces new mathematical approaches to growth modelling and provides practical information on how to use and to interpret
growth charts. National references (US, ARG, BRA, CAN, IND, BEL, GER, IT, NL, PL, SW, SWI, TUR, UK, WHO) for height, weight and body mass index and head circumference for various countries are given as well as growth references for twins, preterm infants and syn-drome specific growth charts for clinical pur-poses. The book for the first time also contains references for height SDS changes, the mod-ern alternative to traditional growth velocity charts.
The book is of greatest interest to all pedia-tricians, to medical students and students of human biology, health workers, nutritionists, medical staff and professionals interested in child and adolescent growth and development.
Schweizerbart Science PublishersStuttgartE
Michael Hermanussen (ed.)
AUXOLOGYStudying Human Growth and DevelopmentWith contributions by 56 internationally reputed expertsIllustrated by Samson Goetze
2013. XII, 324 pp., with 283 gures and 89 tables17 x 24 cm, hardcover ISBN 978-3-510-65278-5 39.90.– €Information + : www.schweizerbart.com/9783510652785
AUXOLOGY – Studying Human Growth and Development
Johannesstr. 3A, 70176 Stuttgart, Germany. Tel. +49 (711) 351456-0 Fax. +49 (711) 351456-99 [email protected] www.schweizerbart.de
Ghada M. Anwar, Cairo, EgyptChristian Aßmann, Bamberg, GermanyPavel Blaha, Prague, Czech RepublicBarry Bogin, Leicestershire, UKJesper L. Boldsen, Odense, DenmarkWalter Bonfig, München, GermanyMarek Brabec, Praha, Czech RepublicFanny Breitman, Buenos Aires, ArgentinaStef van Buuren, Leiden, The NetherlandsSilvia Caino, Buenos Aires, ArgentinaNoel Cameron, Leicestershire, UKTim Cole, London, UKMortada El-Shabrawi, Cairo, EgyptMona El Housseiny, Cairo, EgyptMiranda Fredriks, Leiden, The NetherlandsElena Godina, Moscow, RussiaPetra Golja, Ljubljana, SloveniaCarl Martin Grewe, Berlin-Dahlem, GermanyKomei Hattori, Ibaraki University, JapanKlaus-Peter Herm, Bad Oeynhausen, Germany
Michael Hermanussen, Altenhof, GermanyReinhard Holl, Ulm, GermanyEilin Jopp, Hamburg, GermanyMaria Kaczmarek, Poznan, PolandMagdalena Skrzypczak, Poznan, PolandDiana Mabel Kelmansky, Buenos Aires, ArgentinaAndreas Kersting, Bamberg, GermanySylvia Kirchengast, Vienna, Austria Katja Zdešar Kotnik, Ljubljana, SloveniaHans Lamecker, Berlin-Dahlem, GermanyAndreas Lehmann, Luckenwalde, GermanyHoracio Lejarraga, Buenos Aires, ArgentinaLeslie Sue Lieberman, Oviedo, USA Matthew McIntyre, Orlando, USAJürgen Meier, München, GermanyChristof Meigen, Bonn, GermanyRebekka Mumm, Friedland, GermanyChristina Papageorgopoulou, Komotini, GreeceTilman R. Rohrer, Homburg/Saar, GermanyFrank J. Rühli, Zurich, Switzerland
Emad Salama, Cairo, EgyptTakashi Satake, Matsudo, Chiba, JapanChristiane Scheffler, Potsdam, GermanyMithun Sikdar, Udaipur, Rajasthan, IndiaKaspar Staub, Zurich, SwitzerlandHans Henrik Thodberg, Holte, DenmarkJesus Angel Fernandez-Tresguerres, Madrid, SpainJanina Tutkuviene, Vilnius, LithuaniaStanley Ulijaszek, Oxford, UKMaria Inês Varela-Silva, Leicestershire, UKJerry K.H. Wales, Sheffield, UKUlrich Woitek, Zurich, SwitzerlandCherie L. Yestrebsky, Orlando, USASiegfried Zabransky, Homburg/Saar, GermanyStefan Zachow, Berlin-Dahlem, GermanyElzbieta Zadzinska, Lodz, Poland
AUXOLOGY – Studying Human Growth and DevelopmentContributors
221
Table 36: BELGIUM harmonised
[after Roelants et al. 2009].
Age Height Weight BMI
years mean SD p10 p50 p90 L M S
0 50.0 2.0 2.8 3.3 3.9 0.001 13.2 0.074
0.25 59.6 2.0 4.9 5.6 6.4 -0.1 15.8 0.101
0.5 66.4 2.2 6.4 7.3 8.4 -0.3 16.6 0.079
0.75 70.9 2.4 7.4 8.4 9.7 -0.5 16.7 0.083
1 74.7 2.5 8.1 9.3 10.7 -0.6 16.7 0.080
1.5 81.4 2.8 9.4 10.8 12.5 -1.0 16.3 0.085
2 87.3 3.1 10.6 12.1 14.0 -1.3 15.9 0.084
3 95.3 3.6 12.4 14.3 16.7 -1.4 15.8 0.076
4 102.4 4.1 14.0 16.3 19.2 -1.6 15.6 0.079
5 109.5 4.6 15.8 18.5 22.2 -1.8 15.4 0.086
6 116.4 4.9 17.4 20.7 25.3 -1.9 15.4 0.097
7 123.0 5.3 19.4 23.4 29.2 -1.9 15.5 0.110
8 129.0 5.6 21.8 26.6 33.8 -1.9 15.9 0.122
9 134.3 6.0 24.2 29.8 38.6 -1.9 16.4 0.133
10 139.9 6.4 26.7 33.3 44.0 -1.7 16.9 0.142
11 146.6 6.9 29.6 37.5 50.2 -1.6 17.5 0.147
12 153.2 7.1 33.2 42.3 56.6 -1.5 18.1 0.149
13 158.7 6.9 37.4 47.1 61.9 -1.5 18.8 0.148
14 162.4 6.5 42.0 51.5 66.2 -1.4 19.5 0.143
15 164.7 6.2 45.5 54.9 69.3 -1.4 20.2 0.139
16 165.8 6.0 47.9 57.2 71.8 -1.5 20.8 0.136
17 166.2 6.0 49.1 58.5 73.4 -1.5 21.1 0.135
18 166.3 6.0 49.8 59.2 74.1 -1.6 21.4 0.137
0 50.7 2.1 2.9 3.5 4.1 0.001 13.6 0.085
0.25 61.0 2.2 5.3 6.1 7.0 -0.1 16.4 0.077
0.5 67.9 2.3 6.9 7.9 9.0 -0.2 17.1 0.079
0.75 72.6 2.4 7.9 9.1 10.4 -0.3 17.3 0.091
1 76.3 2.5 8.7 10.0 11.4 -0.4 17.2 0.086
1.5 82.7 2.8 10.1 11.5 13.2 -0.6 16.8 0.087
2 88.4 3.1 11.0 12.6 14.5 -0.7 16.1 0.084
3 96.3 3.6 12.9 14.8 17.2 -1.9 15.9 0.075
4 103.5 4.1 14.6 16.8 19.7 -2.0 15.6 0.078
5 110.3 4.5 16.2 18.8 22.4 -2.2 15.5 0.082
6 117.2 4.9 18.0 21.1 25.5 -2.3 15.4 0.088
7 123.8 5.2 20.0 23.6 29.1 -2.4 15.5 0.096
8 129.9 5.5 22.2 26.5 33.3 -2.4 15.8 0.104
9 135.4 5.8 24.6 29.6 37.9 -2.4 16.1 0.113
10 140.5 6.1 27.0 32.9 42.9 -2.4 16.6 0.122
11 145.8 6.5 29.6 36.4 47.9 -2.4 17.0 0.129
12 152.0 7.0 32.8 40.8 54.0 -2.4 17.6 0.133
13 158.8 7.6 36.5 46.1 61.0 -2.3 18.2 0.135
14 166.0 7.8 41.3 52.5 68.9 -2.2 18.8 0.135
15 171.9 7.7 46.9 58.5 75.1 -2.2 19.5 0.132
16 175.8 7.3 51.6 62.8 79.0 -2.1 20.1 0.128
17 178.1 7.0 54.9 65.9 81.6 -2.0 20.6 0.124
18 179.4 6.9 57.2 68.0 83.5 -2.0 21.0 0.120
11.1
NATIONAL GROWTH REFERENCES
Green numbers indicate synthetic values
220
Green numbers indicate synthetic values | Brown numbers indicate WHO values
Table 35: ARGENTINA harmonised
[after Lejarraga et al. 2009].
Age Height Weight BMI
years mean SD p10 p50 p90 L M S
0 49.3 1.8 2.7 3.2 3.8 0.001 13.1 0.074
0.25 59.8 2.1 5.0 5.8 6.9 -0.1 16.3 0.101
0.5 65.7 2.3 6.2 7.3 8.6 -0.3 16.9 0.079
0.75 70.2 2.4 7.1 8.2 9.6 -0.5 16.7 0.083
1 74.0 2.6 7.7 8.9 10.5 -0.6 16.3 0.080
1.5 80.7 2.9 8.8 10.2 12.0 -1.0 15.7 0.085
2 86.4 3.2 9.8 11.5 13.5 -1.3 15.4 0.084
3 95.0 3.9 12.2 14.2 16.6 -1.5 15.7 0.078
4 101.2 4.5 14.1 16.3 19.2 -1.8 15.9 0.082
5 106.7 4.8 15.5 18.1 21.4 -1.9 15.9 0.089
6 113.0 5.1 17.0 20.1 24.2 -2.0 15.7 0.091
7 118.8 5.5 18.8 22.5 27.6 -1.9 15.9 0.102
8 124.1 6.1 20.8 25.2 31.4 -1.7 16.4 0.107
9 129.2 6.7 22.9 28.2 35.5 -1.6 16.9 0.112
10 134.6 7.3 25.4 31.5 40.0 -1.5 17.4 0.119
11 140.6 7.6 28.5 35.6 45.4 -1.4 18.0 0.124
12 147.0 7.2 32.5 40.5 51.4 -1.3 18.7 0.138
13 152.9 6.7 36.8 45.2 56.7 -1.3 19.4 0.139
14 157.2 6.3 40.6 48.9 60.2 -1.3 19.8 0.128
15 159.6 6.1 43.3 51.3 62.3 -1.3 20.1 0.116
16 160.5 6.1 44.7 52.5 63.3 -1.2 20.4 0.110
17 160.7 6.1 45.4 53.1 63.9 -1.2 20.5 0.110
18 160.7 6.1 45.8 53.4 64.2 -1.1 20.7 0.105
0 50.0 1.8 2.8 3.3 3.9 0.001 13.2 0.085
0.25 61.4 2.1 5.4 6.4 7.4 -0.1 16.9 0.077
0.5 67.6 2.2 6.9 7.9 9.1 -0.2 17.4 0.079
0.75 72.0 2.2 7.8 8.9 10.2 -0.3 17.2 0.091
1 75.7 2.4 8.4 9.6 11.1 -0.4 16.8 0.086
1.5 81.8 2.6 9.5 10.9 12.6 -0.6 16.4 0.087
2 87.8 2.9 10.5 12.2 14.1 -0.7 15.8 0.084
3 96.4 3.4 12.6 14.6 17.1 -1.0 15.8 0.071
4 102.6 4.0 14.3 16.7 19.7 -1.2 15.9 0.072
5 107.9 4.5 15.8 18.7 22.2 -1.4 16.0 0.075
6 114.2 4.8 17.4 20.7 25.0 -1.6 15.9 0.081
7 120.2 5.1 19.3 23.1 28.1 -1.7 16.0 0.092
8 125.9 5.4 21.3 25.7 31.6 -1.8 16.2 0.098
9 131.1 5.8 23.6 28.6 35.5 -1.9 16.6 0.102
10 135.8 6.2 25.9 31.7 39.7 -1.9 17.2 0.108
11 140.3 6.8 28.3 35.0 44.4 -1.9 17.8 0.112
12 145.4 7.5 31.1 39.1 50.0 -1.8 18.5 0.117
13 151.5 8.2 34.8 44.3 56.8 -1.8 19.3 0.119
14 158.4 8.4 39.5 50.5 64.2 -1.7 20.1 0.122
15 164.6 8.2 44.7 56.5 70.6 -1.6 20.9 0.117
16 169.1 7.7 48.9 60.8 74.5 -1.6 21.2 0.115
17 171.7 7.2 51.8 63.3 76.4 -1.6 21.5 0.115
18 172.7 6.9 53.6 64.8 77.5 -1.5 21.7 0.117
Green numbers indicate synthetic values | Brown numbers indicate WHO values
11.1NATIONAL GROWTH REFERENCES
221
1.5 9.4 10.8 12.581.4 2.8
2 10.6 12.1 14.087.3 3.1
-1.3 15.9 0.084
3 12.4 14.3 16.795.3 3.6 12.4 14.3 16.7 -1.4 15.8 0.076
4 14.0 16.3 19.2102.4 4.1 14.0 16.3 19.2 -1.6 15.6 0.079
5 15.8 18.5 22.2109.5 4.6 15.8 18.5 22.2 -1.8 15.4 0.086
6 17.4 20.7 25.3116.4 4.9 17.4 20.7 25.3 -1.9 15.4 0.097
7 19.4 23.4 29.2123.0 5.3 19.4 23.4 29.2 -1.9 15.5 0.110
8 21.8 26.6 33.8129.0 5.6 21.8 26.6 33.8 -1.9 15.9 0.122
9 24.2 29.8 38.6134.3 6.0 24.2 29.8 38.6 -1.9 16.4 0.133
10 26.7 33.3 44.0139.9 6.4 26.7 33.3 44.0 -1.7 16.9 0.142
11 29.6 37.5 50.2146.6 6.9 29.6 37.5 50.2 -1.6 17.5 0.147
12 33.2 42.3 56.6153.2 7.1 33.2 42.3 56.6 -1.5 18.1 0.149
13 37.4 47.1 61.9158.7 6.9 37.4 47.1 61.9 -1.5 18.8 0.148
14 42.0 51.5 66.2162.4 6.5 42.0 51.5 66.2 -1.4 19.5 0.143
15 45.5 54.9 69.3164.7 6.2 45.5 54.9 69.3 -1.4 20.2 0.139
16 47.9 57.2 71.8165.8 6.0 47.9 57.2 71.8 -1.5 20.8 0.136
17 49.1 58.5 73.4166.2 6.0 49.1 58.5 73.4 -1.5 21.1 0.135
18 49.8 59.2 74.1166.3 6.0 49.8 59.2 74.1 -1.6 21.4 0.137
0 50.7 2.1 2.9 3.5 4.1 0.001 13.6 0.085
0.25 61.0 2.2 5.3 6.1 7.0 -0.1 16.4 0.077
0.5 67.9 2.3 6.9 7.9 9.0 -0.2 17.1 0.079
0.75 72.6 2.4 7.9 9.1 10.4 -0.3 17.3 0.091
1 76.3 2.5 8.7 10.0 11.4 -0.4 17.2 0.086
1.5 82.7 2.8 10.1 11.5 13.2 -0.6 16.8 0.087
2 88.4 3.1 11.0 12.6 14.5 -0.7 16.1 0.084
3 96.3 3.6-1.9 15.9 0.075
12.9 14.8 17.2
4 103.5 4.1-2.0 15.6 0.078
14.6 16.8 19.7
5 110.3 4.5-2.2 15.5 0.082
16.2 18.8 22.4
6 117.2 4.9 18.0 21.1 25.5 -2.3 15.4 0.08818.0 21.1 25.5
7 123.8 5.2 20.0 23.6 29.1 -2.4 15.5 0.09620.0 23.6 29.1
8 129.9 5.5 22.2 26.5 33.3 -2.4 15.8 0.10422.2 26.5 33.3
9 135.4 5.8 24.6 29.6 37.9 -2.4 16.1 0.11324.6 29.6 37.9
10 140.5 6.1 27.0 32.9 42.9 -2.4 16.6 0.12227.0 32.9 42.9
11 145.8 6.5-2.4 17.0 0.129
29.6 36.4 47.9
12 152.0 7.0-2.4 17.6 0.133
32.8 40.8 54.0
13 158.8 7.6-2.3 18.2 0.135
36.5 46.1 61.0
14 166.0 7.8-2.2 18.8 0.135
41.3 52.5 68.9
15 171.9 7.7-2.2 19.5 0.132
46.9 58.5 75.1
16 175.8 7.3 51.6 62.8 79.0 -2.1 20.1 0.12851.6 62.8 79.0
17 178.1 7.0 54.9 65.9 81.6 -2.0 20.6 0.12454.9 65.9 81.6
18 179.4 6.9 57.2 68.0 83.5 -2.0 21.0 0.12057.2 68.0 83.5
Green numbers indicate synthetic values
56 16.3 0.080
0 15.7 0.085
.3 15.4 0.084
.5 15.7 0.078
.8 15.9 0.082
.9 15.9 0 0890.0890.089
2.00 15.7 0 0910 0910 0910.0910.091
.99 15.9 0 1020 1020.102
1.77 16.4 0.107
1.66 16.9 0.112
1.55 17.4 0.119
1.44 18.0 0.124
1.33 18.7 0.138
1.33 19.4 0.139
-1.33 19.8 0.128
-1.33 20.1 0.116
-1.22 20.4 0.110
-1.22 20.5 0.110
-1.11 20.7 0.105
.0011 13.2 0.085
-0.11 16.9 0.077
-0.22 17.4 0.079
-0.33 17.2 0.091
-0.44 16.8 0.086
-0.66 16.4 0.087
-0.77 15.8 0.084
-1.00 15.8 0.071
-1.22 15.9 0.072
-1.44 16.0 0.075
-1.66 15.9 0.081
-1.77 16.0 0.092
-1.888 16.2 0.098
-1.99 16.6 0.102
-1.99 17.2 0.108
-1.99 17.8 0.112
-1.88 18.5 0.117
-1.88 19.3 0.119
-1.77 20.1 0.122
-1.66 20.9 0.117
-1.66 21.2 0.115
-1.66 21.5 0.115
-1.55 21.7 0.117
dicate WHO valuesdicate WHO values
87
FINAL HEIGHT PREDICTION
In contrast to the metric scales for height (cm)
and physical time (years) there is no apparent
metric scale for maturation (bone age is not a
metric scale for developmental tempo but relates
to calendar age ). Hewitt and Acheson [1961a,b]
introduced a scoring system, and found a more
rapid increase in unweighted bone scores at pu-
berty than before. Based on similar considera-
tions Tanner and co-workers developed an alter-
native system (Tanner-Whitehouse (TW) skeletal
maturity assessment system ) based on 20 bones
[Tanner et al. 1962]. The TW system defines a
score to each stage, from which a summed ma-
turity score (SMS) was formed ranging from 0
(immature) to 1000 (adult). Tanner later defined
a 13-bone system called RUS (radius, ulna, and
short bone) (TW2 [Tanner et al. 1975]) and
showed that mean maturity score increments per
chronological year differed throughout child-
hood and adolescence, with sharp increments/
yr of RUS scores during mid- and end-pubertal
age. A further refinement of this method (TW3)
was published by Tanner et al. [2001]. Maturi-
ty scores exhibit significant gender dimorphism,
with girls scoring earlier than boys. The Fels bone
age method [Roche et al. 1988] is similar to the
TW method, but involves more bones, more ma-
turity features, and more advanced mathematics;
it is laborious and less common than the Greu-
lich-Pyle /Bayley-Pinneau, and the TW2 method.
Yet, none of these height prediction models are
perfect; the models differ markedly in accuracy
[Onat 1995] (Figure 100).
Unfortunately, all scoring methods turn the scores
for skeletal maturity back into male and female
bone ages, muddling up calendar age, and mean
population versus individual progress in matura-
tion. This uncomfortable semantic confusion still
persists [Hermanussen 2010]. Determining an
individual’s bone age usually causes no prob-
lems per se, but problems arise when describ-
ing bone age progression. Maturity scores ad-
vance with age. But simple ratios such as bone
age/ chronological age that have often been
used in paediatric endocrinology, ignore that the
metric of physical time differs from the internal
dynamics of growth, that is the progress in matu-
rity scores. These ratios cause awkward and age-
dependent artefacts and should be questioned.
Thodberg [personal communication 201
posed to use bone age SD scores instead.
Differences in developmental tempo a
uncertainty of the moment when pubert
inflate height variance so that the associa
tween actual height and final height d
during puberty ( 5.3 Adolescent Growt
The pubertal uncertainty even persists w
endar time is replaced by biological tim
101). This is counterintuitive. Everybod
expect that the prediction error when
biological age would decrease as the ta
is final height) is approached. But this
case.
Also the signs of sexual maturation
used for predictions: pubic hair (PH) s
curs when about 86% of final height
reached in girls, and about 85% in boy
curs when about 91% in girls, and 89
PH4 occurs when 94% in girls and 92
and PH5 occurs when 97% in girls
95% of final height has been achieve
I.e. the Tanner stages may be used
height prediction [Onat 1983], but
prevailed in the clinical routine. Me
often been used to predict height, bu
is too simplistic: the association is gen
short girls tend to add more centimet
girls (Figure 102); and late maturing
end up taller [Onland-Moret et al. 2
103). The association between me
maturation does not hold true in h
tings where menarche may be exc
[Hermanussen et al. 2012b] ( 5.3
of Menarcheal Age). Michael H
Final height predictions shoul
formed before the expected on
berty, i.e. at BA < 12 in boys
10.5 in girls, and there is little ra
repeating a final height predic
puberty.
86
Figure 101: The observed root mean square
(RMS) error of height prediction. The lower lines
include parental height . Dashed line includes
menarche. There is a characteristic plateau in
both sexes, and a mild maximum in the predic-
tion error shortly after peak height velocity [after
Thodberg 2012].
Figure 103: The association between final height
and menarcheal age in over 70,000 Iceland
women born between 1930 and 1988. Both the
secular increase in stature, and the growth ad-
vantage in late menstruating women are visible
[data provided by courtesy of Laufey Tryggvadót-
tir, and Tryggvadóttir et al. 1994].
Figure 100: Mean error of Bayley-Pinneau ,
Roche-Wainer-Thissen [Roche et al. 1975], Tan-
ner-Whitehouse height predictions in Turkish
girls [after Onat 1995].
Figure 102: The remaining height growth after
menarche in girls of different height [after Thod-
berg 2012].
mea
n re
sidu
als
(cm
)
bone age (years)15
B-PRWT 1975RWT (MCSS)TW‘75(+MPS)
TW‘83 (3v)
14131211109
543210-1-2-3
RM
S er
ror
(cm
)
bone age (years)6 8 10 12 14 16 18
43.5
32.5
21.5
10.5
0
includingparents
includingmenarche
rem
aini
ng h
eigh
t at
m
enar
che
(cm
)
height at menarche (cm)175170165160155150145140
16
12
8
4
0
heig
ht (
cm)
menarche (year)8 10 12 14 16 18 20 22
1930-341935-391940-441945-491950-541955-591960-641965-691970-741975-791980-841985-88
180
170
160
150155
165
175
145
5.4FINAL HEIGHT PREDICTIONS
Sample pages
AUXOLOGY – Studying Human Growth and Development
1. Introduction1.1 Some Initial Remarks . . . . . 11.2 A Short Introduction to
Growth . . . . . . . . . . . . . . . 2
2. Basics2.1 Growth References and
Growth Charts . . . . . . . . . . 42.2 Tempo and Amplitude . . . . 82.3 Short Term Growth and
Mini Growth Spurts . . . . . . 102.4 Periodicity in Growth . . . . 122.5 Growth Tracking . . . . . . . . 142.6 Catch-up Growth. . . . . . . . 162.7 Rapid Growth . . . . . . . . . . 182.8 The Growth Plate. . . . . . . . 202.9 Growth Hormone . . . . . . . 242.10 Negative Growth . . . . . . . . 26
3. Body Shape, Composition and Proportions
3.1 Types of Body Shape . . . . . 283.2 Body Composition . . . . . . . 303.3 Determining Body Composi-
tion in Field Studies . . . . . . 323.4 Body Size, Somatotype and
Sports. . . . . . . . . . . . . . . . . 343.5 Fluctuating Asymmetry . . . 36
4. From birth to maturity4.1 Comparative Biology and
Human Life History . . . . . . 384.2 Foetal Programming and
Epigenetics. . . . . . . . . . . . . 424.3 Biological Age . . . . . . . . . . 444.4 Variation in Tempo . . . . . . 484.5 Twins. . . . . . . . . . . . . . . . . 504.6 Very Low Birth Weight
Children . . . . . . . . . . . . . . 524.7 Failure to Thrive during the
First 2 Years . . . . . . . . . . . . 544.8 Signs of Sexual Maturation 564.9 Timing Puberty by Stage
Line Diagrams . . . . . . . . . . 604.10 Menarcheal Age in Egypt . . 624.11 Adolescent Growth Spurt . . 644.12 Body Image and Body Size
during Puberty . . . . . . . . . . 664.13 The Community Effect on
Growth . . . . . . . . . . . . . . . 684.14 The Community Effect in
Swiss Conscripts . . . . . . . . 72
5. Height Predictions 5.1 Final Height . . . . . . . . . . . . 745.2 A Flow Chart to Final
Height Prediction. . . . . . . . 765.3 Target Height . . . . . . . . . . . 785.4 Final Height Prediction . . . 825.5 Factors that Influence Bone
Ageing . . . . . . . . . . . . . . . . 88
6. Prevention and Health6.1 Breast Feeding . . . . . . . . . . 906.2 Infant, Toddler and Child
Nutrition . . . . . . . . . . . . . . 926.3 Short and Tall Stature. . . . . 986.4 Primary Growth Failure . . . 1026.5 Secondary Growth Failure 1046.6 SGA and IUGR . . . . . . . . . 1066.7 The Shortest People: Peri-
centrin mutations. . . . . . . . 1086.8 Growth in Diabetic Patients 1106.9 Body Proportions in Rela-
tion to Health . . . . . . . . . . 1126.10 Social Determinants of
Health . . . . . . . . . . . . . . . . 1146.11 Migrants. . . . . . . . . . . . . . . 1166.12 Childhood Obesity in
Developing Countries . . . . 1186.13 Childhood Obesity: The
Impact of Migration . . . . . . 1206.14 PEM in Children: Anthropo-
metric Evaluation. . . . . . . . 1226.15 Nutrition Transition in
Developing Countries . . . . 1246.16 How Good is the BMI for
Detecting Obesity?. . . . . . . 1266.17 Comments on Obesity . . . . 1286.18 Growth and Pollutants . . . . 130
7. Auxology of the Past7.1 A Short History of the Study
of Human Growth . . . . . . . 1327.2 Secular Trends . . . . . . . . . . 1387.3 Trends in Amplitude and
Tempo . . . . . . . . . . . . . . . . 1407.4 How to Plot Secular Growth
Changes. . . . . . . . . . . . . . . 1427.5 The History of Menarcheal
Age . . . . . . . . . . . . . . . . . . 1447.6 Impact and Pitfalls of
Conscription Data . . . . . . . 1467.7 Conscript Height . . . . . . . . 1507.8 Long Term Changes in Head
Dimensions . . . . . . . . . . . . 1527.9 Harris Lines . . . . . . . . . . . . 1547.10 Growth and Death in the
Past . . . . . . . . . . . . . . . . . . 156
8. Auxological Methods8.1 Requirements for Anthropo-
metric References . . . . . . . 1588.2 Measurement Error in
Anthropometry . . . . . . . . . 1608.3 Standardised Measurements
1628.4 Daily Home-Made Measure-
ments. . . . . . . . . . . . . . . . . 1648.5 Automated Bone Age Deter-
mination . . . . . . . . . . . . . . 1668.6 Knemometry . . . . . . . . . . . 1688.7 Testing for Hormone
Deficiency . . . . . . . . . . . . . 174
9. Statistical Approaches9.1 Statistics for Bunnies . . . . . 1769.2 Growth Velocity . . . . . . . . 1789.3 SDS and LMS. . . . . . . . . . . 1829.4 Synthetic Growth Charts . . 1849.5 Harmonising National
Growth Charts . . . . . . . . . . 1869.6 Stability and Instability in
hSDS Changes . . . . . . . . . . 1889.7 Jump Preserving Smoothing
Technique . . . . . . . . . . . . . 1909.8 Rounding-Off and Heaping 1929.9 Parametric and Non-Param-
etric Regression Models . . . 1949.10 Landmark based Statistical
Shape Analysis. . . . . . . . . . 2009.11 A Bayesian Approach
towards Modelling Growth 2049.12 Methods that still Lack
Adequate Recognition . . . . 206
10. Miscellaneous10.1 Geometry and Auxology . . 20810.2 Finger Length Ratios. . . . . . 21010.3 Patents in Auxology . . . . . . 21210.4 Myths, Tales and Beliefs . . 214
11. Reference Values11.1 National Growth Referen-
ces. . . . . . . . . . . . . . . . . . . 21811.2 References for Preterm
Infants and Twins. . . . . . . . 23411.3 Syndrome Specific Growth
Charts . . . . . . . . . . . . . . . . 24411.4 References for Growth
Velocity . . . . . . . . . . . . . . . 24811.5 References for SD Score
Changes. . . . . . . . . . . . . . . 25611.6 References for Tempo,
Timing and Puberty . . . . . . 25811.7 References for Sitting
Height . . . . . . . . . . . . . . . . 26211.8 Body Proportion Chart . . . . 26411.9 References for MUAC, BF
and Skinfold Thickness . . . 26611.10 References for WC and
WHR . . . . . . . . . . . . . . . . . 27011.11 References and Equations
for Body Composition . . . . 27211.12 Body Surface and Ambigu-
ous Genitalia . . . . . . . . . . . 27611.13 References for IGF1 and
IGFBP3 . . . . . . . . . . . . . . . 278
12. Glossary . . . . . . . . . . . . . . . . 28113. Literature and Internet
Resources . . . . . . . . . . . . . . . 29514. Index. . . . . . . . . . . . . . . . . . . 319
Contents
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AUXOLOGY – Studying Human Growth and Development
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245
11.3SYNDROME SPECIFIC GROWTH CHARTS
Table 58: Prader-Willi syndrome [Hauffa et al. 2000].
Age Height Weight BMIyears M SD L M S L M S
1 70.1 4.9 -0.07 7.4 0.20 -0.84 14.9 0.112 80.2 5.5 -0.07 11.0 0.21 -0.84 16.2 0.133 89.0 6.0 -0.07 14.2 0.23 -0.84 17.3 0.144 96.4 6.4 -0.07 17.4 0.24 -0.84 18.4 0.165 103.0 6.6 -0.07 20.9 0.26 -0.84 19.5 0.176 109.0 6.8 -0.07 24.2 0.27 -0.84 20.3 0.197 114.7 7.0 -0.07 27.4 0.28 -0.84 21.0 0.208 120.1 7.2 -0.07 30.4 0.29 -0.84 21.7 0.219 125.2 7.3 -0.07 33.7 0.30 -0.84 22.3 0.23
10 130.2 7.3 -0.07 37.6 0.31 -0.84 23.0 0.2411 135.0 7.4 -0.07 42.6 0.32 -0.84 23.9 0.2512 139.3 7.4 -0.07 48.5 0.32 -0.84 24.9 0.2613 143.0 7.3 -0.07 54.9 0.32 -0.84 26.2 0.2714 145.8 7.2 -0.07 61.5 0.32 -0.84 27.5 0.2815 147.6 7.0 -0.07 67.7 0.32 -0.84 28.9 0.2816 148.4 6.8 -0.07 73.1 0.32 -0.84 30.3 0.2917 148.5 6.5 -0.07 77.6 0.31 -0.84 31.7 0.2918 148.3 6.2 -0.07 81.2 0.30 -0.84 33.0 0.2919 148.3 5.8 -0.07 83.9 0.29 -0.84 34.1 0.2920 148.6 5.5 -0.07 85.8 0.28 -0.84 35.2 0.29
1 70.2 5.0 0.15 7.4 0.24 -0.71 15.1 0.152 80.2 5.6 0.15 11.0 0.25 -0.71 16.3 0.163 88.8 6.0 0.15 14.3 0.25 -0.71 17.4 0.164 96.2 6.3 0.15 17.6 0.26 -0.71 18.4 0.175 102.8 6.6 0.15 21.0 0.26 -0.71 19.3 0.186 108.9 6.7 0.15 24.3 0.26 -0.71 20.1 0.187 114.8 6.8 0.15 27.6 0.26 -0.71 20.8 0.198 120.7 6.9 0.15 31.0 0.26 -0.71 21.5 0.199 126.8 7.0 0.15 34.9 0.26 -0.71 22.2 0.19
10 132.8 7.0 0.15 39.8 0.26 -0.71 23.0 0.2011 138.8 7.0 0.15 45.5 0.26 -0.71 24.0 0.2012 144.4 7.0 0.15 51.7 0.26 -0.71 25.0 0.2013 149.2 6.9 0.15 57.8 0.25 -0.71 26.1 0.1914 153.0 6.8 0.15 63.3 0.24 -0.71 27.1 0.1915 155.6 6.6 0.15 68.1 0.24 -0.71 28.0 0.1916 157.3 6.4 0.15 72.3 0.23 -0.71 28.9 0.1817 158.2 6.1 0.15 76.0 0.22 -0.71 29.8 0.1718 158.6 5.8 0.15 79.1 0.20 -0.71 30.5 0.1619 158.8 5.5 0.15 81.8 0.19 -0.71 31.2 0.1520 159.1 5.3 0.15 84.3 0.18 -0.71 31.9 0.14
244
Table 57: Silver-Russell syndrome [Wollmann et al.
1995].
Age Heightyears mean SD
2 73.0 5.03 79.2 5.445 91.0 6.16 96.6 6.47 102.0 6.68 107.2 6.89 112.2 6.9
10 117.0 7.011 121.6 7.112 126.0 7.113 130.2 7.014 134.2 6.915 138.0 6.816 141.5 6.6
2 75.1 4.83 81.0 4.945 92.3 5.26 97.7 5.47 103.0 5.68 108.1 5.89 113.0 6.1
10 117.8 6.311 122.4 6.612 126.9 6.913 131.2 7.214 135.3 7.615 139.3 7.916 143.1 8.3
Short stature is a recognised feature of many dys-morphic syndromes . Growth reference charts have been published for many syndromes of which a small number will be presented here. Some of these charts have been published as tables, most as smoothed charts. In general, syndrome specific growth charts give mean values and centiles for height. Some charts also provide information on weight and BMI. Some relate to national referenc-es (e.g. Figure 253). References for head circum-ference have been published for children with Down syndrome [Styles et al. 2002].
Syndrome specific growth charts suffer from a number of serious drawbacks. Many charts are
outdated, and were obtained from biased sam-ples. A lot of data was published before these syndromes were genetically defined. I.e. the charts were derived from patients who looked like that syndrome. We therefore limit this chap-ter to a spectrum of published syndrome specific growth charts that have been clinically used in the past. We strongly recommend high levels of scepticism when using these charts. Particularly Turner syndrome patiences have been shown to exhibit significant variation in the dysmorphic features with many patients who grow and devel-op well within the range of normal girls.
Michael Hermanussen
Table 56: Turner syndrome [Rongen-Westerlaken et al. 1997].
Age Height Weightyears mean SD - 2SD + 2SD mean - 2SD + 2SD
0 47.6 2.5 42.6 52.6 3.0 2.1 4.30.25 56.4 2.6 51.2 61.6 4.4 3.2 6.00.5 62.2 2.6 57.0 67.4 5.8 4.3 7.9
0.75 7.2 5.4 9.61 69.9 2.8 64.3 75.5 8.4 6.4 11.0
1.5 76.1 2.9 70.3 81.9 9.8 7.6 12.62 80.6 3.1 74.4 86.8 10.6 8.5 13.23 87.6 3.4 80.8 94.4 12.2 9.7 15.34 93.7 3.7 86.3 101.1 13.7 10.6 17.75 99.3 3.9 91.5 107.1 15.4 11.7 20.36 104.5 4.2 96.1 112.9 17.3 12.9 23.27 109.5 4.4 100.7 118.3 19.3 14.2 26.48 114.1 4.6 104.9 123.3 21.6 15.6 29.99 118.5 4.8 108.9 128.1 24.0 17.2 33.6
10 122.5 5.0 112.5 132.5 26.6 18.9 37.411 126.3 5.2 115.9 136.7 29.3 20.7 41.412 129.7 5.4 118.9 140.5 32.1 22.7 45.513 132.8 5.5 121.8 143.8 34.9 24.6 49.514 135.7 5.7 124.3 147.1 37.7 26.6 53.315 138.2 5.8 126.6 149.8 40.3 28.6 56.916 140.4 6.0 128.4 152.4 42.8 30.4 60.117 142.3 6.1 130.1 154.5 44.9 32.1 62.818 143.9 6.2 131.5 156.3 46.7 33.6 64.8
adult 146.9 6.4 134.1 159.7
11.3 SYNDROME SPECIFIC GROWTH CHARTS
60
Table 7: Reference values (%) for pubertal development in boys.
Age Genitalia Pubic Hair Testicular volumeyears G2 G3 G4 G5 PH2 PH3 PH4 PH5 4 ml 8 ml 12 ml 15 ml 20 ml
8.0 11.5 1.1 3.2 7.2 1.8 0.18.5 14.9 1.5 5.3 0.0 9.7 2.4 0.29.0 18.8 1.9 8.3 0.1 12.8 3.2 0.39.5 23.1 2.5 12.4 0.2 16.4 4.1 0.7
10.0 28.5 3.3 0.0 17.7 0.7 21.9 5.4 1.2 0.0 0.010.5 34.9 4.9 0.1 24.8 1.9 0.0 0.0 30.1 7.5 2.1 0.2 0.111.0 42.0 7.8 0.3 33.5 4.3 0.2 0.1 40.0 10.9 3.7 0.6 0.211.5 52.0 13.2 1.2 0.0 44.4 9.1 0.7 0.3 53.2 17.1 6.7 2.0 0.512.0 66.5 22.9 3.7 0.2 58.0 18.9 3.1 0.8 69.0 27.7 12.1 5.0 1.312.5 80.8 37.1 9.7 0.9 72.4 34.9 10.0 2.4 81.3 42.3 20.5 10.6 2.913.0 90.5 54.1 20.5 3.2 84.1 53.4 23.1 6.1 89.0 58.4 31.9 19.1 6.113.5 95.8 70.0 35.5 8.4 91.6 69.6 40.5 13.0 93.8 73.3 45.2 30.0 11.614.0 98.3 82.0 52.3 17.0 95.9 81.9 58.7 23.2 97.2 84.9 59.1 42.1 19.614.5 99.3 89.7 67.7 28.5 98.0 89.9 74.8 36.1 99.1 92.1 71.9 54.7 29.515.0 99.7 94.4 79.6 41.7 98.9 94.7 86.5 50.8 99.8 95.9 82.3 67.1 40.515.5 99.9 97.1 87.8 54.6 99.4 97.3 93.1 65.1 100.0 97.9 89.2 77.6 50.916.0 100.0 98.5 92.9 64.6 99.6 98.7 96.4 75.9 98.9 93.1 84.6 58.916.5 99.2 95.8 70.8 99.8 99.4 98.0 82.9 99.4 95.1 88.7 64.117.0 99.6 97.3 74.5 99.9 99.7 98.8 87.4 99.7 96.1 90.6 66.917.5 99.7 98.1 77.2 99.9 99.9 99.3 90.9 99.9 96.6 91.3 68.218.0 99.8 98.7 80.4 100.0 100.0 99.7 93.5 99.9 97.0 91.7 69.1
Table 8: Reference values (%) for pubertal development in girls.
Age Breast Pubic Hair Menarcheyears B2 B3 B4 B5 PH2 PH3 PH4 PH5
8.0 2.1 0.0 1.8 0.58.5 4.9 0.2 3.5 0.0 0.69.0 10.3 0.5 0.0 6.8 0.2 0.0 0.89.5 18.9 1.6 0.1 12.5 0.9 0.2 0.0 1.1
10.0 29.5 4.2 0.3 0.0 20.8 3.0 0.6 0.1 1.610.5 43.0 9.7 1.2 0.2 33.4 8.3 1.9 0.4 2.411.0 59.7 20.0 4.2 0.7 49.6 19.1 5.2 1.2 4.111.5 75.3 35.1 11.0 2.1 65.3 35.3 12.5 3.5 7.912.0 87.1 53.8 22.6 5.3 79.7 54.4 25.4 8.4 15.312.5 94.3 73.2 38.3 11.1 90.4 72.5 43.0 17.0 27.713.0 97.6 87.5 55.6 19.9 95.9 85.8 60.8 29.1 43.913.5 98.9 95.0 71.2 31.1 98.2 93.4 75.5 42.7 60.714.0 99.4 98.1 82.8 42.7 99.2 97.0 85.8 54.9 74.814.5 99.7 99.2 89.9 52.4 99.6 98.6 92.1 64.4 84.815.0 99.8 99.7 93.6 59.9 99.8 99.3 95.7 71.4 91.315.5 99.9 99.8 95.7 65.5 99.9 99.6 97.6 76.8 95.116.0 99.9 99.9 96.9 70.0 99.9 99.7 98.6 81.0 97.216.5 100.0 100.0 97.8 73.6 100.0 99.8 99.0 84.3 98.317.0 98.3 76.5 99.8 99.3 86.7 98.917.5 98.7 79.1 99.8 99.4 88.4 99.318.0 98.9 81.6 99.8 99.5 89.7 99.4
4.9 TIMING PUBERTY BY STAGE LINE DIAGRAM
61
4.9TIMING PUBERTY BY STAGE LINE DIAGRAM
The developmental progress of puberty is a con-tinuous process. But it is difficult to precisely track continuity. Instead we describe the progress in puberty by 5 developmental stages – genitals (boys), pubic hair (boys and girls), and female breast ( 4.8 Signs of Sexual Maturation, pages 58 – 61, Tables 4 – 6). Menarche can be staged (yes/no), and testicular volume can be estimat-ed in millilitres using the orchidometer [Prader 1966]. References of maturation are typically published as age p10, p50 and p90 at which respectively, 10, 50 and 90 percent of the pop-ulation achieve a certain pubertal stage. In clin-ical practice, the physician examines the child, determines the stage appropriate for that child, and compares the child’s age to the ‘normal’ age range p10 – p90 for that stage (Tables 7, 8). This procedure answers the question does this child mature ‘early’, ‘normal’ or ‘late’? and works well if only a classification into ‘early’ vs ‘normal’ vs ‘late’ is needed. But it lacks any sense of continu-ity between ‘early’ and ‘late’.
Stage line diagrams [van Buuren & Ooms 2009, van Buuren 2013] model the probability of the transition between successive categories, in this case, successive stages of puberty. They rely on the assumption that the progress of puberty continuously advances with age, and that the observed data are manifestations of an underly-ing variable, which are linked through a series of additive models with a probit link, one for each category transition. In this model, the transition probability to go from one category to the next smoothly changes with age. Figure 74 is such a
stage line diagram: an age-conditional reference of breast development (B1– B5). The horizontal axis indicates age. The vertical axis indicates maturation status as SDS correcting for age. Low-er values indicate delayed, higher values early maturation. The diagram contains 5 stage lines each corresponding to one of the 5 Tanner stag-es . The observer marks the child’s stage B1– B5 on the stage line corresponding to the child’s age, and connects the mark to the previous measure-ment. The curve gradually tails off as long as the child remains in the same stage. A move to the next stage produces a jump in the curve. The age at which the child reaches the next stage is un-known, and can be anywhere between the two ages surrounding the jump. Steeper jumps occur for measurements that are closer in time. Jumps can span two or more stages. Curves of normally developing children are located roughly between –2 SDS and +2 SDS. Early maturing children are placed near the top, late maturing children near the bottom of the diagram. Diagrams for sexual maturation are available at http://vps.stefvan-buuren.nl/puberty. Figure 75 shows a combined stage line diagram for breast and pubic hair de-velopment and menarche. Stef van Buuren
Stage line diagrams provide quick insights into both status (in SDS) and tempo (in SDS/year) at which the individual puber-tal development progresses. They express status and tempo of discrete changes on a continuous scale.
SDS
age (years)
-1.0
-2.0
-3.0
0.0
1.0
3.0
2.0
2010
B5
B4
B3B2B1
15
SDS
age (years)
-1.0
-2.0
-3.0
0.0
1.0
2.0
201710
BreastPubic HairMenarche
15
Figure 75: Stage line diagram for breast and pu-bic hair development, and menarche.
Figure 74: Stage line diagram of an individual progress in breast development.
Sample pages
9 19.1 6.12 30.0 11.61 42.1 19.69 54.7 29.53 67.1 40.52 77.6 50.91 84.6 58.91 88.7 64.11 90.6 66.96 91.3 68.20 91.7 69.1
che
568164193797883123934
61
and compares the child’s age to the ‘normal’ agerange p10 – p90 for that stage (Tables 7, 8). Thisprocedure answers the question does this child mature ‘early’, ‘normal’ or ‘late’? and works well?if only a classification into ‘early’ vs ‘normal’ vs‘late’ is needed. But it lacks any sense of continu-ity between ‘early’ and ‘late’.
Stage line diagrams [van Buuren & Ooms 2009, van Buuren 2013] model the probability of the transition between successive categories, in this case, successive stages of puberty. They rely on the assumption that the progress of puberty continuously advances with age, and that the observed data are manifestations of an underly-ing variable, which are linked through a series of additive models with a probit link, one for each category transition. In this model, the transitionprobability to go from one category to the nextsmoothly changes with age. Figure 74 is such a
ages surrounding the jump. Steeper jumps occur for measurements that are closer in time. Jumpscan span two or more stages. Curves of normally developing children are located roughly between –2 SDS and +2 SDS. Early maturing children are placed near the top, late maturing children nearthe bottom of the diagram. Diagrams for sexualmaturation are available at http://vps.stefvan-buuren.nl/puberty. Figure 75 shows a combined stage line diagram for breast and pubic hair de-velopment and menarche. Stef van Buuren
Stage line diagrams provide quick insights into both status (in SDS) and tempo (in SDS/year) at which the individual puber-tal development progresses. They express status and tempo of discrete changes on acontinuous scale.
SDS
age (years)
-1.0
-2.0
-3.0
0.0
1.0
3.0
2.0
2010
BB55
BB44
BB333BB222BB11
15
SDS
age (years)
-1.0
-2.0
-3.0
0.0
1.0
2.0
201710
BreastPubic HairP bi H iMenarcheMenarche
15
Figure 75: Stage line diagram for breast and pu-bic hair development, and menarche.
Figure 74: Stage line diagram of an individualprogress in breast development.
45
Biological age refers to the state of maturation or the degree of physical development of a human organism. The tempo at which the biological age of an individual proceeds can differ from the pro-gress in calendar age ; it depends on sex, type of body shape , genetics, ethnicity, and environmen-tal factors [Buckler 1979].
Girls grow up and develop faster than boys during childhood and puberty. On average, puberty starts some 2 years earlier than in boys, and girls tend to reach final height earlier. The progress in biological age is also influenced by body shape: pyknomorph children of both sex-es tend to develop faster and achieve puberty and maximum height up to 2 years earlier than the leptomorph. Differences in biological age between ethnicities are caused both by envi-ronmental (socioeconomic) and genetic fac-tors. The recent improvements in living con-ditions have led to an increase in the rate at which children and adolescents mature ( 7.2 Secular trends; 7.3 Trends in Amplitude and Tempo).
It is the biological, rather than the calendar age that is determined in paediatric screening inves-tigations, in forensic medicine, and in physical education, when identifying the position of a par-ticular child in regard to height, dentition, sexual maturation, cognition abilities etc., among the others.
Height age is an age defined by height. Taller children tend to be older. But the term is mislead-ing and should be abandoned. Body proportions are more sensitive for estimating the progress in maturation (Figures 56, 57).
Proportional age defines the biological age by the change of head – trunk – extremity proportions ( 8.3 Standardised Measurements). Particularly in younger children, the increase in body length large-ly reflects the increase in leg length. The differential dynamics of long bone, rump and head growth is nicely illustrated by the so called Philippine meas-ure (Figure 58), a historic criterion of maturity that was used to define the right time to enter school. Proportional age [Greil 2007] can be estimated by various indexes (Table 3). Christiane Schef er
4.3BIOLOGICAL AGE
Figure 58: Philippine measure: the child either reaches, or does not yet reach, the contralateral ear with the fingers.
Figure 57: Body proportions – the classic illustra-tion of Stratz [1903].
44
Figure 56: Changes of proportion (serial photos of a boy aged 2.5 to 6.5 years) [Schüler 2009].
Table 3: Change of proportions from birth to 18 years [Greil 2007].
Age ThI PSI RFL ScI ThI PSI RFL ScI
0 92.3 72.8 46.1 49.3 92.2 72.6 45.5 50.11 75.5 72.8 41.8 56.1 75.8 72.6 41.4 56.72 74.6 73.0 40.0 64.0 74.3 72.7 39.5 64.83 73.3 74.1 38.0 71.5 72.8 73.7 37.4 72.54 72.7 74.1 36.5 73.5 72.1 73.7 36.0 77.45 72.2 73.8 35.6 79.2 71.9 73.5 35.0 80.26 71.9 73.1 34.9 81.5 71.5 72.9 34.4 82.67 71.6 72.6 34.4 83.6 71.3 72.5 33.9 84.38 71.5 72.4 33.8 85.6 71.3 72.5 33.3 86.79 71.4 72.3 33.3 88.0 71.2 72.5 32.8 89.0
10 71.2 72.3 33.0 90.3 70.9 73.0 32.4 90.811 71.0 72.4 32.6 92.3 70.6 73.7 32.1 91.812 70.9 72.6 32.4 93.7 70.5 74.9 31.7 92.613 70.8 72.9 32.0 94.8 70.5 75.9 31.2 92.614 70.3 72.9 31.4 95.4 70.3 77.1 30.8 91.815 69.9 73.0 31.1 94.9 70.0 77.8 30.5 91.116 69.5 72.6 30.9 94.1 70.0 78.2 30.4 90.517 69.2 72.0 30.9 93.1 70.0 78.5 30.4 90.018 69.9 71.3 30.9 92.3 70.0 78.6 30.5 89.6
ThI: Thoracic Index (= chest depth * 100/chest breadth)PSI: Pelvic-Shoulder Index (= bicristal pelvic breadth *100/ biacromial shoulder breadth)RFL: Relative Foot Length (= foot length*100/leg length)ScI: Scelic Index (= leg length * 100/sitting height )
4.3 BIOLOGICAL AGE