J Appl Physiol 106: 40–48, 2009.
First published November 13, 2008; doi:10.1152/japplphysiol.91123.2008.
Differences between brain mass and body weight scaling to height: potential
mechanism of reduced mass-specific resting energy expenditure
of taller adults
Steven B. Heymsfield,1 Thamrong Chirachariyavej,3,† Im Joo Rhyu,4 Chulaporn Roongpisuthipong,2
Moonseong Heo,5 and Angelo Pietrobelli6
1
Global Center for Scientific Affairs, Merck & Company, Rahway, New Jersey; Departments of 2Medicine and 3Pathology,
Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; 4Department of Anatomy, College
of Medicine, Korea University, Seoul, Korea; 5Department of Epidemiology and Public Health, Albert Einstein College
of Medicine, Bronx, New York; 6Pediatric Unit, Verona University Medical School, Verona, Italy
Submitted 21 August 2008; accepted in final form 11 November 2008
body composition; nutritional requirements
determined by subject body
weight (5b, 23). Since adult body weight increases as a function of height⬃2 (33), taller subjects weigh more and have a
greater energy requirement than their shorter counterparts (5b).
The largest component of energy requirements in most adults
ENERGY REQUIREMENTS ARE LARGELY
† Deceased 23 September 2008.
Address for reprint requests and other correspondence: S. B. Heymsfield,
Clinical Research, Metabolism, Merck Research Laboratories, 126 E. Lincoln
Ave., PO Box 2000, RY34A-A238, Rahway, NJ 07065-0900 (e-mail:
[email protected]).
40
is related to resting energy expenditure (REE), and REE scales
as height⬃1.5 (13). Accordingly, mass-specific resting energy
requirements, defined as REE/body weight, decrease as
height⫺0.5. Tall subjects thus appear to have a smaller magnitude mass-specific REE and from the energetic perspective can
be considered relatively more “efficient” at rest. Greater stature
and associated body weight are thus not accompanied by a
proportionally larger resting energy need.
The mechanism leading to a smaller mass-specific REE with
greater height is unknown, although one hypothesis is that
relative to body weight tall and short subjects differ in their
proportions of heat producing tissues (13,14). Total adipose
tissue free mass (ATFM), skeletal muscle (SM) mass, and bone
mass also scale approximately as height2, so ATFM and
musculoskeletal mass appear to remain stable proportions of
body weight independent of height (14). Adiposity, defined as
%fat or adipose tissue, is largely independent of height (14).
In contrast to these other relatively large compartments,
brain mass at ⬃2% of body weight scales inconsistently to
height with the limited observations variable between men and
women (14). Moreover, some studies of adults fail to detect
significant correlations whereas others observe significant but
relatively weak associations between brain mass and stature (4,
8, 11, 14, 16, 17, 20, 27–31, 36, 38, 43, 44). We recently
reported in a small adult sample that in men (n ⫽ 19) brain
volume measured using magnetic resonance imaging (MRI)
scaled to height with a power of less than 1 and that brain
mass/body weight was negatively associated with stature
(14). Since brain is a high-metabolic-rate organ (⬃240
kcal䡠kg⫺1 䡠day⫺1; Ref. 7), a relatively smaller brain mass may
provide a partial mechanistic explanation for the lower REE/
body weight observed in taller men.
The present study expands on these earlier observations by
testing in a large sample of Thai men the hypothesis that brain
mass scales to height with a power significantly less than that
of body weight (i.e., ⬃2). If valid, this hypothesis would
provide a potential mechanism explaining the observation that
mass-specific REE is smaller in magnitude in tall subjects
compared with their shorter counterparts. To exclude the neurodegenerative effects of aging on our analyses, we also
explored brain mass-body size relations in a subgroup of the
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Heymsfield SB, Chirachariyavej T, Rhyu IJ, Roongpisuthipong C, Heo M, Pietrobelli A. Differences between brain
mass and body weight scaling to height: potential mechanism of
reduced mass-specific resting energy expenditure of taller adults.
J Appl Physiol 106: 40 –48, 2009. First published November 13,
2008; doi:10.1152/japplphysiol.91123.2008.—Adult resting energy
expenditure (REE) scales as height⬃1.5, whereas body weight (BW)
scales as height⬃2. Mass-specific REE (i.e., REE/BW) is thus lower in
tall subjects compared with their shorter counterparts, the mechanism
of which is unknown. We evaluated the hypothesis that high-metabolic-rate brain mass scales to height with a power significantly less
than that of BW, a theory that if valid would provide a potential
mechanism for height-related REE effects. The hypothesis was tested
by measuring brain mass on a large (n ⫽ 372) postmortem sample of
Thai men. Since brain mass-body size relations may be influenced by
age, the hypothesis was secondarily explored in Thai men age ⱕ45 yr
(n ⫽ 299) and with brain magnetic resonance imaging (MRI) studies
in Korean men (n ⫽ 30) age ⱖ20⬍30 yr. The scaling of large body
compartments was examined in a third group of Asian men living in
New York (NY, n ⫽ 28) with MRI and dual-energy X-ray absorptiometry. Brain mass scaled to height with a power (mean ⫾ SEE;
0.46 ⫾ 0.13) significantly smaller (P ⬍ 0.001) than that of BW scaled
to height (2.36 ⫾ 0.19) in the whole group of Thai men; brain
mass/BW scaled negatively to height (⫺1.94 ⫾ 0.20, P ⬍ 0.001).
Similar results were observed in younger Thai men, and results for
brain mass/BW vs. height were directionally the same (P ⫽ 0.09) in
Korean men. Skeletal muscle and bone scaled to height with powers
similar to that of BW (i.e., ⬃2–3) in the NY Asian men. Models
developed using REE estimates in Thai men suggest that brain
accounts for most of the REE/BW height dependency. Tall and short
men thus differ in relative brain mass, but the proportions of BW as
large compartments appear independent of height, observations that
provide a potential mechanistic basis for related differences in REE
and that have implications for the study of adult energy requirements.
BRAIN MASS AND HEIGHT
Thai men age ⱕ45 yr and in a small group of young Korean
men. A secondary study aim was to establish whether large
body compartments such as ATFM, SM mass, and bone mass
also scale in Asian men as height⬃2.
These collective observations would, accordingly, not only
provide insights into the genesis of human whole body REE
but also create a foundation for establishing the fundamental
relations between adult stature and body composition.
METHODS
Experimental Design and Rationale
J Appl Physiol • VOL
consent prior to participation. The NY Asian men were evaluated as
part of a larger cross-sectional body composition study, and brain
imaging studies were not included in the protocol (12, 14, 33).
Data from the three groups of Asian men was used to examine the
study hypothesis and related topics by answering three questions: 1)
How does body weight scale to height? 2) How do brain mass and
brain mass/body weight scale to height? 3) How do adipose tissue,
ATFM, SM, and bone scale to height? Question 1 was examined by
evaluating the scaling of body weight to height in all three groups of
men. Question 2 was examined by evaluating the allometric relationships of brain mass to height in the Thai and Korean men, and
question 3 was evaluated in the corresponding analyses for adipose
tissue mass, ATFM, SM mass, and bone mass in the NY Asian men.
Subjects
The Thai men included those succumbing to accidents, falls,
electrocutions, suicides (e.g., overdoses), and homicides (e.g., gunshots) during the period February 2003–December 2007. Men were
excluded who had any evidence of underlying disease, including
infections, malignancies, or postmortem histological evidence of an
acute or chronic illness. We excluded men succumbing from a
fire-related death or from a treated chronic illness.
The Korean men were healthy young adults recruited through
advertisements on the Korean University web page and local newspapers during the period August 2001–April 2004. Subjects were
entered into the study following completion of a physical exam aimed
at excluding any undetected neurological diseases.
The NY men were mainly Chinese with both parents of Asian
extraction. All were healthy adults and had no evidence of acute or
chronic disease based on a history, screening physical examination,
and blood studies during the accrual period of June 1998 –May 2003.
The Korean and NY Asian men were all well hydrated at the time
of their physical examinations and imaging studies.
Measurements
Body weight and height. Forensic pathologists supervised all of the
Thai autopsies. Trained mortuary technicians measured body weight
and length. Bodies were weighed naked with the same scale (Kern &
Sohn, Balingen, Germany). Body length was measured from head to
heel with a calibrated tape measure. All bodies were refrigerated at the
same temperature (4°C) before weighing.
Body weight and height in the Korean and NY Asian men were
measured via calibrated digital scales and stadiometers, respectively.
Brain mass. Brains from the Thai men were removed within 10 min
of autopsy initiation and prepared as recommended by The College of
American Pathologists (32). The brain and spinal cord were first
separated below the decussation of the pyramids and then promptly
weighed with leptomeninges intact and unopened ventricles. The
brains were then serially sectioned for gross examination followed by
fixation for histological review.
Brain volume was evaluated in the Korean men using 1.5-mm-thick
cross-sectional MRI images acquired via a 1.5-T Magnetom Vision
system (Siemens, Erlangen, Germany) (20). Two trained observers
independently segmented regions of interest, including whole brain,
cerebellum, and lateral ventricles. Cerebrospinal fluid was separated
from brain tissue and measured volumes were derived using V-work
software (CyberMed, Seoul, Korea). The results from the two analysts
were then averaged and converted to whole brain mass for presentation under RESULTS. The MRI measurements are not fully automated
and therefore involve a human judgment component. Accordingly, we
developed three measures of between-observer variation including the
coefficient of variation (CV), the CVs’ interquartile range (IQR) from
multiple analysts, and the intraclass correlation coefficient (ICC). The
respective CV, IQR, and ICC for brain mass evaluation are 2.9%,
1.6%, and 0.97.
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The study focused on Thai men as a large adult autopsy cohort (n ⫽
372) was available to critically test the hypothesis with carefully
collected postmortem estimates of brain mass. The men ranged in
age from 18 to 88 yr, and one concern expressed in earlier studies is
the presence of age-related changes in brain mass (5a, 30, 31). We
therefore also separately analyzed men in this cohort at or below the
age of 45 yr (n ⫽ 299). Additionally, we specifically explored brain
mass-body size relations in a small cohort of healthy young Korean
men (n ⫽ 30) recruited within the restricted age range of ⱖ20⬍30 yr
using MRI to estimate total brain volume. The scaling of large body
compartments, ATFM, SM mass, and bone mass to height was
evaluated in a third healthy cohort of 28 Asian men, largely Chinese,
living in New York (NY), who completed whole body MRI and
dual-energy X-ray absorptiometry (DXA) studies.
The study included only men of Asian extraction since our earlier
investigation of brain mass-body size relations included an ethnically
heterogeneous sample (14) and brain mass (16, 17, 31) and other body
compartments (18) may vary across race groups. However, the country of origin differed between the men, including mainly Thailand,
Korea, and China. The available sample of Thai women was considerably smaller than that of men, particularly when considering those
age ⱕ45 yr, and we thus concentrated the present study on men.
Brain mass-body size relations. The primary sample for brain mass
evaluation included Thai men studied at the time of autopsy (3, 4).
The forensic autopsies were performed at the Department of Pathology, Faculty of Medicine, Ramathibodi Hospital. All of the subjects
selected were Thai adult (age ⱖ18 yr) men who died of a nonbrain
injury with a survival time of less than 15 min. Forensic pathologists
performed all autopsies within 24 h following death since the delay
between death and autopsy can alter brain weight (28, 30). Body
weight, length (i.e., height), and brain weight were evaluated and the
collected data was used to establish the scaling of body weight and
brain weight to height. The Ramathibodi Institutional Review Board
approved the postmortem data collection.
The second subject cohort included healthy young Korean men in
their twenties who were evaluated as part of a program aimed at
developing national reference values for brain volume (19). Brain
volume, converted to mass by use of the reported density of brain
tissue (1.03 kg/l) (37), was evaluated by MRI with 1.5-mm contiguous
slices as previously reported (20). The study was approved by the
Korea University Medical Center, and all subjects signed an informed
consent prior to participation. The collected data was used to evaluate
the scaling of body weight and brain mass to height.
Large body compartment-body size relations. Total body adipose
tissue volume, SM volume, and ATFM were evaluated by whole-body
MRI in Asian men age ⱖ18 yr at the NY Obesity Research Center.
Compartment volumes were converted to mass using previously
reported (7, 37) densities for adipose tissue (0.92 kg/l), SM (1.04
kg/l), and ATFM (1.04 kg/l). Bone mineral content (BMC) was
measured by DXA on the same day and results were converted to
bone mass as BMC/0.54 (12, 37). The collected data was used to
evaluate the scaling of body weight, ATFM, SM mass, and bone mass
to height. The study was approved by the Institutional Review Board
of St. Luke’s-Roosevelt Hospital, and all subjects signed an informed
41
42
BRAIN MASS AND HEIGHT
Body composition. Whole body MRI scans were completed in the
NY Asian men with a 1.5-T General Electric scanner (6X Horizon,
Milwaukee, WI), and images were then segmented by trained and
quality-controlled analysts. Total body adipose tissue and SM volumes were calculated from the cross-sectional image data and converted to mass by using their previously reported tissue densities (7,
37). Adipose tissue-free mass was calculated as body weight minus
adipose tissue mass. Respective CV, IQR, and ICC values for total
body SM and adipose tissue mass are 2.1%, 1.8%, and 0.97 and 2.0%,
2.6%, 0.99.
Bone mineral content was measured for the whole body by use of
a GE Lunar DPX system (Madison, WI, software version 3.6) (12).
The CV for repeated measurements of bone mineral mass is 1.7%.
Statistical Methods
RESULTS
Group Characteristics
The baseline characteristics of the three groups of men are
presented in Table 1. There were 372 total Thai men, 299 of
whom were age ⱕ45 yr. The group as a whole had a mean age
of ⬃36 yr and a body mass index (BMI) of ⬃22 kg/m2. Brain
mass at the time of autopsy in the whole group of men was
1,334 ⫾ 125 g (means ⫾ SD) and was minimally larger
(1,341 ⫾ 121 g) in the younger group of men.
There were 30 Korean men with a mean age of ⬃24 yr with
a BMI of ⬃23 kg/m2. The group brain mass estimated by MRI
was 1,440 ⫾ 102 g.
There were 28 men in the NY Asian group with a mean age
of ⬃35 yr and a BMI of ⬃23 kg/m2. Skeletal muscle was the
largest of the evaluated tissue compartments (⬃28 kg) and with
added bone (⬃5 kg) musculoskeletal mass was ⬃33 kg or 49%
of body weight. Skeletal muscle mass accounted for ⬃60% of
ATFM (⬃54 kg). The total body adipose tissue mass was 13.7
kg or 20% of body weight. Adipose tissue mass was significantly correlated with age (r ⫽ 0.45, P ⬍ 0.01) whereas the
correlations between body weight, ATFM, SM mass, and bone
mass with age were all nonsignificant.
Allometric Analyses
Allometric analyses are presented in Table 2 for body weight
and brain mass in the Thai and Korean men. The allometric
analyses for body weight and body composition for the NY
Asian men are presented in Table 3.
Body weight. Body weight was highly correlated with height
for the Thai, Korean, and NY Asian men (Fig. 1). Body weight
scaled similarly to height with powers of 2.36 ⫾ 0.19 and
2.39 ⫾ 0.22 (means ⫾ SEE) in the whole group of Thai men
(r ⫽ 0.54, P ⬍ 0.001) and in the Thai men age ⱕ45 yr (r ⫽
Table 1. Characteristics of the Thai, Korean, and NY Asian men
Thai Men
N
Age, yr
BW, kg
Height, cm)
BMI, kg/m2
Brain mass, g
AT mass, kg
SM mass, kg
Bone mass, kg
ATFM, kg
Age ⬍45 yr
All Subjects
Korean Men
NY Asian Men
299
30.2⫾7.3
62.5⫾11.1
167.6⫾6.4
22.2⫾3.4
1,341⫾121
NA
NA
NA
NA
372
35.6⫾13.6
62.1⫾10.9
167.1⫾6.5
22.2⫾3.3
1,334⫾125
NA
NA
NA
NA
30
24.3⫾2.2
71.4⫾10.1
175.3⫾3.7
23.2⫾2.9
1,440⫾102
NA
NA
NA
NA
28
34.6⫾14.0
67.3⫾8.5
172.3⫾5.9
22.7⫾2.5
NA
13.7⫾4.4
27.7⫾3.8
5.1⫾0.9
53.5⫾5.9
Data are presented as group mean values ⫾ SD; NA, data not available. AT, adipose tissue; ATFM, adipose tissue-free mass; BMI, body mass index; BW,
body weight; NY, New York; SM, skeletal muscle.
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Baseline subject demographic characteristics are reported as
means ⫾ SD in tables and as mean powers ⫾ standard error of the
estimate (SEE) in the text and figures. The statistical analyses were
carried out via SPSS (SPSS for Windows, 11.5, SPSS, Chicago, IL).
Allometric models. The mathematical foundation for developing
allometric models is extensively reviewed in earlier publications (2,
13, 14, 19, 26). Briefly, the classic general allometric model is
expressed in a form of: Y ⫽ ␣X␤, where Y is outcome (e.g., brain
mass), X is the predictor variable (e.g., height), ␤ is the scaling
exponent or power, and ␣ is the proportionality constant. For instance,
based on the work of Quetelet (34) and many others (14) since his
seminal observations, ␤ approximates 2 in these equations when body
weight (Y) is scaled to height (X).
The scaling of brain mass/body weight can be examined by writing
the general allometric model separately for brain mass and body weight,
both scaled to height: brain mass ⫽ ␣1height␤1 and body weight ⫽
␣2height␤2. Therefore, brain mass/weight ⫽ ␣1/␣2height␤1⫺␤2. If brain
mass and body weight scale differently to height (i.e., ␤1 ⫽ ␤2), short
and tall subjects will not have the same brain mass/body weight. In other
words, the power of height when scaled to brain mass vs. body weight is
not the same.
The hypothesis was tested by examining the allometric relationships between body weight, brain mass, and height in the whole group
of 372 Thai men. Allometric models were developed with coefficients
␣ and ␤ estimated by least squares multiple linear regression analysis
based on log-transformed data in the form of logeY ⫽ loge␣ ⫹
␤logeX ⫹ ε, where ε is error term. Body weight or brain mass was set
as the dependent variable and height and age as independent variables
in the regression models (19, 26). Log ␣ and ␤ values along with R
and SEE values for the series of developed regression models are
presented under RESULTS. Statistical tests of the power of height when
scaled to body weight vs. brain mass within the same group were
performed by testing the significance of ␦ in the following model:
logebrain mass ⫺ logebody weight ⫽ ␥ ⫹ ␦logeheight ⫹ ε. The
sequence of developing these models with related statistical tests were
repeated for the Thai men age ⱕ45 yr and for the Korean men.
Allometric models were similarly developed describing the relations between adipose tissue mass, ATFM, SM mass, and bone mass
with height in the NY Asian men. We also examined these relations
for musculoskeletal mass, derived as the sum of SM and bone mass.
Scatterplots of allometric relations are presented in the figures and
the data in the figures are fit with univariate regression models.
43
BRAIN MASS AND HEIGHT
Table 2. Height-related allometric regression models based on log-transformations for the Thai and Korean men
All Subjects
BW, kg
Thai men
Korean men
Brain mass, g
Thai men
Korean men
Brain/BW, g/kg
Thai men
Korean men
Age⬍45 yr
Ht
Age
Int
SEE
R (P)
Ht
Age
Int
SEE
R (P)
2.36
3.03
NS
NS
⫺7.94
⫺11.40
0.14
0.12
0.54 (⬍0.001)
0.48 (⬍0.01)
2.39
NS
⫺8.12
0.14
0.54 (⬍0.001)
0.46
1.25
⫺0.03
NS
4.93
7.68
0.09
0.07
0.25 (⬍0.001)
0.37 (⬍0.05)
0.52
NS
4.57
0.09
0.21 (⬍0.001)
⫺1.94
⫺1.78
⫺0.06
NS
13.19
12.20
0.15
0.15
0.44 (⬍0.001)
0.25 (0.09)
⫺1.93
⫺0.07
13.19
0.15
0.44 (⬍0.001)
NS, nonsignificant covariate. Ht, height; Int, intercept; SEE, standard error of the estimate.
difference did not reach statistical significance (P ⫽ 0.09).
Brain mass in the two groups of men thus scaled to height with
powers in the range of ⬃0.5–1.2.
Brain mass, when expressed as a ratio to body weight in the
Thai men, scaled negatively to height (Fig. 3) with a power of
⫺1.94 ⫾ 0.20 with age serving as a significant predictor
variable (r ⫽ 0.44, P ⬍ 0.001). Results were nearly identical
when the regression models were developed in the men age
ⱕ45 yr (Table 3). The ratio of brain mass to body weight also
scaled negatively to height (⫺1.78 ⫾ 1.3) in the Korean men,
but the correlation was nonsignificant (P ⫽ 0.18).
Body composition. The allometric analyses of body composition for the NY Asian men are presented in Table 3. The
associations between adipose tissue mass and % of body
weight as adipose tissue with height were both nonsignificant
(r ⫽ 0.07 and r ⫽ ⫺0.16). All of the evaluated lean compartments, ATFM, SM mass, bone mass, and musculoskeletal mass
scaled significantly to height with respective powers of 2.16 ⫾
0.50, 2.24 ⫾ 0.70, 2.75 ⫾ 1.00, and 2.29 ⫾ 0.67 (Fig. 4). Age
was a significant negative predictor in SM and bone mass
regression models. Skeletal muscle mass and musculoskeletal
mass expressed as a fraction of body weight were not significantly correlated with height. With the exception of adipose
tissue mass, the evaluated large body compartments thus scaled
to height with powers in range of ⬃2–3.
Energy expenditure. Our results suggest that tall men have
the same relative amounts of large body compartments (i.e.,
ATFM, skeletal muscle, and bone) compared with short men.
By contrast, our results support the hypothesis that brain
mass/body weight is significantly smaller in tall men. We
modeled the effects these compartmental relations have on
energy expenditure in the Thai men by estimating REE (kcal/
Table 3. Height-related allometric regression models based on log-transformations for the NY Asian men
NY Asian Men
BW, kg
AT, kg
ATFM, kg
SM, kg
SM/Wt, kg/kg
Bone, kg
SM⫹B, kg
(SM⫹B)/Wt, kg/kg
Ht, cm
Age
Int
SEE
R (P)
1.85
NS
2.16
2.24
NS
2.75
2.29
NS
NS
0.34
NS
NS
0.10
NS
NS
0.10
⫺5.33
1.47
⫺7.13
⫺8.24
0.59
12.54
8.33
0.40
0.11
0.29
0.09
0.12
0.07
0.18
0.12
0.07
0.49 (⬍0.01)
0.40 (P⫽0.02)
0.65 (⬍0.001)
0.54 (⬍0.01)
0.45 (⬍0.01)
0.47 (⬍0.01)
0.57 (⬍0.01)
0.45 (⬍0.01)
B, bone; SM, skeletal muscle mass; Wt, weight.
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0.54, P ⬍ 0.001) (Table 2). Body weight scaled with a higher
power to height in the Korean men, but with a larger SEEs
(3.03 ⫾ 1.06). Body weight scaled to height with a power of
1.85 ⫾ 0.64 in the NY Asian men (r ⫽ 0.49, P ⬍ 0.01). Age
was not a significant covariate in any of these regression
models (Table 2). Body weight in the three groups of men thus
scaled to height with powers in the range of ⬃2–3.
Brain mass. Brain mass was significantly negatively correlated with age in the whole group of Thai men but not in those
age ⱕ45 yr (Fig. 2) or the Korean men. Height was also
significantly negatively correlated with age in the whole group
of Thai men (r ⫽ 0.21, P ⬍ 0.001) but not in the Korean men.
Brain mass was significantly correlated with body weight
(r ⫽ 0.31, P ⬍ 0.001), but body weight was not correlated with
age (r ⫽ ⫺0.07, P ⫽ not significant) in the whole group of
Thai men with similar results for the men age ⱕ45 yr. The
correlation between brain mass and body weight was nonsignificant in the Korean men.
The allometric analyses for the Thai and Korean men are
presented in Table 2. Brain mass scaled to height with a power
of 0.46 ⫾ 0.13 (r ⫽ 0.24) (Fig. 3) and 0.52 ⫾ 0.14 (r ⫽ 0.21)
in the whole group of Thai men and men age ⱕ45 yr,
respectively (both P ⬍ 0.001). The allometric regression model
for the whole group of men included age as a significant
predictor variable. Both powers, 0.46 and 0.51, were significantly less (P ⬍ 0.001) than those of the corresponding powers
for body weight scaled to height (2.36 and 2.39).
Brain mass scaled to height with a power of 1.25 ⫾ 0.59
(r ⫽ 0.37, P ⬍ 0.05) in the group of Korean men; age was not
a significant predictor variable. This power of height (i.e., 1.25)
is substantially less than the power of height when body weight
is scaled to height (i.e., 3.03 ⫾ 1.06), although this numerical
44
BRAIN MASS AND HEIGHT
day) according to the Harris-Benedict (HB) equation (REEHB)
(10). We first evaluated the scaling of REEHB to height in the
whole group of Thai men and in those age ⱕ45 yr (Table 4).
Compared with body weight, REEHB scaled with a lower
power to height (⬃1.9 vs. ⬃2.4) and REEHB/body weight
scaled negatively to height with a power of ⬃(⫺0.5) (P ⬍
0.001 for both groups of Thai men).
We then assumed that REE represents the sum of two main
contributions, brain with a mass-specific energy expenditure of
240 kcal䡠kg⫺1 䡠day⫺1 and remaining tissue. This assumption
then allowed us to divide REEHB into brain and nonbrain
components (i.e., REEHB ⫺ [240 ⫻ brain mass in kg)].
Nonbrain REE scaled to height with powers very similar to the
power observed when body weight is scaled to height (⬃2.3 vs.
⬃2.4). Nonbrain REE/weight was nonsignificantly related to
height in the whole group of Thai men (Table 4) and signifi-
Fig. 2. Brain mass vs. age in the Thai men. A vertical line separates the men
above and below the age of 45 yr. The data were fit with a second order
polynomial regression model.
Fig. 4. Adipose tissue free mass (ATFM), skeletal muscle (SM) mass, and
bone mass vs. height in the NY Asian men. The data are fit with univariate
power functions that are shown in the figure.
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Fig. 1. Body weight vs. height in the Thai (top), Korean (middle), and New
York Asian (NY; bottom) men. The plotted data were fit with univariate power
functions that are provided in each panel of the figure. The models are of the
form Y ⫽ ␣X␤ where Y is body weight, X is height, ␤ is the scaling exponent
or power, and ␣ is the proportionality constant. All regression models in the
figure are P ⬍ 0.01 and are presented in Table 2.
Fig. 3. Brain mass (top) and brain mass/body weight (BW) (bottom) vs. height
in the Thai men. The data are fit with univariate power functions that are shown
in the figure. The plotted data were fit with univariate power functions that are
provided in each panel of the figure. The models are of the form Y ⫽ ␣X␤
where Y is brain mass or brain mass/BW, X is height, ␤ is the scaling exponent
or power, and ␣ is the proportionality constant. Both regression models in the
figure are P ⬍ 0.01 and are presented in Table 2.
45
BRAIN MASS AND HEIGHT
Table 4. REE-related allometric regression models based on log-transformations for the Thai men
All Subjects
REEHB, kcal/day
REEHB/BW, kcal 䡠 kg⫺1 䡠 day⫺1
NonBrain REE, kcal/day
NonBrain REE/BW, kcal 䡠 kg⫺1 䡠 day⫺1
Ht
Age
Int
SEE
1.93
⫺0.47
2.35
NS
⫺0.17
⫺0.19
⫺0.21
⫺0.23
⫺2.00
6.25
⫺4.26
4.00
0.08
0.06
0.10
0.06
Age⬍45 yr
R (P)
0.78
0.73
0.78
0.82
(⬍0.001)
(⬍0.001)
(⬍0.001)
(⬍0.001)
Ht
Age
Int
SEE
1.86
⫺0.56
2.23
⫺0.19
⫺0.11
⫺0.15
⫺0.13
⫺0.17
⫺1.83
6.59
⫺3.90
4.53
0.08
0.06
0.10
0.05
R (P)
0.70
0.55
0.69
0.62
(⬍0.001)
(⬍0.001)
(⬍0.001)
(⬍0.001)
HB, Harris-Benedict equation; REE, resting energy expenditure.
cantly related to height with a small negative power of
⫺0.19 ⫾ 0.08 in the younger group of men.
DISCUSSION
Scaling of Body Composition to Height
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Body mass scales across adults approximately as height2, an
observation made almost two centuries ago by Quetelet (34)
and again largely confirmed in the present study. The scaling of
body mass to height reflects the weighted effects of each
anatomical compartment, and our findings in Asian men suggest that ATFM, SM mass, and bone mass all scale to height
with powers of ⬃2. These large compartments, notably the
structurally important musculoskeletal mass compartment, are
the main determinants leading to the scaling of body mass as
height2. As these lean tissue compartments scale across adult
subjects with approximately the same power of height as body
weight, short and tall subjects by inference have roughly the
same relative amounts of ATFM, SM, and bone. By contrast,
adipose tissue mass was highly variable between subjects and
neither total adipose tissue mass nor percent of body weight as
adipose tissue were significantly associated with height. The
scaling across adult populations of body weight as height2 thus
largely reflects the consistent scaling of lean tissues as height2.
These observations in Asian men extend our earlier studies of
both men and women of mixed ethnicity (14).
Body weight in the Thai men scaled as height⬃2 whereas
brain mass scaled as height⬃0.5, the two powers differing
significantly and strongly supporting our study hypothesis.
Brain mass relative to body weight, accordingly, scaled negatively to height, expanding on our findings in a much smaller
sample of ethnically mixed men (14). Excluding older men in
the sample, above the age of 45 yr, did not measurably change
these results for brain mass/body weight, and our findings were
directionally similar but statistically nonsignificant for the
much smaller sample of young Korean men.
Brain mass contributes minimally to total body mass (⬃2%)
(14, 37) and thus only has a small effect on the scaling of body
weight to height. On the other hand, brain has a high massspecific metabolic rate (⬃240 kcal䡠kg⫺1 䡠day⫺1) and accounts
for ⬃20% of energy expended at rest (7). The relatively
smaller brain mass of tall subjects may thus account for the
correspondingly lower REE/body weight also observed in tall
subjects compared with their short counterparts (13). This
hypothesis is supported by our modeling of energy expenditure
derived using REE estimated with the HB equation (10). As
with brain/body weight, REEHB/body weight also scaled negatively to height. Nonbrain REE, by contrast, approached a
scaling relationship to height similar to that of body weight.
Our estimates thus suggest that the previously reported lower
REE/body weight in taller men (13) can largely be accounted
for by their relatively smaller brain mass/body weight.
The only previous study we found in the literature related to
the scaling of human brain mass to height was our own in a
small sample of 19 ethnically mixed men (14). However, many
studies over the past century have examined the relations
between body size and brain mass or volume (e.g., 4, 8, 11, 14,
16, 17, 20, 27–31, 36, 38, 43, 44). Although there are limitations of most of these earlier studies (31), a consistent observation (Table 5) is the generally limited association between
brain mass or volume and stature. Some studies report weak or
modest associations between brain size and height whereas in
others the correlations are nonsignificant. Relations between
brain size and body weight are similarly variable between
studies and reveal only modest associations.
The factors controlling brain mass must therefore differ to
some extent from those controlling the noncentral nervous
system components of body mass. Brain reaches near-peak
mass by the age of 8 –10 yr, long before the remainder of body
mass completes growth processes (5a, 9). Other than aging,
once maximal brain mass is achieved in early adulthood there
is relatively little change in brain size across the life span, with
the exception perhaps of neural tissue loss secondary to severe
semistarvation (36). On the other hand, body mass and most
associated compartments can vary widely during adulthood, as
for example the growth of adipose tissue, SM, bone, liver, and
heart that occurs with the development of obesity (12, 41).
Hormones, such as growth hormone and IGF-1b, have minimal
effects on postnatal brain mass but substantial effects on
somatic tissues such as is observed in patients with acromegaly
(6) or in transgenic rodent models overexpressing growth
hormone (35).
Although brain mass remains relatively stable in mass across
the adult life span, increasing evidence indicates that there are
large between-individual differences in brain mass after controlling for body size that can be accounted for in part by
heritable factors. Data from the Framingham cohort and offspring indicate genetic factors account for 55% of betweenindividual differences in brain white matter volume as measured by MRI (1). Moreover, five genes have been identified
leading to the rare condition referred to as primary microcephaly that is associated with a head circumference ⬎3 SD below
normal and adult brain volumes in the range of 400 –500 cm3
(5). The afflicted individuals are often mildly retarded but may
have a body size at or near that of their unaffected siblings. The
presently known genes causative for primary microcephaly do
not appear to account for the large between-individual difference observed in brain volume among healthy adults (45), but
clearly there are other as yet undiscovered regulators of brain
46
BRAIN MASS AND HEIGHT
Table 5. Representative studies reporting correlations between adult brain mass or volume and body size
Subjects
Comment
Pakkenberg and Voigt (28)
765 men and 325 women; brain evaluated at autopsy.
Holloway (16)
502 men and 165 women judged as “normal adults
⬍age 65 yr” from the database reported in Ref.
28; brain evaluated at autopsy.
Portions of study reported in Ref. 28, men (734) and
women (305); 198 men and 92 women age 18–45
yr; brain evaluated at autopsy.
In adults (724 men, 302 women), multiple regression
with weight and height as brain mass predictors,
only height significant.
Brain mass correlation with height, after controlling
for age, r ⫽ 0.29 for men (P ⫽ 0.001) and 0.04
for women (P ⫽ 0.32).
Height and brain mass, r ⫽ 0.31 in all men (P ⬍
0.001) and 0.20 in women (P ⬍ 0.001); age 18–
45 yr, r ⫽ 0.20 in men (P ⬍ 0.004) and 0.12 in
women (P ⬍ 0.12).
Brain mass correlated with height only in the older
group. Reduced brain mass observed in subjects
with low BMI.
After adjustment for age, correlations between
cerebral volume and height were nonsignificant;
height accounted for 1–4% of brain size within
each sex.
Brain mass correlations with height, r ⫽ 0.05 and
0.21, both nonsignificant.
Brain mass correlations with height, r ⫽ 0.004 (NS)
and 0.33 (P ⫽ 0.04).
Brain mass scaled to height in men (r ⫽ 0.46, P ⫽
0.04) with a power of 0.83 and nonsignificantly
(r ⫽ 0.003, P ⫽ NS) in the women.
Correlations of brain with body weight
nonsignificant; correlation of brain with height
significant in men (r ⫽ 0.37, P ⬍ 0.05) but not
women.
After adjusting for sex, r ⫽ 0.09 and 0.10 for brain
size versus height and weight, both nonsignificant.
Significant correlation between cerebral tissue
volume and height in women (r ⫽ 0.43, P ⫽
0.003) but not men (r ⫽ 0.02, P ⫽ 0.867).
Brain mass, men⬎women at each height grouping;
descriptive increase in brain mass (⬃10%) with
height (⬃150–170/180 cm) for both men and
women.
Reported race, sex, and age differences in brain
mass; sex and race-specific correlations between
brain mass and height, r values 0.15–0.24, all
significant with groups ranging in size from
218–414.
Reported sex and age differences in brain mass;
among major body organs, smallest correlations
between brain mass and combined effects of
weight, height, and age.
Reported sex, age, and BMI differences in brain
mass; brain increases in mass, independent of sex,
by ⬃3.7 g/cm.
Passingham (30)
Skullerud (36)
Witelson (44)
Peters et al. (31)
Heymsfield et al. (14)
64 men and 17 women, ages 45–54 yr; 196 men and
190 women, ages 70–79 yr; brain evaluated at
autopsy.
Caucasian women (58) and men (42) with
nonneurological cancers; brain evaluated at
autopsy.
Healthy Caucasian men (69) and women (46), ages
19–41 yr; brain evaluated with MRI.
Healthy Caucasian men (52) and women (30), ages
19–41 yr; brain evaluated with MRI.
Healthy men (19) and women (57); brain evaluated
with MRI.
Koh et al. (20)
Healthy Korean men (30) and women (30) in their
twenties; brain evaluated with MRI.
Willerman (43)
40 college students; brain evaluated with MRI.
Nopoulos (27)
Healthy men (42) and women (42); brain evaluated
with MRI.
Spann and Dustmann (38)
Males (898) and women (430), ages 15 to 65 yr;
without major illnesses or head trauma; brain
evaluated at autopsy.
Ho et al. (17)
1261 adults, 25–80 yr with those having brain
pathology excluded; brain evaluated at autopsy.
Garby et al. (8)
1,598 healthy or apparently healthy subjects age ⬎16
yr, 1,096 men and 512 women; brain evaluated at
autopsy.
Hartmann et al. (11)
7,965 healthy adults free of brain disorders, 4,488
men and 3,477 women; brain evaluated at autopsy.
growth that will help to unravel whether and how brain and
body mass are related to each other among adults.
Importantly, these collective observations provide a working
basis for understanding the very weak correlations between
brain mass and height or body weight in the many previous
studies that have examined these questions over the past
century and those of the present report.
Relationship to Energy Requirements
Adult stature, in addition to genetic mechanisms (21), is
extremely sensitive to prevailing nutritional and other environmental conditions (15). A premium must thus be placed on
growing taller when conditions allow while at the same time
minimizing the energetic “cost” imposed on adults. The greater
body mass of tall subjects would necessitate larger energy
J Appl Physiol • VOL
requirements, but these costs are minimized by the scaling
patterns observed in the present study.
As an example, body weight approximately doubles (50
kg3100 kg) when the height of a hypothetical man of age 20
yr and BMI 22 kg/m2 increases from 152 cm (5 ft) to 213 cm
(7 ft). Our findings indicate that skeletal muscle and bone
would also approximately double in mass, whereas Garby’s
formula (8), for example, predicts that brain mass would only
increase by 0.26 kg from 1.43 to 1.69 kg. When height
increases from 152 to 213 cm, Hartmann et al.’s analysis (11)
predicts an increase in brain mass of 0.23 kg, and the data
collected in Thai men (Table 2) similarly predicts only a small
relative increase in brain weight of 0.21 kg. REE in our
hypothetical man would be substantially higher had brain
mass, with an energy cost of 240 kcal䡠kg⫺1 䡠day⫺1, also
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Author (Reference No.)
BRAIN MASS AND HEIGHT
47
doubled with greater height and associated weight from 1.43 to
2.86 kg (i.e., a ⌬ of 1.43 kg).
The lower mass-specific REE of tall subjects may also
confer greater fasting endurance (22, 24), thus allowing longer
survival when environmental conditions lead to absence of
food supplies with complete cessation of dietary intake.
to be confirmed and extended in larger ethnically diverse
samples and in women.
Study Limitations
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Although we attempted to narrow our focus to “Asian” men,
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Lastly, we did not evaluate the scaling of body composition
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the “height2” power rule will be detected.
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