Download Climatic Influences on Basal Metabolic Rates Among Circumpolar

AMERICAN JOURNAL OF HUMAN BIOLOGY 14:609 620 (2002)
Climatic In¯uences on Basal Metabolic Rates Among
Circumpolar Populations
WILLIAM R. LEONARD,1* MARK V. SORENSEN,1 VICTORIA A. GALLOWAY,2 GARY J. SPENCER,3
M.J. MOSHER,4 LUDMILLA OSIPOVA,5 AND VICTOR A. SPITSYN6
1
Department of Anthropology, Northwestern University, Evanston, Illinois 60208, USA
2
Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
3
Department of Human Biology and Nutritional Sciences, University of Guelph, Guelph,
Ontario, Canada N1G 2W1
4
Department of Anthropology, University of Kansas, Lawrence, Kansas 66045, USA
5
Institute of Cytology and Genetics, Russian Academy of Sciences, Novosibirsk, Russia
6
Medical Genetic Research Center, Russian Academy of Medical Sciences, Moscow,
115478 Russia
ABSTRACT
This article examines evidence for elevations in basal metabolic rate (BMR) among
indigenous Northern (circumpolar) populations and considers potential mechanisms and the adaptive
basis for such elevations. Data on BMR among indigenous (n = 109 males; 122 females) and nonindigenous (n = 15 males; 22 females) circumpolar groups of North America and Siberia are compiled
and compared to predicted BMRs based on three different references: body surface area (Consolazio et
al., 1963), body mass (Schofield, 1985), and fat-free mass (Poehlman and Toth, 1995). Regardless of
which reference is used, indigenous circumpolar groups show systematic and statistically significant
elevations in BMR ranging from +7% to +19% above predicted values for indigenous men and from +3
to +17% for indigenous women. Nonindigenous males also show elevations in BMR, although not to
the same extent as in indigenous men (deviations = +3 to +14%), whereas nonindigenous females
show no clear evidence of elevated BMRs (deviations = )7 to +5%). This pattern of variation between
indigenous and nonindigenous groups suggests that both functional and genetic factors play a role in
metabolic adaptation to northern climes. Recent studies on the ecology and genetics of thyroid
function offer insights into the mechanisms through which indigenous circumpolar populations may
regulate metabolic rates. Studies of seasonal variation in thyroid hormone levels suggest that indigenous circumpolar populations may have a greater capacity to elevate BMR during severe cold
than nonindigenous groups. Recent twin studies indicate a significant genetic component of thyroid
responses to environmental stressors. Further research exploring the genetics of seasonal variation in
thyroid function and BMR among circumpolar groups would advance understanding of the role that
selection may have played in shaping metabolic variation. Am. J. Hum. Biol. 14:609 620, 2002.
Ó 2002 Wiley-Liss, Inc.
Climatic and ecological factors exert a
strong influence on energy requirements of
humans and other mammalian species.
This influence seems particularly evident in
variation in basal metabolic rates (BMR).
Roberts (1978), in Climate and Human
Variability, found a strong negative correlation between BMR and mean annual
temperature, suggesting that adaptation to
regional climatic stressors, in part, explains
human variation in BMR. Subsequent work
has confirmed that many indigenous populations of the tropics have depressed BMRs
(Henry and Rees, 1991; Soares et al., 1993),
whereas Northern, Arctic populations have
elevated BMRs (Rode and Shephard, 1995;
Shephard and Rode, 1996; Galloway et al.,
2000). Yet while population differences in
BMR have been demonstrated, several im-
ã 2002 Wiley-Liss, Inc.
portant issues are unresolved. First, there
has been considerable debate over how to
best measure deviations from expected
BMR in human populations. Some researchers have maintained that apparent
differences in BMR are the consequence
Contract grant sponsor: the Natural Sciences and Engineering
Research Council of Canada; Contract grant number: OGP0116785; Contract grant sponsor: the National Geographic
Society; Contract grant number: #5829-96; Contract grant
sponsor: the Wenner-Gren Foundation for Anthropological
Research; Contract grant number: Conference 295; Contract
grant sponsor: Northwestern University and the University of
Guelph.
*Correspondence to: William R. Leonard, Department of Anthropology, Northwestern University, 1810 Himan Avenue,
Evanston, IL 60208. E-mail: [email protected]
Received 2 July 2001; Accepted 20 March 2002
Published online in Wiley InterScience (www.interscience.
wiley.com). DOI: 10.1002/ajhb.10072
610
W.R. LEONARD ET AL.
TABLE 1. Summary of studies of basal metabolic rate (BMR) among indigenous circumpolar populations
Study
Ethnic group
Location
Sample
BMR
(kJ/m2/hr)
Percent
deviation1
Reference
1
Inuit
Baffin Island,
Canada
1M
2F
215
210
+28.8
+35.5
Heinbecker, 1928
2
Inuit
Baffin Island,
Canada
1M
4F
171
168
+3.5
+5.8
Heinbecker, 1931
3
Inuit
Eastern
Canadian Arctic
7M
3F
207
194
+25.7
+26.7
Rabinowitch
and Smith, 1936
4
Inuit
Chesterfield Inlet,
Canada
30 M
33 F
195
198
+16.4
+29.4
Crile & Querring,
1939
5
Chippewa
Churchill Bay,
Canada
5M
7F
207
193
+23.2
+25.0
Crile & Querring,
1939
6
Inuit
Alaska, USA:
Anaktuvuk Pass
Gambell
Barter Island
Kotzebue
52 M
21 F
194
179
166
168
+16.0
+10.0
+2.0
+3.0
Rodahl, 1952
7
Inuit
Southampton Is,
NWT, Canada
7M
6F
207
199
+25.1
+30.4
Brown et al. 1954
8
Inuit
Anaktuvuk
Pass, Alaska, USA
6M
209
+25.0
Adams & Covino, 1958
9
Inuit
Baffin Island
Canada
10 M
205
+26.7
Hart et al. 1962
10
Inuit
Anaktuvuk
Pass, Alaska, USA
6M
255
+52.5
Rennie et al. 1962
11
Inuit
Anaktuvuk Pass
Alaska, USA
6M
192
+24.3
Milan et al. 1963
12
Athapascan
Tetlin, Alaska USA
6M
178
+14.8
Milan et al. 1963
13
Inuit
Wainwright
Alaska USA
6M
184
+12.8
Milan & Evanuk, 1967
14
Inuit
Igloolik, NWT
Canada
30 M
22 F
182
166
+18.7
+8.9
Rode & Shephard,
1995
15
Evenki
Evenkia, Siberia
(Russia)
19 M
41 F
180
152
+14.0
+5.6
Galloway et al. 2000
1 16
Buryat
Gakhani, Siberia
(Russia)
50 M
59 F
174
151
+9.2
+3.6
Sorensen et al. 1999
Mean
Males
Females
202
181
+23.6
+19.0
Total
192
+20.0
1
Percent (%) deviation from predicted BMR based on surface area norms (Dubois, 1927; Consolazio et al., 1963).
Note: Study #6 (Rodahl, 1952) was done at four different sites in Alaska (Anaktuvuk Pass, Gambell, Barter Island, Kotzebue). The
total sample was 52 male and 21 female; however, they don't provide information on how many subjects were studied at each location,
simply the BMRs and percent deviations at each site.
of differences in body size, composition,
or proportions, rather than differences
in metabolic intensity (Shetty, 1996). Additionally, even if such population differences are regarded as being biologically
meaningful, it is unclear whether they
are largely genetic or whether they reflect
functional acclimatization. While there has
been much attention given to documenting
climatic and ethnic variation in BMR, much
less attention has been given to the poten-
tial mechanisms that might explain such
variation.
This study specifically considers the evidence for elevations in BMRs, among
indigenous Northern (high latitude) populations. Since the early part of the 20th
century research has indicated increased
BMRs in Arctic and sub-Arctic populations
(Table 1). Previous reviews have broadly
summarized the results of these studies (see
Itoh, 1980, Shephard and Rode, 1996).
611
BMR VARIATION IN CIRCUMPOLAR POPULATIONS
TABLE 2. Descriptive statistics for age, anthropometric characteristic, and BMR for indigenous and nonindigenous
men and women living in circumpolar environments1
Males
Measure
Age (yrs)
Stature (cm)
Weight (kg)
BMI (kg/m2)
SA (m2)
Body fat (%)
FFM (kg)
BMR (kJ/day)
Indigenous
(n = 109)
29.1 ‹ 1.3*
166.8 ‹ 0.9***
63.2 ‹ 1.2***
22.7 ‹ 0.4
1.71 ‹ 0.02***
17.1 ‹ 0.7*
51.7 ‹ 0.7**
7335 ‹ 103
Females
Nonindigenous
(n = 15)
Indigenous
(n = 122)
Nonindigenous
(n = 22)
36.9 ‹ 2.5
174.7 ‹ 4.9
74.2 ‹ 2.6
24.2 ‹ 0.6
1.90 ‹ 0.04
21.5 ‹ 1.0
58.1 ‹ 1.9
7636 ‹ 304
31.1 ‹ 1.1
154.6 ‹ 0.7*
56.4 ‹ 1.1**
23.6 ‹ 0.4*
1.56 ‹ 0.02**
32.7 ‹ 0.6
37.4 ‹ 0.05**
5701 ‹ 84
33.1 ‹ 2.8
157.9 ‹ 1.3
65.5 ‹ 3.3
26.3 ‹ 1.2
1.70 ‹ 0.04
34.2 ‹ 1.7
42.2 ‹ 1.4
5480 ‹ 167
1
All values are mean ‹ SEM.
Indigenous vs. nonindigenous difference are significant at: *P < 0.05; **P < 0.01; ***P < 0.001.
However, there has yet to be a systematic the sample (size and sex composition), 2)
evaluation of metabolic variation in cir- some measure of body size (i.e., weight,
cumpolar groups comparable to that done height, and/or body surface area [SA]), and
for human populations of the tropics. 3) some measure of BMR.
Unfortunately, for the majority of the
Moreover, relatively little attention has
been given to the potential mechanisms for studies, BMR was only expressed per unit
SA (i.e., kcal or kJ/m2/hr), and absolute oxpromoting elevations in metabolic rate.
Thus, the purpose of this article is to ygen consumption (VO2) or energetic (i.e.,
compile and evaluate available BMR data kcal or kJ) measures were not provided.
among indigenous circumpolar populations This is problematic because most studies in
from both North America and Asia. The human and animal energetics now evaluate
data are used to evaluate the evidence for metabolic rates as a function of mass
elevated BMR among circumpolar groups. (weight) or fat-free mass (FFM). ConseAdditionally, potential mechanisms for quently, systematic analysis of BMR variaregulating variation in the BMR are exam- tion was possible for only those studies that
ined, specifically focusing on the role of provided information on body size and body
thyroid hormones. The nature of BMR composition (e.g., percent body fat and/or
variation in these populations is also con- FFM) as well as metabolic measures. These
sidered, i.e., climatic influences on BMR include the studies of Hart et al. (1962) and
largely reflective of functional responses Rode and Shephard (1995) on the Inuit,
(acclimatization), or is there also evidence Galloway et al. (2000) on the Evenki, and
1 Sorensen et al. (1999) on the Buryat. These
for a genetic (adaptive) basis.
combined sources provide a total sample of
231 indigenous subjects (109 males; 122 feMETHODS
males; 16 65 years) that are the basis of
Information on BMR among indigenous the subsequent analyses.
circumpolar populations was compiled from
The studies of Hart et al. (1962), Rode and
our own research and from previously pub- Shephard (1995), and Galloway et al. (2000)
lished studies. A systematic review of the also include data on nonindigenous ``conpublished literature on BMR variation in trol'' subjects that were living in the same
circumpolar publications using sources de- communities as indigenous subjects. Data
rived from previous reviews (Itoh, 1980; for 37 individuals are available (15 males;
Shephard and Rode, 1996) and MEDLINE 22 females; 16 58 years).
searches for the more recent data. Table 1
Table 2 presents the descriptive statistics
provides of a summary of 16 studies on for age, anthropometric characteristics, and
BMR of indigenous northern groups derived BMRs of the indigenous and nonindigenous
from 14 published sources. Data from all men and women. Percent body fat was esstudies were carried out under standard timated from skinfold thicknesses using the
basal conditions (Consolazio et al., 1963). prediction equations of Durnin and WoAdditionally, to be included in Table 1, mersley (1974) for the Rode and Shephard
studies needed to present information on: 1) (1995), Galloway et al. (2000), and Sorensen
612
W.R. LEONARD ET AL.
TABLE 3. Comparison of measured vs. predicted BMR per surface area (kJ/m2/hr) for indigenous
and nonindigenous circumpolar groups1
Group
Males
Indigenous
Nonindigenous
Females
Indigenous
Nonindigenous
Measured BMR/SA
(kJ/m2/hr)
Predicted BMR/SA
(kJ/m2/hr)
Percent deviation from
predicted (%)
179 ‹ 2.4***
167 ‹ 5.0*
159 ‹ 0.6
155 ‹ 1.2
+13.2 ‹ 1.4%
+7.9 ‹ 3.2%
152 ‹ 2.0***
135 ‹ 4.4*
145 ‹ 0.6
144 ‹ 1.6
+4.9 ‹ 1.32
)6.6 ‹ 2.8%
1
All values are mean ‹ SEM. Predicted BMR/SA were calculated based on age- and sex-specific norms compiled by Consolazio et al.
(1963).
2
Differences in percent deviation from predicted between indigenous and nonindigenous samples are statistically significant at
P < 0.001.
Differences between measured and predicted BMRs are statistically significant at: *P < 0.05; ***P < 0.001.
1 et al. (1999) samples, and the equations of
Allen et al. (1956) in the Hart et al. (1962)
sample. Body surface area was calculated
from stature and body weight using the
Dubois (1927; Dubois and Dubois, 1916)
equations.
The nonindigenous subjects are systematically taller and heavier than their indigenous counterparts. Because of their
larger size, the nonindigenous subjects also
have significantly higher body SA than the
indigenous subjects. Additionally, although
the indigenous subjects are relatively
leaner in terms of percent body fat, the
nonindigenous subjects have absolutely
greater FFM. The ethnic differences in size
are relatively greater among men than
women.
Among men, absolute BMRs are higher in
the nonindigenous sample, a reflection of
their larger overall body mass. In contrast,
indigenous women have higher BMRs than
their nonindigenous counterparts, despite
having lower body mass.
RESULTS
Metabolic variation in circumpolar populations
One of the problems in studying metabolic adaptation has been the lack of
agreement on how to best measure deviations from predicted BMR. Historically,
three different approaches have been used:
BMR per unit SA (expressed as a ratio of
kcal or kJ/m2); BMR (kcal or kJ) vs. body
mass (kg); and BMR (kcal or kJ) vs. FFM
(kg). The expression of BMR as a ratio relative to SA stems from the development of
the ``Surface Law'' in the late 19th century.
The surface law states that across organisms of different size, heat loss is propor-
tional to SA (or weight raised to the
two-thirds power). Consequently, most of
the research in both animal and human
energetics during the first half of the 20th
century expressed metabolic rates as a
function of SA. By the middle of the 20th
century, however, research by Kleiber
(1932, 1961), Brody (1945), and Benedict
(1938) demonstrated that across animals of
different size, BMR did not scale in the
manner predicted by the Surface Law, i.e.,
BMR scales to three-fourths (0.75) rather
than the two-thirds (0.66) power of body
weight. As a result, the scaling of BMR
relative to body mass became more widely
used in both mammalian and human energetics. Researchers in comparative mammalian energetics, in fact, have largely
moved away from the SA approach. Most
recently, there has been greater recognition
that sex, age, and ethnic differences in body
composition exert an important influence on
BMR in human populations (e.g., Poehlman
and Toth, 1995; Weinsier et al., 1992),
which has resulted in the BMR increasingly
being expressed relative to FFM in the human energetics literature.
Metabolic variation using each of these
approaches is evaluated to provide a broad
test of increased BMR among circumpolar
groups. This also allows evaluation of the
relative merits of the different approaches.
Table 3 presents mean BMRs per square
meter of surface area (kJ/m2/hr) for the indigenous and nonindigenous groups compared to predicted values based on age- and
sex-specific references compiled by Consolazio et al. (1963). This index, kcal or kJ/ m2/
hr, was the measure of metabolic variation
used in virtually all of the Inuit studies
published before the 1970s. In both sexes
the indigenous subjects have significantly
613
BMR VARIATION IN CIRCUMPOLAR POPULATIONS
TABLE 4. Comparison of measured vs. predicted BMR (kJ/day) based on body mass for indigenous
and nonindigenous circumpolar groups1
Group
Males
Indigenous
Nonindigenous
Females
Indigenous
Nonindigenous
Measured BMR
(kJ/day)
Predicted BMR
(kJ/day)
Percent deviation from
predicted (%)
7,335 ‹ 103***
7,636 ‹ 304
6,848 ‹ 65
7,360 ‹ 166
+7.3 ‹ 1.3%
+3.7 ‹ 3.2%
5,701 ‹ 84*
5,480 ‹ 167*
5,552 ‹ 51
5,931 ‹ 107
+2.8 ‹ 1.3%2
)7.3 ‹ 2.9%
1
All values are mean ‹ SEM. Predicted BMRs were calculated from body mass (kg) based on age- and sex-specific norms presented by
Schofield (1985).
2
Differences in percent deviation from predicted between indigenous and nonindigenous samples are statistically significant at
P < 0.01.
Differences between measured and predicted BMRs are statistically significant at: *P < 0.05; ***P < 0.001.
TABLE 5. Comparison of measured vs. predicted BMR (kJ/day) based on fat-free mass for indigenous and
nonindigenous circumpolar groups1
Group
Males
Indigenous
Nonindigenous
Females
Indigenous
Nonindigenous
Measured BMR
(kJ/day)
Predicted BMR
(kJ/day)
Percent deviation from
predicted (%)
7,335 ‹ 103***
7,636 ‹ 304**
6,196 ‹ 53
6,695 ‹ 150
+18.5 ‹ 1.5%
+13.7 ‹ 3.3%
5,701 ‹ 84***
5,480 ‹ 167
4,853 ‹ 39
5,229 ‹ 109
+17.1 ‹ 1.5%2
+5.1 ‹ 2.9%
1
All values are mean ‹ SEM. Predicted BMRs were calculated from FFM (kg) based on sex-specific equations presented by Poehlman
and Toth (1995).
3 2Differences in percent deviation from predicted between indigenous and nonindigenous samples are statistically significant at
P < 0.001.
Differences between measured and predicted BMRs are statistically significant at: **P < 0.01; ***P < 0.001.
higher BMRs than the nonindigenous subjects, and the apparent ethnic difference is
relatively greater in females than it is in
males.
Additionally, in all four groups measured
BMR/SA significantly deviates from predicted values. Both indigenous men and
women have BMRs that are significantly
higher than predicted, with average elevations of +13% in men and +5% in women
(P < 0.001, for both sexes). Nonindigenous
men also have significantly elevated BMRs,
being 8% above predicted (P < 0.05),
whereas nonindigenous women have BMRs
that are significantly below predicted levels,
by 6 7% (P < 0.05). Thus, when evaluated
relative to SA the indigenous groups show
significant elevations over predicted levels,
by 5 13%. There also are significant ethnic
differences in relative metabolic rates in
females (deviations of +4.9% vs. )6.6%; P <
0.001), but not in males, as nonindigenous
men also show significantly elevated BMR
(deviation of +13.2% vs. +7.9%; P = 0.17).
Deviations from predicted BMRs derived
from estimates based on body weight using
the Schofield (1985) equations are summa-
rized in Table 4. The results are similar to
those for the SA, although the absolute deviations are more modest. Indigenous men
and women both show significant elevations
in BMR of 7% and 3%, respectively (P <
0.001 for men; P < 0.05 for women). In
contrast, nonindigenous males show some
(but not significant) elevation in BMR
(3.7%; P = 0.26), whereas nonindigenous
women again have BMRs that are significantly below predicted levels ()7.3; P <
0.05). Thus, relative to the Schofield reference, both indigenous men and women
show significant elevations in BMR. Ethnic
differences between the indigenous and
nonindigenous groups are evident only in
the females (deviations of +2.9% vs. )7.3%;
P < 0.01).
Table 5 presents mean BMRs for the indigenous and nonindigenous groups compared to predicted values based on FFM
using the sex-specific equations of Poehlman and Toth (1995). Indigenous men and
women have significantly elevated BMRs,
both being 17 19% above predicted levels.
Nonindigenous men also show significant
elevations of 13 14%, while nonindigenous
614
W.R. LEONARD ET AL.
Fig. 2. Summary of percent deviations (‹SEM) between measured and predicted BMRs for indigenous
and nonindigenous circumpolar males relative to the SA
norms of Consolazio et al. (1963), the body mass norms
of Schofield (1985), and the FFM norms of Poehlman
Toth (1995). For indigenous males, measured BMR is
significantly greater than predicted values for all norms
(deviations = +7 to +19%). In the nonindigenous sample, measured BMRs and significantly greater than
predicted values for the SA and FFM norms (range = +3
to +14%).
Fig. 1. Relationship between BMR (kJ/day) and fatfree mass (FFM; kg) in (a) male and (b) female samples
from indigenous circumpolar populations compared to
those of US men and women from Poehlman and Toth
(1995). In men, the scaling relationships are: BMR =
72.2[FFM] + 3,599 (r = 0.48; P < 0.001) for the circumpolar sample and BMR = 78.8 [FFM] + 2,174
(r = 0.69; P < 0.01) for the US sample. In women, the
scaling relationships are: BMR = 87.6[FFM] + 2,404
(r = 0.54; P < 0.001) for the circumpolar sample and
BMR = 78.8[FFM] + 1,944 (r = 0.73; P < 0.01) for the
US sample. Indigenous circumpolar populations show
systematically elevated BMRs per FFM.
women have elevations that are not statistically significant (+5%). Although the
magnitude of the deviations are greater
when BMR is examined relative to FFM, the
pattern of the variation is similar to that
seen with the other approachesÐindigenous
men and women have significantly higher
BMRs than predicted. The main difference
is evident in nonindigenous women who,
compared to the other norms, appear to have
depressed BMRs; however, after correcting
for body composition they have BMRs that
are similar to reference values.
Figure 1a,b present the scaling relationships between BMR and FFM for indigenous
males and females, respectively. The regression lines for the circumpolar groups are
presented along with those of the American
sample of Poehlman and Toth (1995). For
both sexes, the slopes of the regressions for
the indigenous circumpolar populations are
comparable to those of the normative samples (males: b = 72.2 ‹ 14.8 kJ/kg [circumpolar] vs. 78.8 kJ/kg [U.S.]; females
b = 87.6 ‹ 13.6 kJ/kg [circumpolar] vs. 78.8
kJ/kg [U.S.]). In contrast, the Y-intercepts of
the circumpolar regressions are shifted upwards, being significantly higher than the
U.S. sample for males (3,599 ‹ 720 kJ [circumpolar] vs. 2,174 kJ [U.S.]; P < 0.05), but
not females (2,404 ‹ 512 kJ [circumpolar]
vs. 1944 kJ [U.S.]). Thus, results indicate
that for a given level of FFM, indigenous
circumpolar populations have systematically higher metabolic costs.
Overall, the results indicate that indigenous circumpolar populations show systematic and significant elevations in BMR,
regardless of which reference is used. For
indigenous males, deviations from predicted
BMR range from +7 to +19%, compared to
+3 to +14% in nonindigenous males (see
Fig. 2). Indigenous females deviate from
predicted BMR by +3 to +17%, and have
significantly higher metabolic rates than
nonindigenous females ()7 to +5%) (see
Fig. 3). Differences from predicted BMR are
most pronounced when evaluated relative
to FFM and most modest when evaluated
relative to the body mass. Even compared to
other weight-specific BMR reference value,
the Schofield equations produce relatively
high estimates of BMR (Hayter and Henry,
1994), which suggests that these norms are
BMR VARIATION IN CIRCUMPOLAR POPULATIONS
Fig. 3. Summary of percent deviations (‹SEM) between measured and predicted BMRs for indigenous
and nonindigenous circumpolar females relative to the
SA norms of Consolazio et al. (1963), the body mass
norms of Schofield (1985) and the FFM norms of Poehlman Toth (1995). For indigenous females, measured
BMR is significantly greater than predicted values for
all norms (deviations = +3 to +17%). In the nonindigenous sample, measured BMRs are significantly lower
than predicted values for the SA and body weight norms
()7%), and not significantly different from BMRs predicted from FFM. For all three standards, indigenous
circumpolar women have significantly higher BMRs
than their nonindigenous counterparts.
a very conservative benchmark for testing
for elevations in basal metabolism. The
FFM reference is useful in that it is able to
account for differences in body composition
between populations.
The consistent evidence for elevations in
BMR in three of the four ethnic- and sexspecific groups suggests a role for environmental acclimatization. However, the fact
that the elevations in BMR are more pronounced in the indigenous samples (significantly so for females) also suggests that
genetic variation may be operating as well.
In¯uence of thyroid function on basal
metabolic rates
Thyroid function is an important determinant of basal metabolism, since thyroid
hormones promote oxidative metabolism in
most cells (Hadley, 1996). Experimental
evidence indicates that thyroid function is
strongly shaped by environmental factors,
such as changes in temperature (Bojko,
1997; Levine et al., 1995; Salijukov et al.,
1992; Smals et al., 1977; Tkachev et al.,
1991) and nutrition (Danforth and Burger,
1989; Danforth et al., 1979; Key et al., 1950;
Ulijaszek, 1996). The thyroid gland secretes
two types of thyroid hormones T3, Tri-iodo
thyronine, and T4, thyroxine. Secretion of
615
Fig. 4. Seasonal fluctuations in total thyroxine (T4)
levels (mean ‹ SEM) among Russian miners of the
Svalbard Archipelago (78° N) (adapted from Bojko,
1997). Thyroxine levels are significantly increased
during the extreme cold of the winter months.
these hormones is regulated by thyroid
stimulating hormone (TSH) from the pituitary. The vast majority of the thyroid hormone secreted by the thyroid gland is in the
form of thyroxine, which can be converted to
T3 in the peripheral tissues. In the plasma,
most T3 and T4 are bound to proteins;
however, it is only the free (or unbound)
hormones that are biologically active (Hadley, 1996; Hardy, 1981).
Recent work in Russia has examined
patterns of seasonal variation in circulating
thyroid hormone levels among indigenous
and nonindigenous populations. Bojko
(1997) examined seasonal variation in T4
levels among Russian men from a mining
community in Svalbard archipelago, one of
the northern-most outposts in the world. As
shown in Figure 4, thyroxine levels during
the winter months are significantly increased over those in the summer. Similar
research has been done on indigenous Nenet and nonindigenous Russian men living
in the Arkangelsk region of Russia (Tkachev et al., 1991). Both groups show significant increases in T4 levels during the
winter; however, the Nenets show greater
elevations during the winter months, such
that T4 levels are significantly higher than
those of the Russians during the November January period (Fig. 5).
These results suggest that increased
thyroid hormone levels during the winter
may be promoting elevations in basal metabolism in response to the severe cold and
short day lengths of the arctic winter. They
also suggest that indigenous groups may
616
W.R. LEONARD ET AL.
Together, these studies suggest that thyroid function plays a major role in regulating basal metabolism of Northern populations. They also offer a mechanism for
explaining functional/acclimatory responses
and suggest potential avenues through
which natural selection may have operated.
DISCUSSION
Fig. 5. Seasonal fluctuations in total thyroxine (T4)
levels (mean ‹ SEM) among indigenous Nenet and
nonindigenous Russian men of Arckangelst (65° N),
Russia (adapted from Tkachev et al., 1991). Both groups
show significant increases in thyroxine levels during the
winter months; however, the magnitude of the increases
are significantly greater in the Nenets than in the
Russians.
display a more pronounced response during
the winter months. Unfortunately, neither
of these studies measured BMR, so the link
to BMR remains unexplored.
In recent work in Siberia, both BMR and
thyroid hormones were measured on
indigenous (Evenki) and nonindigenous
Russian subjects during a single seasonal
periodÐthe late summer. Table 6 compares
total and free T4 levels in the Evenki and
Russian subjects. As with the Nenets study,
there are no significant differences in total
thyroxine levels between the two ethnic
groups at this time of year. However, free
(unbound) T4 levels in the indigenous
Evenki women are significantly higher than
in the nonindigenous, Russian women (13.2
vs. 11.0 pmol/1; P < 0.05). This finding is
remarkable, because it is Evenki women
who show significant elevations in BMR
compared to Russian counterparts (Galloway et al., 2000).
Additionally, differences in free T4 levels
were considered relative to variation in the
BMR in this sample. Table 7 shows the
partial correlations between BMR and free
T4 levels in the Evenki and Russian samples, adjusting for differences in FFM. In
three of the four groups (all except Russian
men, for whom the sample size is smallest),
free T4 levels are significantly positively
correlated with variation in the BMR. The
results imply that increased BMR is associated with higher free thyroxine levels in
these samples.
Elevations in BMR in circumpolar populations
This study has shown that regardless of
which standards are used, indigenous circumpolar groups show systematic and statistically significant elevations in BMR. The
deviations are most dramatic when assessed relative to FFM and most modest
when assessed relative to the weight-specific Schofield (1985) norms. The elevations
in metabolic rate range from +7% to +19%
above predicted values for indigenous men
and +3 to +17% for indigenous women.
Earlier research among Inuit groups
suggested that their increased BMRs were
largely or entirely attributable to an extreme diet, high in protein and fat (Rodahl,
1952). In the data presented here, dietary
factors are unlikely to play a significant role
in elevating BMRs because most of the
subjects were consuming a mixed diet.1 For
example, dietary analyses on the Evenki
subjects of Central Siberia showed that the
macronutrient composition of the diet was
17% protein (110 120 g/day), 23 24% fat
(70 75 g/day) and 59 60% carbohydrates
(400 450 g/day) (Leonard et al., 1999).
Russian subjects living in the same communities had similar diet compositions
(16% protein [100 150 g/day]; 23 27% fat
[85 90 g/day]; 57 61% carbohydrates
[400 600 g/day]). Among the Buryat of Siberia, protein comprised 12 14% of dietary
energy (55 65 g/day), fat 33 36% of energy (70 85 g/day), and carbohydrates
45 50% of dietary energy derived from
carbohydrates (200 300 g/day). These proportions are markedly different from the
high fat and protein levels reported for the
1
Dietary data collected on the Evenki and Buryat
subjects indicate that they were consuming a mixed
diet. Similarly, Hart et al. (1962:954) noted that all of
their Inuit subjects were consuming ``white man's rations.'' Rode and Shephard's (1995) Inuit sample appears to show age-related variation in diet, with
younger individuals consuming a more mixed, modern
diet, while older members were consuming a more
traditional diet.
617
BMR VARIATION IN CIRCUMPOLAR POPULATIONS
TABLE 6. Total and free thyroxine levels in Evenki and
Russian men and women of Central Siberia1
Group
Males
Evenki
Russian
Females
Evenki
Russian
n
Total
T4 (nmol/l)
TABLE 7. Partial correlations between BMR and free
thyroxine levels, adjusting for variation in fat-free mass
Ethnic group
Free
T4 (pmol/l)
Sex
Evenki
Russian
Males
Females
0.56**
0.42**
0.33
0.53*
19
9
74.4 ‹ 4.3
73.0 ‹ 3.0
13.0 ‹ 1.2
14.6 ‹ 1.0
41
20
85.2 ‹ 2.6
82.9 ‹ 3.7
13.2 ‹ 0.7*
11.0 ‹ 0.3
1
All values are mean ‹ SEM.
Evenki vs. Russian differences are significant at: *P < 0.05.
traditional Inuit diet (280 g animal protein;
135 g fat; 54 g carbohydrates; Rodahl, 1952;
Frisancho, 1993). Thus, while dietary factors may have partly contributed to the very
high BMRs reported in the early Inuit
studies, they do not appear to be responsible
for the systematic elevations in the BMR
shown in this study.
This point is evident in a comparison of
data in Table 1 and Table 3. The early
studies of BMR among Arctic populations
consuming high fat and high protein diets
(e.g., studies 1, 3 5, 7, and 8 in Table 1)
found average elevations in BMR of 25%.
In contrast, the elevations of the present
sample are more modest, averaging 10%.
The results also have implications for assessing the relative merits of alternative
standards for evaluating metabolic variation. Historically, the most widely used approach for evaluating metabolic variation in
humans has been comparison to norms based
on surface area (Dubois, 1927; Consolazio et
al., 1963). However, this approach may be
problematic when comparing diverse human
groups of different body size, composition,
and proportions. One problem is that SA is
estimated from standard equations (DuBois
and DuBois, 1916; Bailey and Briars, 1996)
based on stature and body weight. Because
these equations are sensitive to differences
in overall size and body proportions, their
application to indigenous Northern populations may distort estimates of SA.
Additionally, recent work has underscored the problems of evaluating differences in relative energy costs by using
simple ratios, such as energy per unit SA
(kJ/m2) or energy per unit mass (kJ/kg). For
example, Poehlmam (1996; see also Poehlman and Toth, 1995) has shown that the
use of ratios often results in spurious conclusions about relative energy costs. This is
because the ratio method assumes that the
Correlations
**P < 0.01.
are
statistically
significant
at:
*P < 0.05;
linear relationship between metabolic costs
and the measure of size or SA has a y-intercept of zero, something that is not generally observed in biological variables
(Tanner, 1949). Thus, the ratio method
tends to overestimate relative metabolic
costs in smaller individuals. These findings
suggest that the use ratios of BMR relative
to either SA or mass are problematic
for evaluating deviations from predicted
metabolic rate, especially for populations
that are smaller in size than the reference
sample.
The use of mass-specific standards is an
alternative for evaluating relative deviations in BMR. The Schofield (1985) equations have become the most widely used
weight-specific norms for predicting BMR,
based on the 1985 recommendations of the
FAO/WHO/UNU (1985). However, recent
work has suggested that the Schofield
equations may systematically overestimate
BMR (Hayter and Henry, 1994; Wong et al.,
1996). Hayter and Henry (1994) attribute
this bias to the fact that the Schofield norms
have a disproportionate representation of
Italian subjects in the sample, whose metabolic rates tend to be relatively high. These
authors have suggested that revisions to
the Schofield norm are necessary.
Standards for evaluating BMR relative to
FFM are becoming more widely used because FFM is considered to be the best single
predictor of energy expenditure in humans
(Ravussin and Bogartus, 1989; Weinsier
et al., 1992). This approach appears to have
particular merit given that there is great
variation in body composition across human
populations. However, standardizing BMR
as a simple ratio of FFM (i.e., kJ/kg FFM) is
problematic. Consequently, relationships
between BMR and FFM are most effectively
evaluated using regression and analysis of
covariance (see Poehlman and Toth, 1995;
Weinseir et al., 1992).
It appears that the SA approach for
evaluating metabolic variation is the most
618
W.R. LEONARD ET AL.
problematic. The use of simple ratios, combined with the relatively small body size of
the indigenous sample, may have resulted
in overestimation of relative metabolic
rates. In contrast, evaluation of BMR relative to FFM is particularly useful because
FFM most strongly contributes to variation
in metabolic rate. The use of FFM had
particular benefits because the indigenous
subjects are relatively lean compared to
both the nonindigenous sample and the
reference values. Consequently, after correcting for body composition the samples
were more similar in size (see Table 2 for
indigenous vs. nonindigenous differences in
body mass and FFM). Although the alternative approaches produce different estimates of the magnitude of elevations in
BMR, all indicate a significant increase in
BMR among indigenous circumpolar groups
relative to reference values derived largely
from the Western, industrialized world.
In¯uence of thyroid function on BMR
Thyroid function appears to be an important determinant of variation in the
BMR. A number of studies have demonstrated marked seasonal variation in T3 and
T4 levels among Northern populations,
suggesting the ability to regulate metabolic
rate with seasonal changes in both temperature and day length (Bojko, 1997; Levine et al., 1995; Salijukov et al., 1992;
Smals et al., 1977). Moreover, the recent
work of Tkachev et al. (1991) on seasonal
variation in thyroid function among indigenous and nonindigenous circumpolar
groups suggests that indigenous circumpolar populations have a greater capacity for
elevating BMR in response to severe cold.
Research among indigenous (Evenki) and
nonindigenous (Russian) groups in Central
Siberia shows that free thyroxine levels are
significantly correlated with relative metabolic rates. Thus, variation in the degree of
elevation in BMR is associated with the
thyroid function. It also appears that the
significant differences in relative BMR between Evenki and Russian women are mediated by differences in free T4 levels. These
findings suggest a mechanism for explaining functional/acclimatory responses and
suggest potential avenues through which
natural selection may operate.
A number of recent twin studies also
suggest a significant genetic component to
variation in thyroid function in response to
environmental changes. Specifically, work
by Meikle et al. (1988) indicate that variation in total and free T4 levels has a much
higher heritable component than T3 levels,
which tend to be more environmentally determined. Additionally, other research has
shown significant heritable components to
variation in thyroid hormone levels in response to changes in energy balance. Oppert et al. (1994) found similar increases in
total and free T4 within twin pairs in response to 100 days of overfeeding (by 1,000
kcal/day). Tremblay et al. (1997) examined
changes in thyroid levels and RMR in response to negative energy balance produced
by increased exercise levels. Twin pairs
showed similar changes in both T4 levels
and RMR over the study period. These
findings indicate that not only is there a
heritable component to thyroid hormone
levels but there is also a significant genetic
component to thyroid responses to environmental stressors.
SUMMARY
In conclusion, this study has shown that
regardless of which reference is used, indigenous circumpolar groups show systematic and significant elevations in the BMR.
The deviations are most dramatic when
assessed relative to FFM and most modest
when assessed relative to the weight-specific Schofield reference. The patterns of
variation between indigenous and nonindigenous groups suggest that both functional and genetic factors may play a role in
metabolic adaptation to northern climes.
Thyroid function appears to be an important determinant of variation in the
BMR. Studies of seasonal variation in thyroid hormone levels suggest that indigenous
populations may have a greater capacity to
elevate BMR during severe cold than nonindigenous groups.
Finally, while acclimatization clearly
seems to play a role in these metabolic responses, both the patterns of variation and
recent findings on the genetics of thyroid
function suggest the potential for genetic
adaptations. Further research exploring the
genetics of seasonal variation in thyroid
function and BMR among indigenous circumpolar groups may provide insights into
the role that selection may have played in
shaping metabolic variation.
BMR VARIATION IN CIRCUMPOLAR POPULATIONS
ACKNOWLEDGMENTS
Comments by Dr. Marcia Robertson
helped to greatly improve earlier drafts of
this article.
LITERATURE CITED
Adams T, Covino BG. 1958. Racial variations to standardized cold stress. J Appl Physiol 12:9 12.
Allen TH, Peng MT, Chen KP, Huang TF, Chang C,
Fang HS. 1956. Prediction of blood volume and adiposity in man from weight and cube of height. Metabolism 5:328 345.
Bailey BJR, Briars GL. 1996. Estimating the surface
area of the human body. Stat Med 13:1325 1332.
Benedict FG. 1938. Vital energetics (publication no.
503). Washington, DC: Carnegie Institute.
Bojko ER. 1997. Metabolic changes induced by adaptation to circumpolar conditions in Spitsbergen. Int J
Circumpolar Health 56:134 141.
Brody S. 1945. Bioenergetics and growth. New York:
Reinhold.
Brown GM, Bird GS, Boag LM, Delahaye DJ, Green JE,
Hatcher JD, Page J. 1954. Blood volume and basal
metabolic rate of Eskimos. Metabolism 3:247 254.
Consolazio CF, Johnson RE, Pecora LJ. 1963. Physiological measurements of metabolic functions in man.
New York: McGraw Hill.
Crile GW, Quiring DP. 1939. Indian and Eskimo metabolism. J Nutr 18:361 368.
Danforth E, Burger AG. 1989. The impact of nutrition
on thyroid hormone physiology and action. Annu Rev
Nutr 9:207 227.
Danforth E, Horton ES, O'Connell M, Sims EA, Burger
AG, Ingbar SH, Braverman L, Vagenakis AG. 1979.
Dietary-induced alterations in thyroid hormone metabolism during overnutrition. J Clin Invest 64:
1336 1347.
DuBois EF. 1927. Basal metabolism in health and disease. Philadelphia: Lea and Febiger.
DuBois D, DuBois EF. 1916. A formula to estimate the
approximate surface area if height and weight be
known. Arch Int Med 17:863 871.
Durnin JVGA, Womersley J. 1974. Body fat assessed
from total body density and its estimation from
skinfold thickness: measurements on 481 men and
women aged from 16 to 72 years. Br J Nutr 32:77
97.
FAO/WHO/UNU (Food and Agriculture Organization/
World Health Organization/United Nations University). 1985. Energy and protein requirements: Report of a joint FAO/WHO/UNU expert consultation.
WHO Technical Report Series No. 724. Geneva:
WHO.
Frisancho AR. 1993. Human adaptation and accommodation. Ann Arbor: University of Michigan Press.
Galloway VA, Leonard WR, Ivakine E. 2000. Basal
metabolic adaptation of the Evenki reindeer herders
of Central Siberia. Am J Hum Biol 12:75 87.
Hadley ME. 1996. Endocrinology, 4th edition. Upper
Saddle River, NJ: Prentice Hall.
Hardy RN. 1981. Endocrine physiology. University
Park Press.
Hart JS, Sabean HB, Hildes JA, Depogas F, Hammel
HT, Andersen KL, Irving L, Foy G. 1962. Thermal
and metabolic responses of coastal Eskimos during
night cold. J Appl Physiol 17:953 960.
Hayter JE, Henry CJK. 1994. A reexamination of basal
metabolic rate predictive equations: the importance of
619
geographic origin on subjects in sample selection. Eur
J Clin Nutr 48:702 707.
Heinbecker P. 1928. Studies on the metabolism of Eskimos. J Biol Chem 80:461 475.
Heinbecker P. 1931. Further studies on the metabolism
of Eskimos. J Biol Chem 93:327 336.
Henry CJK, Rees DG. 1991. New predictive equations
for the estimation of basal metabolic rate in tropical
peoples. Eur J Clin Nutr 45:177 185.
Itoh S. 1980. Physiology of circumpolar people. In: Milan FA, editor. The human biology of circumpolar
populations. Cambridge: Cambridge University
Press. p 285 303.
Keys A, Brozek J, Henschel A, Michelson O, Taylor HL.
1950. The biology of human starvation. Minneapolis:
University of Minnesota Press.
Kleiber M. 1932. Body size and metabolism. Hilgardia
6:315 353.
Kleiber M. 1961. The fire of life: An introduction to
animal energetics. Huntington, NY: Krieger.
Leonard WR, Galloway VA, Ivakine E, Osipova L, Kazakovtseva M. 1999. Nutrition, thyroid function and
basal metabolism of the Evenki of Central Siberia. Int
J Circum Health 58:281 295.
Levine VE, Wilber GC. 1949. Fat metabolism in Alaskan Eskimos. Fed Proc 8:95 96.
Levine M, Duffy L, Moore DC, Matej LA. 1995.
Acclimation of a non-indigenous sub-Arctic population: seasonal variation in thyroid function in
interior Alaska. Comp Biochem Physiol 111A:209
214.
Meikle AW, Stringham JD, Woodward MG, Nelson JC.
1988. Heriditary and environmental influences on the
variation in thyroid hormones in normal male twins.
J Clin Endocrinol Metab 66:588 592.
Milan FA, Evonuk E. 1967. Oxygen consumption and
body temperatures of Eskimos during sleep. J Appl
Physiol 22:565 567.
Milan FA, Hannon, JP, Evonuk E. 1963. Temperature
regulation of Eskimos, Indians, and Caucasians in a
bath calorimeter. J Appl Physiol 18:378 382.
Oppert JM, Dussault JH, Tremblay A, Despers JP,
Theriault G, Bouchard C. 1994. Thyroid-hormones
and thyrotropin variations during long-term overfeeding in identical twins. J Clin Endocrinol Metab
79:547 553.
Poehlman ET. 1996. Energy intake and energy
expenditure in the elderly. Am J Hum Biol 8:199
206.
Poehlman ET, Toth MJ. 1995. Mathematical ratios lead
to spurious conclusions regarding age- and sex-related differences in resting metabolic rate. Am J Clin
Nutr 61:482 485.
Rabinowitch IM, Smith FC. 1936. Metabolic studies of
Eskimos in the Canadian Eastern Arctic. J Nutr
12:337 356.
Ravussin E, Bogardus C. 1989. Relationship of genetics, age and physical fitness to daily energy expenditure and fuel utilization. Am J Clin Nutr 49:
968 975.
Rennie DW, Covino BG, Blair MR, Rodahl K. 1962.
Physical regulation of temperature in Eskimos. J Appl
Physiol 17:326 332.
Roberts DF. 1978. Climate and human variability, 2nd
ed. Menlo Park, CA: Cummings.
Rodahl LK. 1952. Basal metabolism of the Eskimo.
J Nutr 48:359 368.
Rode A, Shephard RJ. 1995. Basal metabolic rate of
Inuit. Am J Hum Biol 7:723 729.
Salijukov VB, Lemza SV, Kucher AN, Puzyrev VP.
1992. The role of hereditary factors in phenotypic
variability of hormone levels in the population ge-
620
W.R. LEONARD ET AL.
netically adapted to circumpolar environment. Arct
Med Res 51:143 149.
Schofield WN. 1985. Predicting basal metabolic rate,
new standards and a review of previous work. Hum
Nutr (Clin Nutr) 39C(suppl 1):5 41.
Shephard RJ, Rode A. 1996. Health consequences of
``modernization'': evidence from circumpolar peoples.
Cambridge: Cambridge University Press.
Shetty PS. 1996. Metabolic adaptation in humans: does
it occur? In: Rosetta L, Mascie-Taylor CGN, editors.
Variability in human fertility. Cambridge: Cambridge
University Press. p 125 147.
Sims EAH, Danforth E, Horton ES, Bray GA, Glennon
JA, Salans LB. 1973. Endocrine and metabolic effects
of experimental obesity in man. Recent Prog Horm
Res 29:457 496.
Smals AGH, Ross HA, Kloppenborg PWC. 1977. Seasonal variation in serum T3 and T4 levels in man. J
Clin Endocrinol Metab 44:998 1001.
Soares MJ, Francis DG, Shetty PS. 1993. Predictive
equations for basal metabolic rates of Indian males.
Eur J Clin Nutr 47:389 394.
Sorensen MV, Leonard WR, Mosher MJ, Spencer GJ,
2 Spitsyn VA, Shenin V. 1999. Correlates of variation in
basal metabolism among the Buryat of southern Siberia. Am J Hum Biol 11:130 (abstract).
Tanner JM. 1949. The fallacy of per-weight and persurface are standards and their relation to spurious
correlation. J Appl Physiol 2:1 15.
Tkachev AV, Ramenskaya EB, Bojko JR. 1991. Dynamics of hormone and metabolic state in polar inhabitants depend on daylight duration. Arct Med Res
50(suppl. 6):152 155.
Tremblay A, Poehlman ET, Despres JP, Theriault G,
Danforth E, Bouchard C. 1997. Endurance training
with constant energy intake in identical twins:
changes over time in energy expenditure and related
hormones. Metabolism 46:499 503.
Ulijaszek SJ. 1996. Energetics, adaptation and adaptability. Am J Hum Biol 8:169 182.
Weinsier RL, Shutz Y, Bracco D. 1992. Reexamination
of the relationship of resting metabolic rate to fat-free
mass and to the metabolically active components of
fat-free mass in humans. Am J Clin Nutr 55: 790 794.
Wong WW, Butte NF, Hergenroeder AC, Hill RB, Stuff
JE, Smith EO. 1996. Are basal metabolic rate prediction equations appropriate for female children and
adolescents? J Appl Physiol 81:2407 2414.