AMERICAN JOURNAL OF HUMAN BIOLOGY 20:510–529 (2008)
Original Research Article
Climate Variables as Predictors of Basal Metabolic Rate: New Equations
ANDREW W. FROEHLE
Department of Anthropology, University of California, San Diego, California 92093-0532
ABSTRACT
Estimation of basal metabolic rate (BMR) and daily energy expenditure (DEE) in living humans and in
fossil hominins can be used to understand the way populations adapt to different environmental and nutritional circumstances. One variable that should be considered in such estimates is climate, which may influence between-population variation in BMR. Overall, populations living in warmer climates tend to have lower BMR than those living in
colder climates, even after controlling for body size and composition. Current methods of estimating BMR ignore climate, or deal with its effects in an insufficient manner. This may affect studies that use the factorial method to estimate
DEE from BMR, when BMR is not measured but predicted using an equation. The present meta-analysis of published
BMR uses stepwise regression to investigate whether the inclusion of climate variables can produce a generally applicable model for human BMR. Regression results show that mean annual temperature and high heat index temperature
have a significant effect on BMR, along with body size, age and sex. Based on the regression analysis, equations predicting BMR from body size and climate variables were derived and compared with existing equations. The new equations
are generally more accurate and more consistent across climates than the older ones. Estimates of DEE in living and
fossil humans using the new equations are compared with estimates using previously published equations, illustrating
the utility of including climate variables in estimates of BMR. The new equations derived here may prove useful for
future studies of human energy expenditure. Am. J. Hum. Biol. 20:510–529, 2008.
' 2008 Wiley-Liss, Inc.
The use of reference equations to estimate basal metabolic rate (BMR), the energy required for body maintenance and growth in the absence of digestion and physical
activity, is common practice in human biology and biological anthropology when measurement is not feasible (Ulijaszek, 1995). Subsequent use of these BMR values to estimate daily energy expenditure (DEE) using the factorial
method also occurs frequently. Factorial method studies of
DEE are useful to understanding energy balance in living
human populations, and adaptations to different environmental and nutritional circumstances (e.g. Gamboa and
Garcia, 2007; Leonard et al., 1997, 2005; McNeill et al.,
1988; Panter-Brick, 1993), informing agricultural and
nutritional policy-making by government agencies (FAO,
1950, 1957; FAO/WHO, 1973; FAO/WHO/UNU, 1985), and
investigating the energetic correlates of events in hominin
evolution (Churchill, 2006; Froehle, 2007; Leonard and
Robertson, 1994; Sorensen and Leonard, 2001; SteudelNumbers, 2006).
This approach requires careful consideration of the
sample from which any particular equation was derived,
with regard to a number of variables that influence variation in BMR within humans. One must ensure that the
equation to be used either controls for these variables, or
was derived from a sample that, in terms of these variables, closely resembles the group to which the equation
will be applied (Ulijaszek, 1995). Available equations often
control for many of the relevant variables, but as will be
seen below, none controls for all of them in a systematic
and generally applicable manner. Incomplete consideration of these variables in an equation’s sample and also in
the population under study can lead to inaccuracies in
BMR estimates, which can further produce considerable
error in DEE estimates using the factorial method.
The factorial method, in its simplest form, uses the following equation:
C 2008
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Wiley-Liss, Inc.
DEEðkcal=dÞ ¼ BMRðkcal=dÞ 3 PAL
where PAL stands for physical activity level and represents a coefficient that accounts for energy expenditure
above basal conditions. Note that PAL can have a substantial influence on the error of DEE estimates, and that
even when using measured BMR, the use of PAL can provide inaccurate DEE, especially in highly active populations (Leonard et al., 1997; Spurr et al., 1996a). The use of
focal follows and scan sampling as opposed to subject
activity recall diaries can improve PAL, and thus DEE,
estimates, but this is not always the case (Durnin, 1990;
Leonard et al., 1997; Spurr et al., 1996a).
Although PAL plays an important role in the accuracy
of DEE estimates, the present study focuses on the effect
BMR values have on DEE accuracy with the factorial
method. The use of measured instead of estimated BMR,
for instance, can improve DEE accuracy by up to 19%
(Leonard et al., 1997; Alfonzo-Gonzalez et al., 2004). Obviously this calls for the use of measured values, but field
conditions can often make measurement impractical or
impossible, requiring the use of predictive equations (Ulijaszek, 1995). As the above finding indicates, currently
used equations to estimate BMR are not particularly good,
at least for some populations. In the absence of measured
BMR, then, the use of improved equations could increase
Correspondence to: Andrew W. Froehle, Department of Anthropology,
University of California, San Diego, 9500 Gilman Dr., La Jolla, CA 920930532, USA. E-mail: [email protected]
Received 4 June 2007; Revision received 9 January 2008; Accepted 13
January 2008
DOI 10.1002/ajhb.20769
Published online 6 May 2008 in Wiley InterScience (www.interscience.
wiley.com).
CLIMATE AND BMR IN HUMANS
the accuracy of subsequent DEE estimates. The magnitude of such improvements may be small, on the order of
200 kcal/d, and thus seemingly unimportant (Durnin,
1990). Nevertheless, as an example of the potential uses of
this small quantity of energy, some have found the daily
energy requirements of gestation to be 170 kcal/d
(Thongprasert et al., 1987), and on average 200 kcal could
meet the daily growth needs of 1.4 children between birth
to 4 years old (Butte, 1996). Furthermore, small imbalances in energy intake over expenditure can over time
affect overweight and obesity (Levine, 2003), two major
health issues in population-level nutritional studies.
Thus, errors in DEE estimates on the order of 200 kcal/d
can represent the equivalent of bringing a child to term
and subsequently providing for its normal growth, or the
difference between normal, healthy weight and the health
problems that accompany excessive weight. Concerns of
this nature are quite relevant to applications of the factorial method in human biology and biological anthropology.
In attempting to minimize inaccuracy in BMR and DEE
estimates by choosing a population-appropriate predictive
equation, one must consider several relevant variables.
Body mass and body composition have been shown together to exert the largest influence on BMR, both on an
interspecific scale and within humans (Cunningham,
1980, 1991; Kleiber, 1961; Nelson et al., 1992; Ravussin
et al., 1986; Webb, 1981; Weinsier et al., 1992). These variables are almost universally controlled for in predictive
equations by using body mass or fat-free mass (FFM) as the
independent variable from which BMR is estimated. Some
evidence suggests that BMR in humans also varies with sex
and age, though considerable debate surrounds the precise
nature of these relationships (Buchholz et al., 2001; Calloway and Zanni, 1980; Ferraro et al., 1992; Froehle and
Schoeninger, 2006; Henry, 2000; Holliday, 1986; Keys et al.,
1973; Klausen et al., 1997; Nichols et al., 1990; Piers et al.,
1998; Sathyaprabha, 2000; Vaughan et al., 1991). To control
for these variables, many past BMR meta-analyses have
derived equations estimating BMR from body mass that are
specific to each sex and to age groups.
Climate is another variable thought to influence BMR
in humans, and it is possible that the modern pattern of
BMR variation in humans represents, at least partially, a
series of evolved responses to changes in global climate
and migration into new climatic zones (Leonard et al.,
2005; Wallace, 2005). Nevertheless, no existing predictive
equations deal with this variable’s potential effects in a
systematic manner that is applicable across human populations. According to a great deal of research, climate’s
influence can be summarized as follows: when BMR is
normalized for differences in FFM (henceforth BMRadj),
tropical populations tend to have lower average BMR
than temperate populations, who in turn have lower average BMR than those living in circumpolar regions or at
high elevation (Christin et al., 1993; Galloway et al., 2000;
Henry et al., 1987; Lawrence et al., 1988; Leonard et al.,
2005; Minghelli et al., 1990; Rode and Shephard, 1995;
Soares and Shetty, 1986; Spurr and Reina, 1988, 1989;
Spurr et al., 1992; Ulijaszek and Strickland, 1991). As a
general trend, BMR decreases with increasing mean annual temperature (TMEAN) when body mass is controlled
for (Roberts, 1978).
Additional support for this overall pattern comes from
studies showing that standard reference equations based
on a temperate sample (FAO/WHO/UNU, 1985; Schofield,
511
1985) tend to overestimate BMR in tropical populations
by 3–13% (Cruz et al., 1999; Kashiwazaki et al., 1995;
Leung et al., 2000; McNeill et al., 1987b; Nhung et al.,
2005; Soares and Shetty, 1988; Soares et al., 1993; Spurr
and Reina, 1988; Spurr et al., 1992, 1994), and underestimate BMR in circumpolar or high elevation populations
by 2–10% (Beall et al., 1996; Leonard et al., 2005; Rode
and Shephard, 1995; Snodgrass et al., 2005).
Other studies, however, find good agreement between
temperate-based estimates and measured BMR in nontemperate groups (Alam et al., 2005; Ferro-Luzzi et al.,
1997; Galloway et al., 2000; Lawrence et al., 1988). Such
findings support claims that variation in BMR between
populations is better attributed to factors other than climate. In many tropical populations, the metabolic consequences of undernourishment or high-carbohydrate diets
are cited as potential explanations for lower BMR relative
to Europeans and North Americans (Shetty, 1996). With
regard to circumpolar populations, elevated BMR is often
attributed to high-protein diets (Kormondy and Brown,
1998), whereas morphological and behavioral traits are
emphasized as adaptations to cold, as opposed to evolutionary explanations for the role of elevated BMR in maintaining body temperature (Beall and Steegmann, 2000;
Kormondy and Brown, 1998).
Contrary to these arguments against climate’s influence
on BMR, however, temperate-based reference equations
overestimate BMR by 3–12% in populations of partial or
completely European descent consuming ‘‘western’’ diets,
but that live in warmer subtropical climates (Piers et al.,
1997; Valencia et al., 1994; van der Ploeg et al., 2001,
2002). Moreover, many circumpolar groups that exhibit
higher BMR than expected from temperate standards do
not consume an especially high-protein diet compared
with adults in the United States (Leonard et al., 2005).
Additional research suggests that BMRadj may not differ
between marginally nourished and well nourished segments of the same population (Ferro-Luzzi et al., 1997;
McNeill et al., 1987b; Shetty et al., 1990; Soares and
Shetty, 1991; Spurr and Reina, 1988, 1989; see Shetty,
1999, for a review), suggesting that nutritional differences
between populations may not affect BMR comparisons if
body composition is controlled. An exception to this, however, would be in cases of semi-starvation, where physiological changes resulting from severe energy deficiency
may also be related to a decline in BMR (Grande et al.,
1958; Shephard, 1991; Shetty, 1999).
Finally, research on mitochondrial DNA demonstrates
an association between migration into current and
recently cold/glacial climates and the presence of mutations that uncouple oxidative phosphorylation from ATP
production (Mishmar et al., 2003; Wallace, 2005). Such
mutations have the effect of directing a higher portion of
food energy toward heat production rather than toward
storage in ATP, possibly contributing to the maintenance
of core body temperatures in colder environments. These
mutations are more common in populations that have
encountered glacial or arctic conditions over the past
50,000 years (i.e. circumpolar, European and indigenous
North and South American) than in people living in areas
of the Old World that have maintained tropical or temperate climates (Mishmar et al., 2003; Wallace, 2005). This
may provide evidence for at least a partial genetic and evolutionary basis for the frequently observed correlation
between BMR and climate in living humans.
American Journal of Human Biology
512
A.W. FROEHLE
If one accepts that climate may exert an important
influence on human BMR, then the use of climate-inappropriate BMR equations to estimate DEE using the factorial method becomes potentially problematic. The main
issue is that any initial errors in BMR are magnified in
absolute terms in DEE estimates using the factorial
method. Currently, no available equations deal with climate in a broadly applicable manner while also incorporating the other relevant variables of body mass/composition, sex and age. One current way of dealing with climate
defines climate groups by latitude (e.g. tropical is defined
as residing between the Tropics of Cancer and Capricorn),
ignoring variation in climate that occurs even at the same
latitude (consider, for example, the climates of San Diego,
CA, Baghdad, Iraq, and Amdo, Tibet, all of which lie
within one degree latitude of each other). Another
approach is to incorporate continuous climate variables,
such as TMEAN, into an analysis of BMR. One such study
does examine the relationship between TMEAN and
BMR, but does not include body composition as a predictor
of BMR (Roberts, 1978), thereby ignoring an important
factor known to vary between human groups living in different environments (Shephard, 1991). Also, TMEAN
alone may be a poor indicator of climate, as it ignores
other factors such as humidity and wind (FAO, 1957;
FAO/WHO, 1973).
The present study consists of a meta-analysis of BMR
investigating the effects of several continuous climate variables, as opposed to broad latitude-based climate categories, meanwhile incorporating the influence of body mass/
composition, sex and age. This meta-analysis employs
stepwise-regression in an attempt to elucidate a general
pattern, should one exist, of the manner in which climate
variables affect BMR, and expands beyond TMEAN to
examine the effects of other aspects of climate. If any pattern does emerge, the study is set up such that it will
reflect the relationship between climate and BMR on a
global scale, applicable to human populations in general.
Corresponding new predictive equations for the estimation of BMR will be derived, and will be compared with
existing equations that do not incorporate climate variables in the manner used here. The consequences of including climate variables in estimating BMR will be discussed
in terms of using the factorial method to derive DEE, with
additional consideration of how these new equations
might affect studies of living humans and of fossil hominins. Before describing the methods of this study, a brief
review of commonly used and recently developed equations is warranted.
EXISTING EQUATIONS FOR ESTIMATING BMR
The number of equations discussed here is necessarily
limited, because nearly 100 years of research has produced a large number of predictive equations for BMR
(see Schofield, 1985, for a review). The most commonly
used equations in human biology and biological anthropology in the recent past have been those of Kleiber (1961),
the FAO/WHO/UNU (1985), also known as the Schofield
(1985) equations, and the tropical-population-specific
equations of Henry and Rees (1991). More recently developed, the Oxford Brookes equations (Cole and Henry,
2005; Henry, 2005) have not been widely used or validated
(Weekes, 2007), but they deal with many of the problems
associated with earlier meta-analyses; thus they provide a
American Journal of Human Biology
potentially better alternative to previous equations. All
equations discussed here can be found in Appendix A.
The oldest of the equations discussed here is the
groundbreaking work of Kleiber (1961: referred to as K
below). This equation is still commonly used, especially in
fossil studies, and was derived from a taxonomically
diverse sample of mammals. This study’s importance lies
in its demonstration that body mass, not surface area, is
the major determinant of BMR across mammals, and its
establishment of the [3/4] exponent as the factor by which
BMR scales to body mass, recently the subject of renewed
interest (West and Brown, 2005). Despite its great influence on biology, this equation includes only two humans
from North America, and thus does not account for any
potential climate effects. Accordingly, the equation overestimates BMR in some subtropical populations by 14–
20% (Liu et al., 1995; Scalfi et al., 1993).
The next oldest, and the most commonly used equations
are from the FAO/WHO/UNU (1985), first developed in
slightly different form by Schofield (1985) (referred to as
FAO/S below). These equations are derived from a sample
composed mainly of European and North American subjects from temperate climates, and present unique equations for each of 6 age divisions within each sex. This
allows the investigator to estimate BMR from body mass
where sex and age are known.
The equations published in 1985 represent the latest
output from a long-term program of the Food and Agriculture Organization (FAO) of the United Nations researching the energy needs of different populations worldwide.
In early FAO investigations (1957), the effects of climate
were considered, with the recommendation that energy
expenditure estimates be reduced by 5% for every 108C
increase in TMEAN over a temperate reference standard
of 108C. A similar recommendation was made to elevate
energetic estimates by 3% for every 108C drop in TMEAN
from the reference standard (FAO, 1957). In a later report
(FAO/WHO, 1973), however, climate’s effects were reconsidered, with the assertion ‘‘that there is no quantifiable
basis for correcting the [basal] . . . energy requirements
according to the climate.’’ The 1985 FAO/WHO/UNU
report echoed the 1973 report, but recommended further
study into the subject. The 1985 report also noted that a
small sub-sample of Indian subjects exhibited average
BMR 10% lower than the majority European/North
American sample, but refrained from proposing climate
correction factors.
The FAO/S equations have been widely tested, and provide accurate BMR estimates in some colder temperate
populations (Buccholz et al., 2001; Curtis et al., 1996;
Henry et al., 1999; Lawrence et al., 1988; Seale and Conway, 1999; but see Müller et al., 2004); but, as noted above
a large number of studies find them inaccurate with
regard to tropical, circumpolar and warmer temperate
populations. Criticisms of the FAO/S equations focus on a
number of problems with sampling bias, including the fact
that of roughly 6,000 males aged 10–60 years included in
the study, more than half were members of the Italian
military, a rather homogeneous group (Hayter and Henry,
1994; Shetty et al., 1996). Some have suggested that Italians as a population have abnormally high BMR on the
whole (Henry, 2005; Schofield, 1985), but this view is
based on old data. More recent work shows that the FAO/
S equations overestimate BMR in various groups of Italians (Censi et al., 1998; Maffeis et al., 1993; Scalfi et al.,
513
CLIMATE AND BMR IN HUMANS
Fig. 1.
Geographic distribution of the un-weighted sample.
1993). It may be that the Italian portion of the FAO/S
database is simply composed of inaccurate measurements.
Other critiques focus on the sample’s lack of tropical subjects, which restricts the evaluation of climatic influences
on BMR (Hayter and Henry, 1994; Henry and Rees, 1991;
Shetty et al., 1996).
To deal with the FAO/S climate limitations, Henry and
Rees (1991: referred to as H&R below) produced age- and
sex-specific equations similar to those of FAO/S, but
derived from an entirely tropical sample. Their BMR estimates are 8% lower on average than those of FAO/S, and
though not as extensively tested, the H&R equations provide accurate BMR estimates in some tropical subjects
(Soares et al., 1993). But, like FAO/S, the H&R equations
are limited by their geographically restricted sample, significantly underestimating BMR in Europeans by 5%
(Soares et al., 1993). Less easily explained, the H&R equations also overestimate BMR by 6–8% in some tropical
groups, while giving underestimates of 4–11% in others
(Spurr et al., 1996b; Yamauchi and Ohtsuka, 2000). These
equations also estimate BMR to within 0.5% in North
Americans from the decidedly non-tropical climates of
Philadelphia and Montreal (Soares et al., 1993). Thus,
questions as to the accuracy of H&R remain.
Much more recently, the Oxford Brookes BMR database
was developed and analyzed to provide updated and more
broadly applicable equations (Cole and Henry, 2005;
Henry, 2005: referred to as OB below). This sample
included a large number of subjects living over a wide geographic range, and although some of the same data used
in the FAO/S equations were included, the Italian subjects
were excluded as outliers. The primary analysis produced
age- and sex-specific equations similar in approach to
those of FAO/S and H&R. Further analysis focused on the
relationship between age and BMR, integrating adult and
juvenile data into a ‘‘seamless’’ approach to estimating
BMR (Cole and Henry, 2005). In other words, rather than
producing age-group-specific equations, this analysis
aimed to produce a single equation incorporating the
effects of age, which eliminated the increased error inherent at the margins of age categories in the former
approach. Following this idea, a simpler seamless
approach will be used in part in the present analysis.
Again, the OB equations remain untested (Weekes, 2007).
MATERIALS AND METHODS
Published BMR studies were obtained using the ISI
Web of Knowledge Internet search engine, with each of
the following search terms: BMR, RMR, REE, basal
metaboli*, resting metaboli*, resting energy expenditure,
metaboli* (asterisks indicate truncated searches). The variety of search terms reflects the lack of standardization
between studies, where appropriate BMR measurement
methods are used but the variable is referred to as resting
energy expenditure (REE) or resting metabolic rate
(RMR). Standardized BMR values in humans are measured after a 10–12-h fast (i.e. postabsorptive), with the
subject lying supine and relaxed, under thermoneutral
conditions (20–248C). This is in contrast to the standard
protocol for RMR/REE, which allows measurement after
only a three-hour fast, thus including some energy expenditure associated with digestion which is excluded
from BMR (Blaxter, 1989; Ulijaszek, 1995). Each study
was screened for adherence to standard BMR protocol,
regardless of how the authors labeled the variable. Studies were also screened to ensure that they used a measurement method validated against other proven methods
(Alam et al., 2005; Borghona et al., 2000; Butler et al.,
2004; González-Arévalo et al., 2003; Kato et al., 2002;
McLellan et al., 2002; McNeill et al., 1987a; Murgatroyd
et al., 1993; Stewart et al., 2005; Tissot et al., 1995).
Where reported, data from women measured during the
luteal phase of menstruation or taking oral contraceptives
were excluded, because these factors may elevate BMR
American Journal of Human Biology
514
A.W. FROEHLE
TABLE 1. Characteristics of the two samples
N (Total)
Tropical
Temperate
Circumpolar
Age (years)
Male:female ratio
Body mass (kg)
FFM (kg)
BMR (kcal/d)
Absolute Latitude (8)
Elevation (m)
TMEAN (8C)
LWCT (8C)
HHIT (8C)
MTR (8C)
ATR (8C)
Un-weighted
Weighted
329
78
225
26
32.7 6 1.0 (3–97)
0.495:0.505
60.5 6 0.9 (12.2–125.1)
46.1 6 0.7 (9.8–92.7)
1445 6 14.9 (624–2972)
36.140 6 0.93 (1.293–70.637)
401.6 6 48.3 (0–5150)
14.3 6 0.5 (211.1–29.0)
225.0 6 1.3 (278.3–17.8)
40.7 6 0.4 (24.0–60.0)
45.6 6 0.8 (11.0–70.0)
26.3 6 0.4 (12.5–42.3)
10,512
3,012
7,182
318
35.3 6 0.2 (3–97)
0.458:0.542
62.2 6 0.2 (12.2–125.1)
45.9 6 0.1 (9.8–92.7)
1427 6 2.4 (624–2972)
33.910 6 0.17 (1.293–70.637)
274.5 6 4.9 (0–5150)
15.7 6 0.1 (211.1–29.0)
224.5 6 0.2 (278.3–17.8)
41.5 6 0.1 (24.0–60.0)
45.8 6 0.1 (11.0–70.0)
25.3 6 0.1 (12.5–42.3)
All values are mean 6 SEM, range in parentheses. TMEAN, mean annual temperature; LWCT, lowest monthly windchill temperature; HHIT, highest monthly heat
index temperature; MTR, maximum monthly temperature range; ATR, average monthly temperature range.
(Anantharaman-Barr et al., 1990; Curtis et al., 1996;
Diffey et al., 1997; Ferraro et al., 1992; Piers et al., 1997;
Solomon et al., 1982, but see Piers et al., 1995). It should
be noted that the majority of studies did not comment on
these subject characteristics, and therefore may have
included such women. Additionally, only data from normal, healthy subjects were included (e.g. control subjects
in disease studies), and pregnant and lactating women
were excluded. Where possible, data from obese subjects
were excluded, but the manner in which the database was
assembled (see below) makes it likely that at least some
obese subjects were included in the overall sample. After
screening, data from 159 different studies were used (see
Appendix B) representing 103 different locations worldwide (see Fig. 1). All BMR data were converted to kcal/d,
and corresponding data on FFM (kg), body mass (kg), age
(y) and sex (female 5 0, male 5 1) were included.
For each study’s measurement site(s), latitude, longitude, and elevation were obtained from a free online geographic database (http://www.heavens-above.com/countries.aspx) that compiles information from the United
States Geological Survey and the United States National
Imaging and Mapping Agency. Using these data, the nearest World Meteorological Organization (WMO) weather
station for each site was located using an online database
of the United States National Oceanic and Atmospheric
Administration
(http://www1.ncdc.noaa.gov/pub/data/
inventories/STNLIST-SORTED.TXT). Climate data for
these weather stations are publicly available via the
United States National Centers for Environmental Prediction’s Climate Prediction Center. Monthly summaries of
data are maintained and made accessible online [http://
dss.ucar.edu/datasets/ds512.0/data/ (free registration
required)] by the United States National Center for
Atmospheric Research/University Corporation for Atmospheric Research.
Weather stations were located an average of 14.5 km
from each study site (measured by great circle distance),
and within 45 km for 97 of the 103 measurement sites.
The distance of 45 km was accepted as reasonable (i.e. climate data did not require independent verification), since
for some study sites (e.g. Beijing, Tokyo, New York) measurement could have occurred this far from the weather
station, but still within the same large metropolitan area.
No studies provided details about where subjects came
American Journal of Human Biology
from within such metropolises, aside from naming the
region broadly. Thus, a similar standard was applied to
the entire data set. For six sites the nearest weather station lay outside of the 45 km boundary (range: 52–427
km); in these cases the weather station data were checked
against other independent sources for consistency (Beall
et al., 1996; Goldstein and Beall, 1990; Huq and Asaduzzaman, 1999; Snodgrass et al., 2005).
Climate records for each site were obtained from January 1987 through November 2005, when this study’s data
collection began. To standardize across studies and examine general climate patterns (as opposed to annual outliers), data across this entire period were used for each
study site. For 46 sites, complete datasets of 227 months
were available, and the majority of sites (77) had 192
months (16 years) of available data. Of the remaining 26
sites, 23 had only 6–15 years of data. These sparser datasets were not clustered geographically, however, so they
were kept in the study. Three sites with 3 years of data
were dropped from the study (not included in the totals
above).
Five climate variables were included. Following Roberts
(1978), TMEAN was used, and was calculated by averaging all monthly mean temperatures from the available
data. To expand the treatment of climate, extremes in
temperature and the effects of humidity and wind were
also of interest. Thus, for each site monthly low wind chill
temperature (LWCT) and monthly high heat index temperature (HHIT) were obtained; the lowest extreme value
of LWCT and the highest extreme value of HHIT for each
site were used. Two variables expressing temperature
range were also included by subtracting LWCT from
paired HHIT values for each month. The mean of all such
values for each dataset was calculated to arrive at average temperature range (ATR), and the maximum
monthly temperature range was also obtained (MTR).
Absolute latitude was included, as was membership in
one of three latitude-based climate groups, defined using
standard boundaries: tropical <23.4558 absolute latitude; temperate >23.4558 and <60.0008 absolute latitude; circumpolar >60.0008 absolute latitude. Elevation
was included as a continuous variable and also as a categorical variable (0–1150 m, 1150–2300 m, >2300 m above
sea level). See Table 1 for descriptive data on each dataset.
CLIMATE AND BMR IN HUMANS
Fig. 2.
515
Geographic distribution of the weighted sample.
Potential caveats in the meta-analysis
Two main issues relevant to this meta-analysis merit
brief discussion. The first is that most studies included in
the database do not report individual data, but rather
means for groups of subjects. Thus, all of the data used here
represent group means (where individual data were
reported, means were calculated and incorporated into the
database). This departs from more recent meta-analyses of
BMR data (Cole and Henry, 2005; Henry and Rees, 1991;
Henry, 2005), but was useful in that it allowed a considerable expansion of the sample from a geographical and climatic standpoint. Studies were scrutinized to ensure that
all such group means included in the database came from
closed groups. A group was considered closed when all relevant data came from only one sex, a single age group (<18
years, 18–50 years, and >50 years), and a single study locale. For every study, means for the smallest reported closed
groups were used, resulting in an overall sample size of n 5
329 group means (breakdown by latitude: n 5 78 tropical,
n 5 225 temperate, n 5 26 circumpolar).
The group means used here each represent samples of
between 2 and 521 individual subjects, with an average
sample size of n 5 32. The wide variation in sample size
represented by each group mean, and the use of group
means in general, has the potential to artificially decrease
the observed variance of the sample. In other words, a
sample of 100 group means may have decreased variance
compared with a sample of 100 individual measurements,
which is relevant to the interpretation and validity of this
study’s sample. To investigate whether this was a problem, standard error of the mean (SEM) for this study’s
sample was compared with SEM reported for similarly
sized samples of individuals. The basis for this approach
lies in the relationship between variance (r2) and SEM (r/
n0.5), where samples of similar size with similar SEM will
also have similar variance. This comparison showed no
bias towards decreased variance in this study’s sample as
compared with samples of individual subjects. As an additional precaution, however, two separate analyses were
performed: one using the group means as individual data
points, and a second, weighted approach based on the
sample size represented by each group mean. In the
weighted analysis, a group mean representing 100 subjects would be counted 100 times, whereas it would be
counted only once in the un-weighted analysis. Figure 2
shows the geographical distribution of the weighted
sample.
The second potential caveat regards pooling data from
widely dispersed populations that may live very different
lifestyles. Such data compilation incorporates a number of
variables that could confound the effects of climate on
BMR, including body composition, ethnicity, nutritional
status, varying work habits and exposure to the elements,
and seasonal fluctuations in climate and caloric intake.
Body composition (i.e. body fat percentage) varies with climate and between populations, and indeed some have
suggested that the observed effects of climate on BMR are
simply an artifact of body composition variation (Cunningham 1980, 1991; Nelson et al., 1992). This suggestion
is based on the observation that adipose tissue has little to
no metabolic activity and thus contributes little to BMR
(Cunningham, 1980, 1991). Therefore, when comparing
populations that vary in terms of body fat, the effects of
other variables may be masked or artificially shown to be
significant when body fat variation is not controlled. This
is not a major problem for the present study because FFM
is used as the primary body size variable (though a secondary analysis is conducted using body mass in place of
FFM). Body fat is omitted from FFM, thus controlling for
population-level differences in body composition and
including only metabolically active tissue.
Differences in body composition may underlie other possible confounders of climate’s effects on BMR, and thus
American Journal of Human Biology
516
A.W. FROEHLE
use of FFM to normalize BMR may minimize their statistical interference. For example, although Roberts (1978)
uses body mass to normalize BMR and finds ethnic clines
in the BMR/TMEAN relationship, other studies using
FFM find that different ethnic groups living in similar climates have similar BMRadj (Christin et al., 1993; Henry
et al., 1987; Lawrence et al., 1988; Minghelli et al., 1990;
Soares and Shetty 1986; Spurr and Reina, 1988, 1989;
Spurr et al., 1992; Ulijaszek and Strickland, 1991; males
in Galloway et al., 2000). Other studies, however, find
that ethnically different groups living in the same location
do exhibit significantly different BMRadj (Galloway et al.,
2000; Rode and Shephard, 1995), or that ethnically similar populations living in different climates nonetheless
have similar BMRadj (Luke et al., 2000, 2002). These findings coupled with possible evidence for a genetic basis to
at least some variation in BMR related to climate (Wallace, 2005) indicate that ethnicity and climate likely interact to produce average BMR in any population. Because
the ethnicity of most subjects included here is unknown,
these issues unfortunately cannot be tested using this
sample.
Differences in nutrition between populations should not
confound climate’s influences, since many studies find no
difference in BMRadj between well-nourished and undernourished subjects (Ferro-Luzzi et al., 1997; McNeill
et al., 1987b; Shetty et al., 1990; Soares et al., 1991; Spurr
and Reina, 1988, 1989; Spurr et al., 1992). Seasonality of
climate likewise should not pose a major problem for this
study, because many find no significant daily or seasonal
variation in BMRadj in a variety of environments (Ategbo
et al., 1995; Beall et al., 1996; Schultink et al., 1990; Spurr
et al., 1994; Tohori et al., 1988). Plasqui et al. (2003), however, report a small (62.3% of the mean) but significant
seasonal fluctuation in sleeping metabolic rate, with no
corresponding change in FFM. A number of Japanese
studies have also demonstrated seasonal variation in
BMR, with summer lows and winter highs (Kashiwazaki,
1990), though only one controls for changes in body composition (Tashiro, 1961). Seasonality was impossible to
control in the present study, because the season during
which each subject’s BMR was measured was not often
reported. Finally, lifestyle differences related to exposure
to extreme conditions appear not to influence BMRadj
(Rode and Shephard, 1995; Snodgrass et al., 2005; Yamauchi and Ohtsuka, 2000), nor do differences in level of physical activity (Armellini et al., 2000; Bingham et al., 1989;
Gilliat-Wimberly et al., 2001; Pullicino et al., 1996; Smith
et al., 1997; Taaffe et al., 1995).
Based on each regression analysis, equations were
derived that estimate BMR from any variables shown to
have a significant effect on BMR. In addition to these
equations, equation sets divided according to sex and age
and similar to those presented in previous studies (FAO/S;
H&R; OB) were derived using the significant independent
variables. The present study’s equation sets differ in the
way age groups are divided, using 18 years and 50 years
as borders and resulting in only three age groups per sex
(juveniles <18 years of age, reproductive-age adults 18–50
years of age, and older adults >50 years of age) as opposed
to more in previous studies.
Estimates of BMR using new and previously published
equations were then compared for accuracy using two
samples. One was the un-weighted sample of group means
used in this study, which provided a test of the equations’
accuracy in populations. The other sample (n 5 463) consisted of all individual subject data from published reports
included in this study’s sample, along with a set of individual data from the Yakut of Siberia, previously published
only as group means (Snodgrass and Leonard, personal
communication; Snodgrass et al., 2005). The frequency of
predicted values within 65% and 610% of measured
BMR in each sample was assessed for each equation, and
was broken down by climate to examine inter-climate consistency in the equations. In addition, root mean square
(RMS) error, which indicates the precision of an equation’s
estimates (Freedman et al., 1997), was calculated for each
equation.
RESULTS
See Table 2 for all regression results and significance.
For the un-weighted sample using FFM as the body size
variable, FFM alone was the primary predictor of BMR (P
< 0.001, r2 5 0.776). With FFM and age as predictors, the
equation improved (P < 0.001, r2 5 0.803), and improved
further when FFM, age and TMEAN were all included (P
< 0.001, r2 5 0.815). Finally, adding sex to FFM, age and
TMEAN as predictors, the equation significantly
improved (P < 0.001, r2 5 0.821). None of the other variables had a significant effect on BMR when the above four
were included in the analysis.
TABLE 2. Stepwise regression resultsa
Dataset
Un-weighted
Size variableb
FFM
Statistical analysis
Stepwise regression, with BMR as the independent
variable, was used to analyze both the un-weighted (n 5
329; 78 [24%] tropical, 225 [68%] temperate, 26 [8%] circumpolar) and weighted (n 5 10,512; 3012 [29%] tropical,
7182 [68%] temperate, 318 [3%] circumpolar) samples.
For each sample, two separate analyses were conducted,
one using FFM as the body size variable, and the other
using whole body mass. In addition to the body size variable, each analysis included numerical age, the climate
variables (TMEAN, LWCT, HHIT, ATR, MTR), absolute
latitude, latitude-based climate group, elevation, and
elevation group.
American Journal of Human Biology
Body mass
Weighted
FFM
Body mass
a
Significant variablesc
r2
FFM
FFM, age
FFM, age, TMEAN
FFM, age, TMEAN, sex
Mass
Mass, sex
Mass, sex, age
Mass, sex, age, TMEAN
FFM
FFM, age
FFM, age, HHIT
FFM, age, HHIT, sex
Mass
Mass, age
Mass, age, sex
Mass, age, sex, HHIT
0.776
0.803
0.815
0.821
0.612
0.730
0.798
0.827
0.781
0.813
0.829
0.831
0.619
0.751
0.826
0.836
Where BMR is the dependent variable.
FFM included in analysis to the exclusion of body mass and vice versa.
Significance for all variables P 0.001.
b
c
517
CLIMATE AND BMR IN HUMANS
TABLE 3. New equations
Dataset
Un-weighted
Eq. (1)
Eq. (2)
SET A
SET B
Weighted
Equationa
P
r2
All
All
M <18
F <18
M 18–50
F 18–50
M >50
F >50
M <18
F <18
M 18–50
F 18–50
M >50
F >50
BMR 5 [17.4 (61.3)3FFM] 2 [2.4 (60.8)3A] 2 [3.8 (61.5)3TMEAN] 1 [50.6 (630.9)3SEX] 1 752 (661)
BMR 5 [13.1 (60.93M] 1 [168 (626)3SEX] 2 [4.5 (60.8)3A] 2 [5.3 (61.4)3TMEAN] 1 791 (657)
BMR 5 [22.4 (65.1)3FFM] 1 [1.7 (67.1)3TMEAN] 1 565 (6246)
BMR 5 [17.7 (65.6)3FFM] 2 [1.9 (65.4)3TMEAN] 1 720 (6215)
BMR 5 [23.3 (62.7)3FFM] 2 [4.1 (62.2)3TMEAN] 1 392 (6168)
BMR 5 [12.3 (63.7)3FFM] 2 [3.6 (62.5)3TMEAN] 1 879 (6171)
BMR 5 [9.8 (66.9)3FFM] 2 [10.9 (64.9)3TMEAN] 1 1124 (6401)
BMR 5 [18.9 (65.7)3FFM] 2 [2.8 (65.9)3TMEAN] 1 529 (6249)
BMR 5 [16.9 (64.1)3M] 2 [3.1 (66.9)3TMEAN] 1 703 (6231)
BMR 5 [12.2 (63.8)3M] 2 [3.0 (65.3)3TMEAN] 1 779 (6198)
BMR 5 [14.7 (61.7)3M] 2 [5.6 (62.3)3TMEAN] 1 735 (6131)
BMR 5 [9.2 (62.1)3M] 2 [3.8 (62.2)3TMEAN] 1 852 (6137)
BMR 5 [8.0 (63.9)3M] 2 [11.9 (64.1)3TMEAN] 1 1089 (6292)
BMR 5 [10.6 (65.3)3M] 2 [4.9 (67.6)3TMEAN] 1 656 (6338)
0.003
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
0.821
0.827
0.859
0.766
0.755
0.370
0.641
0.643
0.838
0.760
0.742
0.474
0.723
0.395
All
All
M <18
F <18
M 18–50
F 18–50
M >50
F >50
M <18
F <18
M 18–50
F 18–50
M >50
F >50
BMR 5 [18.0 (60.2)3FFM] 2 [2.4 (60.1)3A] 2 [5.5 (60.3)3HHIT] 1 [26.7 (64.8)3SEX] 1 897 (616)
BMR 5 [13.6 (60.13M] 2 [4.8 (60.1)3A] 1 [147 (64)3SEX] 2 [4.3 (60.3)3HHIT] 1 857 (616)
BMR 5 [23.8 (60.7)3FFM] 1 [2.7 (61.5)3HHIT] 1 437 (671)
BMR 5 [22.0 (60.8)3FFM] 1 [0.7 (60.9)3HHIT] 1 548 (650)
BMR 5 [22.5 (60.4)3FFM] 2 [8.6 (60.5)3HHIT] 1 731 (635)
BMR 5 [19.3 (60.6)3FFM] 2 [4.6 (60.5)3HHIT] 1 726 (637)
BMR 5 [13.6 (60.9)3FFM] 2 [19.8 (61.2)3HHIT] 1 1547 (667)
BMR 5 [18.9 (61.0)3FFM] 2 [11.1 (61.5)3HHIT] 1 925 (673)
BMR 5 [18.5 (60.6)3M] 1 [0.9 (61.6)3HHIT] 1 543 (675)
BMR 5 [15.0 (60.6)3M] 1 [1.0 (61.0)3HHIT] 1 597 (657)
BMR 5 [14.2 (60.3)3M] 2 [9.8 (60.5)3HHIT] 1 1061 (631)
BMR 5 [11.6 (60.3)3M] 2 [4.8 (60.5)3HHIT] 1 842 (632)
BMR 5 [10.0 (60.9)3M] 2 [11.4 (61.5)3HHIT] 1 1224 (6106)
BMR 5 [14.3 (60.8)3M] 2 [5.4 (61.6)3HHIT] 1 522 (691)
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.831
0.836
0.877
0.828
0.805
0.540
0.683
0.643
0.856
0.721
0.792
0.564
0.578
0.615
Group (s)
Eq. (3)
Eq. (4)
SET C
SET D
a
BMR is in kcal/d, FFM and body mass in kg, age in years, and TMEAN and HHIT in 8C. Sexes are assigned the following numerals: male 5 1, female 5 0. For each
parameter, 95% confidence intervals are in parentheses.
For the un-weighted sample using body mass as the
body size variable, body mass alone was the primary predictor of BMR (P < 0.001, r2 5 0.612). With body mass and
sex as predictors, the equation improved (P < 0.001, r2 5
0.730), and improved further when body mass, sex and
age were all included (P < 0.001, r2 5 0.798). Finally, adding TMEAN to body mass, sex and age as predictors, the
equation significantly improved (P < 0.001, r2 5 0.827).
None of the other variables had a significant effect on
BMR when the above four were included in the analysis.
For the weighted sample using FFM as the body size
variable, FFM alone was the primary predictor of BMR (P
< 0.001, r2 5 0.781). With FFM and age as predictors, the
equation improved (P < 0.001, r2 5 0.813), and improved
further when FFM, age and HHIT were all included (P <
0.001, r2 5 0.829). Finally, adding sex to FFM, age and
HHIT as predictors, the equation significantly improved
(P < 0.001, r2 5 0.831). None of the other variables had a
significant effect on BMR when the above four were
included in the analysis.
For the weighted sample using body mass as the body
size variable, body mass alone was the primary predictor
of BMR (P < 0.001, r2 5 0.619). With body mass and age
as predictors, the equation improved (P < 0.001, r2 5
0.751), and improved further when body mass, age and
sex were all included (P < 0.001, r2 5 0.826). Finally, adding HHIT to body mass, age and sex as predictors, the
equation significantly improved (P < 0.001, r2 5 0.836).
None of the other variables had a significant effect on
BMR when the above four were included in the analysis.
For each of the four analyses, a single equation was
derived that predicted BMR from all of the significant variables. Un-weighted sample: Eqs. (1) and (2); weighted
sample: Eqs. (3) and (4). Based on the regression results,
each sample was also divided by sex, and each sex into
three age categories. For each of these subdivisions, an
equation was derived using the remaining significant
body size and climate variables, resulting in four sets of
equations similar to those of previous publications (unweighted sample: Equation Sets A and B; weighted sample: Equation Sets C and D). All new equations are found
in Table 3 along with r2 values and significance.
The predictive power of the new equations was compared with the K, FAO/S, H&R, and OB equations (Figs. 3
and 4). With regard to individuals, none of the new equations was particularly accurate, which was also true for
the previously published equations. Accuracy to within
65% of measured BMR was particularly low, with the
highest proportion of estimates in this range (0.41) coming
from the new Eq. (1) and Set A, and the lowest from K
(0.25). Within 610% of measured BMR, the best equation
was Eq. (1) (0.67), with Set A and OB a close second (0.65).
Again K was the least accurate with only 0.44 of predictions to within 610%.
Between climate zones, the new equations were relatively consistent in their accuracy in temperate and circumpolar individuals (with the exceptions of Sets C and
D), but were considerably less accurate in tropical individuals. Equation (2), for example, was equally accurate in
temperate and circumpolar individuals, but accurate 0.20
less often in tropical individuals. None of the previous
equations demonstrated this much inconsistency between
climates, with the OB equations having an average
between-climate discrepancy of only 0.07. Equation Set C,
however, had the lowest average discrepancy in accuracy
between climates of just 0.05, both within 65% and 610%.
American Journal of Human Biology
518
A.W. FROEHLE
Fig. 3. Predictive accuracy of equations in a sample of individuals.
Bars represent the proportion of estimates for each equation within
65% or 610% of measured BMR. The lower two graphs divide the
sample by climate to show which equations provide the most consistent results from region to region.
Fig. 4. Predictive accuracy of equations in a sample of populations
(group means). Bars represent the proportion of estimates for each
equation within 65% or 610% of measured BMR. The lower two
graphs divide the sample by climate to show which equations provide
the most consistent results from region to region.
The findings on prediction accuracy in populations are
quite different from those in individuals. Overall accuracy
was much higher, with all of the new equations estimating
BMR to within 65% over half of the time (highest: Set A,
0.59). Of the previous equations, both FAO/S and OB were
near the 0.50 mark (0.49 and 0.51, respectively), while
again, K was accurate least frequently (0.29). To within
610% of measured BMR, the new equations’ accuracy
ranged from 0.84 to 0.88 (highest: Set A), whereas the previous equations’ values were not as high (K: 0.50; FAO/S:
0.75; H&R: 0.77; OB: 0.81).
More importantly, the new equations were substantially
more consistent between climate regions when used with
populations, as compared with the previous equations. To
within 65%, the most consistent equation was new Eq. (3)
(0.03 average discrepancy between climates), although K
came in a close second (0.05). The remaining new equations had average between-climate discrepancies of 0.05–
0.17, whereas the other previous equations ranged from
0.11 to 0.29. To within 610% of measured BMR, the most
consistency came from new Equation Set D (0.01),
whereas the remaining new equations had between-climate discrepancies between 0.03 and 0.09. The previous
equations were far more inconsistent here, with betweenclimate discrepancies ranging from 0.09 to 0.26.
In the individual sample, mean error, a measure of overall estimation accuracy, was slightly lower on average in
the new equations (range: 263 kcal/d to 110 kcal/d) than
in the previous equations (range: 2130 kcal/d to 122 kcal/
d) (see Fig. 5). The lowest-magnitude mean error was for
Eq. (3), which was 27 kcal/d, whereas the highest came
from K (2130 kcal/d). When these two equations were
excluded, both the new and old equations had very similar
average absolute mean error (29 kcal/d vs. 31 kcal/d,
respectively). In populations, again the situation was different, with the new equations showing considerably more
accurate mean error (range: 21 kcal/d to 123 kcal/d than
the previous equations (range: 262 kcal/d to 72 kcal/d).
The lowest-magnitude mean error was shared by Eq. (2)
and Set B (21 kcal/d), whereas the largest was from H&R
(72 kcal/d). The average absolute mean error for the new
equations was 10 kcal/d, and 42 kcal/d in the previous
equations.
Root mean square (RMS) error is a measure of the precision with which an equation estimates a dependent variable. Theoretically, 68% of all estimates should fall
within 61 RMS error of the actual value, and 95%
within 62 RMS errors. Smaller RMS error values indicate
American Journal of Human Biology
519
CLIMATE AND BMR IN HUMANS
DISCUSSION
Fig. 5. Mean error 6 1 root mean square (RMS) error for the equations’ BMR estimates in both the individual and population samples.
Mean error is the average of all raw residual values, and indicates
whether or not each equation contains an overall positive or negative
bias. RMS error indicates the magnitude of difference from zero for a
typical residual. In general, 68% of all residuals fall within 61 RMS
error of zero, and 95% within 2 RMS errors (Freedman et al., 1997).
Smaller RMS error values indicate a higher degree of precision with
respect to an equation’s estimates.
greater precision, whereas higher values mean a regression is less precise with regard to a particular sample. For
the individual sample, the largest RMS error value was
for K (286 kcal/d), whereas the lowest (183 kcal/d) was
shared by Equation Sets A and B. None of the other equations, new or old, however, differed from this low value by
more than 25 kcal/d (FAO/S), suggesting that with the
exception of K, all of these equations have relatively the
same level of precision with regard to estimates for individuals (see Fig. 5).
In the population sample, again K had the highest RMS
error (212 kcal/d), whereas the lowest belonged to Equation Set A (105 kcal/d-Set B was a close second at 107
kcal/d). Within the other new equations, RMS errors were
between 7 and 13 kcal/d higher than this low value,
whereas in the previous equations (excluding K), this
range was between 21 and 51 kcal/d. This suggests that
the new equations may be slightly more precise with
regard to populations, but it may partially be an artifact
of using the sample from which the new equations were
derived to test their precision. In any case, with the exception of K, the new and previous equations again appear
to have relatively similar degrees of precision in this
sample.
The above analysis demonstrates that climate variables
can be significant predictors of BMR, and that their inclusion in equations can improve the accuracy of BMR
estimates, at least in populations. Although idiosyncratic
variation likely overpowers any effects of climate in the
analysis of individuals, it appears that factorial method
investigations of DEE at the population level may benefit
from the inclusion of climate factors, especially when
BMR is estimated rather than measured. The overall
effect of climate found here is that for every 18C drop in
TMEAN, BMR increases by roughly 4–5 kcal/d, when controlling for the effects of body size, age and sex. A similar
4–5 kcal/d increase in BMR occurs per 18C decrease in
HHIT, when body size, age and sex are also controlled.
This study’s climate-based adjustments in BMR fall in the
lower range of those which the earliest report of the FAO
(1957) recommends (64–8 kcal/d per 18C above or below
TMEAN of 108C: see FAO, 1957, pp 24–26, 54).
The biological meaning of the significance of either
TMEAN or HHIT is not clear. Both variables are highly
correlated with one another, and with the majority of the
other climate and geographic variables included in the
study (see Table 4). Indeed, there is so much significant
correlation between the different climate variables that it
is impossible to tease out what might be underlying the
overall pattern as it relates to BMR. There are highly significant correlations between absolute latitude and all of
the climate variables, and the highest r-value is between
absolute latitude and TMEAN in both the un-weighted
and weighted samples. The latitude/HHIT relationship,
although highly significant in both samples, is not as close
to unity, and other variables that were not significant in
either stepwise regression analysis have higher r-values
in correlation with latitude.
Thus, examination of the correlation results does not do
much to explain why TMEAN and HHIT are significant
predictors of BMR whereas other variables are not. More
importantly, because TMEAN was the only significant climate variable in one analysis, and HHIT the only one in
the other, this study does not answer the question of
TABLE 4. Pearson correlation for climate and geographical variables*
ABSLAT Elevation
Un-weighted
Elevation 20.223**
TMEAN 20.865**
LWCT
20.771**
HHIT
20.572**
MTR
0.526**
ATR
0.635**
Weighted
Elevation 20.238**
TMEAN 20.888**
LWCT
20.731**
HHIT
20.478**
MTR
0.453**
ATR
0.662**
TMEAN
LWCT
HHIT
MTR
20.108 NS
20.070 NS 0.875**
20.083 NS 0.700** 0.412**
0.172*** 20.668** 20.910** 20.157***
0.085 NS 20.744** 20.918** 20.257** 0.900**
0.023****
0.024**** 0.862**
20.071**
0.583** 0.287**
0.074** 20.621** 20.900** 20.030****
0.064** 20.756** 20.895** 20.104** 0.825**
ABSLAT, Absolute latitude; TMEAN, mean annual temperature; LWCT, lowest
monthly windchill temperature; HHIT, highest monthly heat index temperature;
MTR, maximum monthly temperature range; ATR, average monthly temperature range. NS, Not significant.
*
Pearson correlation r-values.
**
Significant at level of P < 0.001.
***
Significant at level of P < 0.01.
****
Significant at level of P < 0.05.
American Journal of Human Biology
520
A.W. FROEHLE
TABLE 5. Comparison of new BMR and DEE estimates to previous estimates and measurements
Reference
Leonard et al., 1995
Locality (population)
Sex
Age (y)
Body mass
(kg)
TMEANa
PALb
BMR1c
BMR2d
BMR3b
DEE1c
DEE2d
DEE3b
Salcedo, Ecuador (highland)
M
F
M
F
M
F
M
F
M
F
M
F
M
F
32
40
49
34
18–50
18–50
18–50
18–50
32
31
36
33
10–18
10–18
61.3
55.7
55.6
47.8
59.6
51.8
46.0
41.0
53.9
51.0
72.4
64.7
45.1
42.5
12.9
12.9
24.9
24.9
20.0
20.0
19.6
19.6
21.0
21.0
21.0
21.0
26.1
26.1
2.38
1.96
1.58
1.62
2.00
1.50
1.68
1.56
1.48
1.59
1.39
1.53
1.79
1.66
1,601
1,252
1,529
1,226
1,591
1,394
1,383
1,099
1,507
1,266
1,749
1,417
1,440
1,265
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
1,634
1,288
1,892
1,302
n/a
n/a
1,564
1,315
1,413
1,197
1,499
1,253
1,301
1,155
1,533
1,325
1,805
1,451
1,384
1,219
3,127
2,145
2,188
1,916
3,186
2,085
2,319
1,712
2,129
1,794
2,363
2,026
2,605
2,105
3,808
2,458
2,415
1,992
n/a
n/a
n/a
n/a
2,387
2,019
2,609
1,973
n/a
n/a
3,722
2,577
2,233
1,939
2,998
1,880
2,186
1,802
2,269
2,107
2,509
2,220
2,477
2,024
Jipijapa, Ecuador (coastal)
Leonard and
Robertson, 1997
Leonard et al., 1997
Gamboa and Garcia,
2007
Eastern Paraguay (Ache)
Kalahari Desert, Botswana
(!Kung)
Surinda area, Russia
(Evenki)
Surinda area, Russia
(Russian)
Calakmul, Mexcio
a
Obtained from the UCAR database according to this study’s methods, except for Ache and !Kung. Ache and !Kung estimated from published temperature ranges
(Hill et al., 1984; Bentley, 1985).
b
As estimated using this study’s Equation Set B for BMR, with PAL from previous studies used for DEE estimates.
c
As estimated using FAO/S for BMR in the original studies. All reported values converted to kcal/d.
d
As measured in the original studies. All reported values converted to kcal/d.
whether climate-related variation in BMR associates
more with climate norms or climate extremes. This leaves
questions as to the best climate variable to use in BMR
prediction (see FAO, 1957; FAO/WHO, 1973), but does to
some extent support past uses of TMEAN as a BMR predictor (FAO, 1957; Roberts, 1978). Moreover, for the sake
of convenience, TMEAN is a more widely accessible parameter than some of the other climate variables, with
particular regard to fossil hominins and extant human
groups living in remote regions (i.e. those areas not covered by WMO meteorological stations).
In general, the results of accuracy testing here show
that when attempting to develop broad models of DEE in
populations, the use of BMR equations that include climate variables may indeed produce substantial differences in estimates as compared with equations that do
not. In addition to greater overall accuracy, this study’s
new equations are considerably more consistent between
populations living in different climate regions. This is important to consider when comparing subsistence strategies and nutritional status among various living human
groups, or in comparisons of various groups to established
reference standards. The influence of climate is also important to studies of adaptation and competition in fossil
hominins, where arguments using energetics are becoming increasingly important (e.g. Aiello and Wheeler, 2003;
Churchill, 2006; Sorensen and Leonard, 2001; SteudelNumbers et al., 2006).
To demonstrate the manner in which these new climateinclusive equations may alter DEE estimates in both living and fossil humans, two subsequent analyses were conducted. Both studies used Equation Set B as an example,
both because of the set’s relatively high degree of accuracy
and precision, and because the equations predict BMR
from the most easily obtained variables (body mass and
TMEAN, requiring age group rather than numerical age).
First, DEE in several recently studied subsistence-level
populations (Gamboa, and Garcia, 2007; Leonard and
Robertson, 1997; Leonard et al., 1995, 1997) was re-estimated using Equation Set B and data available in the
original publications (see Table 5). New DEE estimates
were compared with previous values using FAO/S to estimate BMR, and were also compared with DEE measureAmerican Journal of Human Biology
ments where available (see Fig. 6). Second, BMR and DEE
were estimated for hypothetical average male and average
female early Homo sapiens, living in three different climate zones based on this study’s living human data: HOT
corresponds to tropical, MILD to temperate, and COLD to
circumpolar/glacial. Fossil body mass and PAL data were
drawn from the literature (see Table 6), and Equation Set
B’s results were compared with previous equations’ estimates (see Fig. 7).
In living humans, the new equations in general provide
different BMR values than FAO/S, which also lead to different estimates for DEE. The largest discrepancies
between current and past estimates of DEE are 19% and
20% in highland Ecuadorians, where the new estimates
are 432–595 kcal/d higher than using FAO/S. Compared
with measured DEE for this population, new estimates
differ by only 86–119 kcal/d, or 2–5%. This runs counter to
the idea that good BMR estimates will only improve DEE
estimates by 10% (Durnin, 1990), and provides support
for the use of these equations in living humans. A difference of 500 kcal/d is roughly equivalent to daily lactation
costs in some subsistence-level populations (Butte et al.,
1997), illustrating that the magnitude of such differences
could have important consequences for the interpretation
of DEE estimates. A similarly large discrepancy (17%; 313
kcal/d) is found in Evenki (herders) women, and again,
the new DEE is far more similar to the measured value
(4% difference; 88 kcal/d). The remaining differences in
predicted values using the new and previous equations
are somewhat more modest (on average 6%; 125 kcal/d;
range of 23–205 kcal/d), and in one case (coastal Ecuadorean males) the previous DEE estimate is actually 6%
closer to the measured value than the new estimate. Even
so, this analysis suggests that at least in some cases, the
inclusion of climate variables can have important consequences for estimating population-level DEE values
and for the study of energy balance. Further improvements in DEE estimates might come through the use
of FFM instead of body mass as the body size variable,
which would better control for regional variation in body
composition.
The analysis of hypothetical early Homo sapiens suggests that the systematic incorporation of climate varia-
CLIMATE AND BMR IN HUMANS
521
Fig. 7. Estimates of BMR and DEE in male and female early Homo
sapiens using the new Equation Set B and the four previously published equations. Three different climate scenarios are used with
Equation Set B, based on average TMEAN for tropical (HOT), temperate (MILD) and circumpolar (COLD) groups in this study’s sample.
Values above each column are DEE estimates in kcal/d. Body mass,
PAL, and temperature values are shown in Table 6 along with
references.
Fig. 6. Comparison of past DEE results of factorial method studies
with new results using this study’s BMR Equation Set B. See Table 5
for references and data. Percent differences were obtained by dividing
new DEE estimates by previous estimates: (DEE3/DEE1)-1. New
DEE values were also compared with measured DEE, where available, in the same manner: (DEE3/DEE2)-1. Absolute differences in
DEE were obtained by subtracting previous values from new estimates, whether the previous values were estimates: (DEE3-DEE1); or
measurements: (DEE3-DEE2).
TABLE 6. Data used for BMR and DEE estimates in
early Homo sapiens
a
Body mass (kg)
PALb
TMEAN (HOT) (8C)c
TMEAN (MILD) (8C)
TMEAN (COLD) (8C)
M
F
65.0
1.84
54.0
1.53
25.0
14.0
26.0
a
Average body mass for early Homo sapiens from McHenry (1992).
Average physical activity level (PAL) for !Kung and Ache foragers from Leonard
and Robertson (1997).
c
Temperatures derived from modern sample used in this study’s analyses.
TMEAN (HOT) is mean of tropical, TMEAN (MILD) is mean of temperate, and
TMEAN (COLD) is mean of circumpolar.
b
bles into energetics estimates in fossil hominins may be
important. The advantage of the new equations is the ability to account for the effects of climate using a single
model. The previous equations can only provide a single
estimate for any body mass value, regardless of geographic origin. Correspondingly, the new and old equations’ estimates differ considerably. For example, com-
pared to this study’s model, K overestimates DEE in
females from all three climate regions, from a low of 34
kcal/d in COLD, to a high of 215 kcal/d in HOT. H&R is
the opposite, underestimating female DEE by 32 kcal/d in
HOT, up to 213 kcal/d in COLD. Both FAO/S and OB provide reasonably similar estimates to the new equations for
females in HOT and MILD climates (within 50 kcal/d), but
underestimate DEE in COLD-climate females by 150 kcal/
d. In males, all four previous equations underestimated
DEE in COLD, from a low of 93 kcal/d (FAO/S) to a high of
338 kcal/d (H&R). In MILD-climate males, most of the
previous equations underestimated DEE by 18 kcal/d (K)
to 132 kcal/d (OB), whereas FAO/S provided a DEE estimate higher than that of Equation Set B by 113 kcal/d. In
males from the HOT climate, H&R gave a quite similar
DEE estimate to Equation Set B (219 kcal/d), whereas
the others overestimated DEE (63 kcal/d in OB, to 226
kcal/d in FAO/S).
The consequences of such discrepancies for studies of
fossil hominin energy ecology can be demonstrated by considering that FAO/S, for example, would predict that a
male and female pair living in glacial Upper Pleistocene
Europe would require the same amount of energy each
day, 5,046 kcal/d, as a similarly sized couple in East
Africa. Meanwhile, using climate data and Equation Set B
provides a slightly higher estimate in Europe of 5,271
kcal/d, and a much lower value for tropical East Africa of
4,771 kcal/d. Not only do both values differ from FAO/S,
they differ from one another by the substantial figure of
500 kcal/d.
In terms of studying foraging efficiency, the availability
of energy for reproduction, and other such comparisons
between populations, the incorporation of climate’s effects
on BMR can provide a substantially different picture than
exists without a consideration of climate. This holds both
in living populations and as applied to fossil hominins.
This study suggests that in the future, where BMR cannot
be measured, assessments of energy expenditure may
benefit from the incorporation of climate variables in preAmerican Journal of Human Biology
522
A.W. FROEHLE
dicting BMR. The new equations presented here provide a
systematic way to incorporate climate’s effects on BMR
variation, so that different populations can be compared
using the same methods and incorporating the same
underlying assumptions. Given the large inaccuracies
that can result from using previous equations such as
those of the FAO/WHO/UNU (1985)/Schofield (1985),
improved estimates using climate variables could prove
very useful to understanding variation in human energy
expenditure.
ACKNOWLEDGMENTS
I thank Margaret J. Schoeninger for helpful comments
and advice. I also thank Josh Snodgrass and Bill Leonard
for their comments, and for sharing their data on the
Yakut of Siberia. Josh Snodgrass and an anonymous
reviewer also provided very helpful comments. Climate
data were provided by the Data Support Section of the Scientific Computing Division at the National Center for
Atmospheric Research. NCAR is supported by grants from
the National Science Foundation. The original sources for
these data were the Climate Prediction Center, the
National Centers for Environmental Prediction, the
National Weather Service, the National Oceanic and
Atmospheric Administration, and the U.S. Department of
Commerce. Geographic data come from the US Geologic
Survey and the National Imaging and Mapping Agency,
compiled by http://heavens-above.com/countries. aspx.
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527
CLIMATE AND BMR IN HUMANS
APPENDIX A: BMR PREDICTION EQUATIONS
TABLE A1. Previously published BMR equations.
Age (y)
Males
Females
Kleiber (1961) (KLE)
BMR 5 703(M)
FAO/WHO/UNU (1985) (FAO)
<3
BMR 5 59.5(M) 2 30.3
BMR 5 58.3(M) 2 31.1
3–10
BMR 5 22.7(M) 1 504.1 BMR 5 20.3(M) 1 485.7
10–18
BMR 5 17.7(M) 1 657.9 BMR 5 13.4(M) 1 692.3
18–30
BMR 5 15.1(M) 1 691.9 BMR 5 14.8(M) 1 486.4
30–60
BMR 5 11.5(M) 1 872.7 BMR 5 8.12(M) 1 845.2
>60
BMR 5 11.7(M) 1 609.0 BMR 5 2.76(M) 1 658.2
Henry and Rees (1991) (H&R)
3–10
BMR 5 27.0(M) 1 403.5 BMR 5 15.1(M) 1 589.1
10–18
BMR 5 20.1(M) 1 506.9 BMR 5 11.2(M) 1 705.0
18–30
BMR 5 13.4(M) 1 668.9 BMR 5 11.5(M) 1 612.1
30–60
BMR 5 11.0(M) 1 754.9 BMR 5 11.5(M) 1 584.8
Oxford Brookes (Henry, 2005)
<3
BMR 5 61.0(M) 2 33.7
BMR 5 58.9(M) 2 23.1
3–10
BMR 5 23.3(M) 1 514
BMR 5 20.1(M) 1 507
10–18
BMR 5 18.4(M) 1 581
BMR 5 11.1(M) 1 761
18–30
BMR 5 16.0(M) 1 545
BMR 5 13.1(M) 1 558
30–60
BMR514.2(M) 1 593
BMR 5 9.74(M) 1 694
>60
BMR 5 13.5(M) 1 514
BMR 5 10.1(M) 1 569
0.75
Terms converted so that all BMR values are in kcal/d. Body mass (M) is in kilograms.
TABLE B1. Geographic, climate and demographic details of this study’s sample with references.
Locality
Latitude Longitude Elevation TMEAN HHIT Nu (M,F)
Adelaide, Australia
234.933
138.600
72
17.0
41.7
6 (4,2)
Adelaide, Australia
Alderly Park, England
Aldershot, England
Anaktuvuk Pass, AK
Ann Arbor, MI
Auckland, New Zealand
Austin, TX
234.933
138.600
53.258
22.995
51.247
20.761
68.143 2151.736
42.271
283.726
236.867
174.767
30.267
297.743
72
172
76
683
261
13
163
17.0
10.2
10.1
28.8
10.5
15.6
20.4
41.7
32.8
33.3
29.4
43.9
30.6
47.2
2M
2M
2M
1M
4 (2,2)
2F
5 (4,1)
Nw (M,F)
Age class*
Reference(s)
119 (95,24)
2
24 M
16 M
34 F
6M
162 (70,92)
48 F
146 (109,37)
3
2
2
2
1
2
2
Clark et al., 1993; Smith et al., 1997;
van der Ploeg et al., 2001; van der
Ploeg et al., 2001
Smith et al., 1999
Goldberg et al., 1995
Ulijaszek and Strickland, 1991
Milan et al., 1962
Katch et al., 1990
Rush et al., 1999
Broeder et al., 1992; Wilmore et al.,
1998
Lemmer et al., 2001
Lemmer et al., 2001
Soares et al., 1993; Piers et al., 1995;
Ferro-Luzzi et al., 1997; Borgonha
et al., 2000; Sathyaprabha, 2000
Davies et al., 1997
Livingstone et al., 1991
Seale and Conway, 1999
Snodgrass et al., 2005
Calloway and Zanni, 1980
Treuth et al., 1998
Hunter et al., 2001
Gower et al., 2000
Fukagawa et al., 1990; Roberts et al.,
1991; Grinspoon et al., 1998
Fukagawa et al., 1990; Bathalon
et al., 2001
Van Pelt et al., 1997; Van Pelt et al.,
2001
Van Pelt et al., 1997; Van Pelt et al.,
2001
Dionne et al., 2004
Dionne et al., 2004
Spurr et al., 1992
Spurr et al., 1994; Dufour et al., 1999
Baltimore, MD
Baltimore, MD
Bangalore, India
39.290
39.290
12.983
276.613
276.613
77.583
2
2
917
13.1
13.1
24.4
45.6
45.6
41.1
Beijing, China
Belfast, Northern Ireland
Beltsville, MD
Berdygestiakh, Russia
Berkeley, CA
Birmingham, AL
Birmingham, AL
Birmingham, AL
Boston, MA
39.900
116.413
54.583
25.933
39.035
276.908
62.117
130.633
37.872 2122.272
33.521
286.803
33.521
286.803
33.521
286.803
42.358
271.060
59
5
32
161
56
173
173
173
11
13.3
9.6
13.1
25.6
14.6
17.2
17.2
17.2
10.9
47.2
28.3
45.6
36.7
37.8
55.0
55.0
55.0
40.0
2 (1,1)
2 (1,1)
2 (1,1)
2 (1,1)
1M
2F
1F
1F
3 (2,1)
12 (6,6)
32 (16,18)
69 (41,28)
125 (50,75)
6M
24
50
19
64 (38,26)
1
2
2
2
3
1
2
3
2
Boston, MA
42.358
271.060
11
10.9
40.0
3 (1,2)
70 (24,46)
3
Boulder, CO
40.015 2105.270
1646
10.5
38.3
4 (2,2)
96 (71,25)
2
Boulder, CO
40.015 2105.270
1646
10.5
38.3
5 (2,3)
106 (66,40)
3
Burlington, VT
Burlington, VT
Call, Colombia
Call, Colombia
44.476
44.476
3.447
3.447
273.213
273.213
276.516
276.516
68
68
745
745
8.6
8.6
24.2
24.2
43.3
43.3
39.4
39.4
1F
1F
16 (8,8)
2F
19 F
12 F
528 (339,189)
135 F
2
3
1
2
2 (1,1)
16 (9,7)
2 (1,1)
19 (9,10)
15 (11,4) 259 (156,103)
2
3
2
APPENDIX B
American Journal of Human Biology
528
A.W. FROEHLE
TABLE B1. (Continued)
Locality
Cambridge, England
Latitude Longitude Elevation TMEAN HHIT Nu (M,F)
Nw (M,F)
Age class
Reference(s)
Prentice et al., 1986; Bingham et al.,
1989; Goldberg et al., 1993;
Goldstone et al., 2002
Beer et al., 1989
Kashiwazaki et al., 1995
Luke et al., 2004
Lazzer et al., 2003
Meunier et al., 2005
Pratley et al., 1994
Thomas et al., 1994
Astrup et al., 1996; Klausen et al.,
1997
McCrory et al., 1998
Kriketos et al., 2000
Burke et al., 1993; Cordain et al.,
1997
Luhrmann et al., 2002
Kinabo and Durnin, 1990; Lawrence
et al., 1990
Arvidsson et al., 2005
Pullicino et al., 1996
Nhung et al., 2005
Curtis et al., 1996
Valencia et al., 1994; Haggarty et al.,
1997
Li et al., 1999
Butte et al., 2001; Butte et al., 2003
Luke et al., 2004
Luke et al., 2000
Rode and Shephard, 1995
Rode and Shephard, 1995
Rode and Shephard, 1995
Peng et al., 2005
Peng et al., 2005
Stettler et al., 1998
Lawrence et al., 1988; Singh et al.,
1989; Minghelli et al., 1990; Diaz
et al., 1991; Frigerio et al., 1992;
Benedek et al., 1995; Heini et al.,
1996
Benedek et al., 1995
Illner et al., 2000
Singhal et al., 2002
Singhal et al., 1997
Henry et al., 2005
Aleman-Mateo et al., 2006
Minghelli et al., 1990
Dolezal and Potteiger, 1998
Lof and Forsum, 2006
Kerckhoffs et al., 1998;
Spaanderman et al., 2000
Kerckhoffs et al., 1998
Ategbo et al., 1995
Alam et al., 2005
Luke et al., 2000
Diffey et al., 1997; Piers et al., 1997
Sanchez-Castillo et al., 2001
Keys et al., 1973
Keys et al., 1973
Cagnacci et al., 2006
Schultink et al., 1990
Jobin et al., 1996; Boivin et al., 2000
Scalfi et al., 1993; Marra et al., 1998;
Rizzo et al., 2005
Rizzo et al., 2005
Buchowski et al., 2000
Buchowski et al., 2000
Segal and Dunalf, 1990; Myerson
et al., 1991; Ratheiser et al., 1998;
Wang et al., 2005
Garby et al., 1987
Puggaard et al., 2002
Henry et al., 1999; Henry et al., 2005
Henry et al., 1989; Hayter and
Henry, 1993; Henry et al., 1999
52.205
0.144
40
9.4
32.8
5 (1,4)
62 (3,59)
2
42.358
217.483
41.850
45.783
45.783
38.981
38.952
55.667
271.106
269.467
287.650
3.083
3.083
276.937
292.334
12.583
11
4086
178
391
391
28
232
0
10.9
6.1
11.3
11.9
11.9
13.1
12.8
9.5
40.0
31.1
47.2
37.2
37.2
45.6
43.3
33.3
2M
2 (1,1)
1M
2 (1,1)
2 (1,1)
1M
1M
3 (1,2)
17 M
19 (7,12)
172 M
50 (23,27)
70 (35,35)
13 M
7M
341 (78,283)
2
2
2
1
3
3
2
2
Davis, CA
Denver, CO
Fort Collins, CO
38.545 2121.739
39.739 2104.984
40.585 2105.084
15
1596
1525
16.5
10.5
10.5
47.8
38.3
38.3
2 (1,1)
2 (1,1)
4 (1,3)
19 (11,8)
94 (49,45)
37 (14,23)
2
2
2
Glessen, Germany
Glasgow, Scotland
50.583
55.862
8.650
24.245
167
68
10.2
9.0
35.0
27.8
2 (1,1)
3F
286 (107,179)
153 F
3
2
Goteborg, Sweden
Guarda Mangla, Malta
Hanol, Vietnam
Headington, England
Hermosillo, Mexico
57.717
11.967
35.891
14.492
21.033
105.850
51.763
21.210
29.067 2110.967
3
1
22
76
195
8.9
19.4
24.9
10.5
25.3
31.7
46.1
51.1
33.3
60.0
2 (1,1)
4F
4 (2,2)
2F
4M
33 (17,18)
50 F
188 (98,90)
12 F
37 M
1
2
2
2
2
Hong Kong, China
Houston, TX
Ibadan, Nigeria
Igbo-Ora/Idere, Nigeria
Igloolik, Canada
Igloolik, Canada
Igloolik, Canada
Kagoshima, Japan
Kagoshima, Japan
Keneba and vicinity, Gambia
Keneba and vicinity, Gambia
22.283
29.763
7.388
7.458
69.400
69.400
69.400
31.600
31.600
13.329
13.329
114.150
295.363
3.896
3.267
281.800
281.800
281.800
130.550
130.550
216.015
218.015
89
15
239
148
1
1
1
89
89
32
32
23.8
20.8
25.5
25.5
211.1
211.1
211.1
19.0
19.0
26.6
26.6
50.0
44.4
42.8
42.8
25.0
25.0
25.0
42.2
42.2
47.8
47.8
1F
4F
2 (1,1)
2 (1,1)
2 (1,1)
6 (3,3)
3 (2,1)
1F
1F
1F
9 (5,4)
19 F
140 F
996 (475,521)
89 (50,39)
16 (14,2)
23 (11,12)
7 (5,2)
12 F
16 F
7F
189 (119,70)
2
2
2
2
1
2
3
2
3
1
2
Keneba and vicinity, Gambia
Kiel, Germany
Kingston, Jamaica
Kingston, Jamaica
Kuala Lumpur, Malaysia
Las Terrazas, Cuba
Lausanne, Switzerland
Lawrence, KS, US
Linkoplng, Sweden
Maastricht, Netherlands
13.329
54.550
18.000
18.000
3.167
22.764
46.533
38.972
58.417
50.850
216.015
9.167
276.800
276.800
101.700
283.242
6.667
295.235
15.617
5.683
32
24
42
42
64
51
857
259
33
64
26.6
10.4
28.3
28.3
28.3
25.5
11.2
13.1
6.9
10.6
47.8
30.6
44.4
44.4
50.6
39.4
35.6
42.8
34.4
38.3
1M
2 (1,1)
2 (1,1)
1M
1F
2 (1,1)
1M
3M
1F
2 (1,1)
28 M
26 (13,13)
31 (17,14)
16 M
51 F
10 (5,5)
16 M
30 M
23 F
23 (11,12)
3
2
1
2
1
3
2
2
2
2
50.850
10.350
23.328
41.879
237.817
19.400
44.962
44.962
44.667
6.930
45.500
40.833
5.883
1.117
90.675
287.843
144.967
299.150
293.179
293.179
10.917
1.717
273.583
14.250
64
228
3
190
58
2224
247
247
25
64
113
0
10.6
27.5
26.5
11.3
16.1
16.6
9.3
9.3
14.5
28.1
7.0
16.8
38.3
48.3
49.4
47.2
41.7
41.7
42.8
42.8
42.8
51.7
38.3
44.4
1M
1F
1F
1M
3M
1F
1M
1M
1F
1F
2M
3F
9M
34 F
37 F
65 M
108 M
34 F
168 M
205 M
12 F
17 F
30 M
158 F
3
2
2
2
2
2
2
3
3
2
2
2
Naples, Italy
Nashville, TN
Nashville, TN
New York, NY
40.833
36.166
36.166
40.714
14.250
286.784
286.784
274.008
0
151
151
2
18.8
15.5
15.5
13.3
44.4
42.2
42.2
44.4
2F
2 (1,1)
2 (1,1)
6 (2,4)
55 F
20 (11,9)
17 (11,6)
97 (31,64)
3
1
2
2
Odense, Denmark
Odense, Denmark
Oxford, England
Oxford, England
55.400
55.400
51.754
51.754
10.383
10.383
21.254
21.254
10
10
66
66
8.9
8.9
10.5
10.5
33.9
33.9
33.3
33.3
2 (1,1)
3F
3 (1,2)
5M
59 (38,21)
80 F
268 (78,190)
68 M
2
3
1
2
Cambridge, MA
Charana, Bolivia
Chicago, IL
Clermont-Ferrand, France
Clermont-Ferrand, France
College Park, MD
Columbia, MO
Copenhagen, Denmark
Maastricht, Netherlands
Manta, Benin
Matlab, Bangladesh
Maywood, IL
Melbourne, Australia
Mexico City, Mexico
Minneapolis, MN, US
Minneapolis, MN, US
Modena, Italy
Mono Province, Benin
Montreal, Canada
Naples, Italy
American Journal of Human Biology
529
CLIMATE AND BMR IN HUMANS
TABLE B1. (Continued)
Locality
Latitude Longitude Elevation TMEAN HHIT Nu (M,F)
Paine, Chile
Palo Alto, CA
233.187 270.750
37.442 2122.142
368
29
14.9
15.8
36.7
41.7
2 (1,1)
4F
Phala, Tibet
Phala, Tibet
Phala, Tibet
Philadelphia, PA
Philadelphia, PA
Phoenix, AZ
30.500
N/A
30.500
N/A
30.500
N/A
39.952 275.164
39.952 275.164
33.448 2112.073
5150
5150
5150
8
8
343
3.4
3.4
3.4
13.3
13.3
24.0
38.9
38.9
38.9
48.3
48.3
47.8
2 (1,1)
3 (2,1)
2 (1,1)
2 (1,1)
1M
5 (2,3)
16 (7,9)
18 (12,6)
5 (1,4)
59 (31,28)
13 M
357 (128,229)
1
2
3
2
3
2
Pollgus and vicinity, Russia
Pollgus and vicinity, Russia
61.842
61.842
95.550
95.550
213
213
21.0
21.0
33.9
33.9
1F
9 (5,4)
2F
127 (62,65)
1
2
Port Moresby, PNG
Porto Alegre, Brazil
Providence, RI
Pune City, India
29.483
230.033
41.824
18.533
147.183
251.200
271.413
73.867
19
34
3
570
26.7
20.1
10.9
24.7
45.6
49.4
41.1
55.0
2 (1,1)
1F
1F
4 (2,2)
17 (9,8)
60 F
10 F
98 (44,64)
2
2
2
2
Quebec City, Canada
Quebec City, Canada
Rio de Janeiro, Brazil
Rochester, MN
Rochester, NY
46.800
46.800
222.900
44.022
43.155
271.250
271.250
243.233
292.470
277.618
71
71
1
322
155
6.2
6.2
24.2
8.4
9.4
37.8
37.8
41.7
44.4
41.1
2 (1,1)
2 (1,1)
1F
2 (1,1)
3 (1,2)
359 (154,205)
319 (146,173)
50 F
253 (100,153)
59 (26,33)
2
3
2
2
2
41.900
12.483
41.900
12.483
32.715 2117.156
32.715 2117.156
14
14
26
26
15.9
15.9
17.7
17.7
40.0
40.0
40.0
40.0
3 (2,1)
2 (1,1)
2M
4 (3,1)
68 (46,22)
108 (56,52)
14 M
47 (37,10)
2
3
2
3
37.775 2122.418
223.533 248.617
37.567
127.000
1.293
103.856
20.150 298.917
46.783 271.300
40.793 277.860
25.017
121.450
30.438 284.281
25.950
143.000
63.137 2142.524
35.700
139.767
60
637
34
1
2335
73
359
6
54
1634
547
20
14.6
20.6
13.2
28.2
14.7
8.2
10.7
23.2
19.9
18.5
22.7
16.7
37.8
45.6
43.9
46.1
42.8
37.8
44.4
50.0
42.8
24.0
28.9
42.8
1F
4 (2,2)
2M
1M
1F
1M
1M
2 (1,1)
1F
2 (1,1)
1M
2 (1,1)
17 F
58 (30,28)
96 M
20 M
12 F
24 M
12 M
223 (102,121)
10 F
16 (9,7)
8M
30 (15,15)
2
1
2
1
2
2
3
2
2
2
2
2
113 F
58 (30,28)
11 M
15 M
52 F
58 M
130 (62,68)
24 F
125 (12,113)
3
2
2
2
2
2
1
2
2
Rome, Italy
Rome, Italy
San Diego, CA
San Diego, CA
San Francisco, CA
Sao Peulo, Brazil
Seoul, South Korea
Singapore
Solis, Mexico
Ste.-Foy, Canada
State College, PA
Taipei, Taiwan
Tallahassee, FL
Tari Basin, PNG
Tetlin, AK
Tokyo, Japan
Tokyo, Japan
Toronto, Canada
Trenton, NJ
Tsukuba Ibarakl, Japan
Ubon, Thailand
Vellore, India
Verona, Italy
Verona, Italy
Wageningen, Netherlands
35.700
43.667
40.217
36.200
15.233
12.933
45.450
45.450
51.967
139.767
279.417
274.743
140.100
104.863
79.133
11.000
11.000
5.667
20
119
11
87
121
205
95
95
14
16.7
8.9
13.7
14.3
27.7
29.0
13.6
13.6
10.1
42.8
44.4
44.4
42.2
52.2
53.3
46.9
48.9
37.8
1F
2 (1,1)
1M
2M
1F
1M
4 (2,2)
1F
4 (1,3)
Wageningen, Netherlands
Wainwright, AK
Zeist, The Netherlands
51.967
5.667
70.637 2160.038
52.100
5.233
14
7
5
10.1
210.8
10.5
37.8
25.0
36.1
1F
1M
2M
Nw (M,F)
16 (8,8)
72 F
28 F
6M
24 M
Age class
3
3
3
2
2
Reference(s)
Aleman-Mateo et al., 2006
Taaffe et al., 1995; Thompson et al.,
1997
Beall et al., 1996
Beall et al., 1996
Beall et al., 1996
Owen et al., 1986; Owen et al., 1987
Owen et al., 1987
Ferraro et al., 1992; Christin et al.,
1993; Tataranni et al., 1994
Katzmarzyk et al., 1994
Katzmarzyk et al., 1994; Galloway
et al., 2000
Yamauchi and Ohtsuka, 2002
Wahrlich and Anjos, 2001
Cullinen and Caldwell, 1998
Chiplonkar et al., 1992; Kanade
et al., 2001
Loos et al., 2006
Loos et al., 2006
Magalhaes et al., 1999
Nielsen et al., 2000
Welle and Nair, 1990; Welle et al.,
1992
Censi et al., 1998; Polito et al., 2000
Meunier et al., 2005
Nichols et al., 1990
Nichols et al., 1990; Morales et al.,
1998
Bronstein et al., 1996
Hoffman et al., 2000
Kim et al., 2001
Stensel et al., 2001
Sanchez-Castillo et al., 2002
Deriaz et al., 1992
Williamson and Kirwan, 1997
Liu et al., 1995
Moffatt and Owens, 1991
Yamauchi and Ohtsuka, 2002
Milan et al., 1962
Yamauchi et al., 2004; Shinagawa
et al., 2005
Ozeki et al., 2000
Buccholz et al., 2001
Schmidt et al., 1996
Doi et al., 2001
Lawrence et al., 1992
McNeill et al., 1987
Maffeis et al., 1993
Armellini et al., 2000
van Raaij et al., 1989; Weststrate
et al., 1990; Voorrips et al., 1993;
Spaaij et al., 1994
Voorrips et al., 1993
Milan and Evonuk, 1967
Velthuis-te Wierik et al., 1995
*Age class: 1, <18 years; 2, 18–50 years; 3, >50 years.
American Journal of Human Biology