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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. 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