Ishida et al.
Journal of Physiological Anthropology(2024) 43:19
https://doi.org/10.1186/s40101-024-00368-1

ORIGINAL ARTICLE

Journal of
Physiological Anthropology

Open Access

Association between thermogenic brown
fat and genes under positive natural selection
in circumpolar populations
Yuka Ishida1, Mami Matsushita2, Takeshi Yoneshiro3, Masayuki Saito2,4 and Kazuhiro Nakayama1*   

Abstract
Background Adaptation to cold was essential for human migration across Eurasia. Non-shivering thermogenesis
through brown adipose tissue (BAT) participates in cold adaptation because some genes involved in the differentiation and function of BAT exhibit signatures of positive natural selection in populations at high latitudes. Whether
these genes are associated with the inter-individual variability in BAT thermogenesis remains unclear. In this study, weevaluated the potential associations between BAT activity and single nucleotide polymorphisms (SNPs) in candidate
gene regions in East Asian populations.
Methods BAT activity induced by mild cold exposure was measured in 399 healthy Japanese men and womenusing fluorodeoxyglucose-positron emission tomography and computed tomography (FDG-PET/CT). The capacity
for cold-induced thermogenesis and fat oxidation was measured in 56 men. Association analyses with physiological traits were performed for 11 SNPs at six loci (LEPR, ANGPTL8, PLA2G2A, PLIN1, TBX15-WARS2, and FADS1) reported
to be under positive natural selection. Associations found in the FDG-PET/CT population were further validated in 84
healthy East Asian men and women, in whom BAT activity was measured using infrared thermography. Associations
between the SNP genotypes and BAT activity or other related traits were tested using multiple logistic and linear
regression models.
Results Of the 11 putative adaptive alleles of the six genes, two intronic SNPs in LEPR (rs1022981 and rs12405556)
tended to be associated with higher BAT activity. However, these did not survive multiple test comparisons. Associations with lower body fat percentage, plasma triglyceride, insulin, and HOMA-IR levels were observed in the FDG-PET/
CT population (P < 0.05). Other loci, including TBX15-WARS2, which is speculated to mediate cold adaptation in Greenland Inuits, did not show significant differences in BAT thermogenesis.
Conclusions Our results suggest a marginal but significant association between LEPR SNPs. However, robust supporting evidence was not established for the involvement of other loci under positive natural selection in cold
adaptation through BAT thermogenesis in East Asian adults. Given the pleiotropic function of these genes, factors
other than cold adaptation through BAT thermogenesis, such as diet adaptation, may contribute to positive natural
selection at these loci.
Keywords Cold adaptation, Brown adipose tissue, Single nucleotide polymorphism, Metabolism, Positive natural
selection*Correspondence:
Kazuhiro Nakayama
knakayama@edu.k.u-tokyo.ac.jp
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Ishida et al. Journal of Physiological Anthropology(2024) 43:19

Background
Homeothermic mammals need to maintain their bodytemperature at a nearly constant level to ensure proper
functioning of their vital organs. Most mammals havedeveloped strategies to maintain stable body temperatures, such as shivering or non-shivering thermogenesis and insulation provided by fur and subcutaneousfat [1]. Natural selection plays an important role in theevolution of these traits in mammalian species living
in cold environments. Modern humans have long been
believed to have evolved cold-adaptive phenotypes during their migration from Africa during the last glacial
period. Compared to other mammals, humans have lessbody hair, a trait that is expected to make a limited contribution to insulation. The high capability for adaptive
thermogenesis observed in human populations in cold
climates, such as the Greenland Inuit, is regarded as aphenotype for coping with cold environments [2]. Nonshivering thermogenesis is primarily mediated by thebrown adipose tissue (BAT). In cold environments, shivering thermogenesis produces heat and increases energy
expenditure; however, after prolonged exposure to cold,
shivering decreases gradually, and non-shivering thermogenesis mechanisms take over [3]. In humans, high BAT
activity during cold exposure has been associated with
reduced reliance on shivering and higher rectal temperatures [4]. Thus, non-shivering thermogenesis may play amore significant role in cold adaptation than shivering
thermogenesis.
The general understanding regarding the genetic basisof cold adaptation via non-shivering thermogenesis
remains fragmented, and only a few candidate loci havebeen reported to date. The gene encoding the mitochondrial uncoupling protein 1 (UCP1), which acts as a partof the thermogenic machinery in BAT, is involved in cold
adaptation in modern humans. Single nucleotide polymorphisms (SNP) in UCP1 have been linked to adaptive
thermogenesis and the development of obesity [5–7].
This SNP has a specific geographical distribution, with aputative adaptive allele involved in high BAT thermogenesis and obesity resistance that is more frequent in highlatitude populations [6, 8]. SNPs in the adrenoceptor
beta 2 and 3 genes are significantly associated with coldinduced activity in BAT [5, 9], although no firm evidence
is available to reject their selective neutrality.
Several other candidate genes for cold adaptationin humans have been reported, mainly from population genetic studies [10–13]. Greenland Inuits have agenomic signature of natural selection related to adaptation to extreme conditions: a locus containing the
T-box transcription factor 15 (TBX15) and tryptophanyltRNA synthetase 2 (WARS2) genes and a cluster of genes
encoding fatty acid desaturases (FADS1–3) [14–16]. In

Page 2 of 10

indigenous Siberian and Central and East Asian (EAs)
living in cold climates, a specific variant that may beunder positive natural selection was found in the phospholipase A2 group 2A gene (PLA2G2A). Angiopoietinlike-8 (ANGPTL8) and perilipin1 (PLIN1) also exhibit
signatures of positive selection in these populations[17]. The allele frequencies of SNPs in the leptin receptor (LEPR), especially rs1137100 (Lys109Arg), are correlated with principal components representing winterin populations worldwide [11, 18]. Moreover, haplotypes
consisting of rs1137100 and tightly linked SNPs exhibit
increased homozygosity in EAs, implying recent positive
selection at this locus [19, 20]. Although the products ofthese genes have been identified as molecules that participate in BAT differentiation and function in rodents[12, 17, 21, 22], it remains unclear whether these genes
play a similar role in humans and whether the adaptive allele indeed renders cold tolerance in humans. We
recently conducted an association study on BAT activity and successfully identified an SNP in the adrenoceptor beta 2 gene significantly associated with variability
in cold-induced thermogenesis [9]. The set of genomic
DNA and phenotypic data used in this study can help
test putative cold-adaptive genes in humans. In thisstudy, we evaluated the association between putative
adaptive alleles of these genes and BAT activity to determine whether these polymorphisms are associated withcold adaptation in humans. In this study, we focused on
EAs because the SNPs of interest are polymorphic inthis population. In addition, the close genetic relationship with Arctic people and variability in their capability
for adaptive thermogenesis [23] suggest that testing theassociation between these genes and cold adaptation in
EAs is an appropriate strategy.

Methods
Study participants

BAT activity was assessed in two independent populations using two measurement methods, fluorodeoxyglucose-positron emission tomography and computed
tomography (FDG-PET/CT) or infrared thermography (IRT), respectively. Details of the participants andthe methods for measuring BAT activity have been
described previously [5, 9, 24]. The FDG-PET/CT population included 399 healthy Japanese men and women(age, 27.1 ± 7.6; body mass index [BMI; kg/m2], 21.9 ± 2.9;
Tables 1 and S1) who were living in or near Sapporo
City, Hokkaido, Japan. The IRT population comprised 84
healthy men and women (age, 26.7 ± 7.0; BMI [kg/m2],
21.5 ± 3.0; Tables 2 and S2) who lived in or near Kashiwa
City, Chiba, Japan, and included individuals of Japanese,
Chinese, Korean, and Taiwanese origin. Their ethnic origins were Japanese (46), Chinese (35), Korean (2), andIshida et al. Journal of Physiological Anthropology(2024) 43:19

Table 1 Characteristics of participants in the FDG-PET/CT
populationAll

BAT-positive BAT-negative P-valuen (men/women) 399 (343/56) 247 (225/22)

152 (118/34)< 0.001Age (years)

27.1 ± 7.6

25.6 ± 6.3

29.6 ± 9.0< 0.001

Height (cm)170.5 ± 7.3

171.0 ± 7.2

169.6 ± 7.3

0.759bWeight (kg)63.7 ± 10.5

63.0 ± 9.5

64.8 ± 11.8

0.006b2

BMI (kg/m )21.9 ± 2.9

21.5 ± 2.6

22.5 ± 3.2

0.003b

BFP (%)

19.0 ± 6.4

17.8 ± 5.5

20.8 ± 7.1

0.189ba

6.1 ± 6.3

7.9 ± 6.4

0.9 ± 0.4

< 0.001

SUVmaxP-values between BAT-positive and BAT-negative phenotypes were calculated asfollows: men-to-women ratio, Pearson’s chi-square test; age and ­SUVmax, Mann–
Whitney U test; other traits, multiple linear regression model adjusted for age
and sex. Continuous variables are presented as means ± standard deviationaData from 325 participants is shown (BAT-positive: n = 242; BAT-negative:n = 83)b

P-value shows the effect of BAT-positive or BAT-negative groups

BAT Brown adipose tissue, BFP Body fat percentage, BMI Body mass index,
FDG-PET/CT Fluorodeoxyglucose-positron emission tomography and computed
tomography, SUVmax Maximum standardized uptake value

Table 2 Body characteristics of the participants in the IRT
population
All (n = 84)
Age (years)

26.7 ± 7.0

Height (cm)

168.1 ± 8.4

Weight (kg)60.7 ± 9.8

BMI (kg/m2)

21.5 ± 3.0

BFP (%)

23.9 ± 8.0∆Temp (°C)

1.9 ± 0.9

Continuous variables are presented as means ± standard deviation
BFP Body fat percentage, BMI Body mass index, ΔTemp BAT activity calculated bysubtracting the skin surface temperature in the control chest region from that in
the supraclavicular fossa region, IRT Infrared thermography

Taiwanese (1) (Table S3). The study design complied withthe principles of the Declaration of Helsinki and wasapproved by the Ethics Committees of the authors’ institutions. All participants provided written informed consent prior to inclusion in these studies.
BAT measurements using FDG‑PET/CT

BAT activity was measured during the winter season(from December to March) using a methodology established and utilized in previous studies. The participants
wore light clothes (a T-shirt and underwear or a disposable lightweight gown) and stayed in a 19 °C roomfor 1 h after fasting for 6 to 12 h. Subsequently, they
were administered 18F-fluoro-2-deoxyglucose (1.66–
5.18 MBq/kg body weight) intravenously and exposed tothe same cold condition for another hour. FDG-PET/CT

Page 3 of 10

examinations using a PET/CT system (Aquiduo; Toshiba
Medical Systems, Ohtawara, Japan) were performed in aroom at 24 °C. BAT activity in a region of interest wasquantified by calculating the maximum standardized
uptake value (­ SUVmax). An S
­ UVmax of 2.0 or greater wasconsidered to indicate high BAT activity (BAT-positive),
while an ­SUVmax of less than 2.0 indicated low activity(BAT-negative) [24, 25]. In 74 samples, BAT-positive/
BAT-negative status was duly documented, but the raw­SUVmax value was absent.
Measurements of cold‑induced thermogenesis (CIT)and fat oxidation (FO)CIT and FO were measured in a 56-men subset of the
FDG-PET/CT population in our previous studies [26, 27].
Whole-body energy expenditure was measured using a
respiratory gas analyzer connected to a ventilated hood
(AR-1; Arco System, Kashiwa, Japan). Energy expenditure adjusted for free fat mass (kg) was used to calculate
the CIT values. The CIT and FO values were calculated
as previously described [26, 27]. ΔFat oxidation was calculated by subtracting the fat oxidation value of pre-cold
exposure from that of post-cold exposure.
BAT measurements using IRT

Measurements were conducted during the winter andsummer (December–March and June–September,respectively). The participants abstained from excessive
drinking, staying up late, and exercising on the previous day. On the day of the experiment, the participants
skipped breakfast and participated in the experiment in
the morning (9:00–12:00). After a 30-min rest in a 27 °C
room, the participants were asked to wear light clothing(thin disposable gowns) and rest in a sitting position for
90 min in a 19 °C air-conditioned room. Thermal images
were acquired using a FLIR-E6-XT thermal imaging camera (Teledyne FLIR, Wilsonville, Oregon, USA). Infrared
thermal images of the upper body were acquired at the
beginning of and during cold exposure (10, 30, 60, and90 min). The captured images were analyzed using FLIR
tools (Teledyne FLIR). We used ∆Temp at 90 min of cold
exposure as an indicator of BAT activity. ΔTemp was calculated by subtracting the skin surface temperature in
the control chest region from that in the BAT-rich supraclavicular fossa region [28].
SNP genotyping

In the FDG-PET/CT population, genomic DNA was prepared from buccal swab cells using the QIAamp DNA
Mini kit (QIAGEN, Hilden, Germany), followed by ethanol precipitation using a Dr. GenTLE precipitation carrier (TAKARA BIO, Kusatsu, Japan). Genomic DNA was
obtained from the saliva of the IRT population using theIshida et al. Journal of Physiological Anthropology(2024) 43:19

OraGene DNA kit (DNA Genotek, Ontario, Canada) and
QIAamp DNA Midi kit (QIAGEN, Hilden, Germany).
SNP genotyping was performed using TaqMan SNP
Genotyping Assays (Thermo Fisher Scientific K.K., Tokyo,
Japan), a Light Cycler Probe Master, and a Light Cycler 96
instrument (Roche Diagnostics K.K., Tokyo, Japan). Elevencandidate SNPs of six gene regions—rs1137100, rs1892535,
rs1022981, rs12405556, and rs4655518 (LEPR); rs2278426(ANGPTL8); rs11573162 (PLA2G2A); rs6496589 (PLIN1);
rs2298080 (TBX15-WARS2) and rs3790553 (WARS2); andrs174547 (FADS1)—were selected based on the literature(Table S4) [11, 12, 14–18]; these were reported as being
under positive natural selection in high-latitude populations or populations living in cold climates. LEPR SNPs
have strong linkage disequilibrium [29, 30]. We analyzed all
the SNPs because they were inconclusive which were truly
important.
Statistical analysis

Agreement with the Hardy–Weinberg equilibrium wasassessed using the χ2 goodness-of-fit test, and the significance level was set at 0.0045, considering the number of
SNPs tested. Associations between SNP genotypes and
BAT activity were evaluated using logistic or multiple linear regression models adjusted for age, sex, test month,or test season, interactions of age and sex, and ethnicity as appropriate, following a previous study [9]. In the
FDG-PET/CT population, three genetic models were
tested for each SNP, additive, dominant, and recessive,
for the putatively selected allele (Table S4). Dependent
variables without a normal distribution were log-transformed. P-values of less than 0.05 were considered toindicate statistical significance in the analyses of CIT,
FO, and the IRT validation study. All statistical tests were
two-sided. SPSS software (IBM, Tokyo, Japan) was used
for all statistical analyses.

Results
Association analyses were performed on data from two
independent populations, collectively comprising 483
individuals in whom BAT activity was measured using
FDG-PET/CT (n = 399) or IRT (n = 84). The characteristics of the participants in each population are summarized in Tables 1, 2, S1, S2, S3, and S5. For the association
test, we selected factors other than SNPs involved in
BAT activity. In the FDG-PET/CT population, the BATpositive group was younger than the BAT-negative
group (25.6 ± 6.3 vs 29.6 ± 9.0, Mann–Whitney U test,
P < 0.001, Table 1). It comprised a higher percentage ofmen (91.1% vs 77.6%, respectively, P < 0.001, Table 1).
In the IRT population, women had a higher ∆Temp
than men (Student’s t-test, P < 0.001; Table 2). Age washigher in the Japanese population than that for the other

Page 4 of 10

population (Mann–Whitney U test, P = 0.014, Table S3).
Furthermore, body characteristics differed between
sexes (Tables S1, S2, and S5). We performed association
tests, including age and sex, with clear causal relationships with BAT activity as covariates. Based on the literature, we selected 11 SNPs from six genomic regions:
LEPR, ANGPTL8, PLA2G2A, PLIN1, TBX15-WARS2,
and FADS1 (Table S4). These SNPs have been reported
to be under positive selection in indigenous circumpolar populations, and the annotated genes are involved in
BAT function in cellular and animal studies [11, 12, 14,
15, 17, 18, 21, 22, 31–33]. There was no deviation from
Hardy–Weinberg equilibrium in the genotype frequencies of any SNPs (P > 0.05, Tables S4 and S6). First, weanalyzed whether these SNPs affected BAT activity in the
FDG-PET/CT population. No significant association wasobserved between genotype and BAT positivity (P > 0.1,
Fig. 1). Considering that binarization might reduce thestatistical power to detect the effect of SNPs on a quantitative trait, we tested the associations between these
SNPs and the log-transformed S­ UVmax in 325 participants, excluding 74 for whom raw S­ UVmax data had notbeen recorded. Of the 11 SNPs, differences in the additive models were noted for two intronic SNPs of LEPR,
rs1022981 and rs12405556 (P = 0.041, β =  − 0.108 and
P = 0.036, β =  − 0.11, respectively; Fig. 2 and Table S7),
although the association did not survive multiple testing correction. Carriers of the putative adaptive T allele
at rs12405556, which showed a trend toward a higher­SUVmax, had significantly lower body fat percentage(BFP), plasma triglyceride levels, insulin levels, and
HOMA-IR levels (Table 3). BAT metabolic activity uses
lipids as substrates, and CIT and FO are auxiliary indicators of BAT activity [26, 27]. We performed association
analyses using an additive model for CIT and FO in 56
male participants for two SNPs with different S­ UVmax
values between the alleles. We failed to find any association between the two LEPR SNPs and CIT or FO (Fig. 3).
Further association analyses were performed using datafrom an independent IRT cohort. There were no differences in BAT activity based on the presence of the allele(Fig. 4).

Discussion
Of the six loci tested, LEPR showed a tendency for carriers of adaptive alleles to have higher BAT activity in
the FDG-PET/CT test (Fig. 2 and Table S7). Notably,
the slight associations with BFP, plasma triglyceride,
insulin, and HOMA-IR levels (Table 3) were consistent with the negative correlation between BAT activity
and body fat [23, 24]. Leptin is produced in adipose tissues and has pleiotropic functions such as controlling
appetite and regulating energy balance via interactions

Ishida et al. Journal of Physiological Anthropology(2024) 43:19

Page 5 of 10

Fig. 1 Results of the logistic regression analysis of BAT-positive/negative and SNP genotypes. The forest plot shows the association
between genotype groups and BAT prevalence in the FDG-PET/CT population (n = 399). Binary variables representing BAT-negative or BAT-positive
statuses were coded as 0 or 1, respectively. Sex (0 = female, 1 = male), the interaction term between age and sex (female: 0 × age, male: 1 × age),a dummy variable representing the study month (0 = December and March, 1 = January and February), and the genotype were included
as independent variables. To consider multiple tests of the number of SNPs and genetic models, the significance level for the genotypes was set
at P = 0.0015. 1Identifiers in the Single Nucleotide Polymorphism Database at the National Center for Biotechnology Information are shown.
2
Putatively selected alleles of each SNP are indicated. 3The tested genetic models were as follows: A, additive model; D, dominant model; and R,
recessive model for the putatively selected allele. SNP, single nucleotide polymorphism; OR, odds ratio; 95% CI, 95% confidence interval. Black dots
and horizontal bars represent ORs in each model and their 95% confidence intervals (CIs), respectively

with the sympathetic nervous system [34–36]. It has
also been reported to mediate sympathetic activation of
BAT [34] or the induction of beige adipocytes [37]. The
lower BFP observed in carriers of the putative adaptive
alleles contrasts with a report that high BFP is associated with increased levels of insulation [38], indicatinga requirement for thermogenesis in cold environments.
The healthier body composition and blood biochemical parameters observed in carriers of this allele could
be attributed to enhanced energy expenditure by leptinmediated BAT activation [34, 37].
Both LEPR SNPs have been identified as quantitative
expression trait loci (eQTL) in peripheral blood cells [39].

Rs12405556 is in a putative enhancer of LEPR in the liver,and rs1022981 has strong linkage disequilibrium withputative promoter and enhancer SNPs of LEPR in sometissues [30]. Other SNPs in strong linkage disequilibriumwith these positively associated SNPs could be genuinely
effective SNPs. Moreover, increased transcript levelsof adaptive alleles have been observed in several tissues[40]. Thus, it is a potential contributor to the maintenance of deep body temperature by promoting LEPR
transcript expression. In contrast, LEPR alleles were notassociated with significant differences in CIT, FO, or BAT
activity as assessed using IRT. These apparently conflicting results may be explained, in part, by the limited

Ishida et al. Journal of Physiological Anthropology(2024) 43:19

Page 6 of 10

Fig. 2 Results of the association study between ­SUVmax and SNP genotypes. A–E SNPs of LEPR; F SNP of ANGPTL8; G SNP of PLA2G2A. Notethe absence of AA homozygotes in this population due to low minor allele frequency. H SNP of PLIN1; I, J SNPs of TBX15 and WARS2; and K SNP
of FADS1. Box plots show the distribution of ­log10SUVmax in the genotype groups of the FDG-PET/CT population (n = 325). In each box plot,
genotypes are shown in the following order: homozygotes of the putatively selected allele (right), heterozygotes (middle), and homozygotesof the counter allele (left). Sex (0 = female, 1 = male), the interaction term between age and sex (female: 0 × age, male: 1 × age), a dummy variable
representing the study month (0 = December and March, 1 = January and February), and the genotype were included as independent variables. Box
plots show median values (central line), mean values (cross mark), and 75th and 25th percentiles (upper and lower boundaries, respectively). The
largest and smallest values are represented as whiskers drawn from the box ends. Outliers are indicated by dots. P-values of genotypes in multiple
linear regression models are shown. N.S., not significant

number of participants, which likely restricts the statistical power. The thermal images acquired by IRT may beaffected by focal fat thickness and blood flow; thus, thesefactors need to be controlled in future studies. The BFP

differed between the sexes in terms of insulation properties. Given these findings, LEPR may be involved in cold
adaptation through BAT activity. However, further studies with larger sample sizes are required.

Ishida et al. Journal of Physiological Anthropology(2024) 43:19

Page 7 of 10

Table 3 Association of rs1022981 and rs12405556 (LEPR) and biochemical and anthropometric parameters in the FDG-PET/CT
population
All (n = 325)

rs1022981

P-valueAA (n = 11)

AG (n = 111)

GG (n = 203)

rs12405556

P-value

GG (n = 11)

GT (n = 111)

TT (n = 203)

Age (years)

26.0 ± 6.9

22.8 ± 3.6

26.8 ± 7.6

25.9 ± 6.7

0.126

22.9 ± 3.8

26.7 ± 7.7

25.9 ± 6.6

0.201

Height (cm)171.3 ± 6.8

171.1 ± 7.7

171.7 ± 6.9

171.1 ± 6.7

0.638

170.9 ± 8.8

171.7 ± 6.7

171.1 ± 6.7

0.535

Weight (kg)64.0 ± 10.1

63.5 ± 9.2

64.9 ± 11.1

63.6 ± 9.5

0.471

65.5 ± 10.6

64.8 ± 11.0

63.6 ± 9.5

0.217

BMI (kg/m2)

21.7 ± 2.8

21.7 ± 2.6

21.9 ± 2.9

21.7 ± 2.7

0.576

22.4 ± 2.6

21.9 ± 3.0

21.7 ± 2.7

0.290

BFP (%)

17.9 ± 5.6

18.9 ± 5.8

18.4 ± 5.8

17.5 ± 5.4

0.086

19.1 ± 5.4

18.6 ± 5.8

17.4 ± 5.5

0.034

T-Cho (mg/dl)

182.0 ± 29.3

187.3 ± 29.8

183.7 ± 32.5

179.9 ± 30.1

0.312

192.5 ± 26.7

184.6 ± 32.5

179.2 ± 30.1

0.175

HDL (mg/dl)

63.1 ± 12.3

59.0 ± 11.1

62.0 ± 11.7

63.7 ± 12.7

0.144

57.7 ± 8.5

62.4 ± 11.9

63.6 ± 12.7

0.142

TG (mg/dl)

74.2 ± 38.5

95.6 ± 64.7

77.0 ± 40.7

72.3 ± 37.0

0.081

112.0 ± 76.2

76.9 ± 37.7

71.5 ± 36.8

0.006

Glucose (mg/dl)

82.4 ± 6.4

81.3 ± 6.6

82.5 ± 6.0

82.6 ± 6.6

0.668

81.3 ± 6.3

82.4 ± 5.9

82.7 ± 6.7

0.504

HbA1c(NGSP) (%) 5.1 ± 0.3

5.0 ± 0.4

5.1 ± 0.2

5.1 ± 0.3

0.519

5.1 ± 0.4

5.1 ± 0.2

5.1 ± 0.3

0.803

Insulin (µIU/ml)

4.6 ± 2.7

5.5 ± 2.5

4.8 ± 2.8

4.4 ± 2.6

0.150

5.7 ± 2.7

5.0 ± 2.9

4.3 ± 2.5

0.024

HOMA-IR0.9 ± 0.6

1.1 ± 0.6

1.0 ± 0.6

0.9 ± 0.6

0.179

1.2 ± 0.6

1.0 ± 0.6

0.9 ± 0.6

0.035

The analysis included data from 325 participants in the FDG-PET/CT population. Continuous data are expressed as the mean ± standard deviation. HOMA-IR was
calculated from fasting glucose × fasting insulin/405. P-values were calculated from statistical tests as follows: age, Kruskal–Wallis test; other traits, multiple linear
regression models adjusted for sex and age (non-normally distributed trait values were log-transformed before the tests)
BMI Body mass index, BFP Body fat percentage, T-Cho Total cholesterol, HDL High-density lipoprotein, TG Triglycerides

Fig. 3 Results of the association study between genotypes and CIT and FO. The box plots show the distribution of values for capacity for CIT (kcal/
day/kg) (A, B) and ∆Fat oxidation (mg/min) (C, D) in the genotype groups. Associations are shown for rs1022981 (LEPR) (A, C) and rs12405556(LEPR) (B, D). In each box plot, genotypes are shown in the following order: homozygotes of the putatively selected alleles (right), heterozygotes(middle), and homozygotes of the counter allele (left). The box plot shows the median values (central line), mean values (cross marks), and 75thand 25th percentiles (upper and lower boundaries, respectively). The largest and smallest values are represented as whiskers drawn from the box
ends. Outliers are indicated by dots. CIT, cold-induced thermogenesis; FO, fat oxidation. ΔFat oxidation was calculated by subtracting the fat
oxidation value of pre-cold exposure from that of post-cold exposure. P-values of genotypes in multiple linear regression models are shown. N.S.,
not significant

Another notable finding of the present study was thelocus involving TBX15 and WARS2, which has been
associated with a wide range of traits and is thought tobe involved in cold adaptation in the Greenland Inuit[15]. The putative adaptive allele at this locus was likely

introgressed from Denisovan hominins, who werealready distributed in high latitudes in East Eurasia before
the arrival of modern humans [14, 15]. Tbx15 and Wars2
participate in the differentiation of brown adipocytes
in mice [21, 41]; however, no association was observed

Ishida et al. Journal of Physiological Anthropology(2024) 43:19

Fig. 4 Results of the association study between BAT activity
and rs1022981 (A) and rs12405556 (B) genotypes. The box
plot shows the distribution of the ΔTemp (BAT temperature)values in the genotype groups at the end of the experiment.
Age, sex (0 = female, 1 = male), season (summer = 0, winter = 1),
ethnicity (Japanese = 0, other = 1), and genotype were included
as independent variables. In each box plot, genotypes are shownin the following order: homozygotes of the putatively selected allele(right), heterozygotes (middle), and homozygotes of the counterallele (left). The box plot shows the median values (central line),mean values (cross marks), and 75th and 25th percentiles (upperand lower boundaries, respectively). The largest and smallest values
are represented as whiskers drawn from the box ends. Outliers areindicated by dots. ΔTemp was calculated by subtracting the skinsurface temperature in the control chest region from that of
the supraclavicular fossa region. P-values of genotypes in multiple
linear regression models are shown

between the capacity for BAT thermogenesis and these
SNPs in the present study. This locus was previously
found to be significantly associated with facial and bodymorphology, such as lip thickness, height, and waist-tohip ratio [32, 42]. Facial morphology appears to play animportant role in cold adaptation in humans [43, 44], anda previous study has suggested that body temperature isregulated by insulation when the BFP is high [38]. Thus,
natural selection at this locus in the Greenland Inuit may
target phenotypes related to insulation, such as body fat
distribution and facial morphology.
ANGPTL8 plays a role in the production of very lowdensity lipoproteins by regulating lipolysis and lipid
accumulation in the liver [17, 45]. PLIN1 is expressedin the adipocytes and plays essential roles in lipolysis
and lipogenesis [46]. The positive selection in thesegenes may predate human dispersal into subarctic
regions. Furthermore, the putative adaptive alleles
were detected at a low frequency in Africa, suggesting that variations arose long before selection began.

Page 8 of 10

Thus, the involvement of ecological factors other than
low temperature is a reasonable consideration [12, 17].
PLA2G2A participates in the generation of free fatty
acids by hydrolyzing phospholipids and influences circulating lipid levels. FADS1 is involved in fatty acidmetabolism modulation. This gene is likely linked toadaptation to animal-rich diets, which are tied to theencoding of delta-5 desaturase enzymes that increase
unsaturation of polyunsaturated fatty acids more efficiently, as evidenced by observations in the Inuit, whorelied on diets based on nutrients sourced from marine
mammals [14]. Based on these reports, aspects beyond
adaptation to cold conditions, such as adaptation tounique diets and efficient energy storage in scenarios
where food is not readily accessible, may explain thenatural selection of these genes.
It should be clarified whether the loci tested hereinteract with other factors that influence BAT activity.
Previous studies have reported sex-based differences in
BAT activity and the CIT [47, 48]. Sex also has a substantial impact on body fat deposition, which can be
influenced by the energy-dissipating nature of BAT andpossibly affects BAT activity by altering the insulation
properties of the body [49]. In particular, LEPR may
need to be examined in detail since LEPR participatesin the regulation of food intake, energy expenditure,and the function of gonadal glands, all of which showed
sex differences [50]. The sex-specific effects of variants at the LEPR on the development of obesity havebeen reported [51]. Although our association analysis
was adjusted for sex, further association analyses withlarger sample sizes are required to address these issues.
This study had some limitations. This study was based
exclusively on SNPs documented in previous population genetics studies. The SNPs tested may not haveadequately captured the variant that was the actual target of natural positive selection. Moreover, how variations in BAT activity are inherited in modern humans
remains unclear. If this trait is highly polygenic, ouranalysis may have limited statistical power because of
the relatively small sample size. In particular, the CIT
and FO measurements and association analyses wereconducted only in 56 males. Further studies of datafrom larger populations are required. Increasing thesample size is indispensable for assessing the interactions between SNPs and factors of interest, such assex, tested season, and ethnicity. Differences in dietarybehavior may affect BAT activity [52]; however, dietary
surveys were not conducted in this study. The inclusion of dietary information is essential for clarifying
the effects of LEPR SNPs on BAT activity. Finally, variants specific to circumpolar populations could not beanalyzed.

Ishida et al. Journal of Physiological Anthropology(2024) 43:19

Conclusions
The present study demonstrates that LEPR may playa role in cold adaptation. In contrast, we did not findrobust evidence for the involvement of other previously
identified “cold-adaptive” genes, including TBX15, inthe variability of thermogenic phenotypes. If there aretraces of natural selection in high-latitude and low-temperature regions and the genomic regions are involved in
BAT differentiation and function, it does not necessarilymean that they are directly involved in the diversity ofhuman thermogenesis. We substantiated the importanceof verifying the results of genomic analyses using actual
human physiological information. This study deepens ourunderstanding of the genomic basis of cold adaptationin humans and provides intriguing insights into humanadaptation and the evolution of thermogenetic diversity.
Abbreviations
95% CI	95% Confidence interval
ANGPTL8	Angiopoietin-like-8 gene
BAT	Brown adipose tissues
BFP	Body fat percentage
BMI	Body mass index
CIT	Cold-induced thermogenesis
EAs	East Asians
eQTL	Quantitative expression trait loci
FADS1	Fatty acid desaturase 1 gene
FDG-PET/CT	Fluorodeoxyglucose-positron emission tomography and computed tomography
FO	Fat oxidation
HDL	High-density lipoprotein
IRT	Infrared thermography
MAF	Minor allele frequency
Models	A, Additive; D, dominant; R, recessive
N.S.	Not significant
OR	Odds ratio
PLA2G2A	Phospholipase A2 group 2A gene
PLIN1	Perilipin1 gene
SNP	Single nucleotide polymorphism
SUVmax	Maximum standardized uptake value
TBX15	T-box transcription factor 15 gene
T-Cho	Total cholesterol
TG	Triglyceride level
UCP1	Uncoupling protein 1 gene
WARS2	Tryptophanyl tRNA synthetase 2 gene

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40101-​024-​00368-1.
Additional file 1: Table S1. Sex differences of participants in the FDG-PET/
CT population. Table S2. Sex differences of participants in the IRT population. Table S3. Nations differences among participants in the IRT population. Table S4. SNP genotyping results in the FDG-PET/CT population (n =399). Table S5. Sex differences of biochemical parameters in the FDG-PET/
CT population (n = 325). Table S6. SNP genotyping results for rs1022981
and rs12405556 (LEPR) in the IRT population (n = 84). Table S7. Resultsof the association study in the FDG-PET/CT population (n = 325). Fig. S1.
Summary of the tested LEPR SNPs.
Acknowledgements
We want to thank all the study participants. We thank Minami Ogawa forassistance with the acquisition of the IRT data.

Page 9 of 10

Authors’ contributions
KN conceived the study. YI, MM, TY, and MS collected data. YI and KN wrote
the manuscript. All the authors have read and approved the final manuscript.
Funding
This work was partly supported by JSPS KAKENHI Grant Numbers JP22H04446,
JP21H02571, and JP23KJ0705.
Availability of data and materials
The data analyzed during the current study are not publicly available owing to
ethical regulations but will be available from the corresponding author upon
reasonable request.

Declarations
Ethics approval and consent to participate
The study design complied with the principles of the Declaration of Helsinki
and was approved by the ethics committees of the authors’ institutions(T2019-0028, SH3957, 22–6, and 22–133). All participants provided written
informed consent prior to inclusion in the study.
Consent for publication
All participants agreed with the publication by documentation.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Department of Integrated Biosciences, Graduate School of Frontier Sciences,
The University of Tokyo, 5‑1‑5 Kashiwanoha, Kashiwa, Chiba 277‑8562, Japan.
2
Department of Nutrition, School of Nursing and Nutrition, Tenshi College,
Sapporo, Hokkaido 065‑0013, Japan. 3 Division of Molecular Physiology
and Metabolism, Tohoku University Graduate School of Medicine, Sendai,
Miyagi 980‑8575, Japan. 4 Laboratory of Biochemistry, Faculty of Veterinary
Medicine, Hokkaido University, Sapporo, Hokkaido 060‑0818, Japan.
Received: 7 June 2024 Accepted: 11 August 2024

References
1. Blix AS. Adaptations to polar life in mammals and birds. J Exp Biol.
2016;219:1093–105.
2. Castellani JW, Young AJ. Human physiological responses to cold exposure: acute responses and acclimatization to prolonged exposure. Auton
Neurosci. 2016;196:63–74.
3. van der Lans AA, Hoeks J, Brans B, Vijgen GH, Visser MG, Vosselman MJ,
et al. Cold acclimation recruits human brown fat and increases nonshivering thermogenesis. J Clin Invest. 2013;123:3395–403.
4. Wakabayashi H, Matsumoto K, Kobori Y, Ebara T, Matsushita M, Kameya T,
et al. Multiorgan contribution to non-shivering and shivering thermogenesis and vascular responses during gradual cold exposure in humans. Eur
J Appl Physiol. 2020;120:2737–47.
5. Yoneshiro T, Ogawa T, Okamoto N, Matsushita M, Aita S, Kameya T, et al.
Impact of UCP1 and beta3AR gene polymorphisms on age-relatedchanges in brown adipose tissue and adiposity in humans. Int J Obes.
2013;37:993–8.
6. Nishimura T, Katsumura T, Motoi M, Oota H, Watanuki S. Experimental
evidence reveals the UCP1 genotype changes the oxygen consumption attributed to non-shivering thermogenesis in humans. Sci Rep.
2017;7:5570.
7. Nakayama K, Miyashita H, Yanagisawa Y, Iwamoto S. Seasonal effectsof UCP1 gene polymorphism on visceral fat accumulation in Japanese
adults. PLoS One. 2013;8:e74720.
8. Hancock AM, Clark VJ, Qian Y, Di Rienzo A. Population genetic analysis of
the uncoupling proteins supports a role for UCP3 in human cold resistance. Mol Biol Evol. 2011;28:601–14.

Ishida et al. Journal of Physiological Anthropology

9.
10.
11.
12.
13.
14.
15.
16.

17.
18.
19.
20.
21.
22.
23.
24.

25.
26.

27.
28.
29.
30.
31.
32.

(2024) 43:19

Ishida Y, Matsushita M, Yoneshiro T, Saito M, Fuse S, Hamaoka T, et al.
Genetic evidence for involvement of β2-adrenergic receptor in brown
adipose tissue thermogenesis in humans. Int J Obes. 2024;48:1110–7.
Cardona A, Pagani L, Antao T, Lawson DJ, Eichstaedt CA, Yngvadottir B,
et al. Genome-wide analysis of cold adaptation in indigenous Siberian
populations. PLoS One. 2014;9:e98076.
Hancock AM, Witonsky DB, Gordon AS, Eshel G, Pritchard JK, Coop G,
et al. Adaptations to climate in candidate genes for common metabolic
disorders. PLoS Genet. 2008;4:e32.
Levy SB, Leonard WR. The evolutionary significance of human brown
adipose tissue: integrating the timescales of adaptation. Evol Anthropol.
2022;31:75–91.
Nakayama K, Ogawa A, Miyashita H, Tabara Y, Igase M, Kohara K, et al.
Positive natural selection of TRIB2, a novel gene that influences visceralfat accumulation, in East Asia. Hum Genet. 2013;132:201–17.
Fumagalli M, Moltke I, Grarup N, Racimo F, Bjerregaard P, Jørgensen ME,
et al. Greenlandic Inuit show genetic signatures of diet and climate
adaptation. Science. 2015;349:1343–7.
Racimo F, Gokhman D, Fumagalli M, Ko A, Hansen T, Moltke I, et al. Archaic
adaptive introgression in TBX15/WARS2. Mol Biol Evol. 2017;34:509–24.
Tobler R, Souilmi Y, Huber CD, Bean N, Turney CSM, Grey ST, et al.
The role of genetic selection and climatic factors in the dispersal of
anatomically modern humans out of Africa. Proc Natl Acad Sci U S A.
2023;120:e2213061120.
Hallmark B, Karafet TM, Hsieh P, Osipova LP, Watkins JC, Hammer MF.
Genomic evidence of local adaptation to climate and diet in indigenous
Siberians. Mol Biol Evol. 2018;36:315–27.
Brown EA. Genetic explorations of recent human metabolic adaptations:hypotheses and evidence. Biol Rev Camb Philos Soc. 2012;87:838–55.
Sazzini M, Schiavo G, De Fanti S, Martelli PL, Casadio R, Luiselli D. Searching for signatures of cold adaptations in modern and archaic humans:
hints from the brown adipose tissue genes. Heredity. 2014;113:259–67.
Voight BF, Kudaravalli S, Wen X, Pritchard JK. A map of recent positive
selection in the human genome. PLoS Biol. 2006;4:e72.
Gburcik V, Cawthorn WP, Nedergaard J, Timmons JA, Cannon B. An essential role for Tbx15 in the differentiation of brown and “brite” but not white
adipocytes. Am J Physiol Endocrinol Metab. 2012;303:E1053–60.
Kuefner MS, Pham K, Redd JR, Stephenson EJ, Harvey I, Deng X, et al.
Secretory phospholipase A(2) group IIA modulates insulin sensitivity andmetabolism. J Lipid Res. 2017;58:1822–33.
Yoneshiro T, Aita S, Matsushita M, Okamatsu-Ogura Y, Kameya T, Kawai Y,
et al. Age-related decrease in cold-activated brown adipose tissue andaccumulation of body fat in healthy humans. Obesity. 2011;19:1755–60.
Saito M, Okamatsu-Ogura Y, Matsushita M, Watanabe K, Yoneshiro T, NioKobayashi J, et al. High incidence of metabolically active brown adipose
tissue in healthy adult humans: effects of cold exposure and adiposity.
Diabetes. 2009;58:1526–31.
Pfannenberg C, Werner MK, Ripkens S, Stef I, Deckert A, Schmadl M, et al.
Impact of age on the relationships of brown adipose tissue with sex andadiposity in humans. Diabetes. 2010;59:1789–93.
Yoneshiro T, Matsushita M, Nakae S, Kameya T, Sugie H, Tanaka S, et al.
Brown adipose tissue is involved in the seasonal variation of coldinduced thermogenesis in humans. Am J Physiol Regul Integr Comp
Physiol. 2016;310:R999–1009.
Matsushita M, Nirengi S, Hibi M, Wakabayashi H, Lee S-i, Domichi M,
et al. Diurnal variations of brown fat thermogenesis and fat oxidation inhumans. Int J Obes. 2021;45:2499–505.
Nirengi S, Wakabayashi H, Matsushita M, Domichi M, Suzuki S, Sukino S,
et al. An optimal condition for the evaluation of human brown adipose
tissue by infrared thermography. PLoS One. 2019;14:e0220574.
Kagawa Y, Nishijima C, Nakayama K, Iwamoto S, Tanaka A, Kamachi K,
et al. Nutrigenetics of Japanese vegetarians with polymorphism in thefatty acid desaturase. J Nutr Food Sci. 2016;6:498.
Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states,
conservation, and regulatory motif alterations within sets of geneticallylinked variants. Nucleic Acids Res. 2011;40:D930–4.
Souza SC, Christoffolete MA, Ribeiro MO, Miyoshi H, Strissel KJ, Stancheva
ZS, et al. Perilipin regulates the thermogenic actions of norepinephrine inbrown adipose tissue. J Lipid Res. 2007;48:1273–9.
Bonfante B, Faux P, Navarro N, Mendoza-Revilla J, Dubied M, Montillot
C, et al. A GWAS in Latin Americans identifies novel face shape loci,

Page 10 of 10

33.
34.
35.
36.
37.
38.
39.
40.
41.

42.

43.
44.
45.

46.
47.
48.
49.
50.
51.

52.

implicating VPS13B and a Denisovan introgressed region in facial variation. Sci Adv. 2021;7:eabc6160.
Hale N. Inuit metabolism revisited: what drove the selective sweep of
CPT1a L479? Mol Genet Metab. 2020;129:255–71.
Haynes WG, Morgan DA, Walsh SA, Mark AL, Sivitz WI. Receptormediated regional sympathetic nerve activation by leptin. J Clin Invest.
1997;100:270–8.
Dhillon H, Zigman JM, Ye C, Lee CE, McGovern RA, Tang V, et al. Leptin
directly activates SF1 neurons in the VMH, and this action by leptin isrequired for normal body-weight homeostasis. Neuron. 2006;49:191–203.
Barrios-Correa AA, Estrada JA, Contreras I. Leptin signaling in the controlof metabolism and appetite: lessons from animal models. J Mol Neurosci.
2018;66:390–402.
Okamatsu-Ogura Y, Nio-Kobayashi J, Iwanaga T, Terao A, Kimura K, Saito
M. Possible involvement of uncoupling protein 1 in appetite control byleptin. Exp Biol Med. 2011;236:1274–81.
Wijers SL, Saris WH, van Marken Lichtenbelt WD. Cold-induced adaptive
thermogenesis in lean and obese. Obesity. 2010;18:1092–9.
Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J,
et al. Systematic identification of trans eQTLs as putative drivers of known
disease associations. Nat Genet. 2013;45:1238–43.
The GTEx Consortium. The GTEx Consortium atlas of genetic regulatory
effects across human tissues. Science. 2020;369:1318–30.
Mušo M, Bentley L, Vizor L, Yon M, Burling K, Barker P, et al. A Wars2
mutant mouse shows a sex and diet specific change in fat distribution,
reduced food intake and depot-specific upregulation of WAT browning.
Front Physiol. 2022;13:953199.
Heid IM, Jackson AU, Randall JC, Winkler TW, Qi L, Steinthorsdottir V, et al.
Meta-analysis identifies 13 new loci associated with waist-hip ratio andreveals sexual dimorphism in the genetic basis of fat distribution. Nat
Genet. 2010;42:949–60.
Noback ML, Harvati K, Spoor F. Climate-related variation of the humannasal cavity. Am J Phys Anthropol. 2011;145:599–614.
Lacruz RS, Stringer CB, Kimbel WH, Wood B, Harvati K, O’Higgins P, et al.
The evolutionary history of the human face. Nat Ecol Evol. 2019;3:726–36.
von Loeffelholz C, Pfeiffer AFH, Lock JF, Lieske S, Döcke S, Murahovschi V,
et al. ANGPTL8 (Betatrophin) is expressed in visceral adipose tissue andrelates to human hepatic steatosis in two independent clinical collectives. Horm Metab Res. 2017;49:343–9.
Brasaemle DL, Subramanian V, Garcia A, Marcinkiewicz A, Rothenberg
A. Perilipin A and the control of triacylglycerol metabolism. Mol Cell
Biochem. 2009;326:15–21.
Fuller-Jackson J-P, Dordevic AL, Clarke IJ, Henry BA. Effect of sex andsex steroids on brown adipose tissue heat production in humans. Eur J
Endocrinol. 2020;183:343–55.
Fletcher LA, Kim K, Leitner BP, Cassimatis TM, O’Mara AE, Johnson JW,
et al. Sexual dimorphisms in adult human brown adipose tissue. Obesity.
2020;28:241–6.
Brychta RJ, McGehee S, Huang S, Leitner BP, Duckworth CJ, Fletcher LA,
et al. The thermoneutral zone in women takes an “arctic” shift compared
to men. Proc Natl Acad Sci. 2024;121:e2311116121.
Picó C, Palou M, Pomar CA, Rodríguez AM, Palou A. Leptin as a key regulator of the adipose organ. Rev Endocr Metab Disord. 2022;23:13–30.
Riestra P, García-Anguita A, Schoppen S, López-Simón L, De Oya M,
Garcés C. Sex-specific association between leptin receptor polymorphisms and leptin levels and BMI in healthy adolescents. Acta Paediatr.
2010;99:1527–30.
Jurado-Fasoli L, Merchan-Ramirez E, Martinez-Tellez B, Acosta FM,
Sanchez-Delgado G, Amaro-Gahete FJ, et al. Association between dietaryfactors and brown adipose tissue volume/18F-FDG uptake in young
adults. Clin Nutr. 2021;40:1997–2008.

Publisher’s NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.