Numao et al. Journal of Physiological Anthropology
https://doi.org/10.1186/s40101-020-00222-0(2020) 39:12

ORIGINAL ARTICLE

Open Access

Association of abdominal fat withmetabolic syndrome components inoverweight women: effect of menopausal
status
Shigeharu Numao1*, Yasutomi Katayama2, Yoshio Nakata3, Tomoaki Matsuo4, Masaki Nakagaichi1 andKiyoji Tanaka3

Abstract
Background: The association between abdominal fat distribution and metabolic syndrome (MetSyn) components
by menopausal status has yet to be explicated. The purpose of this study was to examine a cross-sectional association
between abdominal fat compartments and MetSyn components in pre- and post-menopausal overweight Japanesewomen.
Methods: Of 212 overweight Japanese women, 76 pre-menopausal overweight (BMI ≥ 25) women (PreM age,42.1 ± 5.9 years) and 87 post-menopausal overweight women (PostM: age, 56.2 ± 4.5 years) were analyzed in thisstudy. Measurements were taken for body mass index (BMI), abdominal compartments [visceral fat (VF),subcutaneous fat (SF), superficial subcutaneous fat (SSF), and deep subcutaneous fat (DSF)], serum high-density
lipoprotein cholesterol (HDLC), low-density lipoprotein cholesterol, triglycerides (TG), and fasting plasma glucose(FPG). Abdominal compartments were assessed using computed tomography.
Results: No significant differences were found for BMI, SF, SSF, or DSF between the PreM and PostM. Despite
this, the PreM had a significantly smaller VF area than that of the PostM. However, the difference in VF area
disappeared when age was adjusted for. VFA significantly correlated with HDLC, TG, and FPG independentlyof menopause status.
Conclusions: These results suggest that the effect of menopause status on the association between VF and
MetSyn components is negligible. Abdominal subcutaneous fat compartments were not associated with
MetSyn components in overweight women regardless of menopausal status.
Keywords: Abdominal superficial subcutaneous fat, Abdominal deep subcutaneous fat, Menopause* Correspondence: numashige@nifs-k.ac.jp
1
Department of Sports and Life Sciences, National Institute of Fitness andSports in Kanoya, 1 Shiromizu, Kanoya, Kagoshima 891-2393, Japan
Full list of author information is available at the end of the article© The Author(s). 2020 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 the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence 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 this licence, visit http://creativecommons.org/licenses/by/4.0/.
The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to thedata made available in this article, unless otherwise stated in a credit line to the data.

Numao et al. Journal of Physiological Anthropology(2020) 39:12

Background
Abdominal obesity is considered a precursor to metabolic
syndrome (MetSyn) which is a cluster of risk factors leading
to type 2 diabetes or cardiovascular problems [1]. Visceralfat (VF) is adipose tissue stored in the abdomen which is
associated with development of MetSyn components.
Changes in VF over time, such as increases and decreases
during weight loss, are related to insulin resistance, hypertension, and dyslipidemia [2–7]. Abdominal subcutaneousfat (SF) also contributes to the incidence of MetSyn components [8, 9]. Interestingly, SF is separated into superficial
and deep subcutaneous fat (SSF and DSF, respectively) by
superficial fascia, a thin layer of connective tissue [10], andthese compartments may be associated with metabolic
abnormalities. Other researchers have found that SSF correlates with hepatic insulin resistance [11], insulin concentration [12, 13], and cholesterol concentration [12]. DSF is
associated with glucose disposal [11, 12, 14], insulin concentration [13], cholesterol concentration [12], mean arterypressure [14], and triglyceride concentration [14]. Thus,
SSF and DSF are important factors in MetSyn.
Menopause contributed to increases in body weightand changes in abdominal fat distribution [15–19].
These changes are associated with circulating sex hormone (estrogen) levels, as estrogen therapy influences abdominal fat distribution in post-menopausal women [20].
Although menopause increases VF [15–19], evidences regarding whether it affects changes to the abdominal SF
distribution including SSF and DSF have been limited[19]. Sex hormone levels of overweight women differ fromthat of normal-weight women [21–23]. The difference insex hormone levels could lead to altered abdominal fat
distribution and the MetSyn components. Difference insex hormone levels may change the associations between
abdominal fat distribution and the MetSyn components
between normal-weight and overweight women during
menopause. However, because few studies have compared
the association between abdominal fat distribution and
the MetSyn components in pre- and post-menopausal
overweight women, this association is yet to be explicated.
The purpose of the present study was to compare theabdominal fat compartments, including VF, SF, SSF, andDSF, with the MetSyn components between pre- andpost-menopausal overweight women and examine theassociation between abdominal fat compartments andmetabolic syndrome components in pre- and postmenopausal overweight women.
Methods
ParticipantsParticipants were recruited from three urban communities
in Japan for this study. They were recruited via advertisements in local newspapers from 1999 to 2006. There were
212 Japanese overweight women initially enrolled in thisPage 2 of 8

study. The participants consisted of 100 pre-menopausal
overweight women and 112 post-menopausal overweight
women. Overweight was defined as body mass index(BMI) ≥ 25, according to WHO classification [24]. Premenopause was defined as regularly menstrual cycle
length, post-menopause was defined as no menses for aminimum of 1 year. Menopausal status was confirmed bya self-administered questionnaire. All participants were
not involved in regular exercise training for 6 months
prior to the onset of this study. Regular exercise training
was defined as spontaneous physical activity, such as aerobic exercise, strength exercise, or flexible exercise, ≥ 3session/week. The participants with a smoking habit, defined as having a smoking experiences within the past 3
years (n = 11), were excluded, because women smokers
tend to have a 1.3 times greater VF than women nonsmokers [25]. The participants with high blood glucose
concentration (> 120 mg/dl) (n = 12), receiving hormone
replacement therapy (n = 12) or medications affecting
blood pressure, lipids, or glucose metabolism (n = 14)
were also excluded. Finally, 76 pre-menopausal overweight
women (PreM: age, 42.1 ± 5.9 years) and 87 postmenopausal overweight women (PostM: age, 56.2 ± 4.5
years) were analyzed in this study. All of them completed
all measurements. Participants’ physical characteristics areshown in Table 1. Each participant heard the purpose, design, and risks associated with this study and provided
written informed consent. The present study conformed
Table 1 Participants’ characteristics and metabolic syndrome
components in pre and postmenopausal overweight women
PreM (n = 76)

PostM (n = 87)

p valueAge (years)

42.1 ± 5.965.2 ± 4.5< 0.001

Height (cm)157.4 ± 5.3

154.3 ± 5.4< 0.001

Weight (kg)68.2 ± 6.4

65.2 ± 7.1

0.006%fat (%)

39.5 ± 7.2

39.3 ± 7.2

0.861

2a

BMI (kg/m )1.44 ± 0.04

1.44 ± 0.04

0.844

WC (cm)93.9 ± 7.5

94.3 ± 8.4

0.776

SBP (mmHg)

127 ± 15132 ± 170.072

DBP (mmHg)

80 ± 1183 ± 90.045

MAP (mmHg)

96.4 ± 11.5

99.3 ± 11.7

0.119

TC (mg/dl)

210.8 ± 35.9

230.7 ± 28.8

< 0.001

HDLC (mg/dl)

62.6 ± 12.9

60.9 ± 13.1

0.407

LDLC (mg/dl)

131.2 ± 32.5

148.0 ± 28.2

0.001

TG (mg/dl)a1.91 ± 0.19

2.00 ± 0.19

0.006

FPG (mg/dl)

91.3 ± 7.8

93.6 ± 7.1

0.047

Values indicate mean ± SD
PreM pre-menopausal overweight women, PostM post-menopausal overweight
women, BMI body mass index; WC waist circumference, SBP systolic bloodpressure, DBP diastolic blood pressure, MAP mean artery pressure, TC totalcholesterol, HDLC high-density lipoprotein cholesterol, LDLC low-density
lipoprotein cholesterol, FPG fasting plasma glucose, TG triglyceridesa
The values were transformed log

Numao et al. Journal of Physiological Anthropology(2020) 39:12

to the principles outlined in the Declaration of Helsinki
and was approved by the Comprehensive Human Sciences
review board at the University of Tsukuba.
Anthropometric variables

Height and weight were measured with a wall-mounted
stadiometer and a digital scale, respectively. BMI was
calculated as weight (kg)/height squared (m2). Waist circumference (WC) was measured at the umbilicus withparticipants standing using a non-elastic plastic measuring tape.
Body composition measurements

Skinfold thickness was measured for the estimation ofbody density with an Eiken skinfold caliper at two sites,
the triceps and subscapular. All skinfold thickness measurements were taken on the right side of the body,three times at each site, to the nearest 0.5 mm with themean value recorded by a fully-trained staff member.
Body density was determined with the two skinfold
thickness measurements using the following predictedequation for Japanese [26].
Body density ¼ 1:0897−0:00133½triceps thickness ðmmÞþsubscapular thickness ðmmÞ:The percentage of the amount of body fat to weight (%fat) was estimated from the body density using theBrozek et al. equation [27].

Page 3 of 8

Metabolic syndrome components

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured with a mercury manometer
after the participants had rested at least 20 min in a sitting position. Mean artery pressure (MAP) was determined from the pressure readings using the following
equation:
MAP ¼ DBP þ ðSBP−DBPÞ=3
Blood samples were collected following 12-h of fastingovernight for the determination of serum total cholesterol (TC), high-density lipoprotein cholesterol (HDLC),
low-density lipoprotein cholesterol (LDLC), triglycerides(TG) concentration, and fasting plasma glucose concentration (FPG).
Blood analysis

Samples were divided into 9-ml tubes containing thrombin as the heparin neutralizing agent, and 2-ml tubes
containing EDTA-2Na, heparin-Na, and sodium fluoride.
The 9-ml tubes were centrifuged at 3000 g for 10 min atroom temperature after 30 min of collection. The 2ml tubes were immediately centrifuged at approximately
3000 g for 10 min at 4 °C. The 9-ml tube samples wereused to analyze TC, HDLC, LDLC, and TG. The 2-ml
tube samples were used to analyze FPG. TC was measured by cholesterol oxidase using the ·HDAOS method.
HDLC and LDLC were determined by the heparinmanganese precipitation method. TG was determined by
the GPO·HDAOS method, without Glycerol Blank. FPG
was assayed by a glucose oxidase method.

Abdominal fat area measurements

Cross-sectional images of the abdominal fat (VF, SF,
SSF, and DSF) were scanned by computed tomography(SOMATOM AR.C, Siemens, Germany) while participants were in the supine position. The manner of scanning was a single 5-mm scan with a scanning time of 5 s
that was centered at the level of the umbilicus (fourthand fifth lumbar vertebrae). VF area (VFA) and the SF
area (SFA) were calculated using the Fat Scan software
program (N2system, Osaka, Japan) [28]. Furthermore,
the boundary between the abdominal superficial subcutaneous area (SSFA) and deep subcutaneous fat area
(DSFA) was defined by superficial fascia that existed in
the abdominal subcutaneous fat [10]. To calculate SSFA
and DSFA, fat areas that consisted of DSFA and VFA
were determined by tracing, along with the superficial
fascia. After that, SSFA was determined by subtracting
the area of DSFA and VFA from the predetermined total
abdominal fat area; and DSFA by subtracting the predetermined VFA from the area of DSFA and VFA. The
intraclass correlations of these measurements in our laboratory were 0.95–0.99.

Statistical analysis

All data were expressed as the mean ± standard deviation (SD). Based on a previous study [15], sample size
was calculated to detect a moderate to large effect(Cohen’s d = 0.49–0.92). It was determined that an estimated sample size of 40–134 would be required to haveapproximately an 80% power needed to detect a moderate to large effect at 0.05 significance. The assumption of
normal distribution was confirmed using the Kolmogorov–Smirnov test and skewness. The variables (BMI andTG) were not normally distributed and were logtransformed. F test was used to confirm the assumptionof homoscedasticity. To compare the characteristics and
MetSyn components between the PreM and PostM, an
unpaired t test was used. Analysis of the covariance(ANCOVA) was used to test differences in the abdominal fat distribution adjusted for age, and the MetSyncomponents between the PreM and PostM adjusted forage or VFA. Pearson product-moment and partial correlation coefficients were calculated to determine the association between different abdominal fat areas and theNumao et al. Journal of Physiological Anthropology(2020) 39:12

MetSyn components. A false discovery rate (FDR) adjustment was used to adjust the significance level ofPearson product-moment and partial correlation coefficients for multiple comparisons [29]. In the sensitivity
analyses, outliers were identified and excluded based on
the cutoff for leverage values [30]. Multiple regression
analysis was performed with MetSyn components as
dependent variable and menopause status (pre-menopause 0 and post-menopause 1) and abdominal fat compartments as independent variables in all participants.
Sensitivity analyses of multiple regression analysis did
not performed, because outliers over the cutoff for leverage values [30] were not detected. The effect size (ES)was judged with Cohen’s d (small ≥ 0.20, medium≥ 0.50, or large ≥ 0.80) for comparison of thecharacteristics and MetSyn components between PreM
and PostM. Statistical significance was set at the 0.05 levelof confidence.

Results
Physical characteristics

Participants’ physical characteristics are summarized inTable 1. No significant differences were observed in
BMI, % fat, and WC. There were no differences foundfor SBP, and MAP between the PreM and PostM.
Whereas TC, LDLC, and TG were significantly higher in
the PostM than in the PreM (95% CI 9.9–29.9, ES 0.62;
95% CI 7.4–26.1, ES 0.56; and 95% CI 0.03–0.15, ES
0.48, respectively). Following adjustment for age, significant differences in TC, LDLC, and TG disappeared (95%CI − 16.3–16.7, ES 0.00; 95% CI − 13.9–17.3, ES 0.04;
and 95% CI − 0.06–0.14, ES 0.16, respectively). Moreover, once adjusted for VFA, TC, and LDLC remained
significantly different (95% CI 6.8–27.6, ES 0.53 and 95%
CI 3.1–22.1, ES 0.47, respectively), whereas the difference in TG became insignificant (95% CI − 0.01–0.11,
ES 0.33).
Table 2 Abdominal fat area in pre and postmenopausal
overweight women
PreM (n = 76)

PostM (n = 87)

p value

Adjusted
p valuea

TFA (cm2)

355.9 ± 75.8

377.1 ± 74.5

0.075

0.735

2

266.0 ± 61.5

264.8 ± 57.8

0.894

0.546

SSFA (cm2)

138.3 ± 30.3

131.3 ± 29.1

0.133

0.759

2

DSFA (cm )127.7 ± 41.7

133.5 ± 38.9

0.361

0.264

VFA (cm2)

89.9 ± 34.7

112.3 ± 42.1

< 0.001

0.782

SFA (cm )Values indicate mean ± SD
PreM pre-menopausal overweight women, PostM post-menopausal overweight
women, TFA total abdominal fat area, SFA subcutaneous fat area, SSFA
superficial subcutaneous fat area, DSFA deep subcutaneous fat area, VFA
visceral fat areaaAdjusted for age

Page 4 of 8

Abdominal fat areas

Abdominal fat areas are summarized in Table 2. VFA
was significantly lower in the PreM rather than the
PostM (95% CI 10.4–34.5, ES 0.58). However, following
adjustment for age, significant differences in VFA disappeared (95% CI − 17.0–22.5, ES 0.05). No significant differences were found for SFA, SSFA, and DSFA (95% CI− 19.7–17.2, ES 0.02, 95% CI − 16.2–2.2, ES 0.24, and
95% CI − 6.7–18.3, ES 0.15, respectively) between the
PreM and PostM.
Correlation between the abdominal fat area and themetabolic syndrome components

Correlation coefficients and partial correlation coefficients between the abdominal fat areas and the MetSyncomponents were summarized in Tables 3; Table S1 andTable S2. VFA exhibited significant correlations with TC(r = 0.21, 95%CI 0.06–0.35), HDLC (r = − 0.19, 95%CI − 0.33
to − 0.04), LDLC (r = 0.23, 95%CI 0.08–0.37), andTG (r = 0.36, 95%CI 0.22–0.49) in all participants. In
the PreM, VFA was significantly correlated with TC(r = 0.28, 95%CI 0.06–0.48), LDLC (r = 0.38, 95%CI 0.17–0.56), and TG (r = 0.37, 95%CI 0.16–0.55). Following
adjustment for age and BMI, VFA exhibited significant correlations with HDLC (r = − 0.31, 95%CI − 0.50 to − 0.09),
LDLC (r = 0.28, 95%CI 0.06–0.48), and TG (r = 0.35,
95%CI 0.14–0.53). In PostM, VFA was significantly
correlated with TG (r = 0.27, 95%CI 0.06–0.45).
Following adjustment for age and BMI, the significancewas maintained (Table S1, and Table S2). However, TFA,
SFA, SSFA, and DSFA did not significantly correlate withany of the MetSyn components in both the PreM and
PostM even after adjustment for age and BMI. In thesensitivity analyses, the significance of correlations did
not change in either the PreM or PostM (Supporting
information Tables S3, S4, and S5).
In multiple regression analysis (Table 4), menopause
status was not independent predictor of any of the MetSyncomponents. VFA was the independent predictor of
HDLC (β = − 0.19, 95%CI − 0.11 to − 0.01), TG (β = 0.36,
95%CI 0.001–0.002), and FPG (β = 0.18, 95%CI 0.005–0.06). Other abdominal fat compartments were not significantly correlated with the MetSyn components.

Discussion
The purpose of the present study was to compare theabdominal fat compartments, including VF, SF, SSF, andDSF, with the MetSyn components between pre- andpost-menopausal overweight women and to determine
the differences associated with the abdominal fat compartments and the MetSyn components between preand post-menopausal overweight women. Our results
indicated that although VFA was higher in the PostM
than in the PreM, the difference in VFA disappeared

Numao et al. Journal of Physiological Anthropology(2020) 39:12

Page 5 of 8

Table 3 Pearson product-moment correlation coefficients between abdominal fat areas and metabolic syndrome components inoverweight women
TFA

SFA

SSFA

DSFA

VFA

SBP

DBP

MAP

TC

HDLC

LDLC

TG

FPG

All

0.17

0.17

0.14

0.08

− 0.05

0.09

0.15

0.18

PreM

0.17

0.28

0.18

0.17

− 0.02

0.22

0.13

0.20

PostM

0.14

0.01

0.09

− 0.09− 0.05− 0.11

0.12

0.13

All

0.15

0.10

0.12

− 0.390.06− 0.04− 0.05

0.11

PreM

0.19

0.24

0.18

0.05

0.12

0.05

− 0.05

0.19

PostM

0.12

− 0.040.09− 0.140.01− 0.14− 0.04

0.04

All

0.06

0.05

0.11

− 0.040.06− 0.01− 0.08

0.09

PreM

0.19

0.16

0.21

− 0.020.10

0.02− 0.08

0.18

PostM

− 0.01− 0.020.02− 0.01− 0.020.04− 0.04

0.05

All

0.17

0.11

0.09

− 0.020.06− 0.06− 0.01

0.10

PreM

0.19

0.23

0.12

0.09

0.11

0.06

− 0.02

0.15

PostM

0.19

− 0.050.08− 0.200.03− 0.24− 0.03

0.02

All

0.10

0.17

0.10

0.21*

− 0.19 *

0.23*

0.36*

0.18

PreM

0.03

0.19

0.08

0.28 *− 0.27

0.38*

0.37*

0.11

PostM

0.08

0.09

0.07

0.03

− 0.11− 0.01

0.27*

0.17

SBP systolic blood pressure, DBP diastolic blood pressure, MAP mean artery pressure, TC total cholesterol, HDLC high-density lipoprotein cholesterol, LDLC lowdensity lipoprotein cholesterol, FPG fasting plasma glucose, TG triglycerides, TFA total abdominal fat area, SFA subcutaneous fat area, SSFA superficial
subcutaneous fat area, DSFA deep subcutaneous fat area, VFA visceral fat area. PreM pre-menopausal overweight women, PostM post-menopausal
overweight women*There was a significant correlation after a false discovery rate adjustment

after adjustment for age. SFA, SSFA, and DSFA weresimilar between the PreM and PostM. Furthermore, TC,
LDLC, and TG were significantly higher in the PostM
than in the PreM, while no significant difference in TC
and LDLC were found after adjustment for age. Multiple
regression revealed menopause was not an independent

predictor of MetSyn components, while VFA significantly associated with HDLC, FBG, and TG regardlessof menopause status. These results suggest that age
rather than menopause status affects the association
between abdominal fat compartment and MetSyncomponents.

Table 4 Multiple regression analysis assessing the association between abdominal fat compartments and metabolic syndrome
components
Dependent variable

Model

β

βSE

Standard β

p value

R2

SBP

Model 1: age

0.45

0.14

0.24

0.002

0.06

Model 2: age

0.44

0.14

0.24

0.002

0.09

BMI

86.5

35.9

0.18

0.017

DBP

MAP

Model 1: age

0.26

0.09

0.23

0.004

0.05

Model 2: age

0.25

0.09

0.23

0.004

0.08

BMI

45.4

21.8

0.16

0.039

Model 1: BMI

68.3

25.3

0.21

0.008

0.04

Model 2: BMI

67.6

25.0

0.21

0.008

0.08

age

0.24

0.10

0.18

0.017

TC

Model 1: age

1.41

0.28

0.37

< 0.001

0.13

HDLC

Model 1: VFA− 0.060.03− 0.19

0.018

0.03

LDLC

Model 1: age

1.15

0.27

0.32

< 0.001

0.10

TG

Model 1: VFA

0.002

0.001

0.36

< 0.001

0.13

FBG

Model 1: VFA

0.03

0.01

0.18

0.022

0.03

SBP systolic blood pressure, DBP diastolic blood pressure, MAP mean artery pressure, TC total cholesterol, HDLC high-density lipoprotein cholesterol, LDLC lowdensity lipoprotein cholesterol, FPG fasting plasma glucose, TG triglycerides, BMI body mass index, VFA visceral fat area

Numao et al. Journal of Physiological Anthropology(2020) 39:12

Menopause is believed to influence abdominal fat distribution as VF increases rapidly (2.6 times) following
menopause [31]. VF was higher in post-menopausal
women than pre-menopausal women, even when the
BMI measurements were similar [16–18]. Our findings
expand upon the results of these studies. However, following adjustment for age, significant differences between PreM and PostM in VFA disappeared in ourstudy. This suggests that the difference in VF between
pre-menopausal and post-menopausal overweight women
is not necessary explained by menopause alone. Aging is
also attributed to the difference in VF between premenopausal and post-menopausal overweight women as
shown by meta-analysis [32].
To our knowledge, there has been one study that hascompared the abdominal subcutaneous fat compartments(SSF and DSF) between pre- and post-menopausal women[19]. Lovejoy et al. [19] reported that SSF and DSF area did
not differ between pre- and post-menopausal women atbaseline and at follow-up (after 4 years). Our results also indicated that SFA, SSFA, and DSFA were not significantly
different between the pre- and postmenopausal overweight
women. However, the selection of participants to target
overweight women may influence the results. Especially, asimilar BMI in pre- and post-menopausal overweight
women may contribute to abdominal subcutaneous fat
compartments. In previous study [19], BMI was similar
between pre-menopausal and post-menopausal women.
Because BMI is generally higher in post-menopausal womenthan in pre-menopausal women [32], our results cannot
apply to the populations other than pre-menopausal andpostmenopausal overweight women with a similar BMI.
MetSyn components often deteriorated following
menopause [2, 16, 17]. One of the reasons for this deterioration appears to be the accumulation of excess VF
that occurred after menopause [16, 17]. In our study,
post-menopausal overweight women were significantly
higher in VFA together with TC, LDLC, and TG levels
than the pre-menopausal overweight women. Following
adjustments for VFA, significant differences between the
pre- and post-menopausal overweight women were
found in the TC and LDLC, and the difference in TG
disappeared. This suggests that the accumulation of VF
contributed to the deterioration of the TG levels. In contrast, the deterioration of the TC and LDLC in postmenopausal overweight women could be influenced byother factors than VF, such as the stages of menopause
or aging-related disorders that complicated the TC andLDLC levels. For instance, in our study, significant differences were found in TC and LDLC between the PreM
and PostM until these variables were adjusted for age,and subsequently these differences disappeared. Similar
results were found in Framingham study that reported
increases in TC and LDLC had occurred with aging [33].

Page 6 of 8

A current meta-analysis showed that TC and LDLC
were significantly higher in post-menopausal womenthan in pre-menopausal women [34]. The effects ofaging and menopause on the difference in lipoproteins
between pre-menopausal and post-menopausal women
were also reported [34]. Thus, it seems that metabolicchanges mediated by age could be the cause of deterioration in TC and LDLC levels.
It has been widely known that abdominal fat, especially VF, is related to MetSyn components. However,
the association between abdominal fat compartments
and the MetSyn components might instead be altered bymenopause. Zamboni et al. [16] showed that postmenopausal obese women exhibited fewer significant correlations between abdominal fat (abdominal fat area, visceralfat area, subcutaneous area, and the ratio of the visceraland subcutaneous fat area) and the MetSyn components
than the pre-menopausal obese women. Our study
found that in the separate correlation analysis, premenopausal women had significant correlations between
VFA and TC, LDLC, and TG, while post-menopausal
women had an only significant correlation between VFA
and TG. However, in the multiple regression analysis
using all participants, menopause status was not adopted
as an independent predictor. This suggests menopause
status do not necessarily affect the association between
abdominal fat and the MetSyn components. Nevertheless, VFA was the independent predictor of HDLC, TG,
and FBG. This indicates VF plays an important role in
MetSyn components in overweight women regardless ofmenopause status.
Several previous studies have reported that SSF and DSF
were associated with the MetSyn components [11–14].
However, our results are not in agreement with these results. This discrepancy may be explained by the gender ofthe participants. SSF and DSF were correlated positively
with hepatic insulin resistance in men with type 2 diabetes, while this correlation was not observed in womenwith type 2 diabetes [11]. The relationship between SSF,
DSF, and the MetSyn components for both men andwomen has been observed, but the effect of SSF and DSF
on the MetSyn components is much weaker in womenthan in men [13]. Moreover, SSF and DSF were not associated with glucose disposal in premenopausal women[35] or lipid-lipoprotein in women [36]. These results suggest that different metabolic characteristics exist for SSF
and DSF between men and women. In fact, the differences
noted in the lipolytic response between SSF and DSF in
men has been evaluated with both in vivo [37] and in vitro
studies [38]. Although these results are not yet available
for women, gender differences involved with the metabolic
characteristics for SSF and DSF might be causing the differences found in the levels of SSF, DSF, and the MetSyncomponents between men and women.

Numao et al. Journal of Physiological Anthropology(2020) 39:12

There are several limitations of this study. First, ourparticipants were Japanese women. Japanese adults havea propensity for greater VF than SF compared with otherethnic populations [39, 40]. Therefore, our data likely
does not apply to other ethnic populations. Second,menopause status could not be confirmed by several
hormones [41]. However, none of pre-menopausal overweight women had irregular menstrual cycle length, andnone of post-menopausal overweight women had menses for a minimum of 1 year in the present study. Third,
post-menopausal time periods in post-menopausal
women are not taken into accounts. It is possible that
post-menopausal time periods predict the body fat massand trunk fat [2, 19, 42]. Forth, as our study design wasa cross-sectional study, it is unclear whether the effectsof longitudinal change in the abdominal subcutaneous
fat compartments, including SSF and DSF on the MetSyncomponents in the pre- and post-menopausal overweight women. Further studies are also required to determine that changes to the abdominal subcutaneous fat
compartments contributed to the MetSyn components
in pre- and post-menopausal overweight women.

Conclusions
Despite similar BMI between the pre- and post-menopausal overweight women, post-menopausal women possessed more significant accumulation of VF than premenopausal women. However, the difference in VF between pre-menopausal and post-menopausal overweight
women is likely to be due to aging rather than menopause.
On the other hand, VF was associated with the MetSyncomponents in the overweight women independently ofmenopause status. However, the contribution of abdominal subcutaneous fat compartments to MetSyn components appeared to be minimal in both pre- and postmenopausal overweight women.
Supplementary information
Supplementary information accompanies this paper at https://doi.org/10.
1186/s40101-020-00222-0.
Additional file 1: Table S1. Partial correlation coefficients between
abdominal fat areas and metabolic syndrome components in premenopausal overweight women. Table S2. Partial correlation coefficients
between abdominal fat areas and metabolic syndrome components inpost-menopausal overweight women. Table S3. Pearson productmoment correlation coefficients between abdominal fat areas and metabolic syndrome components in overweight women, excluding participants with high leverage value. Table S4. Partial correlation coefficients
between abdominal fat areas and metabolic syndrome components in
pre-menopausal overweight women, excluding participants with high leverage value. Table S5. Partial correlation coefficients between abdominal fat areas and metabolic syndrome components in post-menopausal
overweight women, excluding participants with high leverage value.
Abbreviations
MetSyn: Metabolic syndrome; VF: Visceral fat; SF: Subcutaneous fat;
SSF: Superficial subcutaneous fat; DSF: Deep subcutaneous fat; BMI: Body

Page 7 of 8

mass index; PreM: Pre-menopausal overweight women; PostM: Postmenopausal overweight women; WC: Waist circumference; %fat: Percentage
of body fat; VFA: Visceral fat area; SFA: Subcutaneous fat area;
SSFA: Abdominal superficial subcutaneous area; DSFA: Abdominal deep
subcutaneous fat area; SBP: Systolic blood pressure; DBP: Diastolic bloodpressure; MAP: Mean artery pressure; TC: Total cholesterol; HDLC: Highdensity lipoprotein cholesterol; LDLC: Low-density lipoprotein cholesterol;
TG: Triglycerides; FPG: Fasting plasma glucose; SD: Standard deviation;
ANCOVA: Analysis of the covariance; ES: Effect size
Acknowledgements
We gratefully acknowledge Kyoko Araki for evaluation of abdominal fat areas
for this study.
Authors’ contributions
SN, YN, and KT conceived and designed the experiments; SN, YK, TM, YN,
MN, and KT performed the experiments; SN, YK, TM, YN, MN, and KT
analyzed and interpreted the data; SN, YK, TM, YN, MN, and KT wrote thepaper. All authors read and approved the final manuscript.
Funding
This research was supported in part by the 21st century COE (Center of
Excellence) program (2002–2003 Nishihira/Tsukuba Project: Promotion ofhealth and sport scientific research, E02), Japan, Ministry of Education,
Culture, Sports, Science and Technology; and by the 2004–2006 Tanaka
Project of Tsukuba Advanced Research Alliance at the University of Tsukuba.
Availability of data and materials
All data generated or analyzed during this study are included in thispublished article.
Ethics approval and consent to participate
We ensure that this work has been carried out in accordance with the Codeof Ethics of the World Medical Association (Declaration of Helsinki) for
experiments involving humans, and informed consent was obtained from allparticipants. The study protocol was approved by the Comprehensive
Human Sciences review board at the University of Tsukuba, Japan.
Consent for publication
This is not applicable as the study does not have individual person’s data.
Competing interests
The authors declared that they have no competing interests.
Author details
Department of Sports and Life Sciences, National Institute of Fitness andSports in Kanoya, 1 Shiromizu, Kanoya, Kagoshima 891-2393, Japan. 2Facultyof Education, Kogakukan University, 1704 Kodakushimoto, Ise, Mie 516-0016,
Japan. 3Faculty of Health and Sport Sciences, University of Tsukuba, 1-1-1
Tennodai, Tsukuba, Ibaraki 305-8577, Japan. 4Occupational Epidemiology
Research Group, National Institute of Occupational Safety and Health, 6-21-1
Nagao, Tamaku, Kawasaki, Kanagawa 214-8585, Japan.
1

Received: 4 February 2020 Accepted: 6 April 2020

References
1. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al.
Harmonizing the metabolic syndrome: a joint interim statement of theInternational Diabetes Federation Task Force on Epidemiology andPrevention; National Heart, Lung, and Blood Institute; American Heart
Association; World Heart Federation; International Atherosclerosis Society;and International Association for the Study of Obesity. Circulation. 2009;
120(16):1640–5.
2. Wu SI, Chou P, Tsai ST. The impact of years since menopause on thedevelopment of impaired glucose tolerance. J Clin Epidemiol. 2001;54(2):117–20.
3. Ryan AS, Nicklas BJ. Reductions in plasma cytokine levels with weight loss
improve insulin sensitivity in overweight and obese postmenopausal
women. Diabetes Care. 2004;27(7):1699–705.

Numao et al. Journal of Physiological Anthropology

4.

5.

6.

7.

8.

9.

10.
11.

12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

22.

23.

24.

(2020) 39:12

Nicklas BJ, Dennis KE, Berman DM, Sorkin J, Ryan AS, Goldberg AP. Lifestyleintervention of hypocaloric dieting and walking reduces abdominal obesity
and improves coronary heart disease risk factors in obese, postmenopausal,
African-American and Caucasian women. J Gerontol A Biol Sci Med Sci.
2003;58(2):181–9.
Okura T, Nakata Y, Yamabuki K, Tanaka K. Regional body compositionchanges exhibit opposing effects on coronary heart disease risk factors.
Arterioscler Thromb Vasc Biol. 2004;24(5):923–9.
Santosa S, Demonty I, Lichtenstein AH, Jones PJ. Cholesterol metabolism
and body composition in women: the effects of moderate weight loss. Int J
Obes (Lond). 2007;31(6):933–41.
Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, et al.
Abdominal visceral and subcutaneous adipose tissue compartments:
association with metabolic risk factors in the Framingham Heart Study.
Circulation. 2007;116(1):39–48.
Abate N, Garg A, Peshock RM, Stray-Gundersen J, Grundy SM. Relationshipsof generalized and regional adiposity to insulin sensitivity in men. J Clin
Invest. 1995;96(1):88–98.
Lovejoy JC, de la Bretonne JA, Klemperer M, Tulley R. Abdominal fat
distribution and metabolic risk factors: effects of race. Metabolism. 1996;
45(9):1119–24.
Goss C. Gray’s Anatomy. Philadelphia: Lea and Febiger; 1985.
Miyazaki Y, Glass L, Triplitt C, Wajcberg E, Mandarino LJ, DeFronzo RA.
Abdominal fat distribution and peripheral and hepatic insulin resistance intype 2 diabetes mellitus. Am J Physiol Endocrinol Metab. 2002;283(6):E1135–43.
Lovejoy JC, Smith SR, Rood JC. Comparison of regional fat distribution andhealth risk factors in middle-aged white and African American women: The
Healthy Transitions Study. Obes Res. 2001;9(1):10–6.
Smith SR, Lovejoy JC, Greenway F, Ryan D, de Jonge L, de la Bretonne J,
et al. Contributions of total body fat, abdominal subcutaneous adipose
tissue compartments, and visceral adipose tissue to the metabolic
complications of obesity. Metabolism. 2001;50(4):425–35.
Kelley DE, Thaete FL, Troost F, Huwe T, Goodpaster BH. Subdivisions ofsubcutaneous abdominal adipose tissue and insulin resistance. Am J Physiol
Endocrinol Metab. 2000;278(5):E941–8.
Toth MJ, Tchernof A, Sites CK, Poehlman ET. Effect of menopausal status on
body composition and abdominal fat distribution. Int J Obes Relat Metab
Disord. 2000;24(2):226–31.
Zamboni M, Armellini F, Milani MP, De Marchi M, Todesco T, Robbi R, et al.
Body fat distribution in pre- and post-menopausal women: metabolic andanthropometric variables and their inter-relationships. Int J Obes Relat
Metab Disord. 1992;16(7):495–504.
Lynch NA, Ryan AS, Berman DM, Sorkin JD, Nicklas BJ. Comparison ofVO2max and disease risk factors between perimenopausal andpostmenopausal women. Menopause. 2002;9(6):456–62.
Ozbey N, Sencer E, Molvalilar S, Orhan Y. Body fat distribution andcardiovascular disease risk factors in pre- and postmenopausal obese
women with similar BMI. Endocr J. 2002;49(4):503–9.
Lovejoy JC, Champagne CM, de Jonge L, Xie H, Smith SR. Increased visceralfat and decreased energy expenditure during the menopausal transition. Int
J Obes (Lond). 2008;32(6):949–58.
Munoz J, Derstine A, Gower BA. Fat distribution and insulin sensitivity inpostmenopausal women: influence of hormone replacement. Obes Res.
2002;10(6):424–31.
Nagata C, Wada K, Nakamura K, Hayashi M, Takeda N, Yasuda K. Associationsof body size and reproductive factors with circulating levels of sex
hormones and prolactin in premenopausal Japanese women. Cancer
Causes Control. 2011;22(4):581–8.
Randolph JF Jr, Sowers M, Gold EB, Mohr BA, Luborsky J, Santoro N, et al.
Reproductive hormones in the early menopausal transition: relationship to
ethnicity, body size, and menopausal status. J Clin Endocrinol Metab. 2003;
88(4):1516–22.
Verkasalo PK, Thomas HV, Appleby PN, Davey GK, Key TJ. Circulating levelsof sex hormones and their relation to risk factors for breast cancer: a crosssectional study in 1092 pre- and postmenopausal women (United
Kingdom). Cancer Causes Control. 2001;12(1):47–59.
Consultation WE. Appropriate body-mass index for Asian populations andits implications for policy and intervention strategies. Lancet. 2004;
363(9403):157–63.

Page 8 of 8

25. Nakanishi K, Nishida M, Ohama T, Moriyama T, Yamauchi-Takihara K.
Smoking associates with visceral fat accumulation especially in women. Circ
J. 2014;78(5):1259–63.
26. Nagamine S. Evaluation of body fatness by skinfold measurements. In:
Asahina KaS R, editor. Physiological Adaptability and Nutritional Status of
the Japanese B: Growth, Work Capacity and Nutrition of Japanese. Tokyo:
Univ. Tokyo Press; 1975.
27. Brozek J, Grande F, Anderson JT, Keys A. Densitometric Analysis of Body
Composition: Revision of Some Quantitative Assumptions. Ann N Y Acad
Sci. 1963;110:113–40.
28. Yoshizumi T, Nakamura T, Yamane M, Islam AH, Menju M, Yamasaki K, et al.
Abdominal fat: standardized technique for measurement at CT. Radiology.
1999;211(1):283–6.
29. Benjamin Y, Hochberg Y. Controlling the false discovery rate - A practical andpowerful approach to multiple testing. J R Statist Soc B. 1995;57(1):289–300.
30. Cohen J, Cohen P, West SG, Aiken LS. Applied multiple regression/correlation analysis for the behavioral sciences. 3rd ed. London: Lawrence
Erlbaum Associates Publishers; 2003.
31. Kotani K, Tokunaga K, Fujioka S, Kobatake T, Keno Y, Yoshida S, et al. Sexual
dimorphism of age-related changes in whole-body fat distribution in theobese. Int J Obes Relat Metab Disord. 1994;18(4):207–2.
32. Ambikairajah A, Walsh E, Tabatabaei-Jafari H, Cherbuin N. Fat mass changes
during menopause: a metaanalysis. Am J Obstet Gynecol. 2019;221(5):393–
409 e50.
33. Hodkinson HM. Diet and maintenance of mental health in the elderly. Nutr
Rev. 1988;46(2):79–82.
34. Ambikairajah A, Walsh E, Cherbuin N. Lipid profile differences during
menopause: a review with meta-analysis. Menopause. 2019;26(11):1327–33.
35. Toth MJ, Sites CK, Cefalu WT, Matthews DE, Poehlman ET. Determinants ofinsulin-stimulated glucose disposal in middle-aged, premenopausal women.
Am J Physiol Endocrinol Metab. 2001;281(1):E113–21.
36. Deschenes D, Couture P, Dupont P, Tchernof A. Subdivision of the
subcutaneous adipose tissue compartment and lipid-lipoprotein levels inwomen. Obes Res. 2003;11(3):469–76.
37. Enevoldsen LH, Simonsen L, Stallknecht B, Galbo H, Bulow J. In vivo human
lipolytic activity in preperitoneal and subdivisions of subcutaneous
abdominal adipose tissue. Am J Physiol Endocrinol Metab. 2001;281(5):E1110–4.
38. Monzon JR, Basile R, Heneghan S, Udupi V, Green A. Lipolysis in adipocytes
isolated from deep and superficial subcutaneous adipose tissue. Obes Res.
2002;10(4):266–9.
39. Gill TP. Cardiovascular risk in the Asia-Pacific region from a nutrition andmetabolic point of view: abdominal obesity. Asia Pac J Clin Nutr. 2001;10(2):85–9.
40. Tanaka S, Horimai C, Katsukawa F. Ethnic differences in abdominal visceralfat accumulation between Japanese, African-Americans, and Caucasians: ameta-analysis. Acta Diabetol. 2003;40(Suppl 1):S302–4.
41. Harlow SD, Gass M, Hall JE, Lobo R, Maki P, Rebar RW, et al. Executive
summary of the Stages of Reproductive Aging Workshop + 10: addressing
the unfinished agenda of staging reproductive aging. J Clin Endocrinol
Metab. 2012;97(4):1159–68.
42. Tremollieres FA, Pouilles JM, Ribot CA. Relative influence of age andmenopause on total and regional body composition changes inpostmenopausal women. Am J Obstet Gynecol. 1996;175(6):1594–600.

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