Content uploaded by Hamidreza Pouragha
Author content
All content in this area was uploaded by Hamidreza Pouragha on Mar 14, 2021
Content may be subject to copyright.
Metabolic Age:
A New Predictor for Metabolic Syndrome
Metabolik Yaş: Metabolik Sendrom İçin Yeni Bir Öngördürücü
Ramin MEHRDAD, Hamidreza POURAGHA*, Mohadeseh VESAL**,
Gholamreza POURYAGHOUB***, Mahdiyeh NADERZADEH****,
Zahra Banafsheh ALEMOHAMMAD*****
Center for Research on Occupational Diseases, Tehran University of Medical Sciences, Iran
*Occupational Health Department, School of Public Health, Tehran University of Medical Sciences, Iran
**Occupational Medicine Department, School of Medicine, Tehran University of Medical Sciences, Iran
***Center for Research on Occupational Diseases, Tehran University of Medical Sciences, Iran
****Center for Air Pollution Research, Institute for Environmental Research Tehran University of Medical Sciences, Tehran, Iran
*****Department of Occupational Medicine, School of Medicine, Occupational Sleep Research Center Baharlou Hospital,
Tehran University of Medical Sciences, Tehran, Iran
Original Article
Turk J Endocrinol Metab
Objective: This study aimed to investigate the prevalence of
metabolic syndrome (MetS) among the employees of the Teh-
ran University of Medical Sciences, along with presenting a pre-
dictor for its identification. Material and Methods: 1583
employees from the Tehran University of Medical Sciences
(TUMS) participated in our cross-sectional study, who were ori-
ginally a part of the enrollment phase in the TUMS Employees’
Cohort study (TEC). Their basic information, physical activity
questionnaire, biochemical blood test, and body composition
were obtained through the Bioelectrical Impedance Analysis
(BIA), blood pressure, anthropometric measurements, and his-
tory of diseases and medication. The prevalence of MetS was
determined according to the criteria of the International Dia-
betes Federation (IDF) and the National Cholesterol Education
Program (NCEP) Adult Treatment Panel-III (ATP-III). Result:
According to the criteria of the IDF, the prevalence of MetS
among total participants was 22.2%, where 21.9% were men
and 22.4% were women. According to the criteria of ATP-III,
the prevalence of MetS was found to be 15%. The prevalence
of obesity (BMI≥30) and hyperglycemia (FBS ≥100 mg/dL)
among the study participants was 23.4% and 9.7%, respecti-
vely. The prevalence of hypertension (SBP ≥130, DBP ≥85
mmHg) and high triglyceride level (TG ≥150 mg/dL) was found
to be 14.6% and 19.6%, respectively, while the prevalence of
reduced high-density lipoprotein in men and women was found
to be 40.3% and 74.7%, respectively.Logistic regression analy-
sis revealed that the predictors of metabolic syndrome were
age, sex, physique rate (the evaluated levels of muscle and
body fat), and metabolic age (where the BMR of a person was
compared to the mean of the BMR of the same age group).
Conclusion: This study introduces metabolic age as a new
predictor of MetS. However, more studies are needed to con-
firm this association.
Keywords: Metabolic syndrome; body composition;
body mass index; physical activity
Amaç: Bu çalışmada, Tahran Tıp Bilimleri Üniversitesi çalışanları
arasında metabolik sendrom (MetS) prevalansını araştırmak ve
tanımlanması için bir öngördürücü sunmak amaçlanmıştır.
Gereç ve Yöntemler: Bu kesitsel çalışmaya, Tahran Tıp Bilim-
leri Üniversitesi [Tehran University of Medical Sciences
(TUMS)]’nden 1583 çalışan katıldı, bu kişiler aslında TUMS Ça-
lışanları Kohort çalışması [TUMS Employees’ Cohort study
(TEC)]nın kayıt aşamasına dâhildi. Temel bilgileri, fiziksel akti-
vite anketi, biyokimyasal kan testi ve vücut kompozisyonu Bi-
yoelektrik Empedans Analizi [Bioelectrical Impedance Analysis
(BIA)], kan basıncı, antropometrik ölçümler ve hastalık ve ilaç
öyküsü aracılığıyla elde edildi. MetS prevalansı, Uluslararası Di-
yabet Federasyonu [International Diabetes Federation (IDF)] ve
Ulusal Kolesterol Eğitim Programı Yetişkin Tedavi Paneli-III [Na-
tional Cholesterol Education Program Adult Treatment Panel-III
(NCEP ATP-III)] kriterlerine göre belirlendi. Bulgular: IDF kri-
terlerine göre toplam katılımcılar arasında MetS prevalansı
%22,2 idi ve %21,9’u erkek, %22,4’ü kadındı. ATP-III kriterle-
rine göre MetS prevalansı %15 olarak bulundu. Çalışma katı-
lımcıları arasında obezite (BKİ ≥30) ve hiperglisemi (AKŞ ≥100
mg/dL) prevalansı sırasıyla %23,4 ve %9,7 idi. Hipertansiyon
(SKB ≥130 mmHg, DKB ≥85 mmHg) ve yüksek trigliserid dü-
zeyi (TG ≥150 mg/dL) prevalansı sırasıyla %14,6 ve %19,6 ola-
rak bulunurken, erkeklerde ve kadınlarda düşük yoğunluklu
lipoprotein prevalansı sırasıyla %40,3 ve %74,7 bulundu. Lojis-
tik regresyon analizi, Mets’in öngördürücülerinin yaş, cinsiyet,
vücut oranı (değerlendirilen kas ve vücut yağı seviyeleri) ve me-
tabolik yaş [kişinin bazal metabolizma hızının (BMR) aynı yaş
grubundakilerin BMR ortalamasıyla karşılaştırılması] olduğunu
ortaya koydu. Sonuç: Bu çalışma, metabolik yaşın MetS için
yeni bir öngördürücü olduğunu ortaya koymaktadır. Ancak, bu
ilişkiyi doğrulamak için daha fazla çalışmaya ihtiyaç vardır.
Anahtar kelimeler: Metabolik sendrom; vücut kompozisyonu;
beden kitle indeksi; fiziksel aktivite
Address for Correspondence: Ramin Mehrdad, Tehran University of Medical Sciences, Iran
Phone: +98 21 66405588 E-mail: mehrdadr@tums.ac.ir
Peer review under responsibility of Turkish Journal of Endocrinology and Metabolism.
Received: 26 Sep 2020 Received in revised form: 18 Nov 020 Accepted: 27 Nov 2020 Available online: 19 Jan 2021
1308-9846 / ® Copyright 2021 by Society of Endocrinology and Metabolism of Turkey.
Publication and hosting by Turkiye Klinikleri.
This is an open access article under the CC BY-NC-SA license (https://creativecommons.org/licenses/by-nc-sa/4.0/)
DOI: 10.25179/tjem.2020-79234
Introduction
Metabolic syndrome is a complex and multi-
risk factor in atherosclerotic cardiovascular
disease (ASCVD) and type 2 diabetes (1).
Metabolic syndrome consists of five risk fac-
tors, including atherogenic dyslipidemia, in-
creased blood pressure, dysglycemia, a
pro-thrombotic and pro-inflammatory state
(2). Metabolic syndrome also multiplies the
potential danger of ASCVD and type 2 dia-
betes by five times (3). About a quarter of the
world’s population i.e., more than one billion
people, are estimated to have metabolic syn-
drome (4). According to various epidemiolog-
ical studies, the incidence rate of metabolic
syndrome is reported between 20% and 45%
(5). Also, many other studies around the
world have been reporting the incidence of
metabolic syndrome. In 2017, a research
work conducted in Iran revealed that the inci-
dence of metabolic syndrome according to the
IDF and ATP III criteria was 30% and 25%,
respectively (4). In another meta-analysis
study in Iran, the incidence of metabolic syn-
drome in 2017 was found to be 31% (6). A
study among Brazilian healthcare providers in
2019 revealed a metabolic syndrome inci-
dence of 24.4% (7), while in the same year,
another study found that the incidence rate of
metabolic syndrome among the Japanese
white-collar workers was 23.1% (8).
Predisposing factors of metabolic syndrome
can be categorized into two groups. The first
group is based on the metabolic syndrome
criteria presented by the IDF, ATP III, and
WHO and consists of hypertension, high
blood sugar, low HDL, high TG and waist cir-
cumference, along with obesity. The second
group is reported in various studies and con-
sists of the predictors of metabolic syn-
drome such as inactivity, age, sex, BMI,
smoking, alcohol, TG/HDL ratio, WHR, body
shape, and serum uric acid (7,9-17).
New predictors for metabolic syndrome are
an important concern for the researchers in
this field. And, body composition analysis
has been introduced as a new predictor for
metabolic syndrome. However, our knowl-
edge in this field needs to improve further.
The purpose of this study was to investigate
the incidence of metabolic syndrome among
the staff of the Tehran University of Medical
Sciences and also to find if some compo-
nents of the body composition analysis such
as metabolic age and physique rating could
act as the predictors of metabolic syndrome.
Material and Methods
Procedure
The data for this cross-sectional study was
taken from the TEC study (Tehran university
of medical sciences Employees’ Cohort
study) during its enrollment phase, which
was collected between January 2017 and
September 2018. The TEC study further in-
tended to enroll 5500 people from their staff
between January 2017 and March 2021
(18). It is designed as a longitudinal study
to track the long-term health of their em-
ployees. Participants were recruited from
different departments. They voluntarily en-
rolled for the research project upon com-
pleting the informed consent form. All the
examinations were performed, and the in-
formation was collected from the partici-
pants in a single day. This study was carried
out according to the Helsinki Declaration
Principles.
Participants
A total of 1583 participants (1012 women
and 571 men), all being TUMS employees
from different divisions (clinical, research,
service, technical, etc.), were enrolled in
this study.
Data collection
The participants were asked about their sex,
age, marital status, ethnicity, age of mar-
riage, education level, occupational group,
shift work, and tobacco usage. Also, to file a
record of diseases in the participants, their
current illnesses were examined through
general medical examinations. The records
included hypertension, type 2 diabetes, hy-
perlipidemia, thyroid diseases, and medica-
tions.
Blood pressure measurement
The blood pressure of the participants was
measured thrice, and the average was re-
ported with a precision of one mmHg. The
participant was made to sit for a 15 min
break, and then the measurements were
taken. An interval of 30 minutes was pro-
vided between the first and second round of
measurements, while a 2-hour interval was
2
given between the second and third rounds.
Blood pressure was measured using a stan-
dard and calibrated clinical mercury
manometer.
Anthropometry
The weight and height of the participants
were measured with a precision of 0.1 kg
and 0.1 cm, respectively. They were wearing
light clothes with no shoes while the meas-
urements were being taken. The waist cir-
cumference was calculated with a precision
of 0.1 cm at the anatomical landmarks such
as the middle of the lower rib margin and
the iliac crest, and the widest portion of the
hip. The BMI was measured as the weight
(kg) of the participant divided by the square
of their height (m).
Blood samples
After 12-hours of fasting, blood samples
were taken between 7 and 9 am. The meas-
ured parameters in the blood samples in-
cluded Fasting Blood Sugar (FBS),
Triglyceride (TG), total cholesterol (CHO),
and high-density and low-density lipoprotein
cholesterol (HDL, LDL).
Body composition
One of the most common methods to study
and analyze the body shape is the body
composition analysis method, which can be
performed using different technologies such
as using of a Caliper, anthropometry, tracer
dilution, densitometry, air displacement
plethysmography, dual-energy X-ray absorp-
tiometry, bioelectrical impedance analyzer,
computed tomography, magnetic resonance
imaging, and 3D body scanning. In the BIA
method, the impedance from different tis-
sues of the body is analyzed to predict the
composition of the body. A very weak elec-
trical current of 800 microamperes with a
frequency of 50 kHz is passed through the
body, and the impedance from the tissues is
measured against this current. Due to the
presence of electrolytes, water demonstrates
high conductivity. However, adipose tissues
show low conductivity (19,20). The body
composition provides quantitative and qual-
itative information on various tissues such as
fat-free mass, fat mass, total water content,
bone mineral density and its content, meta-
bolic age, and the physique rate. At the time
of measurement, all the metal accessories
such as watches, rings, and other jewelry
were removed, and all the measurements
were performed by the same trained per-
sonnel based on the same protocol. The
body composition of participants was meas-
ured and reported using the bioelectrical im-
pedance analysis (BIA) by the Tanita®
MC-780U Body Composition Analyzer.
Metabolic age is determined by comparing
a person’s basal metabolic rate with the av-
erage basal metabolic rate that corresponds
to a similar age group. It is now emerging
as a marker for metabolic health. If the
metabolic age is less than the actual age, it
means that the body is healthy, but if it is
higher than the actual age, it may indicate
that the person is not in good health and
needs to change their eating and exercising
habits and also maybe their lifestyle.
The Physique rate evaluates the levels of
muscle and body fat in an individual. It can
assess which of the nine body types does
one belongs to. The Body Composition Ana-
lyzer can be used to assess whether a per-
son is healthy. It is used to measure the fat
percentage of the body, muscle mass, and
even water and bone content, along with the
physique rating. The nine body types, ac-
cording to the physique rating is as follows:
Hidden Obese: high-fat percentage with a
low level of muscle mass.
Obese: high level of fat percentage with a
standard level of muscle mass.
Solidly-built: high body fat percentage
with a high level of muscle mass.
Under exercised: an average body fat with
a low level of muscle mass.
Standard: an average level of body fat with
average muscle mass.
Standard Muscular: an average amount of
fat percentage with a high level of muscle
mass.
Thin: a low amount of body fat with a low
level of muscle mass.
Thin and Muscular: a low amount of body
fat with a standard level of muscle mass.
Very Muscular: a low amount of body fat
with a high level of muscle mass.
International Physical Activity
Questionnaire- short form (IPAQ-SF)
The physical activity was calculated using
the short form of the IPAQ (International
3
Physical Activity Questionnaire) along with
the MET (the tasks that are equivalent to
metabolic activity) hours per week (MET-
hours/week). The validity of IPAQ has al-
ready been reported (21). Considering the
frequency of participation in the activities
mentioned over the past week, the MET
scores for intense, medium, and hiking ac-
tivities (for at least 10 min) were multiplied
by the time each participant spent on the
activity. The scores for the various activities
were then summarized as MET-mins/week.
Finally, they were categorized into three
groups: low, medium, and high activity.
A HIGH-level of activity was scored upon the
participant’s engagement in vigorously in-
tense activity for at least three days to
achieve a minimum total physical activity of
at least 1500 MET minutes a week OR 7 or
more days of any combination of walking
with moderately intense or vigorously in-
tense activities to achieve a minimum total
physical activity of at least 3000 MET min-
utes a week.
A MODERATE level of physical activity was
scored upon engagement in 3 or more days
of vigorously intense activity and/or walking
at least 30 min per day OR 5 or more days
of moderately intense activity and/or walk-
ing at least 30 min per day OR 5 or more
days of any combination of walking with
moderately intense or vigorously intense ac-
tivities to achieve a minimum total physical
activity of at least 600 MET minutes a week.
Scoring a LOW level of physical activity on
the IPAQ indicated that the participant was
not meeting any of the criteria for either
MODERATE or HIGH levels of physical activ-
ity.
Metabolic syndrome and its components
In this study, the metabolic syndrome was
diagnosed according to the criteria of the
National Cholesterol Education Program
(NCEP) Adult Treatment Panel-III (ATP- III)
and the International Diabetes Federation
(IDF).
The criteria to diagnose the metabolic syn-
drome based on ATP III had to fulfill three or
more of the following:
1. Waist circumference >=102 cm for men
and greater than 88 cm for women,
2. Blood triglycerides >=150 mg/dL or if a
person is on high triglyceride medication,
3. HDL cholesterol level of <40 mg/dL for
men and <50 mg/dL for women,
4. Fasting blood glucose >=100 mg/dL or if
a person is on medication for high blood
sugar,
5. Systolic blood pressure (SBP) >130
mmHg or diastolic blood pressure (DBP)
>=85 mmHg or if an individual is on med-
ication for hypertension.
The diagnostic criteria for the metabolic syn-
drome based on the IDF criteria are:
Obesity that is based on the abdominal cir-
cumference, which is >94 cm in men and
>80 cm in women,
And any two of the following:
Blood triglyceride (TG) more than 150
mg/dL or if a person is on high blood triglyc-
eride therapy,
HDL cholesterol levels of <40 mg/dL in
males and < 50 mg/dL in females,
Fasting blood glucose greater than 100
mg/dL or if a person is on high blood sugar
medication,
Systolic blood pressure >130 mmHg or di-
astolic blood pressure >85 mmHg or if a
person is on medication for hypertension.
Metabolic syndrome was assessed based on
the above-mentioned criteria.
Statistical analysis
For descriptive statistics, means and stan-
dard deviations were used as quantitative
variables, and frequency and percentage
were used as qualitative variables. For uni-
variate analysis, the Chi-square test was uti-
lized for qualitative variables, while for the
quantitative variables, the t-test was used
to compare between the two groups, with
and without metabolic syndrome. The logis-
tic regression analysis was used to realize
which one of our variables were the predic-
tors of metabolic syndrome. The statistical
analyses were done using the IBM SPSS®,
version 24. P-values of less than 0.05 were
considered statistically significant.
Ethical issues
This research was approved by the Ethics
Committee for Research at Tehran University
of Medical Sciences using the code of ethics
IR.TUMS.VCR.REC.1398.246 (10 Jun 2019).
We explained the details of the study, includ-
ing the processes and procedures, to all the
participants just before their enrollment, and
4
they signed and approved the informed con-
sent form. The information of the participants
was coded anonymously and kept confiden-
tial.
Results
The incidence of metabolic syndrome based
on the IDF criteria was equivalent to 22.2%
whereas it was 15% based on the ATP-III
criteria. We used the IDF criteria in this
study since it was stricter (it identifies a
higher percentage of individuals having
metabolic syndrome). In our study, the inci-
dence rate of metabolic syndrome according
to the IDF definition was found to be 21.9%
and 22.4% in males and females, respec-
tively.
Physical activity was also classified into
three categories, namely high, medium, and
low physical activity. According to this study,
70.7% of the overall total population had
low physical activity, where 68.4% were
women, and 74.8% were men. Table 2
shows the details of the physical activity
performed by the participants based on their
gender.
Table 3 presents the variables other than the
IDF criteria, which includes age, sex, physi-
cal activity, metabolic age, and physique rat-
ing in both the groups, with and without
metabolic syndrome.
In the next step, logistic regression was em-
ployed to find the predictors of the meta-
bolic syndrome among the participants. In
this model, Nagelkerke R Square was found
to be 0.176. Table 4 here exhibits the re-
sults.
Discussion
This research study aimed to review the in-
cidence rate of metabolic syndrome and its
predictors among the employees of the
Tehran University of Medical Sciences, re-
sulting in an incidence rate of 22.2% and
15.0% based on the IDF and ATP-III criteria,
respectively.
das Merces et al. in 2019 reported the inci-
dence of metabolic syndrome in Brazilian
healthcare providers as 24.4% based on the
ATP-III criteria, which is about 10% more
than our results (7). However, in another
study, Brazilian healthcare providers showed
a much lower incidence rate of 4.5% (22).
This discrepancy may be due to different
categorizations of the participants in both
the studies based on the existence of meta-
bolic syndrome. Moreover, the results of
Mango et al. were closer to ours. In a 2019
study, the incidence of metabolic syndrome
in the Japanese white-collar employees was
found to be 19.5% (8). In the same year,
the incidence rate of metabolic syndrome in
Iranian petrochemical workers was revealed
as 18.4% as per the ATP-III criteria (23),
and in Korean staff, the incidence of meta-
bolic syndrome was found to be 19.8% ac-
cording to the criteria of ATP-III (9). In a
study close to our research, the incidence of
5
*According to IDF criteria.
TG: Triglyceride; HDL: High-density lipoprotein; FBS: Fasting Blood Sugar; BP: Blood pressure; BMI: Body mass index.
Total Male Female
n=1583 n=571 n=1012 p value Incidence*
Mean (SD) Mean (SD) Mean (SD) n (%)
Age (year) 43.0 (8.7) 44.0 (9.2) 42.4 (8.4) 0.002 -
TG (mg/dL) 119.1 (58.7) 137.4 (73.0) 108.7 (45.8) <0.001 311 (19.6)
HDL (mg/dL) 43.3 (9.3) 42.2 (8.0) 43.9 (9. 9) 0.007 986 (62.3)
FBS (mg/dL) 86.0 (20.8) 89.7 (26.2) 84.1 (16.7) <0.001 153 (9.7)
BP systolic (mmHg) 115.7 (12.8) 120.7 (12.7) 112.9 (12.0) <0.001 230 (14.5)
BP diastolic (mmHg) 77.4 (8.2) 79.8 (8.6) 76.1 (7.8) <0.001
BMI (kg/m2) 27.2 (4.5) 27.6 (4.3) 27.0 (4.7) <0.001 371 (23.4)
Waist circumference (cm) 88.7 (11.6) 96.1(9.6) 84.5 (10.4) <0.001 985 (62.2)
Metabolic Age (year) 41.1 (12.5) 42.9 (11.8) 40.0 (12.8) <0.001 -
Table 1. Basic information on the data and elements of metabolic syndrome and their incidence.
metabolic syndrome in Iranian health work-
ers was found to be 22.4% (24). Also, the
incidence of metabolic syndrome among
hospital health workers in Nigeria and Kenya
were 24.2% and 34%, respectively (25,
26). The incidence was also 21.2% in devel-
oping countries such as Ghana, as per the
IDF criteria, where the incidence was proven
to be greater in females than in males (11).
These results were similar to our study, as
well. In 2016, the incidence of metabolic
syndrome in Japanese healthcare workers
was 8.7% (27), which was very different
from our results and could be due to the dif-
ferences in the lifestyle. A meta-analysis of
the metabolic syndrome among the Chinese
people was reported to be 24.5% (28),
while the incidence among employees who
participated in the Aragon Workers’ Health
Study (AWHS) was reported as 27.1% (29).
Many studies have compared the incidence
of metabolic syndrome between the em-
ployees with sedentary jobs and the ones
with active jobs. The difference can be seen
in some studies; for example, a study re-
ported an incidence of metabolic syndrome
in office workers to be 33% but found only
a 14% incidence rate in firefighters (30).
Various studies have also reported the inci-
dence of the metabolic syndrome within dif-
ferent societies, which ranges from 20% to
35%, especially in developing countries.
This incidence accounts for at least one-fifth
of the population and could be an alarming
6
Physical activity status Total n (%) Male n (%) Female n (%) p value
Low 1119 (70.7) 427 (74.8) 692 (68.4)
Moderate 366 (23.1) 114 (20) 252 (24.9) 0.027
High 98 (6.2) 30 (5.3) 68 (6.7)
Table 2. Levels of physical activity among the participants based on their gender.
With Metabolic Syndrome Without Metabolic Syndrome
Total Male Female Total Male Female
n=352 n=125 n=227 n=1231 n=446 n=785 p value
Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)
Age (year) 47.0 (7.9) 47.3 (8.8) 46.8 (7.3) 41.8 (8.6) 43.0 (9.1) 41.1 (8.2) <0.001
Physical Activity 481.1 (619.2) 401.5 (484.2) 524.9 (679.2) 544.4 (799.4) 533.7 (884.2) 550.3 (747.5) <0.001
met/min/week
Metabolic Age 48.6 (11.2) 50.0 (10.9) 47.8 (11.3) 38.9 (12.0) 40.9 (11.3) 37.8 (12.3) <0.001
(year)
Physique Rating 29.3 (8.1) 26.1 (7.1) 31.1 (8.0) 34.8 (9.9) 31.1 (9.6) 36.1(9.7) <0.001
Table 3. Descriptive statistics of the study variables in both the groups, with and without metabolic syndrome.
95% C. I for EXP(B)
Variable B Odds Ratio Sig Lower Upper
Metabolic age 0.037 1.037 0.002 1.014 1.061
Physique rating 0.028 0.972 0.039 0.946 0.999
Age 0.050 1.051 <0.001 1.028 1.074
Sex 0.299 1.349 0.034 1.023 1.780
Constant -4.648 0.010 <0.001
Table 4. Metabolic syndrome predicting the elements.
SD: Standard deviation.
signal for ASCVD and type 2 diabetes (31).
Abdominal obesity is the most common part
of metabolic syndrome, and many studies
have shown a direct relationship between
abdominal obesity and the incidence of
metabolic syndrome. These studies also val-
idate our results (32-34).
Some studies argue that metabolic syn-
drome occurs at an older age. For example,
at the age of 29 years, the incidence of
metabolic syndrome accounted for as much
as one-third of the total population, while at
the age of 50 years, half of the population
was having metabolic syndrome.
In our study, the mean±SD of the age in the
group with metabolic syndrome was found
to be 47±7.9 years, whereas the mean ±SD
of the age in the non-metabolic syndrome
group was shown as 41.8±8.6 years.
Many studies have explored the predictive
components of metabolic syndrome, such as
levels of HDL, TG, FBS, waist circumference,
and elevated blood pressure (35).
In addition to the components of MetS de-
scribed by the criteria of the IDF, NCEP ATP-
III, and WHO, other predictors have also
been introduced in various studies, such as
inactivity, age, sex, BMI, smoking, alcohol
usage, TG to HDL ratio, waist to hip ratio,
serum uric acid, and leptin (7,9-16,36). How-
ever, in our study, age and sex were identi-
fied as predictors of metabolic syndrome
based on the logistic regression analysis.
Metabolic age is a new term used to de-
scribe the overall fitness and the metabolic
activity of an individual and is obtained by
comparing the basal metabolism of a person
with the mean basal metabolism of the
same age group. If the metabolic age of a
person was found higher than their chrono-
logical age, it indicated a level of basic me-
tabolism with low physical activity. Metabolic
age can be a useful tool for assessing the
metabolic status of individuals. A study by
the European Society of Cardiology (ESC)
used metabolic age as one of the predictors
for cardiovascular disorders in people hav-
ing a higher metabolic age than their
chronological age (37).
In addition to the known variables of meta-
bolic syndrome, we also used results of body
composition analysis as probable predictors
of metabolic syndrome. However, our main
target was to confirm if the body composi-
tion results could predict metabolic syn-
drome. The results indicated that metabolic
age and physique rating could be considered
as independent predictors of metabolic syn-
drome.
We also investigated if metabolic age could
predict metabolic disorders in individuals.
Basal Metabolic Rate (BMR) changes with
age (38). Metabolic age is the comparison of
the BMR of a person with the mean BMR of
the same age group (39). According to the
logistic regression analysis, metabolic age
can be a new predictor for metabolic syn-
drome. Also, physique rating, which indi-
cates the body-type (40), can be a simple
predictor of metabolic syndrome. In 2016, a
study found that type 2 diabetic individuals
were significantly different from the control
in terms of physique rating (41).
Our results might open the door to the world
of metabolic syndrome.
The R-square of the model of regression in
our study reached 0.176, which is not good
enough, and the odds ratios were also small.
Hence, we recommend other researchers to
consider studies with more variables that
could affect the metabolic syndrome. This
can help in creating a better picture of meta-
bolic syndrome along with its anticipating
factors.
Conclusion
This study introduces metabolic age as a
new predictor of metabolic syndrome. How-
ever, more studies are needed to confirm
this association.
Source of Finance
During this study, no financial or spiritual
support was received neither from any phar-
maceutical company that has a direct con-
nection with the research subject, nor from
a company that provides or produces med-
ical instruments and materials which may
negatively affect the evaluation process of
this study.
Conflict of Interest
No conflicts of interest between the authors
and/or family members of the scientific and
medical committee members or members of
the potential conflicts of interest, counsel-
ing, expertise, working conditions, share
holding and similar situations in any firm.
7
Authorship Contributions
Idea/Concept: Ramin Mehrdad, Mohadeseh
Vesal; Design: Zahra Banafsheh Alemoham-
mad; Control/Supervision: Mohadeseh
Vesal; Data Collection and/or Processing:
Gholamreza Pouryaghoub, Mohadeseh
Vesal; Analysis and/or Interpretation: Ramin
Mehrdad; Literature Review: Writing the Ar-
ticle: Hamidreza Pouragha, Mohadeseh
Vesal; Critical Review: Ramin Mehrdad.
Acknowledgment
This cross-sectional study was performed
with the support, collaboration, and financ-
ing of the TUMS employees’ cohort (TEC)
study (Grant no: 97-01-159-38078). The
paper has been prepared based on the data
from the enrolment phase of the cohort
study
References
1. Grundy SM. Metabolic syndrome: a multiplex car-
diovascular risk factor. J Clin Endocrinol Metab.
2007;92:399-404. PMID: 17284640
2. Expert Panel on Detection, Evaluation, and Treat-
ment of High Blood Cholesterol in Adults. Executive
Summary of The Third Report of The National Cho-
lesterol Education Program (NCEP) Expert Panel on
Detection, Evaluation, And Treatment of High Blood
Cholesterol In Adults (Adult Treatment Panel III).
JAMA. 2001;285:2486-2497. PMID: 11368702
3. Grundy SM, Cleeman JI, Daniels SR, Donato KA,
Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Sa-
vage PJ, Smith SC Jr, Spertus JA, Costa F; American
Heart Association; National Heart, Lung, and Blood
Institute. Diagnosis and management of the meta-
bolic syndrome: an American Heart Association/Na-
tional Heart, Lung, and Blood Institute Scientific
Statement. Circulation. 2005;112:2735-2752. Er-
ratum in: Circulation. 2005;112:e297. Erratum in:
Circulation. 2005;112:e298. PMID: 16157765
4. Saklayen MG. The global epidemic of the metabolic
syndrome. Curr Hypertens Rep. 2018;20:12. PMID:
29480368; PMCID: PMC5866840
5. Bonora E, DeFronzo RA. Diabetes Complications,
Comorbidities and Related Disorders (1st ed).
Cham; Springer; 2018;1-726.
6. Dalvand S, Niksima SH, Meshkani R, Ghanei Ghesh-
lagh R, Sadegh-Nejadi S, Kooti W, Parizad N, Za-
hednezhad H, Afrisham R. Prevalence of metabolic
syndrome among iranian population: a systematic
review and meta-analysis. Iran J Public Health.
2017;46:456-467. PMID: 28540261; PMCID:
PMC5439034
7. das Merces MC, Santana AIC, Lua I, da Silva DAR,
E Silva DS, Gomes AMT, Miranda MCDM, Barbosa
CDS, Magalhães LBNC, Coelho JMF, Servo MLS, Por-
tella DDA, de Souza MC, Lago SB, Araújo EMQ, Mar-
ques SC, Figueiredo VP, D'Oliveira Júnior A.
Metabolic syndrome among primary health care
nursing professionals: a cross-sectional population-
based study. Int J Environ Res Public Health.
2019;16:2686. PMID: 31357596; PMCID:
PMC6696523
8. Narisada A, Suzuki K. Association between procras-
tination, white-collar work and obesity in Japanese
male workers: a cross-sectional study. BMJ Open.
2019;9:e029931. PMID: 31740465; PMCID:
PMC6887083
9. Jeong HS. The relationship between workplace en-
vironment and metabolic syndrome. Int J Occup En-
viron Med. 2018;9:176-183. PMID: 30325358;
PMCID: PMC6466990
10.Adejumo EN, Adejumo AO, Azenabor A, Ekun AO,
Enitan SS, Adebola OK, Ogundahunsi OA. Anthro-
pometric parameter that best predict metabolic
syndrome in South west Nigeria. Diabetes Metab
Syndr. 2019;13:48-54. PMID: 30641748
11.Ofori-Asenso R, Agyeman AA, Laar A. Metabolic
syndrome in apparently "healthy" Ghanaian adults:
a systematic review and meta-analysis. Int J Chro-
nic Dis. 2017;2017:2562374. PMID: 29130065;
PMCID: PMC5654269
12. Mentoor I, Kruger M, Nell T. Metabolic syndrome and
body shape predict differences in health parameters in
farm working women. BMC Public Health.
2018;18:453. PMID: 29618342; PMCID: PMC5885298
13.Cuschieri S, Vassallo J, Calleja N, Pace N, Mamo J.
The effect of age, gender, TG/HDL-C ratio and be-
havioral lifestyles on the metabolic syndrome in the
high risk Mediterranean Island population of Malta.
Diabetes Metab Syndr. 2017;11:S321-S327. PMID:
28283398
14.Chen BD, Yang YN, Ma YT, Pan S, He CH, Liu F, Ma
X, Fu ZY, Li XM, Xie X, Zheng YY. Waist-to-height
ratio and triglycerides/high-density lipoprotein cho-
lesterol were the optimal predictors of metabolic
syndrome in Uighur men and women in Xinjiang,
China. Metab Syndr Relat Disord. 2015;13:214-
220. PMID: 25781351
15.Cheong KC, Ghazali SM, Hock LK, Subenthiran S,
Huey TC, Kuay LK, Mustapha FI, Yusoff AF, Mustafa
AN. The discriminative ability of waist circumfe-
rence, body mass index and waist-to-hip ratio in
identifying metabolic syndrome: Variations by age,
sex and race. Diabetes Metab Syndr. 2015;9:74-78.
PMID: 25819369
16.Yu TY, Jee JH, Bae JC, Jin SM, Baek JH, Lee MK, Kim
JH. Serum uric acid: a strong and independent pre-
dictor of metabolic syndrome after adjusting for
body composition. Metabolism. 2016;65:432-440.
PMID: 26975535
17.Haghighatdoost F, Sarrafzadegan N, Mohammadi-
fard N, Asgary S, Boshtam M, Azadbakht L. Asses-
sing body shape index as a risk predictor for
cardiovascular diseases and metabolic syndrome
among Iranian adults. Nutrition. 2014;30:636-644.
PMID: 24800666
18. Nedjat S, Mehrdad R, Yunesian M, Pouragha H, Biagi
V, Monazzam-Esmaeelpour MR. Prospective cohort
study on the social determinants of health: Tehran
University of Medical Sciences employees` cohort
(TEC) study protocol. BMC Public Health.
2020;20:1703. PMID: 33187513; PMCID:
PMC7666496
8
19.Kuriyan R. Body composition techniques. Indian J
Med Res. 2018;148:648-658. PMID: 30666990;
PMCID: PMC6366261
20.Khalil SF, Mohktar MS, Ibrahim F. The theory and
fundamentals of bioimpedance analysis in clinical
status monitoring and diagnosis of diseases. Sen-
sors (Basel). 2014;14:10895-10928. PMID:
24949644; PMCID: PMC4118362
21.Vasheghani-Farahani A, Tahmasbi M, Asheri H, As-
hraf H, Nedjat S, Kordi R. The Persian, last 7-day,
long form of the International Physical Activity
Questionnaire: translation and validation study.
Asian J Sports Med. 2011;2:106-116. PMID:
22375226; PMCID: PMC3289200
22.Vidigal Fde C, Ribeiro AQ, Babio N, Salas-Salvadó J,
Bressan J. Prevalence of metabolic syndrome and
pre-metabolic syndrome in health professionals:
LATINMETS Brazil study. Diabetol Metab Syndr.
2015;7:6. PMID: 25717347; PMCID: PMC4339435
23. Khosravipour M, Shahmohammadi M, Athar HV. The
effects of rotating and extended night shift work on
the prevalence of metabolic syndrome and its com-
ponents. Diabetes Metab Syndr. 2019;13:3085-
3089. PMID: 31765983
24.Niazi E, Saraei M, Aminian O, Izadi N. Frequency of
metabolic syndrome and its associated factors in
health care workers. Diabetes Metab Syndr.
2019;13:338-342. PMID: 30641722
25. Adeoye AM, Adewoye IA, Dairo DM, Adebiyi A, Lack-
land DT, Ogedegbe G, Tayo BO. Excess metabolic
syndrome risks among women health workers com-
pared with men. J Clin Hypertens (Greenwich).
2015;17:880-884. PMID: 26053898; PMCID:
PMC4629712
26.Garrido RA, Semeraro MB, Temesgen SM, Simi MR.
Metabolic syndrome and obesity among workers at
Kanye Seventh-Day Adventist Hospital, Botswana.
S Afr Med J. 2009;99:331-334. PMID: 19588794
27.Hidaka T, Hayakawa T, Kakamu T, Kumagai T, Hiruta
Y, Hata J, Tsuji M, Fukushima T. Prevalence of me-
tabolic syndrome and its components among Japa-
nese workers by Clustered Business Category. PLoS
One. 2016;11:e0153368. PMID: 27082961;
PMCID: PMC4833411
28.Li R, Li W, Lun Z, Zhang H, Sun Z, Kanu JS, Qiu S,
Cheng Y, Liu Y. Prevalence of metabolic syndrome
in Mainland China: a meta-analysis of published
studies. BMC Public Health. 2016;16:296. PMID:
27039079; PMCID: PMC4818385
29.Ledesma M, Hurtado-Roca Y, Leon M, Giraldo P, Po-
covi M, Civeira F, Guallar E, Ordovas JM, Casasnovas
JA, Laclaustra M. Association of ferritin elevation
and metabolic syndrome in males. Results from the
Aragon Workers' Health Study (AWHS). J Clin En-
docrinol Metab. 2015;100:2081-2089. PMID:
25695891
30.Strauß M, Foshag P, Przybylek B, Horlitz M, Lucia A,
Sanchis-Gomar F, Leischik R. Occupation and me-
tabolic syndrome: is there correlation? A cross sec-
tional study in different work activity occupations of
German firefighters and office workers. Diabetol
Metab Syndr. 2016;8:57. PMID: 27555885; PMCID:
PMC4994412
31.Grundy SM. Metabolic syndrome update. Trends
Cardiovasc Med. 2016;26:364-373. PMID:
26654259
32.Engin A. The definition and prevalence of obesity
and metabolic syndrome. In: Engin AB, Engin A,
eds. Obesity and Lipotoxicity (1st ed). Cham; Sprin-
ger; 2017;1-17.
33.Ritchie SA, Connell JM. The link between abdominal
obesity, metabolic syndrome and cardiovascular di-
sease. Nutr Metab Cardiovasc Dis. 2007;17:319-
326. PMID: 17110092
34.Paley CA, Johnson MI. Abdominal obesity and me-
tabolic syndrome: exercise as medicine? BMC
Sports Sci Med Rehabil. 2018;10:7. PMID:
29755739; PMCID: PMC5935926
35. Engin AB, Engin A. Obesity and Lipotoxicity (1st ed).
Cham; Springer; 2017;1-624.
36.Ghadge AA, Khaire AA. Leptin as a predictive mar-
ker for metabolic syndrome. Cytokine.
2019;121:154735. PMID: 31154250
37.Gascuena R, Gomez Paredes L, Munoz Gutierrez J,
Mendiguren JM, Calderon L, Cogollos M, Lledo C.
Improvement in metabolic profile, metabolic age,
and body composition by a diet and exercise prog-
ramme for obese and overweighted young adults.
European Heart Journal. 2017;38:3432.
https://academic.oup.com/eurheartj/article/38/sup
pl_1/ehx504.P3432/4089935
38.Henry C. Mechanisms of changes in basal metabo-
lism during ageing. European journal of clinical nut-
rition. 2000;54:S77-S91.
https://www.nature.com/articles/1601029
39.Hertel J, Friedrich N, Wittfeld K, Pietzner M, Budde
K, Van der Auwera S, Lohmann T, Teumer A, Völzke
H, Nauck M, Grabe HJ. Measuring Biological Age via
Metabonomics: The Metabolic Age Score. J Pro-
teome Res. 2016;15:400-410. PMID: 26652958
40.Carter JEL. Part 1: The Heath-Carter Anthropomet-
ric Somatotype-Instruction Manual. 2002:3-4.
(Date of access: 31.1.2013)
https://eclass.uoa.gr/modules/document/file.php/
MED1353/Heath-CarterManual%20%281%29.pdf
41.Owolabi LF, Adebisi SS, Danborno B, Buraimoh AA.
Comparative evaluation of body composition analy-
sis in type-2 diabetes mellitus patients and healthy
Nigerians using bioelectric impedance analysis tech-
nique. Nigerian Journal of Basic and Clinical Scien-
ces. 2016;13:13-18.
https://www.njbcs.net/article.asp?issn=0331-
8540;year=2016;volume=13;issue=1;spage=13;e
page=18;aulast=Owolabi
9