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ORIGINAL CONTRIBUTION
Metabolic syndrome in Spanish adolescents and its associationwith birth weight, breastfeeding duration, maternal smoking,and maternal obesity: a cross-sectional study
Emilio Gonzalez-Jimenez • Miguel A. Montero-Alonso •
Jacqueline Schmidt-RioValle • Carmen J. Garcıa-Garcıa •
Cristina Padez
Received: 27 November 2013 / Accepted: 8 July 2014
� Springer-Verlag Berlin Heidelberg 2014
Abstract
Purpose The metabolic syndrome (MetS) in adolescents
is a growing problem. The objectives were to verify the
association among early predictors such as birth weight,
breastfeeding, maternal weight status, smoking during
pregnancy, and the development of MetS.
Methods A cross-sectional study was performed of 976
children and adolescents, 10–15 years of age, at schools in
the provinces of Granada and Almeria (Spain). For this
purpose, we analyzed the physical characteristics as well as
the biochemical markers of the participants with a view to
ascertaining the prevalence of the MetS. Relevant data
were also extracted from the clinical histories of their
mothers.
Results It was found that 3.85 % of the female subjects
and 5.38 % of the male subjects in the sample population
suffered from MetS. In both sexes, there was an association
between birth weight and positive MetS diagnosis
(OR 1.27). For both males and females, there was an
inverse association between the length of time that they had
been breastfed and positive MetS diagnosis (OR1–3 months
3.16; OR4–6 months 1.70; OR[6 months 0.13). There was also
a significant association between maternal weight
(ORoverweight 30.79; ORobesity 49.36) and cigarette con-
sumption during pregnancy (OR 1.47) and the subsequent
development of MetS in the children of these mothers.
Conclusions Those subjects born with a higher than
average birth weight had a greater risk of developing MetS
in childhood and adolescence. Breastfeeding children for
longer than 6 months protected them from MetS in their
early years as well as in their teens. Other risk factors for
MetS were maternal smoking during pregnancy as well as
maternal overweight and obesity.
Keywords Metabolic syndrome � Adolescents � Related
factors
Introduction
Metabolic syndrome (MetS) is defined as a group of risk
factors that when occurring together, increase the risk of
cardiovascular disease and type 2 diabetes mellitus (DM2)
[1, 2]. Until recently, the clustering of such factors had
only been reported in adults. Nevertheless, there are now a
growing number of studies that point to the presence of
MetS in adolescents, probably due to the widespread
E. Gonzalez-Jimenez (&)
Department of Nursing, Faculty of Nursing, University of
Granada, C/Santander No 1, 52071 Melilla, Spain
e-mail: [email protected]
M. A. Montero-Alonso
Department of Statistics and O.R., Faculty of Social Sciences,
University of Granada, C/Santander No 1, 52071 Melilla, Spain
e-mail: [email protected]
J. Schmidt-RioValle
Department of Nursing, Faculty of Health Sciences, University
of Granada, Av/Madrid s/n, 18071 Granada, Spain
e-mail: [email protected]
C. J. Garcıa-Garcıa
Department of Forensic Medicine Toxicology and Physical
Anthropology, Faculty of Medicine, University of Granada,
Av/Madrid s/n, 18071 Granada, Spain
e-mail: [email protected]
C. Padez
Department of Life Sciences, Research Centre for Anthropology
and Health, University of Coimbra, Apartado 3046,
3001-401 Coimbra, Portugal
e-mail: [email protected]
123
Eur J Nutr
DOI 10.1007/s00394-014-0740-x
increase in obesity in young people throughout the world
[3].
The prevalence of MetS in children and adolescents
varies depending on the definition of risk factors and the
population analyzed [4]. However, generally speaking, few
studies have specifically focused on population samples of
young people [5]. Recently, the International Diabetes
Federation (IDF) published a set of guidelines for the
diagnosis of MetS in children and adolescents with a view
to establishing a simple unified definition [6]. According to
this definition, the identification of MetS in adolescents is
based on a waist circumference of C94 cm in males and of
C80 cm in females. This is evidently linked to other risk
factors, such as fasting glucose levels of 100–125 mg/dl,
triglyceride levels of C150 mg/dl, high-density lipopro-
tein-cholesterol (HDL-cholesterol) levels lower than
40 mg/dl in males and 50 mg/dl in females, along with a
blood pressure of C130/85 mmHg [7]. However, it is also
true that as yet, this definition has not been tested on
subjects belonging to different ethnic groups [8, 9].
The identification of risk factors that lead to the devel-
opment of MetS in youngsters is crucial for its prevention
and rapid detection [10]. Accordingly, it has been sug-
gested that a higher than average birth weight is associated
with the early development of insulin resistance as well as
of MetS [11–13]. For example, McCance et al. [14] studied
a population of Pima Indians and found a significant rela-
tion between a higher than average birth weight and the
risk of metabolic disorders in childhood.
Another risk factor in the early development of MetS is an
excessively short period (or lack) of maternal breastfeeding
in the first year of infancy [15]. According to recent studies,
maternal breastfeeding for longer than 6 months helps to
prevent cardiovascular disorders and more specifically, MetS
in the early years of childhood [16, 17]. Nevertheless, the
lack of significant results in other studies [18] has led to some
controversy over the protective effect of maternal lactation in
regard to the development of MetS in infancy.
Unhealthy maternal habits, such as smoking during
pregnancy, also appear to be linked to the childhood
development of MetS. After studying a cohort of 406
young Australians, Huang et al. [19] found that the children
of women that had smoked during the gestation period had
a heavier birth weight. This finding was corroborated in
subsequent studies, such as Behl et al. [20], who concluded
that early exposure of the unborn infant to the chemical
components in cigarette smoke is an important risk factor
in the early development of serious disorders (e.g., MetS)
in childhood and adolescence.
Similarly, the maternal weight of pregnant mothers is
also conducive to their children subsequently developing
MetS [21]. For example, studies such as Ryckman et al.
[22] suggest that maternal obesity during the gestation
period can lead to the early development of MetS. In the
same line, Boney et al. [23] also found that children whose
mothers had been obese during pregnancy were more likely
to develop MetS in their childhood years.
In view of this research, the first objective of this study
was to ascertain whether a higher than normal birth weight
is directly related to the early development of MetS. The
second objective was to verify the correlation between the
duration of maternal breastfeeding and the early develop-
ment of MetS. The third was to discover whether there was
a relation between maternal weight and maternal smoking
during the 9 months of gestation and the child’s subsequent
development of MetS.
Methods
Study design and population
The sample population of this cross-sectional study was
composed of 976 children and adolescents, 10–15 years of
age (519 females and 457 males) from the sixth grade of
primary school to the third year of secondary school. The
subjects attended 18 schools in the provinces of Granada
and Almeria (Spain). The study had been previously
approved by the Board of Education of the Andalusian
Regional Government (Granada and Almeria Delegations)
and also authorized by the directors of the participating
schools. Moreover, the informed consent of the parents and
guardians of the subjects was obtained previous to begin-
ning the study. Moreover, all of the mothers signed a written
authorization that allowed access to their clinical histories.
This research was performed in strict compliance with the
international code of medical ethics established by the
World Medical Association and the Declaration of Helsinki.
Anthropometric measurements and blood pressure
Each subject underwent a complete anthropometric eval-
uation to ascertain his/her nutritional status. This evalua-
tion was performed according to the recommendations of
the European Pediatric Association (Body Composition
Analysis Protocol). The variables assessed were weight,
height, and body mass index [weight (kg)/height (m2)]. The
subjects were weighed on a self-calibrating Seca� 861
Class (III) Digital Floor Scale with a precision of up to
100 g. Their height was measured with a Seca� 214*
portable stadiometer. For this purpose, each subject was
asked to stand erect with back, buttocks, and heels in
continuous contact with the vertical height rod of the sta-
diometer and head orientated in the Frankfurt plane. The
horizontal headpiece was then placed on top of the sub-
ject’s head to measure his/her height.
Eur J Nutr
123
Overweight and obesity, as defined by the international
standards of Cole et al. [24], corresponded to values higher
than the 85th and 95th percentiles, respectively, for BMI-
for-age and sex. Waist circumference was measured using
the horizontal plane midway between the lowest rib and the
upper border of the iliac crest at the end of normal inspi-
ration expiration. Hip circumference was measured at the
maximum extension of the buttocks as viewed from the
right side. For both measurements, a Seca� automatic roll-
up measuring tape (precision of 1 mm) was used to mea-
sure subjects in a standing position with their arms hanging
at their sides in a normal anatomical position. The waist-to-
hip ratio (WHR) was calculated by dividing the waist cir-
cumference by the hip circumference. Also evaluated were
the triceps, biceps, subscapular, and suprailiac skinfolds,
which were measured with a Holtain� skinfold calliper
with a precision of 0.1–0.2 mm. The skinfold measure-
ments were then used to calculate the percentage of body
fat. Previously, however, it was necessary to determine
body density for both sexes with Brook’s [25] equation.
Male subjects
Density: 1:1690�00788
� Log10 ½triceps; biceps; subscapular; and suprailiac�
Female subjects
Density: 1.2063-0.0999
� Log10 ½triceps, biceps, subscapular, and suprailiac]
After determining body density, the Siri [26] equation
was then used to calculate the body fat percentage:
Body fat percentage: 4:95=densityð Þ�4:5½ � � 100
Blood pressure levels were calculated with a previously
calibrated aneroid sphygmomanometer and a Littmann�
stethoscope. The subjects were asked to sit down and relax
for at least 10 min before their blood pressure was mea-
sured and recorded. They were requested to rest their back
against the back of the chair. They were also asked not to
cross their legs so that both of their feet were in direct
contact with the floor. Their right arm, which had to be free
of tight clothing, was extended, flexed at the elbow, and at
heart level. All calculations were made on the right arm
and compared with international reference standards. The
results were interpreted based on Korotkoff phase I for the
systolic blood pressure value and phase V for the diastolic
blood pressure value. A blood pressure of C130/85 mmHg
was regarded as a risk factor of MetS.
Serum biochemical examination
At 8:00 a.m. after a 12-h overnight fast, 10 ml of blood was
extracted by venipuncture from the antecubital fossa of the
right arm with a disposable vacuum blood collection tube.
In the 4 h after the extraction, all samples were centrifuged
at 3,500 rpm for 15 min (Z400 K, Hermle, Wehingen,
Germany). The red blood cells were thus separated, and the
serum was finally frozen at -80 �C for its subsequent
analysis.
Only the glucose was measured immediately after col-
lection. Plasma insulin was determined with an ELISA kit
[27]. Blood glucose (mg/dl) was measured by using the
colorimetric enzymatic method (GOD-PAP Method,
Human Diagnostics, Germany). The HBA1c (glycosylated
hemoglobin) was measured with high-performance liquid
chromatography using a National Glycohemoglobin Stan-
dardization Program certified automated analyzer (model
HLC-723 G7; Tosoh, Tokyo, Japan, intra-assay coefficient
of variation \0.8 %, inter-assay coefficient of variation
\0.5 %) and was standardized according to the Diabetes
Control and Complications Trial.
The low-density lipoproteins (LDL-C) and high-density
lipoproteins (HDL-C) as well as the total cholesterol and
the triglycerides were calculated by means of the enzy-
matic colorimetric method with an Olympus analyzer.
Serum levels of homocysteine were calculated by im-
munoenzymatic assay (DRG Instruments) with a sensitivity
of 1 lmol/l. Intra-assay and inter-assay variation coeffi-
cients were 7 and 9 %, respectively. Ceruloplasmin was
determined by immunoturbidimetric assay (Architect
ci8200, Abbott, Abbott Park IL), with an intra-assay vari-
ation coefficient of 3.7 %, an inter-assay precision of up to
4 %, and a reference of 20–60 mg/dl.
MetS definition
The diagnosis of MetS in adolescents was based on the
criteria established by the IDF [7]. Accordingly, for chil-
dren 10–16 years of age, the diagnosis of the MetS was
based on abdominal obesity (waist circumference [90th
percentile) in combination with two or more clinical val-
ues: fasting blood glucose levels of 100–125 mg/dl; serum
triglyceride levels C150 mg/dl; HDL-cholesterol levels
\40 mg/dl (male subjects) and \50 mg/dl (female sub-
jects); and blood pressure C130/85 mmHg.
Other health-related variables
A retrospective study was carried out of the clinical his-
tories of each mother. The birth weight of the student,
maternal lactation period, maternal consumption of ciga-
rettes during pregnancy, and maternal weight status during
pregnancy (normal weight, overweight, and obesity) were
extracted from the histories of each mother. A question-
naire, especially devised to study and record these
Eur J Nutr
123
variables, was elaborated and validated by the members of
the research team.
Statistical analysis
Descriptive statistics are displayed as arithmetic
mean ± SD or percentages (%). All skewed distributions
were log transformed for analysis. The comparison of male
and female subjects with and without a positive diagnosis
of the MetS was performed with Student’s t test for inde-
pendent samples or Welch’s approximation, depending on
whether the variances were the same or different in the case
of quantitative variables. For qualitative variables, a com-
parable contingency table analysis was conducted with the
chi-squared test. When this was not applicable, the gener-
alization of Fisher’s exact test was used. An exact logistic
regression was employed to discover which variables of
them other, pregnancy, and childbirth were associated with
MetS. The statistical analyses were carried out with the
SPSS statistical package (version 20.0; SPSS Inc, Chicago,
IL) and STATA (version 11.2; StataCorp, College Station,
TX) where a p value of \0.05 denoted statistical
significance.
Results
Table 1 shows the clinical laboratory data of the subjects,
distributed according to the positive or negative diagnosis
of MetS. Levels of basal insulin, basal glucose, glycosyl-
ated hemoglobin, and basal NEFA were higher in all sub-
jects with MetS, whereas homocysteine and ceruloplasmin
were higher only in male subjects with MetS. In the case of
basal glucose, values were higher in the group with MetS.
Total cholesterol levels were lower in all subjects with
MetS though the group of healthy subjects had higher
HDL-C values.
Table 2 lists the measures of dispersion for the defining
parameters of MetS for the female subjects, and Table 3
shows these measures of dispersion for the male subjects.
According to the results of our study, 3.85 % of the female
subjects were positively diagnosed with MetS as opposed
to 5.38 % of the male subjects who suffered from this
disorder. Consequently, in the sample population, MetS
evidently had a higher prevalence in the male population
than in the female population. In the case of variables such
as BMI, body fat (%), abdominal obesity, blood pressure,
and basal glucose, higher mean values were observed in
both sexes in the group diagnosed with MetS as compared
to those without MetS. In the case of triglycerides, higher
values were found in the male subjects with MetS though
not in the female subjects. Moreover, as expected, the
subjects with MetS had lower levels of HDL-C.
Table 4 shows the distribution of the defining parame-
ters of MetS, according to the sex of the subjects. Statis-
tically significant gender-specific differences were found
for triglycerides. It is striking that none of the 20 females
subjects diagnosed with MetS had triglyceride levels
higher than or equal to 150 mg/dl.
The analysis of all the defining parameters of MetS
showed a clear correlation between abdominal obesity
(waist circumference) and systolic and diastolic blood
pressure (Pearson correlation of 0.578 and 0.455, respec-
tively (p \ 0.001). No statistically significant differences
were found for the other parameters (p [ 0.05).
Regarding birth weight, Table 5, mean values were
higher for both sexes in the group with a positive MetS
diagnosis. In regard to maternal lactation, there were sig-
nificant differences for male and female subjects that did
not have MetS. More specifically, 91.2 % of all the sub-
jects without MetS (regardless of gender) had been
Table 1 Biochemical
characteristics of the subjects,
based on sex and positive/
negative diagnosis of MetS
Data are mean values ± SD
Non-metabolic syndrome Metabolic syndrome
Female subjects
(n = 499)
Male subjects
(n = 434)
Female subjects
(n = 20)
Male subjects
(n = 23)
Mean ± SD Mean ± SD Mean ± SD Mean ± SD
Basal insulin (mcU/ml) 20.1 ± 9.07 20.8 ± 9.81 21.8 ± 10.33 23.5 ± 12.44
Basal glucose (mg/dl) 82.7 ± 24.46 84.0 ± 28.40 147.9 ± 52.07 130.8 ± 51.27
HBA1c (%) 4.6 ± 1.8 4.7 ± 2.27 5.2 ± 1.85 5.3 ± 2.09
Total cholesterol 82.4 ± 17.48 81.0 ± 15.67 77.3 ± 8.29 78.4 ± 10.48
Triglyceride (mg/dl) 123.9 ± 43.00 125.9 ± 49.90 116.7 ± 4.45 222.9 ± 150.93
HDL-C (mg/dl) 40.3 ± 2.42 40.4 ± 2.58 34.5 ± 1.82 35.1 ± 2.68
LDL-C (mg/dl) 93.9 ± 24.07 92.8 ± 22.43 84.7 ± 2.48 94.5 ± 24.91
Homocysteine (lmol/l) 9.2 ± 3.90 9.1 ± 3.75 8.8 ± 2.63 9.3 ± 3.94
NEFA (mmol/l) 0.2 ± 0.15 0.2 ± 0.15 0.3 ± 0.17 0.3 ± 0.20
Ceruloplasmin (mg/dl) 26.5 ± 16.23 28.0 ± 17.5 28.4 ± 18.62 32.5 ± 23.74
Eur J Nutr
123
breastfed for more than 6 months. In contrast, in the group
diagnosed with MetS, only 20 % of the females and 34.8 %
of the males had been breastfed for more than 6 months.
The results obtained in this study indicate an inverse
association between the duration of maternal breastfeeding
and the diagnosis of MetS [OR1–3 months 3.16; 95 % CI
(0.92–10.85); OR4–6 months 1.70; 95 % CI (0.39–7.45);
OR[6 months 0.13; 95 % CI (0.03–0.65)]. This was partic-
ularly the case for subjects of both sexes that had been
breastfed for longer than 6 months.
In reference to the weight status of the subjects’ mothers
during pregnancy, the fitting of the logistic regression
model showed that there was a relation between the weight
status of the mother during the 9-month gestation period.
The fit of the logistic regression model reflects a salient
relation between the weight status of the pregnant mothers
and the early development of MetS in their children:
ORoverweight 30.78; 95 % CI (11.01; 86.09) and ORobesity
49.36; 95 % CI (10.38; 234.68).
Our results indicated that the mothers of 5.0 % of the
female subjects diagnosed with MetS were of normal
weight during their pregnancy. In contrast, the mothers of
the female subjects without MetS generally had a more
satisfactory weight status during their pregnancy (i.e.,
88.8 % were of normal weight). Similarly, the mothers of
the male subjects diagnosed with MetS also had a poorer
weight status during their pregnancy (i.e., only 26.1 %were
of normal weight). Generally speaking, the mothers of the
male subjects without MetS also had a more satisfactory
weight status during pregnancy with a normal weight
prevalence of 88.5 %. Regarding the daily smoking habits
of the mothers during pregnancy, the results showed that
this variable had a direct influence on the early develop-
ment of MetS in their children [OR 1.47; 95 % CI (1.03;
2.05)]. In fact, all of the mothers of the female subjects
diagnosed with MetS had smoked during pregnancy. In this
sense, 35 % reported that their daily consumption had been
more than 20 cigarettes. In the case of the mothers of the
healthy female subjects, 83.4 % had not smoked during
pregnancy.
Regarding the mothers of the male subjects diagnosed
with MetS, only 4.3 % of them had not smoked at all
during pregnancy, and 34.8 % said that they had smoked
more than 20 cigarettes per day during the gestation period.
In the case of the mothers of male subjects without MetS,
84.6 % had not smoked during pregnancy, and only 4.8 %
of the mothers said that they had smoked more than 20
cigarettes per day (see Table 5).
Discussion
The results obtained in this study indicate a relatively high
prevalence of MetS in the sample population, particularly
in the male subjects. When compared to the results of other
authors who also used the definition of the IDF [7], our
percentages were higher than those in Aguayo Calcena [28]
(2.9 %) but lower than those obtained by the Studies to
Table 2 Measures of dispersion of the defining parameters of MetS for females, based on the positive or negative diagnosis of MetS
Variables Non-metabolic syndrome (n = 499) Metabolic syndrome (n = 20) p value*
Mean ± SD Min P25 P50 P75 Max Mean ± SD Min P25 P50 P75 Max
Weight (kg) 52.3 ± 10.66 25.1 45.0 51.6 57.8 89.5 68.8 ± 9.35 52.2 62.4 69.5 75.8 87.4 \0.001
Height (cm) 157.9 ± 6.96 131.0 153.7 157.7 163.0 181.0 161.0 ± 7.32 147.2 157.2 160.6 165.3 178.9 \0.001
BMI (kg/m2) 20.8 ± 3.43 14.0 18.4 20.4 22.7 32.0 26.5 ± 3.42 20.1 23.2 26.2 29.6 32.2 \0.001
Body fat (%) 28.9 ± 8.04 10.7 22.6 28.7 34.9 49.6 32.8 ± 6.95 17.8 27.1 34.1 37.6 47.1 \0.001
Obesity (waist
circumference
in cm)
70.6 ± 9.31 49.0 64.0 69.5 75.0 105.0 84.9 ± 5.68 78.0 80.0 83.5 89.3 98.0 \0.001
Triglycerides
(mg/dl)
123.9 ± 43.00 102.0 113.0 115.0 121.0 467.0 116.7 ± 4.45 112.0 113.3 115.0 118.0 129.0 0.450
HDL-C (mg/dl) 40.3 ± 2.42 31.0 40.0 41.0 42.0 43.0 34.5 ± 1.82 31.0 33.0 34.0 36.0 38.0 \0.001
Systolic blood
pressure
(mmHg)
116.0 ± 14.87 70.0 106.0 115.0 125.0 162.0 132.9 ± 11.07 100.0 130.0 135.5 138.8 149.0 \0.001
Diastolic blood
pressure
(mmHg)
63.6 ± 8.77 40.0 58.0 62.0 70.0 90.0 69.1 ± 7.87 50.0 63.0 69.5 74.8 86.0 0.006
Basal
glucose(mg/dl)
82.7 ± 24.46 70.0 72.0 73.0 83.0 205.0 147.9 ± 52.07 88.0 91.8 185.0 194.0 210.0 \0.001
MetS based on the IDF criteria for adolescents 10–15 years of age
* Student’s t test
Eur J Nutr
123
Treat or Prevent Pediatric Type 2 Diabetes (STOPP-T2D)
Prevention Study Group* [29] (9.5 %) and Tapia [1]
(18.6 %) for a sample population of adolescents in Spain.
According to Pan and Pratt [30], the increased prevalence
of MetS in the adolescent population is directly related to a
rejection of traditional dietary habits and the growing
tendency of young people to adopt a more sedentary life-
style with more time spent watching television, using the
computer, and playing video games.
The results of the biochemical analysis confirmed dif-
ferences in the values obtained for basal insulin, HBA1c,
LDL-C, NEFA, and ceruloplasmin in the group of male
and female subjects diagnosed with MetS as compared to
the group of healthy subjects. These results coincided with
those of Chen et al. [31] and Wang et al. [32] in a cross-
sectional study of 624 adolescents in China.
Among the physical characteristics, birth weight was
found to be directly related to the development of MetS,
given the fact that this variable had higher than average
values in both the male and female subjects with MetS. In
contrast, Eriksson et al. [33] and Khuc et al. [34], who
surveyed a population of 357 adolescents in Chile, did not
find any association between birth weight and the early
development of the MetS.
In regard to maternal lactation, our results showed an
inverse association between the length of time that the
subjects had been breastfed and a positive MetS diagnosis
in childhood and adolescence. Accordingly, there was a
higher prevalence of MetS in the male and female subjects
who had not been breastfed as babies. These results are in
consonance with those obtained in other studies of a similar
population of children and adolescents [35, 36]. As also
highlighted by Ekelund et al. [37], the most important
benefits of maternal breastfeeding in terms of MetS pre-
vention were for those subjects who had been breastfed for
more than 6 months.
Furthermore, our results highlight the importance of the
weight status of mothers during the 9 months of pregnancy,
which has an impact on the potential development of MetS
in their offspring. In this sense, the children of mothers
who had suffered from overweight or obesity during
pregnancy were more likely to develop MetS than those
children whose mothers were of normal weight. These
results agree with those obtained in previous research [38].
Finally, maternal smoking during pregnancy was also
found to be a risk factor. Similarly to other research [39,
40], our study found that cigarette consumption by mothers
during the gestation period increased the risk of their
children developing MetS in childhood and adolescence.
In conclusion, the results of our study show the presence
of metabolic disorder in the sample population. The inci-
dence of MetS was especially striking in the male subjects.
Birth weight was closely related to the potentialTa
ble
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\0
.00
1
Tri
gly
ceri
des
(mg
/dl)
12
5.9
±4
9.9
01
05
.01
13
.01
15
.01
20
.04
22
.02
22
.9±
15
0.9
31
10
.01
15
.01
24
.03
98
.05
27
.00
.00
6
HD
L-C
(mg
/dl)
40
.4±
2.5
82
9.0
40
.04
1.0
42
.04
3.0
35
.01
±2
.68
30
.03
3.0
35
.03
7.0
41
.0\
0.0
01
Sy
sto
lic
blo
od
pre
ssu
re(m
mH
g)
11
8.4
±1
5.4
08
1.0
11
0.0
11
8.0
13
0.0
16
2.0
13
8.8
±1
0.5
41
10
.01
36
.01
39
.01
42
.01
60
.0\
0.0
01
Dia
sto
lic
blo
od
pre
ssu
re(m
mH
g)
64
.1±
9.1
04
0.0
59
.06
2.0
70
.01
00
.07
1.4
8±
7.7
55
0.0
70
.07
2.0
76
.08
2.0
\0
.00
1
Bas
alg
luco
se(m
g/d
l)8
4.0
±2
8.4
07
0.0
72
.07
3.0
83
.02
47
.01
30
.83
±5
1.2
78
3.0
88
.09
4.0
19
1.0
20
1.0
\0
.00
1
Met
Sb
ased
on
the
IDF
crit
eria
for
ado
lesc
ents
10
–1
5y
ears
of
age
*S
tud
ent’
st
test
Eur J Nutr
123
Table 4 Distribution of the
defining parameters of MetS,
based on sex
* Student’s t test
Variables Non-metabolic syndrome p value* Metabolic syndrome p value*
Females
(n = 499)
Males
(n = 434)
Females
(n = 20)
Males
(n = 23)
n (%) n (%) n (%) n (%)
Obesity (waist circumference in cm)
\90th percentile 399 (80) 335 (77.2) 0.336 – – –
C90th percentile 100 (20) 99 (22.8) 20 (100) 23 (100)
Triglycerides (mg/dl)
\150 mg/dl 488 (97.8) 421 (97) 0.536 20 (100) 15 (65.2) 0.004
C150 mg/dl 11 (2.2) 13 (3) 0 (0) 8 (34.8)
HDL-C (mg/dl)
C40 mg/dl 424 (85) 374 (86.2) 0.641 20 (100) 21 (91.3) 0.491
\40 mg/dl 75 (15) 60 (13.8) 0 (0) 2 (8.7)
Systolic blood pressure (mmHg)
\130 mmHg 395 (79.2) 323 (74.4) 0.102 3 (15) 2 (8.7) 0.650
C130 mmHg 104 (20.5) 111 (25.6) 17 (85) 21 (91.3)
Diastolic blood pressure (mmHg)
\85 mmHg 492 (98.6) 424 (97.7) 0.335 19 (95) 23 (100) 0.465
C85 mmHg 7 (1.4) 10 (2.3) 1 (5) 0 (0)
Blood pressure
Normal systolic/diastolic 393 (78.8) 322 (74.2) 0.104 3 (15) 2 (8.7) 0.650
High systolic/diastolic 106 (21.2) 112 (25.8) 17 (85) 21 (91.3)
Basal glucose (mg/dl)
\100 mg/dl 477 (95.6) 411 (94.7) 0.544 9 (45) 14 (60.9) 0.366
C100 mg/dl 22 (4.4) 23 (5.3) 11 (55) 9 (39.1)
Table 5 Duration of maternal
breastfeeding, maternal
smoking, and maternal weight
during pregnancy according to
the sex of the offspring and their
positive/negative diagnosis of
MetS
* Chi-squared test
Variables Non-metabolic syndrome p value* Metabolic syndrome p value*
Females
(n = 499)
Males
(n = 434)
Females
(n = 20)
Males
(n = 23)
n (%) n (%) n (%) n (%)
Birth weight (kg)
Mean ± SD 3.17 ± 0.46 3.17 ± 0.52 0.862 3.85 ± 0.45 3.73 ± 0.45 0.385
P25/P50/P75 3.0/3.3/3.5 3.0/3.3/3.4 3.5/3.9/4.2 3.4/3.7/4.2
Duration of maternal breastfeeding
Never 8 (1.6) 20 (4.6) 0.007 4 (20.0) 6 (26.1) 0.552
1–3 months 18 (3.6) 6 (1.4) 8 (40.0) 5 (21.7)
4–6 months 18 (3.6) 12 (3.8) 4 (20.0) 4 (17.4)
[6 months 455 (91.2) 396 (91.2) 4 (20.0) 8 (34.8)
Cigarette consumption
0 cigarettes 416 (83.4) 367 (84.6) 0.315 – 1 (4.3) 0.683
\10 cigarettes 44 (8.8) 33 (7.6) 5 (25.0) 8 (34.8)
10–20
cigarettes
23 (4.6) 13 (3.0) 8 (40.0) 6 (26.1)
[20 cigarettes 16 (3.2) 21 (4.8) 7 (35.0) 8 (34.8)
Maternal weight
Normal weight 443 (88.8) 384 (88.5) 0.571 1 (5.0) 6 (26.1) 0.215
Overweight 39 (7.8) 30 (6.9) 10 (50.0) 9 (39.1)
Obesity 17 (3.4) 20 (4.6) 9 (45.0) 8 (34.8)
Eur J Nutr
123
development of this disorder. In this regard, both male and
female subjects with MetS had a higher than average birth
weight in comparison with those without MetS. Maternal
breastfeeding was found to protect newborns from subse-
quently developing MetS in adolescence, particularly when
the duration of the lactation period was longer than
6 months. There was also an important relation between
the weight status of mothers during pregnancy and the
possibility of their children subsequently developing MetS.
In this regard, the subjects whose mother had been over-
weight or obese during the gestation period were more
vulnerable to MetS. Nevertheless, this result should be
interpreted with caution since the effect of maternal weight
during the gestation period could be oversized by the effect
of other variables such as family eating habits. Finally, a
significant association was also found between maternal
smoking during pregnancy and the subsequent develop-
ment of MetS in their children.
Conflict of interest The authors declare they have no competing
interests.
References
1. Tapia Ceballos L (2007) Sındrome metabolico en la infancia. An
Pediatr (Barc) 66:159–166
2. Bahar A, Hosseini Esfahani F, Asghari Jafarabadi M, Mehrabi Y,
Azizi F (2013) The structure of metabolic syndrome components
across follow-up survey from childhood to adolescence. Int J
Endocrinol Metab 11:16–22
3. Montero-Alonso MA, Gonzalez-Jimenez E (2013) Valoracion del
estado nutricional, resistencia insulınica y riesgo cardiovascular
en una poblacion de adolescentes de la ciudades de Granada y
Almerıa. Nutr Hosp 28:802–806
4. Ford ES, Li C (2008) Defining the metabolic syndrome in chil-
dren and adolescents: will the real definition please stand up?
J Pediatr 152:160–164
5. Mangge H, Summers KL, Meinitzer A, Zelzer S, Almer G, Prassl
R, Schnedl WJ, Reininghaus E, Paulmichl K, Weghuber D, Fuchs
D (2013) Obesity-related dysregulation of the Tryptophan–Kyn-
urenine metabolism: role of age and parameters of the metabolic
syndrome. Obesity (Silver Spring) 22:195–201
6. Ekelund U, Anderssen S, Andersen LB, Riddoch CJ, Sardinha
LB, Luan J, Froberg K, Brage S (2009) Prevalence and correlates
of the metabolic syndrome in a population-based sample of
European youth. Am J Clin Nutr 89:90–96
7. Zimmet P, Alberti KG, Kaufman F, Tajima N, Silink M, Arsla-
nian S, Wong G, Bennett P, Shaw J, Caprio S, IDF Consensus
Group (2007) The metabolic syndrome in children and adoles-
cents: an IDF consensus report. Pediatr Diabetes 8:299–306
8. Korner A, Kratzsch J, Gausche R, Schaab M, Erbs S, Kiess W
(2007) New predictors of the metabolic syndrome in children:
role of adipocytokines. Pediatr Res 61:640–645
9. Simmons RK, Alberti KGMM, Gale EAM (2010) The metabolic
syndrome: useful concept or clinical tool? Report of a WHO
Expert Consultation. Diabetologia 53:600–605
10. Ahmadi A, Gharipour M, Nouri F, Sarrafzadegan N (2013)
Metabolic syndrome in Iranian youths: a population-based study
on junior and high schools students in rural and urban areas.
J Diabetes Res 2013:738485
11. Ekelund U, Ong KK, Linne Y, Neovius M, Brage S, Dunger DB,
Wareham NJ, Rossner S (2007) Association of weight gain in
infancy and early childhood with metabolic risk in young adults.
J Clin Endocrinol Metab 92:98–103
12. Crespo PS, Prieto Perera JA, Lodeiro FA, Azuara LA (2007)
Metabolic syndrome in childhood. Public Health Nutr
10:1121–1125
13. Agius R, Savona-Ventura C, Vassallo J (2013) Transgenerational
metabolic determinants of fetal birth weight. Exp Clin Endocrinol
Diabetes 121:431–435
14. McCance D, Pettitt D, Hanson R, Jacobsson L, Knowler W,
Bennett P (1994) Birth weight and non-insulin dependent dia-
betes: thrifty genotype, thrifty phenotype, or surviving baby
genotype? BMJ 308:942–945
15. Stuebe A (2009) The risks of not breastfeeding for mothers and
infants. Rev Obstet Gynecol 2:222–231
16. Verd S, Barriuso L, Gich I, Gutierrez A, Nadal-Amat J, Carreras E
(2013) Risk of early breastfeeding cessation among symmetrical,
small for gestational age infants. Ann Hum Biol 40:146–151
17. Oddy WH (2012) Infant feeding and obesity risk in the child.
Breastfeed Rev 20:7–12
18. Yang Z, Huffman SL (2013) Nutrition in pregnancy and early
childhood and associations with obesity in developing countries.
Matern Child Nutr 9:105–119
19. Huang RC, Burke V, Newnham JP, Stanley FJ, Kendal GE,
Landau LI, Oddy WH, Blake KV, Palmer LJ, Beilin LJ (2007)
Perinatal and childhood origins of cardiovascular disease. Int J
Obes 31:236–244
20. Behl M, Rao D, Aagaard K, Davidson TL, Levin ED, Slotkin TA,
Srinivasan S, Wallinga D, White MF, Walker VR, Thayer KA,
Holloway AC (2013) Evaluation of the association between
maternal smoking, childhood obesity, and metabolic disorders: a
national toxicology program workshop review. Environ Health
Perspect 121:170–180
21. Sanchez-Muniz FJ, Gesteiro E, Esparrago Rodilla M, Rodrıguez
Bernal B, Bastida S (2013) Maternal nutrition during pregnancy
conditions the fetal pancreas development, hormonal status and
diabetes mellitus and metabolic syndrome biomarkers at birth.
Nutr Hosp 28:250–274
22. Ryckman KK, Borowski KS, Parikh NI, Saftlas AF (2013)
Pregnancy complications and the risk of metabolic syndrome for
the offspring. Curr Cardiovasc Risk Rep 7:217–223
23. Boney CM, Verma A, Tucker R, Vohr BR (2005) Metabolic
syndrome in childhood: association with birth weight, maternal
obesity, and gestational diabetes mellitus. Pediatrics 115:290–296
24. Cole TJ, Bellizzi MC, Flega KM, Dietz WH (2000) Establishing
a standard definition for child overweight and obesity worldwide:
international survey. BMJ 320:1–6
25. Brook CGD (1971) Determination of body composition of chil-
dren from skinfold measurements. Arch Dis Child 46:182–184
26. Siri WE (1961) Body composition from fluid spaces and density:
analysis of methods. In: Brozeck J, Henschel A (eds) Techniques
for measuring body composition. National Acad Sci National Res
Council, Washington
27. Berson S, Yellow RS (1959) Quantitative aspects of the reaction
between insulin and insulin-binding antibody. J Clin Invest
38:1996–2016
28. Aguayo Calcena AA (2011) Prevalencia del sındrome metabolico
en una poblacion de ninos obesos en el Paıs Vasco, su relacion
con la resistencia a la insulina, las adipoquinas y ghrelina. (PhD
thesis). Universidad del Paıs Vasco
29. Studies to Treat or Prevent Pediatric Type 2 Diabetes (STOPP-
T2D) Prevention Study Group* (2008) Prevalence of the meta-
bolic syndrome among a racially/ethnically diverse group of U.S.
Eur J Nutr
123
eighth-grade adolescents and associations with fasting insulin and
homeostasis model assessment of insulin resistance levels. Dia-
betes Care 31:2020–2025
30. Pan Y, Pratt CA (2008) Metabolic syndrome and its association
with diet and physical activity in US adolescents. J Am Diet
Assoc 108:276–286
31. Chen SK, Luo JS, Qin YF, Fan X, Tang Q, Feng Y (2011)
Epidemiological study on the association between obesity with
metabolic syndrome in obese children and adolescents of Nan-
ning city, Guangxi. Zhonghua Liu Xing Bing Xue Za Zhi
32:969–972
32. Wang SQ, Liu YJ, Zhan J, Liu XL, Feng Q, Gong J, Talbott EO,
He QQ (2013) Determinants of metabolic syndrome in Chinese
schoolchildren. Asia Pac J Public Health 4. doi:10.1177/
1010539513496137
33. Eriksson JG, Osmond C, Kajantie E, Forsen TJ, Barker DJP
(2006) Patterns of growth among children who later develop type
2 diabetes or its risk factors. Diabetologia 49:2853–2858
34. Khuc K, Blanco E, Burrows R, Reyes M, Castillo M, Lozoff B,
Gahagan S (2012) Adolescent metabolic syndrome risk is
increased with higher infancy weight gain and decreased with
longer breastfeeding. Int J Pediatr 5:478610
35. Yang Z, Huffman SL (2013) Nutrition in pregnancy and early
childhood and associations with obesity in developing countries.
Matern Child Nutr 9:105–119
36. De Armas MG, Megıas SM, Modino SC, Bolanos PI, Guardiola
PD, Alvarez TM (2009) Importance of breastfeeding in the
prevalence of metabolic syndrome and degree of childhood
obesity. Endocrinol Nutr 56:400–403
37. Ekelund U, Anderssen S, Andersen LB, Riddoch CJ, Sardinha
LB, Luan J, Froberg K, Brage S (2009) Prevalence and correlates
of the metabolic syndrome in a population-based sample of
European youth. Am J Clin Nutr 89:90–96
38. Ornoy A (2011) Prenatal origin of obesity and their complica-
tions: gestational diabetes, maternal overweight and the para-
doxical effects of fetal growth restriction and macrosomia.
Reprod Toxicol 32:205–212
39. Merritt TA, Mazela J, Adamczak A, Merritt T (2012) The impact
of second-hand tobacco smoke exposure on pregnancy outcomes,
infant health, and the threat of third-hand smoke exposure to our
environment and to our children. Prz Lek 69:717–720
40. Noda T (2013) Effects of tobacco smoke on fetus and children.
Nihon Rinsho 71:454–458
Eur J Nutr
123
