Simple anthropometric measurements to predict dyslipidemias in Mexican school-age children: a cross-sectional study

doi:10.4236/ojpm.2011.13023

Paper Menu >>

Journal Menu >>

Vol.1, No.3, 171-181 (2011)

doi:10.4236/ojpm.2011.13023

Open Journal of Preventive Medicine

Simple anthropometric measurements to predict

dyslipidemias in Mexican school-age children: a

cross-sectional study

Maria del Carmen Caamaño1,2, Olga Patricia García1, María del Rocío Arellano1,

Karina de la Torre-Carbot1, Jorge L. Rosado1,2*

1School of Natural Sciences, Universidad Autónoma de Querétaro, Querétaro, Mexico;

*Corresponding Author: jlrosado@prodigy.net.mx

2Disease and Development Center for Chronic Diseases, Querétaro, Mexico.

Received 8 September 2011; revised 14 October 2011; accepted 27 October 2011.

ABSTRACT

Objective: The purpose of this study was to i-

dentify the best predictors of dyslipidemias in

Mexican obese children using different anthro-

pometric and body composition measurements.

Methods: In an observational, cross-sectional

study, 905 children from 5 schools were meas-

ured for weight, height, waist and hip circum-

ference, and triceps and subscapular skinfolds.

A fasting blood sample was taken from a ran-

dom sub-sample of 306 children to determine

lipid profile. Abnormal total cholesterol, LDL,

HDL, triglycerides, total cholesterol to HDL ratio,

and LDL to HDL ratio, were determined. Logistic

regressions and ROC analysis were carried out

to determine the best anthropometric predictors

of these risk factors. Results: Prevalence of ele-

vated total cholesterol, triglycerides and LDL

cholesterol was 14%, 56% and 58%, respectively.

In logistic regressions, BMI and triceps skinfold

had the highest odds ratios to predict elevated

total cholesterol (1.05, 95% CI: 0.97 - 1.14; 1.07,

1.01 - 1.13, respectively), triglycerides (1.19, 1.11

- 1.27; 1.12, 1.08 - 1.17, respectively), LDL cho-

lesterol (1.11, 1.04 - 1.18; 1.09, 1.05 - 1.14, re-

spectively), total cholesterol to HDL ratio (1.06,

1.00-1.14; 1.07,1.03-1.12, respectively) and LDL

to HDL ratio risk (1.08,1.01-1.15; 1.07, 1.03-1.12,

respectively). After BMI and triceps skinfold,

subscapular skinfold also predicted dyslipide-

mias, except for low HDL; both skinfolds had a

narrower odds ratio confidence interval than

BMI. In ROC analysis, subscapular skinfold was

the best predictor of elevated triglycerides with

an AUC ≥ 0.7. Conclusion: Anthropometric

measurements are not strongly associated with

dyslipidemias in Mexican children. However,

since triceps and subscapular skinfolds were be-

tter predictors than other anthropometry meas-

ures, they may be a simple way to predict dys-

lipidemias in Mexican children.

Keywords: Cardiovascular Risk; Dyslipidemia;

Lipids; Anthropometry and Children

1. INTRODUCTION

Obesity is a major public health problem in Mexican

children [1,2]. About 26% of children between 5 to 12

years of age are overweight or obese, and this age group

had the highest rates of obesity increase in the past 10

years. The high prevalence of obesity in Mexican popu-

lation is associated with the increased incidence in

chronic diseases observed in recent years [3,4]. An ex-

cess in body fat is associated with insulin resistance and

metabolic syndrome which results in a greater probabil-

ity of developing type 2 diabetes and hyperlipidemia

[5,6]. Dyslipidemia and the consequent cardiovascular

(CV) diseases constitute the first cause of mortality in

Mexican adults [7]. In children, obesity produces an in-

crease in the prevalence of CV risk factors such as high

concentration of total cholesterol, tryglicerides or low

concentrations of HDL [8-11].

Anthropometric measures are useful and practical

methods for screening and surveillance of childhood o-

besity [12]. The most widely used are weight and height

which are used to determine Body Mass Index (BMI),

waist and hip circumferences, and skinfolds measure-

ments [13-16]. Some studies have recommended to iden-

tify children with dyslipidemia by measuring BMI or

waist circumference (WC) [11,15,17-19]. However, other

studies have suggested that different anthropometry and

body composition measurements, such as skinfold mea-

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

172

surements and waist-to-height ratio (WHtR), could pre-

dict better the risk for CV diseases [10,20,21].

It has been observed that dyslipidemias, such as low

high-density lipoprotein cholesterol (HDL), elevated low-

density lipoprotein cholesterol (LDL) and triglycerides

(TG) are associated with body fat, even in the absence of

elevated body weight [17,22]. In addition, fat distribu-

tion may differ according to gender or ethnicity, thus

these variables may affect the relationship between obe-

sity and dyslipidemias [17,23].

The objective of this study was to identify the best

predictors of abnormal lipoproteins and triglycerides in

obese children from elementary schools in Queretaro,

Mexico, using different anthropometric measurements.

2. METHODS

2.1. Subjects and Place of the Study

Children aged 6 to 12 y from 5 elementary schools in

the city of Querétaro, Mexico, participated in the study.

Parents of all children from 1st to 6th grade received oral

and written information about the study and those that

accepted to participate signed a consent form. The study

was performed in accordance with the Helsinki Declara-

tion, and the study protocol was approved by the Internal

Committee of Human Research of the University of

Querétaro.

From 905 children that participated, 17% were over-

weight and 18% were obese [24], according to the WHO

cut off criteria [25] (according to international cut-off

points (IOTF) [26]: 21% were overweight and 13% were

obese) [24]. A sub-sample was selected to include ap-

proximately the same proportion of children with over-

weight or obesity than with normal weight to participate

in the present study. Three hundred and six children par-

ticipated in a cross-sectional study. A calculation of 300

children was established to detect a significant Area

Under the Curve (AUC) of 0.6 to predict dyslipidemias

from each anthropometry measurement compared with

0.5 considering a type I error of 0.05 and a type II error

of 0.20. An AUC value of 0.5 means that the prediction

is equal to chance, and AUC value of 1 means perfect

prediction. This sample size was calculated with Med-

Calc software V.9.6.4.0 (Mariakerke, Belgium).

2.2 Measurements

2.2.1. Anthropometry

Anthropometry and body composition were measured

in all children by trained and standardized staff. Parents

of enrolled children received written notification with

the date of their measurements and were instructed that

their children not to eat anything for the previous 12

hours. Measurements were taken on school ground, in a

room specifically assigned by school authorities and

conditioned for this study. Anthropometry included

weight, height, waist, hip circumference, and subscapu-

lar and triceps skinfolds. A fasting blood sample was

taken to measure plasma lipids concentration.

Anthropometric measurements were performed in du-

plicate by trained nutritionists following standard pro-

cedures [27]. Children were weighed in light clothes,

without sweater or jacket and without shoes using an

electronic scale (SECA, Erecta 844, Hamburg, Germany)

to the nearest 1 g. Height was measured using portable

stadimeters (SECA, Bodymeter 208, Germany) with an

accuracy of 0.1 cm. Waist and hip circumference were

measured to the nearest 0.1 cm using flexible bands

(SECA). Children’s triceps and subscapular skinfolds

thickness were measured on the child’s right side fol-

lowing standard procedures to the nearest 1 mm with a

Lange caliper (Beta Technology, Inc, Cambridge, MD).

BMI for age was calculated according to the World

Health Organization growth curves references [28]: chil-

dren were identified as overweight when their BMI-for-

age Z-score was >1SD and as obese when BMI-for-age

Z-score was > 2SD. Waist to height ratios (WHtR) were

also calculated.

2.2.2. Lipid Profile

Fasting blood samples were centrifuged at 1800 -

2000 rpm during 15 minutes and plasma was separated

and stored at –20˚C until subsequent analysis. Total

cholesterol, HDL-cholesterol and triglycerides, were

measured by enzymatic/colorimetric methods using a

commercial kit (Sera-Pak Kit Bayer Diagnostics,

France). LDL was calculated with Friedwald’s equation

[29].

According to the National Cholesterol Education Pro-

gram (1992), the cut off values to determine children at

risk of a CV disease are: total cholesterol > 170 mg/dL,

HDL < 35 mg/dL, LDL > 110 mg/dL. High triglycerides

is considered with blood concentrations > 130 mg/dL for

children above 10 y and > 100 mg/dL for children aged

10 years or less. Also, total cholesterol to HDL ratio

>3.5 and LDL to HDL ratio >2.2 were considered risk

factors [30,31].

2.3. Statistical Analysis

Descriptive analysis included central tendency meas-

urements and abnormal lipids or triglycerides prevalence.

Logistic regressions were performed to determine the

odds ratio as a measure to predict the probability to pre-

sent a dyslipidemia according with the cut-off previously

described for total cholesterol, LDL, HDL, triglycerides,

total cholesterol to HDL ratio, and LDL to HDL ratio

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

173

based on continuous anthropometry variables adjusting

for age. The Receiver Operating Characteristics (ROC)

analysis was carried out to evaluate the accuracy of di-

agnosis of children with a dyslipidemia according to

each anthropometry measurements. The AUC was used

as a measure of overall performance to predict dyslipi-

demias from each anthropometry measurement. A sig-

nificant p value means that the AUC is significantly dif-

ferent from 0.5. All analyses were also performed strati-

fying by gender. The software used for statistical analy-

sis was SPSS v 18.0 (Chicago Il)

3. RESULTS

Demographic characteristics of subjects included in

the study are described in Table 1. Boys had higher

WHR and WHtR than girls, and girls had a larger hip

circumference. Plasma lipids concentration is described

in Table 2. High total cholesterol concentration was

found in 13.5% of the children. Fifty six percent of the

studied children presented high TG concentrations; a

higher proportion of girls (66%) compared with boys

(49%) presented high TG concentration. Fifty eight per-

cent of children presented elevated LDL concentrations,

while 24.8% showed total cholesterol to HDL ratio > 3.5

and 64.8% showed a LDL to HDL ratio > 2.2. Obese

children had significantly higher LDL, total cholesterol

and triglycerides than normal weight and overweight

children.

In logistics regression, all dyslipidemias, except for

low HDL cholesterol, were associated with one or more

anthropometric measures. BMI and triceps skinfold had

the highest odds ratios to predict elevated lipids or

triglycerides concentration. Odds ratio for BMI and tri-

ceps skinfold, respectively were: for high total choles-

terol: 1.05, (95%CI) 0.97 - 1.14; 1.07, 1.01 - 1.13, for

high triglycerides: 1.19, 1.11 - 1.27; 1.12, 1.08 - 1.17,

for high LDL cholesterol: 1.11, 1.04 - 1.18; 1.09, 1.05 -

1.14, for high total cholesterol to HDL ratio: 1.06, 1.00 -

1.14; 1.07, 1.03 - 1.12 and for LDL to HDL ratio: 1.08,

1.01 - 1.15; 1.07, 1.03 - 1.12. After BMI and triceps

skinfold the next best predictor of plasma lipids was

subscapular skinfold. The odds ratio confidence inter-

val for both skinfolds was narrower than BMI’s (Figure

1). When stratifying logistics regressions by gender, less

anthropometric measures could predict dyslipidemias a-

mong boys, but the results were similar to overall results.

Table 1. Anthropometry characteristics of children that participated in the study.

Anthropometry measurements All Boys Girls Normal weight Overweight Obese

N 306 161 145 94 74 137

Age (y) 9.3 ± 1.6 9.3 ± 1.6 9.4 ± 1.6 9.5 ± 1.6 9.4 ± 1.6 9.3 ± 1.6

Boys (%) 52.6 100 0 54.3 47.3 54.0

Girls (%) 47.4 0 100 45.7 52.7 46.0

BMI Zscore<1 (%) 30.8 31.9 29.7 100 0 0

BMI Zscore>1 (%) 24.3 21.9 26.9 0 100 0

BMI Zscore>2 (%) 44.9 46.2 43.4 0 0 100

BMI (Kg/m2) 20.9 ± 4.1 20.6 ± 4.1 21.1 ± 4.1 16.5 ± 1.6 a 20.0 ± 1.6 b 24.3 ± 3.0 c

BMIfor age (Z score) 1.6 ± 1.3 1.7 ± 1.4 1.5 ± 1.2 0.0 ± 0.7 a 1.6 ± 0.3 b 2.7 ± 0.6 c

Triceps skinfold (mm) 19.0 ± 6.1 18.5 ± 6.4 19.5 ± 5.7 12.8 ± 3.6 a 18.1 ± 3.9 b 23.6 ± 4.2 c

Subescapular skinfold (mm) 14.5 ± 6.8 13.9 ± 6.9 15.1 ± 6.8 7.7 ± 2.6 a 12.7 ± 3.6 b 19.9 ± 5.4 c

Waist circumference (cm) 70.3 ± 11.2 70.8 ± 11.5 69.8 ± 10.8 59.5 ± 5.7 a 67.6 ± 6.1 b 79.0 ± 8.6 c

Hip circumference (cm) 80.0 ± 10.1 78.8 ± 9.8 a 81.4 ± 10.4 b 71.7 ± 6.8 a 78.3 ± 7.1 b 86.5 ± 8.9 c

Waist to hip ratio 0.9 ± 0.1 0.9 ± 0.1 a 0.9 ± 0.1 b 0.8 ± 0.0 a 0.9 ± 0.0 b 0.9 ± 0.1 c

Skinfolds sum (mm) 33.4 ± 12.4 32.4 ± 12.7 34.6 ± 12.0 20.4 ± 5.9 a 30.8 ± 6.7 b 43.5 ± 8.6 c

Waist to height ratio 51.6 ± 6.9 52.4 ± 7.1 a 50.8 ± 6.7 b 43.8 ± 3.0 a 49.9 ± 3.1 b 57.6 ± 4.0 c

Values are means ± SD or %; a,b,cDifferent letters represent significant difference between groups (p < 0.05 in ANOVA).

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

174

Table 2. Lipids concentration in school-age children.

Lipids

concentration All Boys Girls Normal Overweight Obesity

Total cholesterol

(mg/dL) (306) 136.4 ± 31.1(161) 138.1 ± 30.3(145) 134.5 ± 31.9(94) 131.1 ± 28.1 a(74) 128.3 ± 30.5 a (137) 144.2 ± 31.8b

Triglycerides

(mg/dL) (301) 118.7 ± 48.8(157) 111.6 ± 46.6a(144) 126.4 ± 50.1b(93) 103.6 ± 48.6 a(74) 114.4 ± 45.5 a (133) 131.6 ± 47.8b

HDL Cholesterol

(mg/dL) (302) 48.2 ± 10.7(159) 49.1 ± 10.5 (143) 47.1 ± 10.9 (93) 48.4 ± 8.7 (74) 47.8 ± 11.7 (134) 48.3 ± 11.5

LDL Cholesterol

(mg/dL) (296) 118.2 ± 33.3(156) 117.6 ± 31.3(140) 118.8 ± 35.5(93) 114.2 ± 34.9 a(74) 109.5 ± 35.1 a (128) 126.0 ± 29.4b

Total cholesterol

to HDL ratio (300) 2.9 ± 0.8 (157) 2.9 ± 0.8 (143) 3.0 ± 0.9 (92) 2.8 ± 0.7 a (73) 2.8 ± 0.9 a (134) 3.1 ± 0.9b

LDL cholesterol to

HDL ratio (293) 2.6 ± 0.9 (153) 2.5 ± 0.9 (140) 2.6 ± 0.9 (91) 2.4 ± 0.8 a (73) 2.4 ± 1.0 a (128) 2.8 ± 0.9b

Values are (n) mean ± SD; a,bDifferent letters represent significance level of p < 0.05 i n ANOVA among gender or BMI category explain.

Among girls, all types of dyslipidemias were predicted

by some anthropometric measures; skinfolds had similar

odds ratio than BMI but narrower confidence intervals.

HDL cholesterol was better predicted only in girls by

BMI percentile and WC (Figures 2-3).

The ROC analysis showed that the only indicator of

dyslipidemia that was fairly well predicted by an an-

thropometric measurement with an AUC ≥ 70 was high

TG by subscapular skinfold (Table 3). Among boys, the

best predicted dyslipidemias were high TG by the WHR

and high LDL cholesterol by the triceps skinfold; among

girls, only high TG values were well predicted by sub-

scapular skinfold with the highest AUC value followed

by BMI, WHtR, WC and the sum of both skinfolds.

4. DISCUSSION

The prevalence of obesity in school-age Mexican

children is high, similar to some developed countries [32]

and is one of the highest in the world [1]. As found in

other studies in obese children with similar age groups

[8,33], the most prevalent dyslipidemia in the present

study was high concentration of TG, followed by high

concentrations of LDL cholesterol.

In the population studied, dyslipidemias are not con-

sistently present in obese children and non-obese chil-

dren may present abnormal lipids concentration, which

makes difficult to identify children at risk. For this rea-

son the AUC values from ROC curves to predict abnor-

mal lipids and triglycerides concentration from most

anthropometric measurements were slightly lower than

those reported from obese children in other populations.

For instance, in Argentinean school-age children AUC

values to predict low HDL and high triglycerides were

0.87, 0.83, and 0.84 for BMI, WC and WHtR, respect-

tively [34] and in Chinese adolescents the AUC to pre-

dict clustering of risk factors from BMI in girls and boys

were 0.85 and 0.76, and from WC were 0.82 and 0.78,

respectively. The highest AUC value found in the pre-

sent study was 0.72, an acceptable value to predict high

triglycerides from subscapular skinfold. The highest

AUC values from anthropometric measures, BMI, WC

or WHtR, that have been recognized as good predictors

of CV disease in other studies [19,35] differed from boys

to girls and overall were lower than skinfolds to predict

high triglycerides.

Body mass index had the highest odds ratio in logis-

tics regression, which means that the higher the BMI, the

higher the risk to have elevated lipids or triglycerides.

However the confidence interval for these odds ratio was

much wider than other anthropometry measurements that

had a similar odds ratio, which means that there is a

higher variability of the association between BMI and

lipids and TG concentrations than there is with other

anthropometric measurements. That may be the reason

why in ROC curves, triceps and subscapular skinfolds

seemed to diagnose similarly or even better than BMI,

WC, or WHtR.

Although different anthropometry measurements have

been evaluated to predict body fat or dyslipidemias, few

studies have compared skinfolds thickness with BMI or

waist measurements to identify children at risk of a CV

disease. Results from similar studies are controversial;

some studies have found similar results than the ones

found in the present study. Teixeira et al. [10] concluded

that trunk skinfolds predict CV disease as well as DXA

body fat variables did in Portuguese pre-adolescents.

Maffeis et al. [36] found that subscapular and triceps

skinfolds, as well as WC, may be helpful in identifying

prepubertal children with an adverse blood-lipids profile

and hypertension.

In contrast, some studies have recommended different

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

175

Figure 1. Odds ratio (±95%CI) of different anthropometry measurements to predict dyslipidemias from logistic regressions adjusted

for age.

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

176

Figure 2. Odds ratio (± 95%CI) of different anthropometry measurements to predict dyslipidemias from logistic regressions adjusted

for age in boys.

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

177

Figure 3. Odds ratio (±95% CI) of different anthropometry measurements to predict dyslipidemias from logistic regressions adjusted

for age in girls.

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

178

Table 3. Area under the curve from Receiving Operating Characteristics (ROC) to predict dyslipidemia from anthropometry measurements.

Values are areas under the curve in the receivers operating characteristics analysis (95% CI) 1The cut offs considered at risk are: Total Cholesterol >170 mg/dL, triglycerides < 130 mg/dL in children >10y; triglyc-

erides<110 in children ≤ 10 y, LDL cholesterol >110 mg/dL, HDL cholesterol < 35 mg/dL, VLDL cholesterol < Total cholesterol to HDL ratio > 3.5 and LDL to HDL ratio > 2.2 Different to 0.50 * p < 0.05, **p <

0.01.

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

179

5. ACKNOWLEDGEMENTS

measurements than skinfolds to adequately predict dys-

lipidemias. Geiss et al. [11] concluded that height-to-

weight indices, such as BMI, in prepubescent German

children, are best predictors of CV risk factors. Feedman

et al. [37] also reported that WHtR is better than skin-

folds to predict adult CV risk in a bi-racial sample of

American children; although, in the same study, the pre-

diction from the sum of skinfolds thickness did not differ

much from WHtR’s.

We are thankful to the schools that kindly agreed to participate in

this study. We would like to thank Juana Ramirez Anguiano, Paola

García Juarez and Abigail Dominguez Chavero for their dedicated

work, and to Elba Suaste for her special touch when taking blood from

children and her assistance with laboratory work.

No conflicts of interest are stated, the study was partially funded by

Conacyt Mexico

The different predictors found among studies may be

related to ethnicity, gender and age which can affect the

relationship between the ratio of subcutaneous fat to

total body fat [12,38,39] and consequently anthropome-

try predictors of dyslipidemias may differ according to

such demographic characteristics [40-42]. For in- stance,

in the present study high LDL cholesterol was better

predicted in boys than in girls, probably because differ-

ences of fat deposition between boys and girls. Similarly

the distinct ethnic characteristics of our sample may re-

sult in a different fat deposition distribution which could

have led to a better prediction of dyslipidemias with tri-

ceps and subscapular skinfolds.

REFERENCES

[1] Perichart-Perera, O., Balas-Nakash, M., Ortiz-Rodriguez,

V., et al. (2008) A program to improve some cardio-

vascular risk factors in Mexican school age children. Sa-

lud Pública de México, 50, 218-226.

[2] Olaiz-Fernandez, G., Rivera-Domarco, J., Shama-Levy,

T., et al. (2006) Encuesta Nacional de Salud y Nutricion

2006. Instituto Nacional de Salud Pública, Cuernavaca,

Mexico.

[3] Aregullin-Eligio, E.O. and Alcorta-Garza, M.C. (2009)

Prevalence and risk factors of high blood pressure in

Mexican school children in Sabinas Hidalgo. Salud

Pública de México, 51, 14-18.

[4] Velazquez-Monroy, O., Rosas Peralta, M., Lara-Esqueda,

A., et al. (2003) Prevalence and interrelations of non-

communicable chronic diseases and cardiovascular risk

factors in Mexico. Final outcomes from the National

Health Survey 2000. Archivos de cardiologia de Mexico,

73, 62-77.

It is known that skinfold measurement, such as triceps

and subscapular thickness, are a direct measure of body

fat. Even though skinfolds’ thickness do not predict vis-

ceral fat as well as other anthropometry measurements,

such as WC or WHtR [43], they have been well accepted

measurements to predict body fat [44], and consequently

they could be good predictors of dyslipidemias.

[5] Perichart-Perera, O., Balas-Nakash, M., Schiffman-Sele-

chnik, E., et al. (2007) Obesity increases metabolic syn-

drome risk factors in school-aged children from an urban

school in Mexico city. Journal of the American Dietetic

Association, 107, 81-91. doi:10.1016/j.jada.2006.10.011

In order to improve the accurateness of dyslipidemia

prediction from triceps and subscapular skinfolds, cutoff

values according to age and gender in specific ethnos,

such as Mexican children, should be determined. Addo

& Himes et al. [45] established triceps and subscapular

skinfold thickness cut offs for age and gender in Ameri-

can children. A limitation of the present study was that

the sample size was insufficient to suggest cutoff values

stratified by age and gender in Mexican children or to

test the cutoff values already reported [45]. Therefore,

future research studies with a larger sample size are

recommended to define cutoff values for age and gender

that are appropriate for Mexican children, and to confirm

its efficacy as part of the strategies of public health pro-

grams for the prevention and control of CV disease.

[6] Haffner, S.M. (2006) Relationship of metabolic risk fac-

tors and development of cardiovascular disease and dia-

betes. Obesity (Silver Spring), 14, 121S-127S.

doi:10.1038/oby.2006.291

[7] Velázquez-Monroy, O., Barinagarrementería-Aldatz, F.

S., Rubio-Guerra, A.F., et al. (2007) Morbidity and mor-

tality by ischemic heart disease and stroke in Mexico

2005. Archivos de Cardiologia de Mexico, 77, 31-39.

[8] Del-Rio-Navarro, B. E., Velazquez-Monroy, O., Lara-

Esqueda, A., et al. (2008) Obesity and metabolic risks in

children. Archives of Medical Research, 39, 215-221.

doi:10.1016/j.arcmed.2007.07.008

[9] Bueno, G., Moreno, L. A., Bueno, O., et al. (2007) Meta-

bolic risk-factor clustering estimation in obese children.

Journal of Physiology and Biochemistry, 63, 347-355.

doi:10.1007/BF03165 766

In conclusion, there is a high prevalence of obesity

and dyslipidemias in Mexican children; the major health

concern is the high triglycerides concentration. Anthro-

pometric measurements are not strongly associated with

dyslipidemias in Mexican children. However, since tri-

ceps and subscapular skinfolds were better predictors

than other anthropometry measures, they may be a sim-

ple way to predict dyslipidemias in Mexican children.

[10] Teixeira, P.J., Sardinha, L.B., Going, S.B., et al. (2001)

Total and regional fat and serum cardiovascular disease

risk factors in lean and obese children and adolescents.

Obesity Research, 9, 432-442. doi:10.1038/oby.2001.57

[11] Geiss, H.C., Parhofer, K.G. and Schwandt, P. (2001) Pa-

rameters of childhood obesity and their relationship to

cardiovascular risk factors in healthy prepubescent chil-

dren. Journal of Obesity and Related Metabolic Disor-

ders, 25, 830-837. doi:10.1038/sj.ijo.0801594

Openly accessible at

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

180

[12] Wang, J., Thornton, J.C., Kolesnik, S., et al. (2000) An-

thropometry in body composition. An Overview, Annals

of the New York Academy of Sciences, 904, 317-326.

doi:10.1111/j.1749-6632.2000.tb06474.x

[13] Siminialayi, I.M., Emem-Chioma, P.C., and Dapper, D.V.

(2008) The prevalence of obesity as indicated by BMI

and waist circumference among Nigerian adults attend-

ing family medicine clinics as outpatients in Rivers State.

Nigerian Journal of Medicine: Journal of the National

Association of Resident Doctors of Nigeria, 17, 340-345.

[14] Hubert, H., Guinhouya, C.B., Allard, L., et al. (2009)

Comparison of the diagnostic quality of body mass index,

waist circumference and waist-to-height ratio in screen-

ing skinfold-determined obesity among children. Journal

of Science and Medicine in Sport/Sports Medicine Aus-

tralia, 12, 449-451.

[15] Shalitin, S. and Phillip, M. (2008) Frequency of cardio-

vascular risk factors in obese children and adolescents

referred to a tertiary care center in Israel, Hormone re-

search, 69, 152-159. doi:10.1159/000112588

[16] WHO (1995) Report of the WHO Expert Commettee

Physical status: The use and interpretation of anthropom-

etry. Geneva.

[17] Botton, J., Heude, B., Kettaneh, A., et al. (2007) Cardio-

vascular risk factor levels and their relationships with

overweight and fat distribution in children: the Fleurbaix

Laventie Ville Sante II study, Metabolism: Clinical and

Experimental, 56, 614-622.

doi:10.1016/j.metabol.2006.12.006

[18] Ng, V.W., Kong, A.P., Choi, K.C., et al. (2007) BMI and

waist circumference in predicting cardiovascular risk fac-

tor clustering in Chinese adolescents. Obesity (Silver

Spring), 15, 494-503. doi:10.1038/oby.2007.588

[19] Katzmarzyk, P.T., Srinivasan, S.R., Chen, W., et al. (2004)

Body mass index, waist circumference, and clustering of

cardiovascular disease risk factors in a biracial sample of

children and adolescents. Pediatrics, 114, e198-e205.

doi:10.1542/peds.114.2.e198

[20] Hara, M., Saitou, E., Iwata, F., et al. (2002) Waist-to-

height ratio is the best predictor of cardiovascular dis-

ease risk factors in Japanese schoolchildren. Journal of

atherosclerosis and thrombosis, 9, 127-132.

doi:10.5551/jat.9.127

[21] Lin, W. Y., Lee, L. T., Chen, C. Y., et al. (2002) Optimal

cut-off values for obesity: Using simple anthropometric

indices to predict cardiovascular risk factors in Taiwan.

Journal of Obesity and Related Metabolic Disorders, 26,

1232-1238. doi:10.1038/sj.ijo.0802040

[22] Zwiauer, K.F., Pakosta, R., Mueller, T., et al. (1992)

Cardiovascular risk factors in obese children in relation

to weight and body fat distribution. Journal of the Ame-

rican College of Nutrition, 11, 41S-50S.

[23] Lear, S.A., Toma, M., Birmingham, C.L., et al. (2003)

Modification of the relationship between simple anthro-

pometric indices and risk factors by ethnic background.

Metabolism: Clinical and Experimental, 52, 1295-1301.

doi:10.1016/S0026-0495(03)00196-3

[24] Rosado, J.L., del, R.A.M., Montemayor, K., et al. (2008)

An increase of cereal intake as an approach to weight

reduction in children is effective only when accompanied

by nutrition education: A randomized controlled trial.

Nutrition Journal, 7, 28. doi:10.1186/1475-2891-7-28

[25] WHO (2006) WHO child growth standards: length/

height-for-age, weight-for-age, weight-for-length, weight-

for-height and body mass index-for-age. Methods and

development, WHO, Geneva.

[26] Cole, T.J., Bellizzi, M.C., Flegal, K.M., et al. (2000)

Establishing a standard definition for child overweight

and obesity worldwide: international survey. British Me-

dical Journal, 320, 1240-1243.

doi:10.1136/bmj.320.7244.1240

[27] Lohman, T.G., Roche, A.F. and Martorell, R. (1988)

Anthropometric standardization reference manual. Hu-

man Kinetics, Champaign, 39-54.

[28] WHO (2006) WHO Child growth standards. Geneva.

[29] Friedewald, W.T., Levy, R.I., and Fredrickson, D.S. (1972)

Estimation of the concentration of low-density lipopro-

tein cholesterol in plasma, without use of the preparative

ultracentrifuge. Clinical Chemistry, 18, 499- 502.

[30] Bersot, T.P., Pepin, G.M., and Mahley, R.W. (2003) Risk

determination of dyslipidemia in populations character-

ized by low levels of high-density lipoprotein cholesterol,

American Heart Journal, 146, 1052-1059.

doi:10.1016/S0002-8703(03)00516-7

[31] Inigo-Martinez, J., Elcarte-Lopez, R., Reparaz-Abaitua,

F., et al. (1996) Changes in mean serum lipid levels in

pediatric and adolescent population of Navarra between

1987 and 1993. Revista Española de Cardiología, 49,

166-173.

[32] Benson, L., Baer, H.J., and Kaelber, D.C. (2009) Trends

in the diagnosis of overweight and obesity in children

and adolescents: 1999-2007. Pediatrics, 123, e153-e158.

doi:10.1542/peds.2008-1408

[33] Hamidi, A., Fakhrzadeh, H., Moayyeri, A., et al. (2006)

Obesity and associated cardiovascular risk factors in Ira-

nian children: A cross-sectional study. Pediatrics Inter-

national: Official Journal of the Japan Pediatric Society,

48, 566-571.

[34] Hirschler, V., Molinari, C., Maccallini, G., et al. (2011)

Comparison of different anthropometric indices for iden-

tifying dyslipidemia in school children. Clinical Bioche-

mistry, In Press. doi:10.1016/j.clinbiochem.2011.02.004

[35] Savva, S.C., Tornaritis, M., Savva, M.E., et al. (2000)

Waist circumference and waist-to-height ratio are better

predictors of cardiovascular disease risk factors in chil-

dren than body mass index. Journal of Obesity and Re-

lated Metabolic Disorders, 24, 1453-1458.

doi:10.1038/sj.ijo.0801401

[36] Maffeis, C., Pietrobelli, A., Grezzani, A., et al. (2001) Waist

circumference and cardiovascular risk factors in prepu-

bertal children. Obesity Resear ch, 9, 179-187.

doi:10.1038/oby.2001.19

[37] Freedman, D.S., Serdula, M.K., Srinivasan, S.R., et al.

(1999) Relation of circumferences and skinfold thick-

nesses to lipid and insulin concentrations in children and

adolescents: the Bogalusa Heart Study. American Jour-

nal of Clinical Nutrition, 69, 308-317.

[38] Freedman, D.S., Wang, J., Thornton, J.C., et al. (2008)

Racial/ethnic differences in body fatness among children

and adolescents. Obesity (Silver Spring), 16, 1105-1111.

doi:10.1038/oby.2008.30

[39] Wells, J.C. (2001) A critique of the expression of paedia-

tric body composition data. Archives of Disease in Child-

hood, 85, 67-72. doi:10.1136/adc.85.1.67

M. del C. Caamaño et al. / Open Journal of Preventive Medicine 1 (20 11) 171-181

181

[40] Van Vliet, M., von Rosenstiel, I.A., Schindhelm, R.K., et

al. (2009) Ethnic differences in cardiometabolic risk pro-

file in an overweight/obese paediatric cohort in the

Netherlands: A cross-sectional study. Cardiovascular di-

abetology, 8, 2. doi:10.1186/1475-2840-8-2

[41] Lee, S., Kuk, J.L., Hannon, T.S., et al. (2008) Race and

gender differences in the relationships between anthro-

pometrics and abdominal fat in youth. Obesity (Silver

Spring), 16, 1066-1071. doi:10.1038/oby.2008.13

[42] Moore, D.B., Howell, P.B., and Treiber, F.A. (2002)

Adiposity changes in youth with a family history of car-

diovascular disease: impact of ethnicity, gender and so-

cioeconomic status. Journal of the Association for Aca-

demic Minority Physicians: the official publication of the

Association for Academic Minority Physicians, 13, 76-

83.

[43] Ball, G.D., Huang, T.T., Cruz, M.L., et al. (2006) Pre-

dicting abdominal adipose tissue in overweight Latino

youth. International Journal of Pediatric Obesity (IJPO):

An Official Journal of the International Association for

the Study of Obesity, 1, 210-216.

[44] Brodie, D.A. (1988) Techniques of measurement of body

composition. Part I. Sports Medicine, 5, 11-40.

doi:10.2165/00007256-198805010-00003

[45] Addo, O.Y. and Himes, J.H. (2010) Reference curves for

triceps and subscapular skinfold thicknesses in US chil-

dren and adolescents. American Journal of Clinical Nu-

trition, 91, 635-642. doi:10.3945/ajcn.2009.28385