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Factors associated with insulin resistance and non-alcoholic fatty liver disease among youths
Atherosclerosis 204 (2009) 538–543
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Factors associated with insulin resistance and non-alcoholic fatty liver disease among youths Roya Kelishadi a,∗ , Stephen R. Cook b , Babak Amra c , Atoosa Adibi c a
Pediatric Preventive Cardiology Department, Isfahan Cardiovascular Research Centre, Isfahan University of Medical Sciences, P.O. Box 81465-1148, Isfahan, Iran Department of Pediatrics, University of Rochester Medical Center, New York, USA c Isfahan School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran b
a r t i c l e
i n f o
Article history: Received 3 July 2008 Received in revised form 19 August 2008 Accepted 17 September 2008 Available online 9 October 2008 Keywords: Insulin resistance Fatty liver Adolescents Lifestyle Oxidation Inflammation Apolipoprotein
a b s t r a c t Objectives: To compare the cardio-metabolic risk factors, fitness and lifestyle among adolescents with and without weight disorders and/or metabolic abnormality, and to identify the factors associated with insulin resistance and non-alcoholic fatty liver disease (NAFLD) in this age group. Methods: This cross-sectional study comprised 100 adolescents (12–18 years) consisting of four subgroups of normal weight/obese with and without components of the metabolic syndrome. Fasting blood glucose, insulin, lipid profile, apolipoproteins A, B, CRP, oxidized-LDL, malondialdehyde and alanine aminotransferase (ALT) were examined. Cardiorespiratory fitness (CRF) and the sonographic findings of liver and carotid intima media thickness were determined. Results: Overall 95 participants completed all tests. Serum lipids, lipoproteins, the markers of inflammation and oxidative stress as well as the C-IMT of normal weight children with a metabolic abnormality were similar to obese children. CRF had the highest inverse correlation with HOMA-IR and ALT. Physical activity and healthy eating index had similar inverse correlation with HOMA-IR and ALT. ApoB/ApoA-I had significant independent association with upper quartiles of HOMA-IR and ALT. Waist circumference and ApoB/ApoA-I ratio had the highest odds ratio in increasing the risk of insulin resistance and NAFLD, whereas CRF followed by healthy eating index decreased this risk significantly. C-IMT was significantly associated with insulin resistance and NAFLD. Conclusions: We found significant associations between insulin resistance and NAFLD, and similar risk factors and protective factors for these two inter-related disorders; in this regard the role of CRF and apolipoprotein B to apolipoprotein A-I (ApoB/ApoA-I) ratio in the pediatric age group is underscored. © 2008 Elsevier Ireland Ltd. All rights reserved.
1. Introduction The high prevalence of sedentary lifestyle, low physical fitness as well as clustering of cardio-metabolic risk factors among youths make them prone to chronic diseases in adulthood [1]. The etiology of the metabolic syndrome and its contribution to chronic diseases is complex with insulin resistance as the underlying mechanism [2]. Moreover, insulin resistance and non-alcoholic fatty liver disease (NAFLD) appear to be correlated from childhood [3]. In addition, the severity of obesity, central adiposity and hypertriglyceridemia are considered as the main predictors of NAFLD in children [4]. Cardiorespiratory fitness (CRF) is shown to be more strongly correlated to metabolic risk than physical activity [5]. Some studies demonstrated an inverse association between physical activity and
∗ Corresponding author. Tel.: +98 311 3377881–8; fax: +98 311 3373435. E-mail addresses: [email protected], [email protected] (R. Kelishadi). 0021-9150/$ – see front matter © 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.atherosclerosis.2008.09.034
the metabolic risk independent of body fatness [6]. CRF and systemic inflammation may be correlated to NAFLD among children [4,7]. Oxidative stress may mediate the effects of insulin resistance on NAFLD in children [8]. Furthermore, dietary pattern can affect inflammation and insulin resistance in adults [9], no previous study has assessed such association among children. The ratio of apolipoprotein B to apolipoprotein A-I (apoB/apoA-I) may be a better marker than lipid profile in predicting cardiovascular risk, and may have significant independent association with insulin resistance [10]. Comparatively no previous study has determined the specific contribution of apolipoproteins to insulin resistance, cardio-metabolic risk factors and NAFLD among children. Although fatness has a pivotal role in such risk factors, but some obese children might have no associated metabolic abnormality, and some normal weight subjects may have a clustering of cardiometabolic abnormalities [11,12]. The objectives of this study were to compare the cardiometabolic risk factors, fitness and lifestyle behaviors among
R. Kelishadi et al. / Atherosclerosis 204 (2009) 538–543
children with and without weight disorders and/or metabolic abnormality, as well as to identify the contribution of the aforementioned parameters with insulin resistance and NAFLD in this age group.
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The physical activity pattern was assessed by a validated questionnaire including nine different metabolic equivalent (MET) levels [6]. 2.4. Laboratory methods
2. Methods 2.1. Study population This cross-sectional study was conducted in 2007–2008 among 100 adolescents, aged 12–18 years. Based on power calculations, a sample size of 88 was deemed large enough to test our hypothesis, and we increased it to 100. Participants consisted of 25 obese children with normal blood glucose, lipid profile and blood pressure considered as the phenotypically obese metabolically normal (POMN) group, 25 children with the criteria of the metabolic syndrome, i.e. at least three out of five risk factors [13] considered as phenotypically obese metabolically abnormal (POMA) group, 25 children with normal weight with normal blood glucose, lipid profile and blood pressure, considered as normal weight metabolically normal (NWMN) group, and 25 children with normal weight and at least one of the cardio-metabolic risk factors mentioned in the NWMN group, considered as the normal weight metabolically abnormal (NWMA) group. The first two groups were consecutively recruited from among obese children who were referred to the Obesity and Metabolic Syndrome Research Clinic of the Pediatric Preventive Cardiology Department, Isfahan Cardiovascular Research Center (ICRC). The last two groups were selected from the classmates of the first two groups. After screening 91 students, we reached to the necessary number (i.e. 25) of subjects with at least one of the aforementioned risk factors, the most common being low serum HDL-cholesterol followed by high triglycerides level. The samples of the NWMN group were randomly selected from among the screened students without any risk factor (n = 66). Children of all four groups were matched for age and sex. Eligibility criteria for participation included being 12–18-year-old, and being non-smoker. Those subjects with syndromal obesity, endocrine disorders, presence of any physical disability, and or history of chronic medication use were excluded from the survey. None of the participants had history and symptoms of infection during the two weeks before the study. We considered the Declaration of Helsinki, and the Ethics Committee of ICRC (NIH code: FWA 0000t8578) approved the study. Written informed consent and oral assent were obtained from all parents and children, respectively. 2.2. Anthropometric measurement and clinical examination The same team examined all study participants. Anthropometric measurement was conducted by calibrated instruments. Body mass index (BMI) was calculated on the basis of Centers for Disease Control and Prevention (CDC) growth charts [14]. Blood pressure (BP) was measured under standard protocol. The percentage of body fat was determined using dual energy absorptiometry by using Omron body fat monitor instrument (Omron, HBF-300, Japan) that was validated in our previous studies [15]. 2.3. Assessment of dietary and physical activity behaviors Nutritional assessment was performed by means of three 24-hfood records (once per week; two school days and one weekend). The Healthy Eating Index (HEI) was computed to measure participants’ diet quality [16].
Fasting blood samples were analyzed in the laboratory at ICRC, that meets the standards of the National Reference Laboratory (WHO-Collaborating Center in Tehran), and is also under the quality control of the CDC, USA, and St. Rafael University, Leuven, Belgium. Fasting blood glucose (FBG), total cholesterol (TC), LDL-cholesterol (LDL-C), HDL-cholesterol (HDL-C) and triglycerides (TG) were measured enzymatically (Pars Azmoun) on Hitachi autoanalyzer (Tokyo, Japan). Apolipoprotein A and B (apoA, B) were measured using immunoturbidometry by the same auto analyzer. Alanine aminotransferase (ALT) was determined on fresh blood samples. CRP and stress oxidatives, i.e. malondialdehyde (MDA), and conjugated diene (CDE) were measured by methods previously described [15]. The concentration of ox-LDL in plasma was measured with an ELISA procedure by using the murine monoclonal antibody mAb-4E6 as capture antibody and a peroxidase-conjugated antiapolipoprotein B antibody recognizing ox-LDL bound to the solid phase (Mercodia AB, Uppsala, Sweden). Plasma insulin was measured by radioimmunoassay (LINCO Research Inc). Insulin resistance (IR) was calculated on the basis of homeostasis model assessment of IR (HOMA-IR). 2.5. Assessment of cardiorespiratory fitness Symptom-limited cardiopulmonary exercise test was performed by standard protocol by using an electrically braked cycle ergometer (model 800; Zan; Germany). CRF was expressed relative to body weight (mL/kg/min) [17]. 2.6. Sonographic studies of liver and carotid-intima-media thickness Liver ultrasonography was performed by the same radiologist by using the same instrument (Siemens G50 serial GEE 3160). Any degree of increased liver echogenicity was documented. The same cardiologist conducted the studies for measurement of carotid intima media thickness (C-IMT) by using high resolution carotid ultrasonographic study as described previously [18]. 2.7. Definition of abnormal values We defined insulin resistance as the upper quartile of HOMA-IR, and high apoB/apoA ratio as the highest quartile of this ratio. Elevated ALT level was defined as ALT level >30 U/L. Given that among children, NAFLD has distinct characteristics, often different from those found in adults, and different cut-off values of serum ALT have been used for its definition. As reviewed by Schwimmer [19], different definitions used for pediatric NAFLD have major limitations. Serum ALT activity that is widely used as a screening tool for the detection of fatty liver in children is insufficient as a single measure for the determination of NAFLD. On the other hand, as the most commonly used imaging technique to clinically evaluate for the presence of hepatic echogenicity, liver ultrasonography is considered a good screening test for NAFLD; however, it has high rates of both false-positive and false-negative results. Therefore, in order to reduce the limitations of each definition and to reach to a higher probability of detecting fatty liver among children, we defined NAFLD as the co-existence of elevated serum ALT values and increased liver echogenicity in the sonographic evaluation. Metabolic syndrome was defined according to the criteria of the
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2007 Consensus of the International Diabetes Federation provided for children and adolescents [13]. 2.8. Statistical methods The characteristics of study participants were compared by using analysis of variance (ANOVA) for quantitative variables and chi-square for qualitative variables. Pearson correlations were used to examine bivariate relations of variables with HOMA-IR and ALT as continuous variables. Multiple regression analysis was performed to assess the correlates of upper quartiles of HOMA-IR and ALT. Selection of predictor variables was done using a forward stepwise fashion with strict variable entry and elimination criteria in each predictors group. For assessment of the independent associations of different variables with the dependent variables, seven models were applied with adjustment for different variables. Consequently, the final model only included those measures that made independent contribution to upper quartiles of HOMA-IR and ALT. Logistic regression was used to assess associations of the variables assessed with insulin resistance and NAFLD. Analyses were performed using the SPSS for Windows (version 15.0; SPSS Inc., Chicago, IL). ALT, CRP, insulin, HOMA-IR, and TG levels were natural log transformed to normalize distributions. 3. Results Of the children included to the study, 95 (51 girls, 44 boys) completed all tests. The mean age of participants was 15.6 ± 1.4 years, without significant difference between groups. The most prevalent abnormalities in the NWMA group were high TG (41.6%, n = 10) and low HDL-C (37.5%, n = 9), respectively; the rest (20.9%, n = 5) had a combination of these two risk factors. The mean value of serum lipids, lipoproteins, ApoB/ApoA-I ratio, insulin, HOMA-IR, CRP, ox-
LDL, MDA and ALT, as well as C-IMT was higher in the POMA group followed by the POMN, NWMA and NWMN groups. The mean HDLC and CRF was lower in the POMA than in other groups (Table 1). Within the same BMI category, subjects with high CRF had significantly (p = 0.01) lower WC compared with subjects with low CRF (data not shown). Across all groups, CRF had the highest inverse correlation with HOMA-IR and ALT. Physical activity level and HEI had near similar inverse correlation with HOMA-IR and ALT. The highest correlation of anthropometric indexes with HOMA-IR and ALT was found for WC, fat mass and BMI, respectively (Supplementary online Table 1). As presented in Table 2, CRF, PA and HEI had inverse associations with upper quartiles of HOMA-IR and ALT, which remained significant after adjustment for age, gender, pubertal status, BMI and WC. ApoB/ApoA-I ratio, C-reactive protein and malondialdehyde had significant associations with upper quartiles of HOMA-IR and ALT, which were independent to anthropometric measures and lifestyle habits. After adjustment for all variables, ApoB/ApoA-I ratio remained in the model and had significant independent association with upper quartiles of HOMA-IR and ALT. After controlling for age, gender and pubertal status, each variable considered one-at-a-time made a significant additional contribution to the prediction of insulin resistance and NAFLD. Overall, the variables assessed explained 65% of variation in insulin resistance and 67% of variation in NAFLD; the strongest predictors being TG, ApoB/ApoA-I ratio, and CRP, respectively. In addition, ALT explained 9% of variation in insulin resistance, and HOMA-IR explained 8% of variation in NAFLD (Supplementary online Table 2). Logistic regression analysis revealed that after adjustment for age, gender and pubertal status, respectively, WC, ApoB/ApoAI ratio, fat mass and BMI, followed by CRP, the number of the metabolic syndrome components, TG, ox-LDL, C-IMT and MDA increased the risk of insulin resistance and NAFLD among chil-
Table 1 Descriptive characteristics of participants.
Body mass index (kg/m2 ) Body mass index percentile Waist circumference (cm) Waist circumference percentile Subcutaneous fat (mm) Body fat mass (kg) Total cholesterol (mg/dL) LDL cholesterol (mg/dL) HDL cholesterol (mg/dL) Triglycerides (mg/dL) Apolipoprotein A-1 (mg/dL) Apolipoprotein B (mg/dL) ApoB/ApoA-I ratio Fasting blood glucose (mg/dL) Insulin (U/mL) HOMA-IR Alanine aminotransferase (ALT) (U/L) C-reactive protein (mg/L) Oxidized LDL (U/L) Malondialdehyde (mol/L) Systolic blood pressure, mmHg Diastolic blood pressure (mmHg) Healthy eating index score (0–100) Physical activity (MET-h/day) Cardiorespiratory fitness (mL/kg/min) Carotid intima media thickness (mm) Insulin resistance (%) Non-alcoholic fatty liver disease (%)
NWMN (n = 23)
NWMA (n = 24)
POMN (n = 24)
POMA (n = 24)
20.7 (0.5)b 77thb 68.2 (1.4)b 55thb 15.4 (0.2)b 24.1 (2.2)b 157.8 (7.9) 102.4 (2.1) 41.5 (2.9) 97.1 (12.4) 139.1 (14.4) 67.5 (7.1) 0.47 81.7 (8.2)b,c 16.1 (2.3) 1.6 (0.4) 19.8 (4.1) 0.8 (0.02)c 65.4 (12.5) 0.5 (0.01) 115.1 (21.1) 68.7 (15.4) 72.4 (12.5) 47.2 (10.7) 18.7 (2.2) 0.29 (0.02) 1.8 3.2
20.2 (0.7)a 75tha 67.2 (1.7)a 57tha 15.7 (0.1)a 24.7 (2.1)a 174.8 (6.5) 115.2 (2.4) 35.2 (1.7) 132.7 (14.1) 135.4 (12.7)c 72.4 (8.1) 0.52c 82.5 (6.2)a,c 20.4 (2.1) 3.4 (0.7) 25.5 (4.2) 1.01 (0.05)c 70.1 (15.4) 0.7 (0.01) 119.2 (21.4)c 74.2 (17.2) 69.2 (10.2)c 45.2 (11.7)c 15.2 (2.4)c 0.35 (0.02)c 6.4 5.6
26.4 (0.5)d 96thd 80.7 (1.2)d 92thd 18.2 (0.4)d 30.1 (1.5)d 168.1 (6.3) 107.8 (2.1)a 38.4 (1.7) 112.4 (12.5) 137.1 (11.4)a 70.5 (7.4) 0.51 81.5 (6.5)d 23.1 (2.5) 4.1 (0.9) 32.4 (8.6) 0.9 (0.01) 69.8 (16.2) 0.7 (0.02) 121.5 (20.2) 77.1 (18.2) 68.1 (8.7)d 44.6 (10.1)d 15.7 (2.5)d 0.37 (0.04) 17.2 12.4
26.2 (0.6)c 97thc 82.4 (1.1) 96thc 18.4 (0.5)c 30.7 (2.2)c 179.2 (7.2) 117.8 (3.2) 32.1 (2.4) 139.3 (11.4) 125.3 (12.4) 78.2 (7.5) 0.61 83.4 (7.2)c 26.2 (2.4) 4.8 (0.5) 37.7 (9.2) 1.4 (0.02) 77.2 (18.1) 0.9 (0.01) 128.4 (25.1) 77.2 (17.5)c 67.2 (7.4)c 43.7 (10.2)c 14.8 (2.1)c 0.41 (0.05) 79.1 21.1
NWMN: normal weight metabolically normal; NWMA: normal weight metabolically abnormal; POMN: phenotypically obese metabolically normal; POMA: phenotypically obese metabolically abnormal; data are means (SD) unless indicated otherwise; all analyses are adjusted for age and sex; HOMA-IR = (fasting insulin(mU/L) × fasting glucose(mmol/L) /22.5. All differences are significant between groups except those marked by following superscripts: a : p > 0.05 vs. NWMN group; b :p > 0.05 vs. NWMA group; c : p > 0.05 vs. POMN group; d : p > 0.05 vs. POMA group.
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Table 2 Association of variables assessed with upper quartiles of HOMA-IR and alanine aminotransferase. Upper quartile of HOMA-IR
Upper quartile of ALT
ˇ (SE)
P
R2
ˇ (SE)
P
0.31 (0.02) 0.37 (0.04) 025 (0.01) 0.27 (0.02) 0.45 (0.07) 0.34 (0.02) 0.30 (0.02) 0.28 (0.01) −0.32 (0.02) (0.38 (0.03) (0.37 (0.01) 0.25 (0.04)
0.03 0.03 0.04 0.03 0.001 0.02 0.03 0.04 0.02 0.04 0.04 0.04
0.31 0.35 0.22 0.34 0.51 0.41 0.35 0.27 0.29 0.32 0.38 0.34
0.28 (0.03) 0.35 (0.01) 0.21 (0.01 0.25 (0.02) 0.42 (0.03) 0.39 (0.04) 0.34 (0.01) 0.21 (0.01) (0.28 (0.03) (0.24 (0.02) (0.28 (0.03) 0.24 (0.01)
0.04 0.03 0.04 0.04 0.001 0.01 0.02 0.04 0.04 0.04 0.03 0.04
Model II: Adjusted for age, age, gender, pubertal status and BMI Waist circumference (cm) 0.41 Triglycerides (mg/dL) 0.30 ApoB/ApoA-I ratio 0.51 C-reactive protein (mg/L) 0.35 Malondialdehyde (mol/L) 0.34 Oxidized LDL (U/L) 0.32 Healthy eating index score (0–100) 0.31 Physical activity (MET-h/day) 0.35 Cardiorespiratory fitness (mL/kg/min) 0.44
0.35 (0.02) 0.27 (0.02) 0.43 (0.07) 0.32 (0.03) 0.28 (0.01) 0.27 (0.02) (0.27 (0.02) (0.31 (0.02) (0.37 (0.01)
0.02 0.04 0.001 0.02 0.03 0.04 0.03 0.04 0.04
0.34 0.32 0.50 0.37 0.29 0.28 0.24 0.27 0.38
0.34 (0.01) 0.24 (0.03) 0.41 (0.02) 0.31 (0.02) 0.21 (0.01) 0.21 (0.01) (0.22 (0.02) (0.22 (0.02) (0.28 (0.03)
0.04 0.04 0.02 0.03 0.04 0.04 0.04 0.04 0.03
Model III: Adjusted for age, gender, pubertal status, BMI and WC ApoB/ApoA-I ratio 0.37 C-reactive protein (mg/L) 0.28 Malondialdehyde (mol/L) 0.24 Healthy eating index score (0–100) 0.29 Physical activity (MET-h/day) 0.32 Cardiorespiratory fitness (mL/kg/min) 0.38
0.31 (0.01) 0.25 (0.02) 0.21 (0.01) (0.24 (0.03) (0.30 (0.02) (0.32 (0.02)
0.03 0.04 0.04 0.04 0.04 0.04
0.32 0.40 0.19 0.21 0.17 0.31
0.27 (0.02) 0.35 (0.02) 0.17 (0.02) (0.20 (0.02) (0.18 (0.01) (0.22 (0.02)
0.04 0.01 NS 0.04 NS 0.04
Model IV: Adjusted for age, gender, pubertal status, BMI, WC and HEI ApoB/ApoA-I ratio 0.35 C-reactive protein (mg/L) 0.25 Malondialdehyde (mol/L) 0.22 Physical activity (MET-h/day) 0.29 Cardiorespiratory fitness (mL/kg/min) 0.34
0.30 (0.02) 0.22 (0.03) 0.20 (0.02) (0.27 (0.01) (0.30 (0.02)
0.03 0.04 0.04 0.04 0.04
0.30 0.32 0.14 0.18 0.30
0.25 (0.02) 0.27 (0.01) 0.17 (0.02) 0.19 (0.01) (0.21 (0.01)
0.04 0.04 NS NS 0.04
Model V: Adjusted for age, gender, pubertal status, BMI, WC and PA ApoB/ApoA-I ratio 0.32 C-reactive protein (mg/L) 0.24 Malondialdehyde (mol/L) 0.21 Healthy eating index score (0–100) 0.25 Cardiorespiratory fitness (mL/kg/min) 0.32
0.28 (0.01) 0.22 (0.03) 0.21 (0.02) (0.20 (0.01) (0.28 (0.02)
0.04 0.04 0.04 0.04 0.04
0.28 0.28 0.18 0.20 0.27
0.24 (0.02) 0.24 (0.01) 0.19 (0.02) (0.20 (0.01) (0.20 (0.02)
0.04 0.04 NS 0.04 0.04
Model VI: Adjusted for age, gender, pubertal status, BMI, WC and CRF ApoB/ApoA-I ratio 0.31 C-reactive protein (mg/L) 0.23 Malondialdehyde (mol/L) 0.20 Healthy eating index score (0–100) 0.24 Physical activity (MET-h/day) 0.26
0.27 (0.01) 0.21 (0.01) 0.20 (0.01) (0.21 (0.01) (0.26 (0.02)
0.04 0.04 0.04 0.04 0.04
0.25 0.25 0.17 0.20 0.19
0.22 (0.02) 0.22 (0.01) 0.18 (0.04) (0.20 (0.02) 0.18 (0.02)
0.04 0.04 NS 0.04 NS
0.04 0.04
0.24 0.17
0.21 (0.01) 0.18 (0.01)
0.04 NS
0.04
0.22
0.20 (0.02)
0.04
2
R
Model I: Adjusted for age, gender and pubertal status 0.37 Body mass index (kg/m2 ) Waist circumference (cm) 0.42 Cholesterol/HDL ratio 0.24 Triglycerides (mg/dL) 0.31 ApoB/ApoA-I ratio 0.52 C-reactive protein (mg/L) 0.37 Malondialdehyde (mol/L) 0.31 Oxidized LDL (U/L) 0.29 Healthy eating index score (0–100) 0.38 Physical activity (MET-h/day) 0.41 Cardiorespiratory fitness (mL/kg/min) 0.44 Carotid intima media thickness (mm) 0.32
Model VII: Adjusted for age, gender, pubertal status, BMI, WC, HEI, PA and CRF ApoB/ApoA-I ratio 0.28 0.25 (0.03) Malondialdehyde (mol/L) 0.20 0.19 (0.01) Model VIII: Adjusted for age, gender, pubertal status, BMI, WC, HEI, PA, CRF, CRP and MDA ApoB/ApoA-I ratio 0.27 0.24 (0.01)
Only significant associations are presented. Body mass index, waist circumference, body fat mass, cholesterol/HDL ratio, triglycerides, ApoB/ApoA-I ratio, C-reactive protein, malondialdehyde, oxidized LDL, healthy eating index score, physical activity, cardiorespiratory fitness and carotid intima media thickness were included in each model (unless the model was adjusted for that variable), pubertal status was classified according to Tanner stages; ˇ: regression coefficients; SE: standard error; R2 : standardized coefficient of determination; ALT: alanine aminotransferase; BMI: body mass index; WC: waist circumference; HEI: healthy eating index score; PA: physical activity; CRF: cardiorespiratory fitness; MDA: malondialdehyde; the upper quartile of HOMA-IR was 4.2–5.4, and the upper quartile of ALT was 37.7–46.9 U/L.
dren studied. CRF followed by HEI decreased this risk significantly (Table 3). 4. Discussion In this study, that to the best of our knowledge is the first of its kind, we found that the lipids, lipoproteins, the markers of inflam-
mation and oxidative stress as well as the C-IMT of normal weight children with a metabolic abnormality (NWMA) were similar to obese children. Irrespective of obesity and metabolic abnormality, fitness had the highest inverse correlation with HOMA-IR and ALT; and among the anthropometric measures, WC had the highest correlation with these parameters. Furthermore we found that after adjustment for all covariates, ApoB/ApoA-I ratio had significant
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Table 3 Logistic regression analysisa for factors associated with insulin resistance and NAFLD. Insulin resistance
BMI (quartiles) WC (quartiles) Fat mass (quartiles) CRF (quartiles) HEI score (quartiles) C-IMT (quartiles) TG (quartiles) No. of MetS components ApoB/ApoA-I ratio (quartiles) C-reactive protein (quartiles) Oxidized LDL (quartiles) Malondialdehyde (quartiles) HOMA-IR (quartiles) NAFLD (present vs. absent)
Non-alcoholic fatty liver disease
Odds ratio
95%CI
Odds ratio
95%CI
2.4 3.8 2.8 0.6 0.8 1.3 1.7 1.8 3.1 2.1 1.4 1.2
1.4–3.2 2.1–4.3 1.5–3.6 0.4–0.9 0.3–0.9 1.04–2.5 1.1–2.6 1.2–2.7 1.8–4.2 1.4–3.1 1.07–2.8 1.03–2.1
2.7 3.5 2.7 0.7 0.8 1.2 1.5 1.6 3.2 1.9 1.5 1.3 2.4
1.5–3.4 2.4–4.6 1.2–3.7 0.2–0.9 0.1–0.9 1.03–2.1 1.07–2.8 1.1–3.01 1.4–4.5 1.1–2.8 1.05–2.6 1.04–2.7 1.8–3.7
2.5
1.8–3.1
Only significant values are presented in the table. Variables included in the model: body mass index (BMI), waist circumference (WC), subcutaneous fat, body fat mass total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides (TG), fasting blood glucose, number of metabolic syndrome (MetS) components, ApoB/ApoA-I ratio, C-reactive protein, oxidized LDL, malondialdehyde, alanine aminotransferase (ALT), systolic blood pressure, diastolic blood pressure, healthy eating index (HEI) score, physical activity, cardiorespiratory fitness, non-alcoholic fatty liver disease (NAFLD) and carotid intima media thickness (C-IMT). a Adjusted for age, gender and pubertal status (classified according to Tanner stages) NAFLD: non-alcoholic fatty liver disease.
independent association with upper quartiles of HOMA-IR and ALT. WC and ApoB/ApoA-I ratio had the highest odds ratio in increasing the risk of insulin resistance and NAFLD among the children studied, whereas CRF followed by HEI decreased this risk significantly. We documented significant associations between insulin resistance and NAFLD, and similar risk factors and protective factors for these two inter-related disorders. Insulin resistance and NAFLD might be early markers of mechanisms predisposing to future metabolic events and atherosclerosis. In our study, C-IMT was significantly associated with insulin resistance and NAFLD; this finding might suggest that the liver and the vessels share common mediators; it should be confirmed through longitudinal studies. The association of apolipoproteins with metabolic syndrome and insulin resistance has been previously documented among adult population [20]. Our findings highlight the importance of measuring apolipoproteins, and not only the traditional risk factors, in the pediatric age group. Consistent with a recent study among healthy men who showed less visceral adipose tissue accumulation and more favorable plasma lipoprotein-lipid profiles in the highest tertile of CRF [21], we found that within the same BMI category, children with high CRF had significantly lower WC compared with subjects with low CRF. There are conflicting results about the association of CRF with cardiometabolic risk factors in children; a study found that for the same level of aerobic fitness, the lipid profile was more favorable in normal than in overweight adolescents [22], whereas in another study, visceral adipose tissue, but not CRF, was a significant predictor of markers of the insulin resistance syndrome [23]. HOMA and fasting insulin are positively associated with adiposity in children, however, it is suggested that the health hazards of high fatness in children could be counteracted by having high levels of CRF [24]. Consistent with some previous studies among youths, we found significant independent association between CRF and insulin resistance but such independent association is not confirmed by some previous studies that showed this association is mediated, at least in part, through fatness [25]. It is suggested that the differences between the results of these studies might be because of ethnic differences in body composition and in the age of the children studied. Taken together, these findings suggest that interventions designed to prevent insulin resistance and related metabolic disorders should focus not only on reducing fatness but also improving fitness.
We found that ALT was higher in obese children with metabolic disorder (POMA group) than in normal weight children with metabolic disorders (NWMA group). This finding is in line with a previous study that showed the severity of obesity is considered as one of the main predictors of NAFLD in children [4]. However, in our study although the obese groups did not have morbid obesity and their mean BMI corresponded to the 96–97th percentile, but the markers of insulin resistance and NAFLD were considerably high among them. This finding is in line with studies conducted among adult populations which showed that among Asians the increased health risks associated with obesity occur at lower BMI levels [26]. Genetic predisposition or early-life adverse events may contribute to adverse body fat patterning and its related complications, notably in non-European populations [27]. Our findings showed that a healthy diet might act as a protective factor reducing the risk of insulin resistance and NAFLD among children. Although this association is documented among adults [28] such experience is limited in the pediatric age group. 4.1. Study limitations The most important limitation of our study is its cross-sectional nature. Second, we did not assess visceral fat by imaging techniques; however, WC has been shown to be a reliable marker of visceral adiposity tissue in children. Third is the recall bias for the process of recalling and recording food intake and physical activity habits. Fourth, we used a dietary score and did not analyze the precise nutrient and energy intake of the subjects studied, however, the practical use of this score has been documented in previous studies among children. The controversies existing in the methods applied for measurement of ox-LDL, and the limitations of ELISA procedures used for its determination [29] should be considered as well. 5. Conclusion In this study, we documented significant associations between insulin resistance and NAFLD, and similar risk factors and protective factors for these two inter-related disorders. Among the factors studied, fitness had the highest inverse correlation with HOMA-IR and ALT. ApoB/ApoA-I ratio had significant independent association with upper quartiles of HOMA-IR and ALT. WC and ApoB/ApoA-I
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ratio had the highest odds ratio in increasing the risk of insulin resistance and NAFLD, whereas fitness and healthy dietary habits decreased this risk significantly. The association of C-IMT with insulin resistance and NAFLD might suggest that the liver and the vessels share common mediators. Our findings should be confirmed through longitudinal studies. Acknowledgment Grant/funding support: This study is funded by Isfahan Cardiovascular Research Center, a WHO Collaborating center in the EMR, and affiliated to Isfahan University of Medical Sciences. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atherosclerosis.2008.09.034. References [1] Carnethon MR, Gulati M, Greenland P. Prevalence and cardiovascular disease correlates of low cardiorespiratory fitness in adolescents and adults. JAMA 2005;294:2981–8. [2] Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2005;365:1415–28. [3] Roberts EA. Pediatric nonalcoholic fatty liver disease (NAFLD): a “growing” problem? J Hepatol 2007;46:1133–42. [4] Quiros-Tejeira RE, Rivera CA, Ziba TT, et al. Risk for nonalcoholic fatty liver disease in Hispanic youth with BMI > or = 95th percentile. J Pediatr Gastroenterol Nutr 2007;44:228–36. [5] Rizzo NS, Ruiz JR, Hurtig-Wennlof A, et al. Relationship of physical activity, fitness, and fatness with clustered metabolic risk in children and adolescents: the European youth heart study. J Pediatr 2007;150:388– 94. [6] Kelishadi R, Razaghi EM, Gouya MM, et al. Association of physical activity and the metabolic syndrome in children and adolescents: CASPIAN Study. Horm Res 2007;67:46–52. [7] Sartorio A, Del Col A, Agosti F, et al. Predictors of non-alcoholic fatty liver disease in obese children. Eur J Clin Nutr 2007;61:877–83. [8] Schwimmer JB, Deutsch R, Rauch JB, et al. Obesity, insulin resistance, and other clinicopathological correlates of pediatric nonalcoholic fatty liver disease. J Pediatr 2003;143:500–5. [9] Ruidavets JB, Bongard V, Dallongeville J, et al. High consumptions of grain, fish, dairy products and combinations of these are associated with a low prevalence of metabolic syndrome. J Epidemiol Community Health 2007;6: 810–7. [10] Thompson A, Danesh J. Associations between apolipoprotein B, apolipoproteinAI, the apolipoprotein B/AI ratio and coronary heart disease: a literaturebased meta-analysis of prospective studies. J Intern Med 2006;259:481–92.
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[11] Kelishadi R, Cook SR, Motlagh ME, et al. Metabolically obese normal weight and phenotypically obese metabolically normal youths: the CASPIAN Study. J Am Diet Assoc 2008;108:82–90. [12] Ramachandran A, Snehalatha C, Yamuna A, et al. Insulin resistance and clustering of cardiometabolic risk factors in urban teenagers in southern India. Diab Care 2007;30:1828–33. [13] Zimmet P, Alberti KG, Kaufman F, et al. The metabolic syndrome in children and adolescents–an IDF consensus report. Pediatr Diab 2007;8:299–306. [14] Kuczmarski RJ, Ogden CL, Grummer-Strawn LM. CDC growth charts: United States. Adv Data 2000;314:1–27. [15] Kelishadi R, Sharifi M, Khosravi A, Adeli K. Relationship between C-reactive protein and atherosclerotic risk factors and oxidative stress markers among young persons 10–18 years old. Clin Chem 2007;53:456–64. [16] Lee Y, Mitchell DC, Smiciklas-Wright H, Birch LL. Diet quality, nutrient intake, weight status, and feeding environments of girls meeting or exceeding recommendations for total dietary fat of the American Academy of Pediatrics. Pediatrics 2001;107:E95. [17] Vanderburgh PM, Katch FI. Ratio scaling of VO2max penalizes women with larger percent body fat, not lean body mass. Med Sci Sports Exerc 1996;28:1204–8. [18] Sabri MR, Kelishadi R. The thickness of the intimal and medial layers of the carotid arteries, and the index of left ventricular mass, in children of patients with premature coronary arterial disease. Cardiol Young 2007;17:609–16. [19] Schwimmer JB. Definitive diagnosis and assessment of risk for nonalcoholic fatty liver disease in children and adolescents. Semin Liver Dis 2007;27(3):312–8. [20] Sierra-Johnson J, Romero-Corral A, Somers VK, et al. ApoB/apoA-I ratio: an independent predictor of insulin resistance in US non-diabetic subjects. Eur Heart J 2007;28:2637–43. [21] Arsenault BJ, Lachance D, Lemieux I, et al. Visceral adipose tissue accumulation, cardiorespiratory fitness, and features of the metabolic syndrome. Arch Intern Med 2007;167:1518–25. [22] Mesa JL, Ruiz JR, Ortega FB, et al. Aerobic physical fitness in relation to blood lipids and fasting glycaemia in adolescents: influence of weight status. Nutr Metab Cardiovasc Dis 2006;16:285–93. [23] Owens S, Gutin B, Barbeau P, et al. Visceral adipose tissue and markers of the insulin resistance syndrome in obese black and white teenagers. Obes Res 2000;8:287–93. [24] Ruiz JR, Rizzo NS, Ortega FB, et al. Markers of insulin resistance are associated with fatness and fitness in school-aged children: the European Youth Heart Study. Diabetologia 2007;50:1401–8. [25] Lee S, Bacha F, Gungor N, Arslanian SA. Cardiorespiratory fitness in youth: relationship to insulin sensitivity and beta-cell function. Obesity (Silver Spring) 2006;14:1579–85. [26] WHO/IASO/IOTF. The Asia-Pacific Perspective. Redefining Obesity and Its Treatment. Sydney: Health Communications. WHO (1998): Obesity: Prevention and managing the global epidemic. Geneva: WHO 2000. [27] Lear SA, Humphries KH, Kohli S, et al. Visceral adipose tissue accumulation differs according to ethnic background: results of the Multicultural Community Health Assessment Trial (M-CHAT). Am J Clin Nutr 2007;86:353–9. [28] McKeown NM, Meigs JB, Liu S, et al. Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort. Diab Care 2004;27:538–46. [29] Itabe H, Ueda M. Measurement of plasma oxidized low-density lipoprotein and its clinical implications. J Atheroscler Thromb 2007;14:1–11.
Contents lists available at ScienceDirect
Atherosclerosis journal homepage: www.elsevier.com/locate/atherosclerosis
Factors associated with insulin resistance and non-alcoholic fatty liver disease among youths Roya Kelishadi a,∗ , Stephen R. Cook b , Babak Amra c , Atoosa Adibi c a
Pediatric Preventive Cardiology Department, Isfahan Cardiovascular Research Centre, Isfahan University of Medical Sciences, P.O. Box 81465-1148, Isfahan, Iran Department of Pediatrics, University of Rochester Medical Center, New York, USA c Isfahan School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran b
a r t i c l e
i n f o
Article history: Received 3 July 2008 Received in revised form 19 August 2008 Accepted 17 September 2008 Available online 9 October 2008 Keywords: Insulin resistance Fatty liver Adolescents Lifestyle Oxidation Inflammation Apolipoprotein
a b s t r a c t Objectives: To compare the cardio-metabolic risk factors, fitness and lifestyle among adolescents with and without weight disorders and/or metabolic abnormality, and to identify the factors associated with insulin resistance and non-alcoholic fatty liver disease (NAFLD) in this age group. Methods: This cross-sectional study comprised 100 adolescents (12–18 years) consisting of four subgroups of normal weight/obese with and without components of the metabolic syndrome. Fasting blood glucose, insulin, lipid profile, apolipoproteins A, B, CRP, oxidized-LDL, malondialdehyde and alanine aminotransferase (ALT) were examined. Cardiorespiratory fitness (CRF) and the sonographic findings of liver and carotid intima media thickness were determined. Results: Overall 95 participants completed all tests. Serum lipids, lipoproteins, the markers of inflammation and oxidative stress as well as the C-IMT of normal weight children with a metabolic abnormality were similar to obese children. CRF had the highest inverse correlation with HOMA-IR and ALT. Physical activity and healthy eating index had similar inverse correlation with HOMA-IR and ALT. ApoB/ApoA-I had significant independent association with upper quartiles of HOMA-IR and ALT. Waist circumference and ApoB/ApoA-I ratio had the highest odds ratio in increasing the risk of insulin resistance and NAFLD, whereas CRF followed by healthy eating index decreased this risk significantly. C-IMT was significantly associated with insulin resistance and NAFLD. Conclusions: We found significant associations between insulin resistance and NAFLD, and similar risk factors and protective factors for these two inter-related disorders; in this regard the role of CRF and apolipoprotein B to apolipoprotein A-I (ApoB/ApoA-I) ratio in the pediatric age group is underscored. © 2008 Elsevier Ireland Ltd. All rights reserved.
1. Introduction The high prevalence of sedentary lifestyle, low physical fitness as well as clustering of cardio-metabolic risk factors among youths make them prone to chronic diseases in adulthood [1]. The etiology of the metabolic syndrome and its contribution to chronic diseases is complex with insulin resistance as the underlying mechanism [2]. Moreover, insulin resistance and non-alcoholic fatty liver disease (NAFLD) appear to be correlated from childhood [3]. In addition, the severity of obesity, central adiposity and hypertriglyceridemia are considered as the main predictors of NAFLD in children [4]. Cardiorespiratory fitness (CRF) is shown to be more strongly correlated to metabolic risk than physical activity [5]. Some studies demonstrated an inverse association between physical activity and
∗ Corresponding author. Tel.: +98 311 3377881–8; fax: +98 311 3373435. E-mail addresses: [email protected], [email protected] (R. Kelishadi). 0021-9150/$ – see front matter © 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.atherosclerosis.2008.09.034
the metabolic risk independent of body fatness [6]. CRF and systemic inflammation may be correlated to NAFLD among children [4,7]. Oxidative stress may mediate the effects of insulin resistance on NAFLD in children [8]. Furthermore, dietary pattern can affect inflammation and insulin resistance in adults [9], no previous study has assessed such association among children. The ratio of apolipoprotein B to apolipoprotein A-I (apoB/apoA-I) may be a better marker than lipid profile in predicting cardiovascular risk, and may have significant independent association with insulin resistance [10]. Comparatively no previous study has determined the specific contribution of apolipoproteins to insulin resistance, cardio-metabolic risk factors and NAFLD among children. Although fatness has a pivotal role in such risk factors, but some obese children might have no associated metabolic abnormality, and some normal weight subjects may have a clustering of cardiometabolic abnormalities [11,12]. The objectives of this study were to compare the cardiometabolic risk factors, fitness and lifestyle behaviors among
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children with and without weight disorders and/or metabolic abnormality, as well as to identify the contribution of the aforementioned parameters with insulin resistance and NAFLD in this age group.
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The physical activity pattern was assessed by a validated questionnaire including nine different metabolic equivalent (MET) levels [6]. 2.4. Laboratory methods
2. Methods 2.1. Study population This cross-sectional study was conducted in 2007–2008 among 100 adolescents, aged 12–18 years. Based on power calculations, a sample size of 88 was deemed large enough to test our hypothesis, and we increased it to 100. Participants consisted of 25 obese children with normal blood glucose, lipid profile and blood pressure considered as the phenotypically obese metabolically normal (POMN) group, 25 children with the criteria of the metabolic syndrome, i.e. at least three out of five risk factors [13] considered as phenotypically obese metabolically abnormal (POMA) group, 25 children with normal weight with normal blood glucose, lipid profile and blood pressure, considered as normal weight metabolically normal (NWMN) group, and 25 children with normal weight and at least one of the cardio-metabolic risk factors mentioned in the NWMN group, considered as the normal weight metabolically abnormal (NWMA) group. The first two groups were consecutively recruited from among obese children who were referred to the Obesity and Metabolic Syndrome Research Clinic of the Pediatric Preventive Cardiology Department, Isfahan Cardiovascular Research Center (ICRC). The last two groups were selected from the classmates of the first two groups. After screening 91 students, we reached to the necessary number (i.e. 25) of subjects with at least one of the aforementioned risk factors, the most common being low serum HDL-cholesterol followed by high triglycerides level. The samples of the NWMN group were randomly selected from among the screened students without any risk factor (n = 66). Children of all four groups were matched for age and sex. Eligibility criteria for participation included being 12–18-year-old, and being non-smoker. Those subjects with syndromal obesity, endocrine disorders, presence of any physical disability, and or history of chronic medication use were excluded from the survey. None of the participants had history and symptoms of infection during the two weeks before the study. We considered the Declaration of Helsinki, and the Ethics Committee of ICRC (NIH code: FWA 0000t8578) approved the study. Written informed consent and oral assent were obtained from all parents and children, respectively. 2.2. Anthropometric measurement and clinical examination The same team examined all study participants. Anthropometric measurement was conducted by calibrated instruments. Body mass index (BMI) was calculated on the basis of Centers for Disease Control and Prevention (CDC) growth charts [14]. Blood pressure (BP) was measured under standard protocol. The percentage of body fat was determined using dual energy absorptiometry by using Omron body fat monitor instrument (Omron, HBF-300, Japan) that was validated in our previous studies [15]. 2.3. Assessment of dietary and physical activity behaviors Nutritional assessment was performed by means of three 24-hfood records (once per week; two school days and one weekend). The Healthy Eating Index (HEI) was computed to measure participants’ diet quality [16].
Fasting blood samples were analyzed in the laboratory at ICRC, that meets the standards of the National Reference Laboratory (WHO-Collaborating Center in Tehran), and is also under the quality control of the CDC, USA, and St. Rafael University, Leuven, Belgium. Fasting blood glucose (FBG), total cholesterol (TC), LDL-cholesterol (LDL-C), HDL-cholesterol (HDL-C) and triglycerides (TG) were measured enzymatically (Pars Azmoun) on Hitachi autoanalyzer (Tokyo, Japan). Apolipoprotein A and B (apoA, B) were measured using immunoturbidometry by the same auto analyzer. Alanine aminotransferase (ALT) was determined on fresh blood samples. CRP and stress oxidatives, i.e. malondialdehyde (MDA), and conjugated diene (CDE) were measured by methods previously described [15]. The concentration of ox-LDL in plasma was measured with an ELISA procedure by using the murine monoclonal antibody mAb-4E6 as capture antibody and a peroxidase-conjugated antiapolipoprotein B antibody recognizing ox-LDL bound to the solid phase (Mercodia AB, Uppsala, Sweden). Plasma insulin was measured by radioimmunoassay (LINCO Research Inc). Insulin resistance (IR) was calculated on the basis of homeostasis model assessment of IR (HOMA-IR). 2.5. Assessment of cardiorespiratory fitness Symptom-limited cardiopulmonary exercise test was performed by standard protocol by using an electrically braked cycle ergometer (model 800; Zan; Germany). CRF was expressed relative to body weight (mL/kg/min) [17]. 2.6. Sonographic studies of liver and carotid-intima-media thickness Liver ultrasonography was performed by the same radiologist by using the same instrument (Siemens G50 serial GEE 3160). Any degree of increased liver echogenicity was documented. The same cardiologist conducted the studies for measurement of carotid intima media thickness (C-IMT) by using high resolution carotid ultrasonographic study as described previously [18]. 2.7. Definition of abnormal values We defined insulin resistance as the upper quartile of HOMA-IR, and high apoB/apoA ratio as the highest quartile of this ratio. Elevated ALT level was defined as ALT level >30 U/L. Given that among children, NAFLD has distinct characteristics, often different from those found in adults, and different cut-off values of serum ALT have been used for its definition. As reviewed by Schwimmer [19], different definitions used for pediatric NAFLD have major limitations. Serum ALT activity that is widely used as a screening tool for the detection of fatty liver in children is insufficient as a single measure for the determination of NAFLD. On the other hand, as the most commonly used imaging technique to clinically evaluate for the presence of hepatic echogenicity, liver ultrasonography is considered a good screening test for NAFLD; however, it has high rates of both false-positive and false-negative results. Therefore, in order to reduce the limitations of each definition and to reach to a higher probability of detecting fatty liver among children, we defined NAFLD as the co-existence of elevated serum ALT values and increased liver echogenicity in the sonographic evaluation. Metabolic syndrome was defined according to the criteria of the
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2007 Consensus of the International Diabetes Federation provided for children and adolescents [13]. 2.8. Statistical methods The characteristics of study participants were compared by using analysis of variance (ANOVA) for quantitative variables and chi-square for qualitative variables. Pearson correlations were used to examine bivariate relations of variables with HOMA-IR and ALT as continuous variables. Multiple regression analysis was performed to assess the correlates of upper quartiles of HOMA-IR and ALT. Selection of predictor variables was done using a forward stepwise fashion with strict variable entry and elimination criteria in each predictors group. For assessment of the independent associations of different variables with the dependent variables, seven models were applied with adjustment for different variables. Consequently, the final model only included those measures that made independent contribution to upper quartiles of HOMA-IR and ALT. Logistic regression was used to assess associations of the variables assessed with insulin resistance and NAFLD. Analyses were performed using the SPSS for Windows (version 15.0; SPSS Inc., Chicago, IL). ALT, CRP, insulin, HOMA-IR, and TG levels were natural log transformed to normalize distributions. 3. Results Of the children included to the study, 95 (51 girls, 44 boys) completed all tests. The mean age of participants was 15.6 ± 1.4 years, without significant difference between groups. The most prevalent abnormalities in the NWMA group were high TG (41.6%, n = 10) and low HDL-C (37.5%, n = 9), respectively; the rest (20.9%, n = 5) had a combination of these two risk factors. The mean value of serum lipids, lipoproteins, ApoB/ApoA-I ratio, insulin, HOMA-IR, CRP, ox-
LDL, MDA and ALT, as well as C-IMT was higher in the POMA group followed by the POMN, NWMA and NWMN groups. The mean HDLC and CRF was lower in the POMA than in other groups (Table 1). Within the same BMI category, subjects with high CRF had significantly (p = 0.01) lower WC compared with subjects with low CRF (data not shown). Across all groups, CRF had the highest inverse correlation with HOMA-IR and ALT. Physical activity level and HEI had near similar inverse correlation with HOMA-IR and ALT. The highest correlation of anthropometric indexes with HOMA-IR and ALT was found for WC, fat mass and BMI, respectively (Supplementary online Table 1). As presented in Table 2, CRF, PA and HEI had inverse associations with upper quartiles of HOMA-IR and ALT, which remained significant after adjustment for age, gender, pubertal status, BMI and WC. ApoB/ApoA-I ratio, C-reactive protein and malondialdehyde had significant associations with upper quartiles of HOMA-IR and ALT, which were independent to anthropometric measures and lifestyle habits. After adjustment for all variables, ApoB/ApoA-I ratio remained in the model and had significant independent association with upper quartiles of HOMA-IR and ALT. After controlling for age, gender and pubertal status, each variable considered one-at-a-time made a significant additional contribution to the prediction of insulin resistance and NAFLD. Overall, the variables assessed explained 65% of variation in insulin resistance and 67% of variation in NAFLD; the strongest predictors being TG, ApoB/ApoA-I ratio, and CRP, respectively. In addition, ALT explained 9% of variation in insulin resistance, and HOMA-IR explained 8% of variation in NAFLD (Supplementary online Table 2). Logistic regression analysis revealed that after adjustment for age, gender and pubertal status, respectively, WC, ApoB/ApoAI ratio, fat mass and BMI, followed by CRP, the number of the metabolic syndrome components, TG, ox-LDL, C-IMT and MDA increased the risk of insulin resistance and NAFLD among chil-
Table 1 Descriptive characteristics of participants.
Body mass index (kg/m2 ) Body mass index percentile Waist circumference (cm) Waist circumference percentile Subcutaneous fat (mm) Body fat mass (kg) Total cholesterol (mg/dL) LDL cholesterol (mg/dL) HDL cholesterol (mg/dL) Triglycerides (mg/dL) Apolipoprotein A-1 (mg/dL) Apolipoprotein B (mg/dL) ApoB/ApoA-I ratio Fasting blood glucose (mg/dL) Insulin (U/mL) HOMA-IR Alanine aminotransferase (ALT) (U/L) C-reactive protein (mg/L) Oxidized LDL (U/L) Malondialdehyde (mol/L) Systolic blood pressure, mmHg Diastolic blood pressure (mmHg) Healthy eating index score (0–100) Physical activity (MET-h/day) Cardiorespiratory fitness (mL/kg/min) Carotid intima media thickness (mm) Insulin resistance (%) Non-alcoholic fatty liver disease (%)
NWMN (n = 23)
NWMA (n = 24)
POMN (n = 24)
POMA (n = 24)
20.7 (0.5)b 77thb 68.2 (1.4)b 55thb 15.4 (0.2)b 24.1 (2.2)b 157.8 (7.9) 102.4 (2.1) 41.5 (2.9) 97.1 (12.4) 139.1 (14.4) 67.5 (7.1) 0.47 81.7 (8.2)b,c 16.1 (2.3) 1.6 (0.4) 19.8 (4.1) 0.8 (0.02)c 65.4 (12.5) 0.5 (0.01) 115.1 (21.1) 68.7 (15.4) 72.4 (12.5) 47.2 (10.7) 18.7 (2.2) 0.29 (0.02) 1.8 3.2
20.2 (0.7)a 75tha 67.2 (1.7)a 57tha 15.7 (0.1)a 24.7 (2.1)a 174.8 (6.5) 115.2 (2.4) 35.2 (1.7) 132.7 (14.1) 135.4 (12.7)c 72.4 (8.1) 0.52c 82.5 (6.2)a,c 20.4 (2.1) 3.4 (0.7) 25.5 (4.2) 1.01 (0.05)c 70.1 (15.4) 0.7 (0.01) 119.2 (21.4)c 74.2 (17.2) 69.2 (10.2)c 45.2 (11.7)c 15.2 (2.4)c 0.35 (0.02)c 6.4 5.6
26.4 (0.5)d 96thd 80.7 (1.2)d 92thd 18.2 (0.4)d 30.1 (1.5)d 168.1 (6.3) 107.8 (2.1)a 38.4 (1.7) 112.4 (12.5) 137.1 (11.4)a 70.5 (7.4) 0.51 81.5 (6.5)d 23.1 (2.5) 4.1 (0.9) 32.4 (8.6) 0.9 (0.01) 69.8 (16.2) 0.7 (0.02) 121.5 (20.2) 77.1 (18.2) 68.1 (8.7)d 44.6 (10.1)d 15.7 (2.5)d 0.37 (0.04) 17.2 12.4
26.2 (0.6)c 97thc 82.4 (1.1) 96thc 18.4 (0.5)c 30.7 (2.2)c 179.2 (7.2) 117.8 (3.2) 32.1 (2.4) 139.3 (11.4) 125.3 (12.4) 78.2 (7.5) 0.61 83.4 (7.2)c 26.2 (2.4) 4.8 (0.5) 37.7 (9.2) 1.4 (0.02) 77.2 (18.1) 0.9 (0.01) 128.4 (25.1) 77.2 (17.5)c 67.2 (7.4)c 43.7 (10.2)c 14.8 (2.1)c 0.41 (0.05) 79.1 21.1
NWMN: normal weight metabolically normal; NWMA: normal weight metabolically abnormal; POMN: phenotypically obese metabolically normal; POMA: phenotypically obese metabolically abnormal; data are means (SD) unless indicated otherwise; all analyses are adjusted for age and sex; HOMA-IR = (fasting insulin(mU/L) × fasting glucose(mmol/L) /22.5. All differences are significant between groups except those marked by following superscripts: a : p > 0.05 vs. NWMN group; b :p > 0.05 vs. NWMA group; c : p > 0.05 vs. POMN group; d : p > 0.05 vs. POMA group.
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Table 2 Association of variables assessed with upper quartiles of HOMA-IR and alanine aminotransferase. Upper quartile of HOMA-IR
Upper quartile of ALT
ˇ (SE)
P
R2
ˇ (SE)
P
0.31 (0.02) 0.37 (0.04) 025 (0.01) 0.27 (0.02) 0.45 (0.07) 0.34 (0.02) 0.30 (0.02) 0.28 (0.01) −0.32 (0.02) (0.38 (0.03) (0.37 (0.01) 0.25 (0.04)
0.03 0.03 0.04 0.03 0.001 0.02 0.03 0.04 0.02 0.04 0.04 0.04
0.31 0.35 0.22 0.34 0.51 0.41 0.35 0.27 0.29 0.32 0.38 0.34
0.28 (0.03) 0.35 (0.01) 0.21 (0.01 0.25 (0.02) 0.42 (0.03) 0.39 (0.04) 0.34 (0.01) 0.21 (0.01) (0.28 (0.03) (0.24 (0.02) (0.28 (0.03) 0.24 (0.01)
0.04 0.03 0.04 0.04 0.001 0.01 0.02 0.04 0.04 0.04 0.03 0.04
Model II: Adjusted for age, age, gender, pubertal status and BMI Waist circumference (cm) 0.41 Triglycerides (mg/dL) 0.30 ApoB/ApoA-I ratio 0.51 C-reactive protein (mg/L) 0.35 Malondialdehyde (mol/L) 0.34 Oxidized LDL (U/L) 0.32 Healthy eating index score (0–100) 0.31 Physical activity (MET-h/day) 0.35 Cardiorespiratory fitness (mL/kg/min) 0.44
0.35 (0.02) 0.27 (0.02) 0.43 (0.07) 0.32 (0.03) 0.28 (0.01) 0.27 (0.02) (0.27 (0.02) (0.31 (0.02) (0.37 (0.01)
0.02 0.04 0.001 0.02 0.03 0.04 0.03 0.04 0.04
0.34 0.32 0.50 0.37 0.29 0.28 0.24 0.27 0.38
0.34 (0.01) 0.24 (0.03) 0.41 (0.02) 0.31 (0.02) 0.21 (0.01) 0.21 (0.01) (0.22 (0.02) (0.22 (0.02) (0.28 (0.03)
0.04 0.04 0.02 0.03 0.04 0.04 0.04 0.04 0.03
Model III: Adjusted for age, gender, pubertal status, BMI and WC ApoB/ApoA-I ratio 0.37 C-reactive protein (mg/L) 0.28 Malondialdehyde (mol/L) 0.24 Healthy eating index score (0–100) 0.29 Physical activity (MET-h/day) 0.32 Cardiorespiratory fitness (mL/kg/min) 0.38
0.31 (0.01) 0.25 (0.02) 0.21 (0.01) (0.24 (0.03) (0.30 (0.02) (0.32 (0.02)
0.03 0.04 0.04 0.04 0.04 0.04
0.32 0.40 0.19 0.21 0.17 0.31
0.27 (0.02) 0.35 (0.02) 0.17 (0.02) (0.20 (0.02) (0.18 (0.01) (0.22 (0.02)
0.04 0.01 NS 0.04 NS 0.04
Model IV: Adjusted for age, gender, pubertal status, BMI, WC and HEI ApoB/ApoA-I ratio 0.35 C-reactive protein (mg/L) 0.25 Malondialdehyde (mol/L) 0.22 Physical activity (MET-h/day) 0.29 Cardiorespiratory fitness (mL/kg/min) 0.34
0.30 (0.02) 0.22 (0.03) 0.20 (0.02) (0.27 (0.01) (0.30 (0.02)
0.03 0.04 0.04 0.04 0.04
0.30 0.32 0.14 0.18 0.30
0.25 (0.02) 0.27 (0.01) 0.17 (0.02) 0.19 (0.01) (0.21 (0.01)
0.04 0.04 NS NS 0.04
Model V: Adjusted for age, gender, pubertal status, BMI, WC and PA ApoB/ApoA-I ratio 0.32 C-reactive protein (mg/L) 0.24 Malondialdehyde (mol/L) 0.21 Healthy eating index score (0–100) 0.25 Cardiorespiratory fitness (mL/kg/min) 0.32
0.28 (0.01) 0.22 (0.03) 0.21 (0.02) (0.20 (0.01) (0.28 (0.02)
0.04 0.04 0.04 0.04 0.04
0.28 0.28 0.18 0.20 0.27
0.24 (0.02) 0.24 (0.01) 0.19 (0.02) (0.20 (0.01) (0.20 (0.02)
0.04 0.04 NS 0.04 0.04
Model VI: Adjusted for age, gender, pubertal status, BMI, WC and CRF ApoB/ApoA-I ratio 0.31 C-reactive protein (mg/L) 0.23 Malondialdehyde (mol/L) 0.20 Healthy eating index score (0–100) 0.24 Physical activity (MET-h/day) 0.26
0.27 (0.01) 0.21 (0.01) 0.20 (0.01) (0.21 (0.01) (0.26 (0.02)
0.04 0.04 0.04 0.04 0.04
0.25 0.25 0.17 0.20 0.19
0.22 (0.02) 0.22 (0.01) 0.18 (0.04) (0.20 (0.02) 0.18 (0.02)
0.04 0.04 NS 0.04 NS
0.04 0.04
0.24 0.17
0.21 (0.01) 0.18 (0.01)
0.04 NS
0.04
0.22
0.20 (0.02)
0.04
2
R
Model I: Adjusted for age, gender and pubertal status 0.37 Body mass index (kg/m2 ) Waist circumference (cm) 0.42 Cholesterol/HDL ratio 0.24 Triglycerides (mg/dL) 0.31 ApoB/ApoA-I ratio 0.52 C-reactive protein (mg/L) 0.37 Malondialdehyde (mol/L) 0.31 Oxidized LDL (U/L) 0.29 Healthy eating index score (0–100) 0.38 Physical activity (MET-h/day) 0.41 Cardiorespiratory fitness (mL/kg/min) 0.44 Carotid intima media thickness (mm) 0.32
Model VII: Adjusted for age, gender, pubertal status, BMI, WC, HEI, PA and CRF ApoB/ApoA-I ratio 0.28 0.25 (0.03) Malondialdehyde (mol/L) 0.20 0.19 (0.01) Model VIII: Adjusted for age, gender, pubertal status, BMI, WC, HEI, PA, CRF, CRP and MDA ApoB/ApoA-I ratio 0.27 0.24 (0.01)
Only significant associations are presented. Body mass index, waist circumference, body fat mass, cholesterol/HDL ratio, triglycerides, ApoB/ApoA-I ratio, C-reactive protein, malondialdehyde, oxidized LDL, healthy eating index score, physical activity, cardiorespiratory fitness and carotid intima media thickness were included in each model (unless the model was adjusted for that variable), pubertal status was classified according to Tanner stages; ˇ: regression coefficients; SE: standard error; R2 : standardized coefficient of determination; ALT: alanine aminotransferase; BMI: body mass index; WC: waist circumference; HEI: healthy eating index score; PA: physical activity; CRF: cardiorespiratory fitness; MDA: malondialdehyde; the upper quartile of HOMA-IR was 4.2–5.4, and the upper quartile of ALT was 37.7–46.9 U/L.
dren studied. CRF followed by HEI decreased this risk significantly (Table 3). 4. Discussion In this study, that to the best of our knowledge is the first of its kind, we found that the lipids, lipoproteins, the markers of inflam-
mation and oxidative stress as well as the C-IMT of normal weight children with a metabolic abnormality (NWMA) were similar to obese children. Irrespective of obesity and metabolic abnormality, fitness had the highest inverse correlation with HOMA-IR and ALT; and among the anthropometric measures, WC had the highest correlation with these parameters. Furthermore we found that after adjustment for all covariates, ApoB/ApoA-I ratio had significant
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Table 3 Logistic regression analysisa for factors associated with insulin resistance and NAFLD. Insulin resistance
BMI (quartiles) WC (quartiles) Fat mass (quartiles) CRF (quartiles) HEI score (quartiles) C-IMT (quartiles) TG (quartiles) No. of MetS components ApoB/ApoA-I ratio (quartiles) C-reactive protein (quartiles) Oxidized LDL (quartiles) Malondialdehyde (quartiles) HOMA-IR (quartiles) NAFLD (present vs. absent)
Non-alcoholic fatty liver disease
Odds ratio
95%CI
Odds ratio
95%CI
2.4 3.8 2.8 0.6 0.8 1.3 1.7 1.8 3.1 2.1 1.4 1.2
1.4–3.2 2.1–4.3 1.5–3.6 0.4–0.9 0.3–0.9 1.04–2.5 1.1–2.6 1.2–2.7 1.8–4.2 1.4–3.1 1.07–2.8 1.03–2.1
2.7 3.5 2.7 0.7 0.8 1.2 1.5 1.6 3.2 1.9 1.5 1.3 2.4
1.5–3.4 2.4–4.6 1.2–3.7 0.2–0.9 0.1–0.9 1.03–2.1 1.07–2.8 1.1–3.01 1.4–4.5 1.1–2.8 1.05–2.6 1.04–2.7 1.8–3.7
2.5
1.8–3.1
Only significant values are presented in the table. Variables included in the model: body mass index (BMI), waist circumference (WC), subcutaneous fat, body fat mass total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides (TG), fasting blood glucose, number of metabolic syndrome (MetS) components, ApoB/ApoA-I ratio, C-reactive protein, oxidized LDL, malondialdehyde, alanine aminotransferase (ALT), systolic blood pressure, diastolic blood pressure, healthy eating index (HEI) score, physical activity, cardiorespiratory fitness, non-alcoholic fatty liver disease (NAFLD) and carotid intima media thickness (C-IMT). a Adjusted for age, gender and pubertal status (classified according to Tanner stages) NAFLD: non-alcoholic fatty liver disease.
independent association with upper quartiles of HOMA-IR and ALT. WC and ApoB/ApoA-I ratio had the highest odds ratio in increasing the risk of insulin resistance and NAFLD among the children studied, whereas CRF followed by HEI decreased this risk significantly. We documented significant associations between insulin resistance and NAFLD, and similar risk factors and protective factors for these two inter-related disorders. Insulin resistance and NAFLD might be early markers of mechanisms predisposing to future metabolic events and atherosclerosis. In our study, C-IMT was significantly associated with insulin resistance and NAFLD; this finding might suggest that the liver and the vessels share common mediators; it should be confirmed through longitudinal studies. The association of apolipoproteins with metabolic syndrome and insulin resistance has been previously documented among adult population [20]. Our findings highlight the importance of measuring apolipoproteins, and not only the traditional risk factors, in the pediatric age group. Consistent with a recent study among healthy men who showed less visceral adipose tissue accumulation and more favorable plasma lipoprotein-lipid profiles in the highest tertile of CRF [21], we found that within the same BMI category, children with high CRF had significantly lower WC compared with subjects with low CRF. There are conflicting results about the association of CRF with cardiometabolic risk factors in children; a study found that for the same level of aerobic fitness, the lipid profile was more favorable in normal than in overweight adolescents [22], whereas in another study, visceral adipose tissue, but not CRF, was a significant predictor of markers of the insulin resistance syndrome [23]. HOMA and fasting insulin are positively associated with adiposity in children, however, it is suggested that the health hazards of high fatness in children could be counteracted by having high levels of CRF [24]. Consistent with some previous studies among youths, we found significant independent association between CRF and insulin resistance but such independent association is not confirmed by some previous studies that showed this association is mediated, at least in part, through fatness [25]. It is suggested that the differences between the results of these studies might be because of ethnic differences in body composition and in the age of the children studied. Taken together, these findings suggest that interventions designed to prevent insulin resistance and related metabolic disorders should focus not only on reducing fatness but also improving fitness.
We found that ALT was higher in obese children with metabolic disorder (POMA group) than in normal weight children with metabolic disorders (NWMA group). This finding is in line with a previous study that showed the severity of obesity is considered as one of the main predictors of NAFLD in children [4]. However, in our study although the obese groups did not have morbid obesity and their mean BMI corresponded to the 96–97th percentile, but the markers of insulin resistance and NAFLD were considerably high among them. This finding is in line with studies conducted among adult populations which showed that among Asians the increased health risks associated with obesity occur at lower BMI levels [26]. Genetic predisposition or early-life adverse events may contribute to adverse body fat patterning and its related complications, notably in non-European populations [27]. Our findings showed that a healthy diet might act as a protective factor reducing the risk of insulin resistance and NAFLD among children. Although this association is documented among adults [28] such experience is limited in the pediatric age group. 4.1. Study limitations The most important limitation of our study is its cross-sectional nature. Second, we did not assess visceral fat by imaging techniques; however, WC has been shown to be a reliable marker of visceral adiposity tissue in children. Third is the recall bias for the process of recalling and recording food intake and physical activity habits. Fourth, we used a dietary score and did not analyze the precise nutrient and energy intake of the subjects studied, however, the practical use of this score has been documented in previous studies among children. The controversies existing in the methods applied for measurement of ox-LDL, and the limitations of ELISA procedures used for its determination [29] should be considered as well. 5. Conclusion In this study, we documented significant associations between insulin resistance and NAFLD, and similar risk factors and protective factors for these two inter-related disorders. Among the factors studied, fitness had the highest inverse correlation with HOMA-IR and ALT. ApoB/ApoA-I ratio had significant independent association with upper quartiles of HOMA-IR and ALT. WC and ApoB/ApoA-I
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ratio had the highest odds ratio in increasing the risk of insulin resistance and NAFLD, whereas fitness and healthy dietary habits decreased this risk significantly. The association of C-IMT with insulin resistance and NAFLD might suggest that the liver and the vessels share common mediators. Our findings should be confirmed through longitudinal studies. Acknowledgment Grant/funding support: This study is funded by Isfahan Cardiovascular Research Center, a WHO Collaborating center in the EMR, and affiliated to Isfahan University of Medical Sciences. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.atherosclerosis.2008.09.034. References [1] Carnethon MR, Gulati M, Greenland P. Prevalence and cardiovascular disease correlates of low cardiorespiratory fitness in adolescents and adults. JAMA 2005;294:2981–8. [2] Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2005;365:1415–28. [3] Roberts EA. Pediatric nonalcoholic fatty liver disease (NAFLD): a “growing” problem? J Hepatol 2007;46:1133–42. [4] Quiros-Tejeira RE, Rivera CA, Ziba TT, et al. Risk for nonalcoholic fatty liver disease in Hispanic youth with BMI > or = 95th percentile. J Pediatr Gastroenterol Nutr 2007;44:228–36. [5] Rizzo NS, Ruiz JR, Hurtig-Wennlof A, et al. Relationship of physical activity, fitness, and fatness with clustered metabolic risk in children and adolescents: the European youth heart study. J Pediatr 2007;150:388– 94. [6] Kelishadi R, Razaghi EM, Gouya MM, et al. Association of physical activity and the metabolic syndrome in children and adolescents: CASPIAN Study. Horm Res 2007;67:46–52. [7] Sartorio A, Del Col A, Agosti F, et al. Predictors of non-alcoholic fatty liver disease in obese children. Eur J Clin Nutr 2007;61:877–83. [8] Schwimmer JB, Deutsch R, Rauch JB, et al. Obesity, insulin resistance, and other clinicopathological correlates of pediatric nonalcoholic fatty liver disease. J Pediatr 2003;143:500–5. [9] Ruidavets JB, Bongard V, Dallongeville J, et al. High consumptions of grain, fish, dairy products and combinations of these are associated with a low prevalence of metabolic syndrome. J Epidemiol Community Health 2007;6: 810–7. [10] Thompson A, Danesh J. Associations between apolipoprotein B, apolipoproteinAI, the apolipoprotein B/AI ratio and coronary heart disease: a literaturebased meta-analysis of prospective studies. J Intern Med 2006;259:481–92.
543
[11] Kelishadi R, Cook SR, Motlagh ME, et al. Metabolically obese normal weight and phenotypically obese metabolically normal youths: the CASPIAN Study. J Am Diet Assoc 2008;108:82–90. [12] Ramachandran A, Snehalatha C, Yamuna A, et al. Insulin resistance and clustering of cardiometabolic risk factors in urban teenagers in southern India. Diab Care 2007;30:1828–33. [13] Zimmet P, Alberti KG, Kaufman F, et al. The metabolic syndrome in children and adolescents–an IDF consensus report. Pediatr Diab 2007;8:299–306. [14] Kuczmarski RJ, Ogden CL, Grummer-Strawn LM. CDC growth charts: United States. Adv Data 2000;314:1–27. [15] Kelishadi R, Sharifi M, Khosravi A, Adeli K. Relationship between C-reactive protein and atherosclerotic risk factors and oxidative stress markers among young persons 10–18 years old. Clin Chem 2007;53:456–64. [16] Lee Y, Mitchell DC, Smiciklas-Wright H, Birch LL. Diet quality, nutrient intake, weight status, and feeding environments of girls meeting or exceeding recommendations for total dietary fat of the American Academy of Pediatrics. Pediatrics 2001;107:E95. [17] Vanderburgh PM, Katch FI. Ratio scaling of VO2max penalizes women with larger percent body fat, not lean body mass. Med Sci Sports Exerc 1996;28:1204–8. [18] Sabri MR, Kelishadi R. The thickness of the intimal and medial layers of the carotid arteries, and the index of left ventricular mass, in children of patients with premature coronary arterial disease. Cardiol Young 2007;17:609–16. [19] Schwimmer JB. Definitive diagnosis and assessment of risk for nonalcoholic fatty liver disease in children and adolescents. Semin Liver Dis 2007;27(3):312–8. [20] Sierra-Johnson J, Romero-Corral A, Somers VK, et al. ApoB/apoA-I ratio: an independent predictor of insulin resistance in US non-diabetic subjects. Eur Heart J 2007;28:2637–43. [21] Arsenault BJ, Lachance D, Lemieux I, et al. Visceral adipose tissue accumulation, cardiorespiratory fitness, and features of the metabolic syndrome. Arch Intern Med 2007;167:1518–25. [22] Mesa JL, Ruiz JR, Ortega FB, et al. Aerobic physical fitness in relation to blood lipids and fasting glycaemia in adolescents: influence of weight status. Nutr Metab Cardiovasc Dis 2006;16:285–93. [23] Owens S, Gutin B, Barbeau P, et al. Visceral adipose tissue and markers of the insulin resistance syndrome in obese black and white teenagers. Obes Res 2000;8:287–93. [24] Ruiz JR, Rizzo NS, Ortega FB, et al. Markers of insulin resistance are associated with fatness and fitness in school-aged children: the European Youth Heart Study. Diabetologia 2007;50:1401–8. [25] Lee S, Bacha F, Gungor N, Arslanian SA. Cardiorespiratory fitness in youth: relationship to insulin sensitivity and beta-cell function. Obesity (Silver Spring) 2006;14:1579–85. [26] WHO/IASO/IOTF. The Asia-Pacific Perspective. Redefining Obesity and Its Treatment. Sydney: Health Communications. WHO (1998): Obesity: Prevention and managing the global epidemic. Geneva: WHO 2000. [27] Lear SA, Humphries KH, Kohli S, et al. Visceral adipose tissue accumulation differs according to ethnic background: results of the Multicultural Community Health Assessment Trial (M-CHAT). Am J Clin Nutr 2007;86:353–9. [28] McKeown NM, Meigs JB, Liu S, et al. Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort. Diab Care 2004;27:538–46. [29] Itabe H, Ueda M. Measurement of plasma oxidized low-density lipoprotein and its clinical implications. J Atheroscler Thromb 2007;14:1–11.