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Intra-abdominal fat is related to metabolic syndrome and non-alcoholic fat liver disease in obese youth

Abstract

Background

Previous studies have shown an association between adiposity, especially intra-abdominal adipose tissue, and hemodynamic/metabolic comorbidities in adults, however it is not clear in pediatric population. The aim of the study was to analyze the relationship between non-alcoholic fatty liver disease (NAFLD) and components of metabolic syndrome (MS) with values of intra-abdominal (IAAT) and subcutaneous (SCAT) adipose tissue in obese children and adolescents.

Methods

Cross-sectional study. Subjects: 182 obese sedentary children and adolescents (aged 6 to 16 y), identified by the body mass index (BMI). Measurements: Body composition and trunk fat by dual-energy X-ray absorptiometry- DXA; lipid profile, blood pressure and pubertal stage were also assessed. NAFLD was classified as absent (0), mild (1), moderate (2) and severe (3), and intra-abdominal and subcutaneous abdominal fat thickness were identified by ultrasound. The MS was identified according to the cut offs proposed by World Health Organization adapted for children and adolescents. The chi-square test was used to compare categorical variables, and the binary logistic regression indicated the magnitude of the associations adjusted by potential cofounders (sex, age, maturation, NAFLD and HOMA-IR).

Results

Higher quartile of SCAT was associated with elevated blood pressure (p = 0.015), but not associated with NAFLD (p = 0.665). Higher IAAT was positively associated with increased dyslipidemia (p = 0.001), MS (p = 0.013) and NAFLD (p = 0.005). Intermediate (p = 0.007) and highest (p = 0.001) quartile of IAAT were also associated with dyslipidemia, independently of age, sex, maturation, NAFLD and HOMA-IR (homeostatic model assessment-insulin resistance).

Conclusion

Obese children and adolescents, with higher IAAT are more prone to develop MS and NAFLD than those with higher values of SCAT, independent of possible confounding variables.

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Background

Changes observed in modern society in the last decades have led to increasing prevalence of childhood obesity in developed and developing countries [1]. Pediatric obesity is associated with a large variety of metabolic/hemodynamic comorbidities, such as the metabolic syndrome (MS) and non-alcoholic fat liver disease (NAFLD) [2, 3].

Deeper analysis have shown that, not only the total, but the distribution of excessive adipose tissue seems to determine the development of these comorbidities and, therefore, abdominal obesity (intra-abdominal adipose tissue [IAAT]) is point out as more harmful to human health than subcutaneous adipose tissue (SCAT) [4, 5].

It is well documented that adulthood health is affected by risk factors which may determine cardiovascular diseases, such as dyslipidaemia, diabetes type II and hypertension at early age [68] and is fundamental to detect these potential risk factors to prevent or treat obesity. On the other hand, it is not clear whether the above mentioned influence of body fat distribution occur among obese children and adolescents, due to the lack of research about the relationship between IAAT and SCAT with MS and NAFLD in these populations.

The purpose of the present study is to analyze the relationship of NAFLD and components of MS with values of IAAT and SCAT, in a sample of sedentary obese children and adolescents.

Methods

Participants and setting

The present study included 182 children and adolescents of both genders (51.6% females), who met the inclusion criteria: (a) obesity identified by body mass index (BMI) according to Cole et al. (2000) [9], (b) aged between 6 and 16 years (at evaluation date) and (c) no regular physical activity practice in the last three months prior to the assessment and no disease or limitation that does not allowed the practice (d) sign a written informed consent form for the participation in the study (participants and parents/guardians).

The number of participants at each age ranged from 20 (6 and 7 years), 44 (8 and 9 years), 51 (10 to 12 years) 48 (13 and 14 years) and 19 (15 and 16 years).

The ethical committee of the FCT/UNESP approved this study (Protocol number 07/2009).

Anthropometry

Body weight (BW) was measured with an electronic scale (precision 0.1 kg [Filizzola PL 150, Filizzola® Ltda]) and the height with a wall-mounted stadiometer (precision 0.1 cm [Sanny®, São Paulo, Brazil]), with the subjects wearing light clothing and no shoes. Anthropometric measurements were performed by trained researchers, according to standardized techniques [10].

Dual energy X-ray absorptiometry

Percentage of body fat (%BF) and amount of trunk fat mass (TFM [kg]) were estimated by a Dual-energy X-ray absorptiometry- DXA scanner (Lunar DPX-NT; General Electric Healthcare, Little Chalfont, Buckinghamshire [software version 4.7]). The measurement took approximately 15 minutes and the subjects remained in a supine position during the scanning, wearing light clothing while lying flat on their back with arms by the side, without moving during the measurement. The results were transmitted to a connected computer for further analysis, according to the manufacturer orientation.

All Dual-energy X-ray absorptiometry- DXA measurements were made at the laboratory of the São Paulo State University Campus of Presidente Prudente, in a room with controlled temperature. Each morning before beginning the measurements, the equipment was calibrated by the same researcher and, according to the references provided by the manufacturer.

Pubertal stages

Pubertal stage was self-assessed by the participants aged older than 10 years. A standardized series of drawings was given to the subjects to assess their own pubertal development according to Tanner, et al. (1969, 1970) [11, 12]. Girls received drawings with the five pubertal stages of Tanner with breast and female pubic hair development, with appropriate descriptions. Boys received drawings with the five pubertal stages of Tanner with male genitalia and pubic hair development, with appropriate descriptions. It was asked to the subjects to select the drawing stage that best indicated his/her own development. The results were placed in a locked box by each subject to guarantee the integrity and anonymity of the subjects. The five stages of Tanner of pubic hair development were grouped in three classifications: Group 1 = subjects aged under 10 years and those who answered stage 1; Group 2 = subjects who answered stages 2 and 3; Group 3 = Subjects who answered stages 4 and 5.

Blood samples

After a 12 hour fasting, blood samples were collected, by nurses, on tubes containing EDTA, an anticoagulant for blood samples. All blood samples were analysed in a private laboratory. Triglycerides, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), insulin and glucose were analysed, using the colorimetric method (dyslipidemia was identified as any modification in the lipid variables). The dosage of insulin was performed using the Elisa Kit (RayBio® Human Insulin ELISA Kit), according to the manufacturer instructions.

Blood pressure

Systolic blood pressure (SBP) and diastolic blood pressure (DBP) values were measured with an electronic device (MX3Plus; Omron Corporation, Kyoto, Kansai, Japan), previously validated for pediatric populations [13]. The measurements were taken after 15 minutes of rest in a sitting position. Two measures were taken on the right arm, with a 2-minute interval between them and mean value of both measures was used. Two types of cuffs were used according to arm circumference. The classification of elevated blood pressure (EBP) was made by 95th centile or higher for age, gender and height, according to National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents (NHBPEP, 2004) [14].

Metabolic syndrome

The presence of MS was identified according to the cut offs proposed by World Health Organization adapted for children and adolescents [15]. The diagnosis of MS was based in the presence of, at least, three criteria. 1) Obesity: BMI ≥ 95th centile; 2) Glucose homeostasis: defined by hyperinsulinemia according to the norms for pubertal stage: pre-pubertal ≥15 mU/L, mid-puberty (stages 2–3) ≥ 30 mU/L [16], and post-pubertal ≥20 mU/L (adult value) and/or fasting glucose ≥ 110 mg/dL; 3) Hypertension: systolic blood pressure (SBP) ≥ 95th centile for age, gender and height [14]; 4) Dyslipidaemia: triglycerides >155 mg/dL, high density lipoprotein (HDL) < 35 mg/dL and total cholesterol ≥ 95th centile. Homeostasis model assessment-insulin resistance (HOMA-IR) was calculated according to the formula proposed by Matthews et al. (1985) [17]: HOMA-IR = Fasting insulin (mU/mL) X fasting glucose (mgl/dL)/405.

Ultrasound

An ultrasound equipment (Toshiba Aplio Model Tochigi-ken, Japan) was used to assess the fat accumulation and the morphology of the liver (size of the left and right lobes), by a specialist medical doctor. NAFLD was stratified as followed: absent (0), mild (level 1), moderate (level 2) and severe (level 3). However, the sample was dichotomized: Absence and Presence, because of the lack of subjects with severe level. The ultrasound also measured intra-abdominal (IAAT) and subcutaneous (SCAT) abdominal adipose tissue thickness (cm). The values of IAAT and SCAT were stratified into quartile (Quartile1; Quartile2-3; Quartile4).

Statistical analysis

Numerical variables were presented as mean and standard deviation. Test-T was used for comparison of the general variables between sexes. Chi-square test compared the categorical variables. Binary logistic regression (odds ratio [OR] and 95% confidence interval [OR95%CI]) indicated the magnitude of the associations adjusted by potential confounders (sex, age, maturation, classification of NAFLD and HOMA-IR). Statistical significance was set at 5% and the Statistical Package for Social Science, version 17.0 (SPSS Inc, Chicago, Illinois) was used for all analyses.

Results

The comparison between male and female subjects (Table 1), showed similarity for age (p = 0.994), all biochemical variables, including, insulin resistance (p = 0.356) and dyslipidemia (p = 0.391) and blood pressure at rest (SBP with p = 0.426 and DBP with p = 0.551) and elevated (p = 0.740). The prevalence of MS was high in both genders (male 52.3% and female: 42.6%; p = 0.381). Boys presented lower percentage of body fatness (p = 0.012), however, higher IAAT than girls (p = 0.001).

Table 1 General characteristics of obese youth stratified by sex (n = 182)

Higher quartile of SCAT was associated with EBP (chi-square with p = 0.015 (Table 2), but not with NAFLD (p = 0.665). However, IAAT was positively associated with increased occurrence of dyslipidemia (p = 0.001), MS (p = 0.013) and NAFLD (p = 0.005). Hyperinsulinemia was not associated with SCAT and IAAT. The classification of NAFLD was positively associated with insulin resistance (Table 3), and marginally associated with MS (p = 0.075).

Table 2 Association between abdominal adipose tissue, non-alcoholic fatty liver disease and metabolic syndrome in obese youth (n = 182)
Table 3 Association between non-alcoholic fatty liver disease and metabolic syndrome components in obese children and adolescents (n = 182)

In the multivariate model, statistically significant associations in the chi-square test were analyzed under adjustment of potential confounders (sex, age, maturation, presence of NAFLD and HOMA-IR). The association between higher quartile of SCAT and EBP did not reach statistical significance, in both adjusted models (Model 1- OR = 1.86 [0.70 – 4.91]; p = 0.210; Model 2- OR = 1.90 [0.71 – 5.05]; p = 0.195). On the other hand, both intermediate (OR = 3.33 [1.39 – 8.00]; p = 0.007) and highest (OR = 5.23 [1.92 – 14.2]; p = 0.001) quartile of IAAT were significantly associated to dyslipidemia, independently of age, sex, maturation, NAFLD and HOMA-IR (Table 4).

Table 4 Multivariate model for association between abdominal adipose tissue and metabolic syndrome components (n = 182)

Discussion

Despite seeming higher in obese youth, MS prevalence is not clear, because there is not yet a standard reference table worldwide accepted [18]. In our obese sample, boys (52.3%) and girls (44.7%) presented higher prevalence of the outcome than in another study with obese Brazilian adolescents (27,6%) [19] that used the same classification for MS. Moreover, the prevalence of NAFLD in our obese sample (26.4%) was similar to those recently observed in other overweight/obese adolescents (27,7%) [20]. Therefore, MS and NAFLD should be a relevant concern in developing countries, which experienced rapid industrialization process during the last decades, but without a consistent health politics related to nutrition and promotion of physical activity.

NAFLD was significantly associated with increased fasting insulin and glucose and marginally with MS. Previously, Ayonrinde et al. (2011) [21] identified in 1170 Australian adolescents that fat accumulation in the liver is related to decreased values of insulin. Other components of MS presented similar result of association with NAFLD, which is in accordance with previous studies, that refers to NAFLD as the “hepatic manifestation” of the MS [22].

Regarding the association between IAAT and dyslipidemia, the scientific literature has offered support to the idea that insulin resistance plays key role in the genesis of the NAFLD and components of MS [4], mainly in cases when the abdominal obesity is present. Adipose tissue has an endocrine function that produces multiple adipokines causing inflammatory process and insulin resistance [4, 23, 24]. Visceral adipose tissue located at abdominal region has particularities related to higher lipolysis, higher release of adipokines and these characteristics increase its atherosclerotic capacity [4].

Adipokines like tumor necrosis factor-α (TNF-α) and interleukine-6 are largely produced by abdominal fat and have been related to insulin resistance. TNF-α interferes on insulin metabolism cascade, activating proinflammatory pathways that impair insulin signalling at the level of the insulin receptor substrate proteins, causing insulin resistance [25]. Besides, TNF-α inhibits adiponectin expression, causes elevation in serum free fat acid by stimulation of lipolysis and hepatic lipogenesis, contributing for insulin resistance and may lead to development of NAFLD [4, 23, 26], justifying our findings.

Our results indicated that IAAT was more important than SCAT in the genesis of the MS and NAFLD [27]. Although SCAT has been associated with blood pressure and, in fact, the above mentioned pathways are strongly related to endothelial dysfunction [4], the significance was lost after the adjustments. Various studies have shown that, the increase of free fat acid in circulation corroborates with formation of low density lipoproteins (LDL) e very low density lipoproteins (VLDL) associated to reduction on high density lipoproteins (HDL) and very high density lipoproteins (VHDL), which results in atherosclerosis and elevation of arterial pressure [28, 29].

Limitations of the study should be highlighted. The cross-sectional design constitutes a limitation because it is not possible to establish a causality of the involved variables. Moreover, the absence of pro-inflammatory adipokines analysis should be considered in the interpretation of the findings. In addition, the NAFLD was not diagnosed by liver biopsy, which is considered the gold standard, otherwise, the ultrasound is considered a useful tool for initial screening [30, 31], based on parameters such as hepatorenal echo contrast and liver echogenicity [32]. Another reason is the unclear perform of serum aminotransferases, such as ALT and AST to diagnosis NAFLD in adults and children [33].

Furthermore, interventional studies are necessary to offer consistent support to our proposed model of analysis.

Conclusions

In summary, our findings identified that, in obese adolescents, intra-abdominal fat seems to be more relevant to the development of MS and NAFLD than subcutaneous fat, and these results are independent of possible confounding variables. However, future studies with larger sample are necessary to confirm this evidence and perhaps, suggest a cut off of visceral fat to predict MS and NAFLD.

Abbreviations

NAFLD:

Nonalcohoolic fatty liver disease

MS:

Metabolic syndrome

IAAT:

Intra-abdominal adipose tissue

SCAT:

Subcutaneous adipose tissue

BMI:

Body mass index

HOMA-IR:

Homeostasis model assessment-insulin resistance

BW:

Body weight

%BF:

Percentage of body fat

TFM:

Trunk fat mass

TC:

Total cholesterol

HDL-C:

High-density lipoprotein cholesterol

LDL-C:

Low density lipoproteins

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

EBP:

Elevated blood pressure

TNF-α:

Tumor necrosis factor-α

VLDL:

Very low density lipoproteins.

References

  1. Gupta N, Goel K, Shah P, Misra A: Childhood obesity in developing countries: epidemiology, determinants, and prevention. Endocr Rev. 2012, 33 (1): 48-70. 10.1210/er.2010-0028.

    Article  CAS  PubMed  Google Scholar 

  2. Sundaram SS, Phil Z, Kristen N: The metabolic syndrome and non-alcoholic fatty liver disease in children. Curr Opin Pediatr. 2009, 21 (4): 529-535. 10.1097/MOP.0b013e32832cb16f.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Tominaga K, Fujimoto E, Suzuki K, Hayashi M, Ichikawa M, Inaba Y: Prevalence of non-alcoholic fatty liver disease in children and relationship to metabolic syndrome, insulin resistance, and waist circumference. Environ Health Prev Med. 2009, 14 (2): 142-149. 10.1007/s12199-008-0074-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Després JP, Lemieux I: Abdominal obesity and metabolic syndrome. Nature. 2006, 444 (7121): 881-887. 10.1038/nature05488.

    Article  PubMed  Google Scholar 

  5. Huang PL: eNOS, metabolic syndrome and cardiovascular disease. Trends Endocrinol Metab. 2009, 20 (6): 295-302. 10.1016/j.tem.2009.03.005.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Sinaiko A: Obesidade, resistência à insulina e síndrome metabólica. J Pediatr (Rio J). 2007, 83 (1): 3-5. 10.2223/JPED.1585.

    Article  Google Scholar 

  7. Fernandes RA, Zanesco A: Early physical activity promotes lower prevalence of chronic diseases in adulthood. Hypertens Res. 2010, 33: 926-931. 10.1038/hr.2010.106.

    Article  PubMed  Google Scholar 

  8. Fernandes RA, Christofaro DG, Casonatto J, Codogno JS, Rodrigues EQ, Cardoso ML, Kawaguti SS, Zanesco A: Prevalence of dyslipidemia in individuals physically active during childhood, adolescence and adult age. Arq Bras Cardiol. 2011, 97 (4): 317-323. 10.1590/S0066-782X2011005000083.

    Article  PubMed  Google Scholar 

  9. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH: Establishing a standard definition for child overweight and obesity worldwide: international survey. Bmj. 2000, 320 (7244): 1240-1243. 10.1136/bmj.320.7244.1240.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Lohman T, Roche A, Martorell R: Anthropometric standardization reference manual. Champaign, Illinois. Human Kinetics. 1988, 3-8.

    Google Scholar 

  11. Marshall WA, Tanner JM: Variations in pattern of pubertal changes in girls. Arch Dis Child. 1969, 44 (235): 291-303. 10.1136/adc.44.235.291.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Marshall WA, Tanner JM: Variations in the pattern of pubertal changes in boys. Arch Dis Child. 1970, 45 (239): 13-23. 10.1136/adc.45.239.13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Christofaro DG, Casonatto J, Polito MD, Cardoso JR, Fernandes R, Guariglia DA, Gerage AM, de Oliveira AR: Evaluation of the Omron MX3 Plus monitor for blood pressure measurement in adolescents. Eur J Pediatr. 2009, 168 (11): 1349-1354. 10.1007/s00431-009-0936-x.

    Article  PubMed  Google Scholar 

  14. National High Blood Pressure Education Program Working Group on High Blood Pressure in Children and Adolescents: The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics. 2004, 114 (2): 555-576.

    Article  Google Scholar 

  15. Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diab Med. 1998, 15: 539-553. 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S.

    Article  CAS  Google Scholar 

  16. Goran MI, Gower BA: Longitudinal study on pubertal insulin resistance. Diabetes. 2001, 50 (11): 2444-2450. 10.2337/diabetes.50.11.2444.

    Article  CAS  PubMed  Google Scholar 

  17. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985, 28: 412-419. 10.1007/BF00280883.

    Article  CAS  PubMed  Google Scholar 

  18. Moraes AC, Fulaz CS, Netto-Oliveira ER, Reichert FF: Prevalence of metabolic syndrome in adolescents: a systematic review. Cad Saude Publica. 2009, 25 (6): 1195-1202. 10.1590/S0102-311X2009000600002.

    Article  PubMed  Google Scholar 

  19. Caranti DA, de Mello MT, Prado WL, Tock L, Siqueira KO, de Piano A, et al: Short- and long-term beneficial effects of a multidisciplinary therapy for the control of metabolic syndrome in obese adolescents. Metabolism. 2007, 56 (9): 1293-1300. 10.1016/j.metabol.2007.05.004.

    Article  CAS  PubMed  Google Scholar 

  20. Lira Ana RF, Oliveira Fernanda LC, Escrivão Maria AMS, Colugnati Fernando AB, Taddei José AAC: Esteatose hepática em uma população escolar de adolescentes com sobrepeso e obesidade. J Pediatr (Rio J). 2010, 86 (1): 45-52.

    CAS  Google Scholar 

  21. Ayonrinde OT, Olynyk JK, Beilin LJ, Mori TA, Pennell CE, de Klerk N, Oddy WH, Shipman P, Adams LA: Gender-specific differences in adipose distribution and adipocytokines influence adolescent nonalcoholic fatty liver disease. Hepatology. 2011, 53 (3): 800-809. 10.1002/hep.24097.

    Article  CAS  PubMed  Google Scholar 

  22. Barshop NJ, Francis CS, Schwimmer JB, Lavine JE: Nonalcoholic fatty liver disease as a comorbidity of childhood obesity. Ped Health. 2009, 3 (3): 271-281. 10.2217/phe.09.21.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Van Gaal LF, Mertens IL, De Block CE: Mechanisms linking obesity with cardiovascular disease. Nature. 2006, 444 (7121): 875-880. 10.1038/nature05487.

    Article  CAS  PubMed  Google Scholar 

  24. Ronti T, Lupattelli G, Mannarino E: The endocrine function of adipose tissue: an update. Clin Endocrinol (Oxf). 2006, 64 (4): 355-365.

    CAS  Google Scholar 

  25. Lorenzo M, Fernandez-Veledo S, Vila-Bedmar R, Garcia-Guerra L, De Alvaro C, Nieto-Vazquez I: Insulin resistance induced by tumor necrosis factor-alpha in myocytes and brown adipocytes. J Anim Sci. 2008, 86 (14 Suppl): E94-E104.

    CAS  PubMed  Google Scholar 

  26. Plomgaard P, Bouzakri K, Krogh-Madsen R, Mittendorfer B, Zierath JR, Pedersen BK: Tumor necrosis factor-alpha induces skeletal muscle insulin resistance in healthy human subjects via inhibition of Akt substrate 160 phosphorylation. Diabetes. 2005, 54 (10): 2939-2945. 10.2337/diabetes.54.10.2939.

    Article  CAS  PubMed  Google Scholar 

  27. Wajchenberg BL: Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev. 2000, 21 (6): 697-738. 10.1210/er.21.6.697.

    Article  CAS  PubMed  Google Scholar 

  28. Matía MP, et al: Nutrition and metabolic syndrome. Rev Esp Salud Publica. 2007, 81 (5): 489-505. 10.1590/S1135-57272007000500006.

    Article  Google Scholar 

  29. Souza MSF, Leme RB, Franco RR: Síndrome metabólica em adolescentes com sobrepeso e obesidade. Rev paul pediatr [serial on the Internet]. 2007, 25 (3): 214-220. 10.1590/S0103-05822007000300004.

    Article  Google Scholar 

  30. Shannon A, Alkhouri N, Carter-Kent C, Monti L, Devito R, Lopez R, Feldstein AE, Nobili V: Ultrasonographic quantitative estimation of hepatic steatosis in children with Nafld.Jpgn. J Pediatr Gastroenterol Nutr. 2011, 53: 190-195. 10.1097/MPG.0b013e31821b4b61.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Mazhar SM, Shiehmorteza M, Sirlin CB: Noninvasive assessment of hepatic steatosis. Clin Gastroenterol Hepatol. 2009, 7: 135-140. 10.1016/j.cgh.2008.11.023.

    Article  PubMed  Google Scholar 

  32. Dasarathy S, Dasarathy J, Khiyami A, Joseph R, Lopez R, McCullough AJ: Validity of real time ultrasound in the diagnosis of hepatic steatosis: a prospective study. J Hepatol. 2009, 51: 1061-1067. 10.1016/j.jhep.2009.09.001.

    Article  PubMed  Google Scholar 

  33. Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, Grundy SM, Hobbs HH: Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatol. 2004, 40: 1387-1395. 10.1002/hep.20466.

    Article  Google Scholar 

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Correspondence to Loreana Sanches Silveira.

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Authors’ contributions

LSS participated in the design of the study, was the main responsible for collection, analysis and interpretation of data, and also drafting the manuscript. PAM and BMMA carried out the Dual energy X-ray absorptiometry involved in analysis and interpretation of data and drafted the manuscript. PMS carried out the immunoassays and also in critical revision of the paper. RAF participated in the design of the study and performed the statistical analysis. DGDC participated in the design of the study and review the manuscript. IFFJ conceived of the study and involved in revising the manuscript critically for important intellectual content. All authors read and approved the final manuscript.

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Silveira, L.S., Monteiro, P.A., Antunes, B.d.M.M. et al. Intra-abdominal fat is related to metabolic syndrome and non-alcoholic fat liver disease in obese youth. BMC Pediatr 13, 115 (2013). https://doi.org/10.1186/1471-2431-13-115

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