1. bookVolume 8 (2014): Edizione 5 (October 2014)
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eISSN
1875-855X
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01 Jun 2007
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6 volte all'anno
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Open Access

Association of adiposity, measures of metabolic dysregulation, and elevated alanine aminotransferase in subjects with normal body mass index

Pubblicato online: 04 Feb 2017
Volume & Edizione: Volume 8 (2014) - Edizione 5 (October 2014)
Pagine: 585 - 596
Dettagli della rivista
License
Formato
Rivista
eISSN
1875-855X
Prima pubblicazione
01 Jun 2007
Frequenza di pubblicazione
6 volte all'anno
Lingue
Inglese

1. Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, et al. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology. 2007; 45:846-54.10.1002/hep.21496Apri DOISearch in Google Scholar

2. Church TS, Kuk JL, Ross R, Priest EL, Biltoft E, Blair SN. Association of cardiorespiratory fitness, body mass index, and waist circumference to nonalcoholic fatty liver disease. Gastroenterology. 2006; 130: 2023-30.10.1053/j.gastro.2006.03.019Apri DOISearch in Google Scholar

3. Park SH, Kim BI, Kim SH, Kim HJ, Park DI, Cho YK, et al. Body fat distribution and insulin resistance: beyond obesity in nonalcoholic fatty liver disease among overweight men. J Am Coll Nutr. 2007; 26:321-6.10.1080/07315724.2007.10719618Apri DOISearch in Google Scholar

4. Duvnjak M, Lerotic I, Barsic N, Tomasic V, Virovic Jukic L, Velagic V. Pathogenesis and management issues for non-alcoholic fatty liver disease. World J Gastroenterol. 2007; 13:4539-50.10.3748/wjg.v13.i34.4539Apri DOISearch in Google Scholar

5. Bouloumie A, Curat CA, Sengenes C, Lolmede K, Miranville A, Busse R. Role of macrophage tissue infiltration in metabolic diseases. Curr Opin Clin Nutr Metab Care. 2005; 8:347-54.10.1097/01.mco.0000172571.41149.52Apri DOISearch in Google Scholar

6. Wan YP, Xu RY, Fang H, Lu LP, Zhang XM, Cai W. [The prevalence of non-alcoholic fatty liver disease and its related risk factors in 1180 school children in Shanghai]. Zhonghua Gan Zang Bing Za Zhi. 2007; 15: 644-8.Search in Google Scholar

7. Kim HJ, Kim HJ, Lee KE, Kim DJ, Kim SK, Ahn CW, et al. Metabolic significance of nonalcoholic fatty liver disease in nonobese, nondiabetic adults. Arch Intern Med. 2004; 164:2169-75.10.1001/archinte.164.19.2169Apri DOISearch in Google Scholar

8. Hsieh SD, Yoshinaga H, Muto T. Waist-to-height ratio, a simple and practical index for assessing central fat distribution and metabolic risk in Japanese men and women. Int J Obes Relat Metab Disord. 2003; 27:610-6.10.1038/sj.ijo.0802259Apri DOISearch in Google Scholar

9. Chumlea WM, Guo SS. Assessment and prevalence of obesity: application of new methods to a major problem. Endocrine. 2000; 13:135-42.10.1385/ENDO:13:2:135Apri DOISearch in Google Scholar

10. Chumlea WC, Guo SS, Kuczmarski RJ, Flegal KM, Johnson CL, Heymsfield SB, et al. Body composition estimates from NHANES III bioelectrical impedance data. Int J Obes Relat Metab Disord. 2002; 26:159 6-609.10.1038/sj.ijo.080216712461676Apri DOISearch in Google Scholar

11. Plan and operation of the Third National Health and Nutrition Examination Survey, 1988-94. Series 1: programs and collection procedures. Vital Health Stat 1. 1994;32:1-407.Search in Google Scholar

12. Chumlea WC, Guo SS, Zeller CM, Reo NV, Baumgartner RN, Garry PJ, et al. Total body water reference values and prediction equations for adults. Kidney Int. 2001; 59:2250-8.10.1046/j.1523-1755.2001.00741.x11380828Apri DOISearch in Google Scholar

13. Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller D, Friedl K, et al. Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys. Am J Clin Nutr. 2003; 77:331-40.10.1093/ajcn/77.2.331Search in Google Scholar

14. Sui X, LaMonte MJ, Laditka JN, Hardin JW, Chase N, Hooker SP, et al. Cardiorespiratory fitness and adiposity as mortality predictors in older adults. JAMA. 2007; 298:2507-16.10.1001/jama.298.21.2507Search in Google Scholar

15. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985; 28:412-9.10.1007/BF00280883Apri DOISearch in Google Scholar

16. Wallace TM, Levy JC, Matthews DR. Use and abuse of HOMA modeling. Diabetes Care. 2004; 27:1487-95.10.2337/diacare.27.6.1487Apri DOISearch in Google Scholar

17. Ruhl CE, Everhart JE. Leptin concentrations in the United States: relations with demographic and anthropometric measures. Am J Clin Nutr. 2001; 74: 295-301.10.1093/ajcn/74.3.295Search in Google Scholar

18. Muntner P, He J, Chen J, Fonseca V, Whelton PK. Prevalence of non-traditional cardiovascular disease risk factors among persons with impaired fasting glucose, impaired glucose tolerance, diabetes, and the metabolic syndrome: analysis of the Third National Health and Nutrition Examination Survey (NHANES III). Ann Epidemiol. 2004; 14:686-95.10.1016/j.annepidem.2004.01.002Search in Google Scholar

19. Prati D, Taioli E, Zanella A, Della Torre E, Butelli S, Del Vecchio E, et al. Updated definitions of healthy ranges for serum alanine aminotransferase levels. Ann Intern Med. 2002; 137:1-10.10.7326/0003-4819-137-1-200207020-00006Apri DOISearch in Google Scholar

20. Rohrmann S, Crespo CJ, Weber JR, Smit E, Giovannucci E, Platz EA. Association of cigarette smoking, alcohol consumption and physical activity with lower urinary tract symptoms in older American men: findings from the third National Health And Nutrition Examination Survey. BJU International. 2005; 96:77-82.10.1111/j.1464-410X.2005.05571.xApri DOISearch in Google Scholar

21. Chitturi S, Farrell GC, Hashimoto E, Saibara T, Lau GK, Sollano JD. Non-alcoholic fatty liver disease in the Asia-Pacific region: definitions and overview of proposed guidelines. J Gastroenterol Hepatol. 2007; 22:778-87.10.1111/j.1440-1746.2007.05001.xApri DOISearch in Google Scholar

22. Alberti KG, Zimmet P, Shaw J. The metabolic syndrome-a new worldwide definition. Lancet. 2005; 366(9491):1059-62.10.1016/S0140-6736(05)67402-8Search in Google Scholar

23. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes (Lond). 2008; 32:959-66.10.1038/ijo.2008.11287750618283284Apri DOISearch in Google Scholar

24. Demerath EW, Schubert CM, Maynard LM, Sun SS, Chumlea WC, Pickoff A, et al. Do changes in body mass index percentile reflect changes in body composition in children? Data from the Fels Longitudinal Study. Pediatrics. 2006; 117:e487-95.10.1542/peds.2005-057216510627Search in Google Scholar

25. Dervaux N, Wubuli M, Megnien JL, Chironi G, Simon A. Comparative associations of adiposity measures with cardiometabolic risk burden in asymptomatic subjects. Atherosclerosis. 2008; 201:413-7.10.1016/j.atherosclerosis.2007.11.03218191136Search in Google Scholar

26. Han TS, Lean ME, Seidell JC. Waist circumference remains useful predictor of coronary heart disease. BMJ. 1996; 312:1227-8.10.1136/bmj.312.7040.1227e23509728634585Search in Google Scholar

27. Kvist H, Sjostrom L, Tylen U. Adipose tissue volume determinations in women by computed tomography: technical considerations. Int J Obes. 1986; 10:53-67.Search in Google Scholar

28. Smith SR, Lovejoy JC, Greenway F, Ryan D, deJonge L, de la Bretonne J, et al. Contributions of total body fat, abdominal subcutaneous adipose tissue compartments, and visceral adipose tissue to the metabolic complications of obesity. Metabolism. 2001; 50:425-35.10.1053/meta.2001.2169311288037Apri DOISearch in Google Scholar

29. Han TS, Carter R, Currall JE, Lean ME. The influence of fat free mass on prediction of densitometric body composition by bioelectrical impedance analysis and by anthropometry. Eur J Clin Nutr. 1996; 50:542-8.Search in Google Scholar

30. Ryo M, Maeda K, Onda T, Katashima M, Okumiya A, Nishida M, et al. A new simple method for the measurement of visceral fat accumulation by bioelectrical impedance. Diabetes Care. 2005; 28: 451-3.10.2337/diacare.28.2.45115677816Apri DOISearch in Google Scholar

31. Nagai M, Komiya H, Mori Y, Ohta T, Kasahara Y, Ikeda Y. Development of a new method for estimating visceral fat area with multi-frequency bioelectrical impedance. Tohoku J Exp Med. 2008; 214:105-12.10.1620/tjem.214.10518285667Search in Google Scholar

32. Kim JA, Park HS. Association of abdominal fat distribution and cardiometabolic risk factors among obese Korean adolescents. Diabetes Metab. 2008; 34:126-30.10.1016/j.diabet.2007.10.01218289908Apri DOISearch in Google Scholar

33. Rexrode KM, Carey VJ, Hennekens CH, Walters EE, Colditz GA, Stampfer MJ, et al. Abdominal adiposity and coronary heart disease in women. JAMA. 1998; 280:1843-8.10.1001/jama.280.21.18439846779Search in Google Scholar

34. Kuk JL, Katzmarzyk PT, Nichaman MZ, Church TS, Blair SN, Ross R. Visceral fat is an independent predictor of all-cause mortality in men. Obesity (Silver Spring). 2006; 14:336-41.10.1038/oby.2006.4316571861Apri DOISearch in Google Scholar

35. Romero-Corral A, Lopez-Jimenez F, Sierra-Johnson J, Somers VK. Differentiating between body fat and lean mass-how should we measure obesity? Nat Clin Pract Endocrinol Metab. 2008; 4:322-3.Search in Google Scholar

36. Romero-Corral A, Somers VK, Sierra-Johnson J, Korenfeld Y, Boarin S, Korinek J, et al. Normal weight obesity: a risk factor for cardiometabolic dysregulation and cardiovascular mortality. Eur Heart J. 2010; 31: 737-46.10.1093/eurheartj/ehp487283867919933515Apri DOISearch in Google Scholar

37. Rosito GA, Massaro JM, Hoffmann U, Ruberg FL, Mahabadi AA, Vasan RS, et al. Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the Framingham Heart Study. Circulation. 2008; 117:605-13.10.1161/CIRCULATIONAHA.107.74306218212276Apri DOISearch in Google Scholar

38. Chaston TB, Dixon JB. Factors associated with percent change in visceral versus subcutaneous abdominal fat during weight loss: findings from a systematic review. Int J Obes (Lond). 2008; 32:619-28.10.1038/sj.ijo.080376118180786Apri DOISearch in Google Scholar

39. Ross R, Rissanen J, Hudson R. Sensitivity associated with the identification of visceral adipose tissue levels using waist circumference in men and women: effects of weight loss. Int J Obes Relat Metab Disord. 1996; 20:533-8.Search in Google Scholar

40. Lovejoy JC, Smith SR, Rood JC. Comparison of regional fat distribution and health risk factors in middle-aged white and African American women: The Healthy Transitions Study. Obes Res. 2001; 9: 10-6.10.1038/oby.2001.211346662Apri DOISearch in Google Scholar

41. Carroll JF, Chiapa AL, Rodriquez M, Phelps DR, Cardarelli KM, Vishwanatha JK, et al. Visceral fat, waist circumference, and BMI: impact of race/ethnicity. Obesity (Silver Spring). 2008; 16:600-7.10.1038/oby.2007.9218239557Apri DOISearch in Google Scholar

42. Yokoyama H. [Gamma glutamyl transpeptidase (gammaGTP) in the era of metabolic syndrome]. Nihon Arukoru Yakubutsu Igakkai Zasshi. 2007; 42:110-24.Search in Google Scholar

43. Lee MY, Koh SB, Koh JH, Nam SM, Shin JY, Shin YG, et al. Relationship between γ-glutamyltransferase and metabolic syndrome in a Korean population. Diabet Med. 2008; 25:469-75.10.1111/j.1464-5491.2008.02415.x18346161Apri DOISearch in Google Scholar

44. Monami M, Bardini G, Lamanna C, Pala L, Cresci B, Francesconi P, et al. Liver enzymes and risk of diabetes and cardiovascular disease: results of the Firenze Bagno a Ripoli (FIBAR) study. Metabolism. 2008; 57: 387-92.10.1016/j.metabol.2007.10.01518249212Apri DOISearch in Google Scholar

45. Kim DJ, Noh JH, Cho NH, Lee BW, Choi YH, Jung JH, et al. Serum γ-glutamyltransferase within its normal concentration range is related to the presence of diabetes and cardiovascular risk factors. Diabet Med. 2005; 22:1134-40. 10.1111/j.1464-5491.2005.01581.x16108838Apri DOISearch in Google Scholar

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