Yusuf BOZKUŞ, Umut MOUSA, Nazlı KIRNAP, Özlem İYİDİR, Lala RAMAZANOVA, Aslı NAR, Neslihan TÜTÜNCÜ

Kadınlarda metabolik sendromun alternatif prediktörleri

Amaç: Metabolik sendrom kriterler içinde yer almayan ve adipozite göstergesi kabul edilen parametrelerin, metabolik sendromu olan ve olmayan kadınlar arasında farklılık gösterip göstermediğini ortaya koymak ve bu parametrelerin metabolik sendromu predikte eden kesim noktalarını saptamak amaçlanmıştır. Gereç ve Yöntem: Kilo vermek için başvuran, bilinen glikoz metabolizma bozukluğu olmayan 393 kadın birey (18-70 yaş) alındı. Bu bireylerden NCEP ATPIII kriterlerine göre metabolik sendromu olan ve olmayanlar tespit edildikten sonra, tüm katılımcıların antropometrik ölçümleri ve vücut yağ dağılımı ölçüldü ve laboratuar parametrelerine bakıldı. ROC eğrileri çizildi ve eğri altındaki alanlar hesaplandı ve parametrelerin metabolik sendromu predikte eden kesim noktaları ve bu kesim noktalarının duyarlılık ve özgüllük oranları belirlendi. Bulgular: Metabolik sendromu predikte eden parametrelerden, vücut kitle indeksi, boyun çevresi, visseral yağ miktarı, gövde yağ yüzdesi, HOMAIR indeksi ve insülin düzeylerinin, eğri altındaki alanları 0,7'nin üstünde olduğu; kalça çevresi, bel-kalça oranı, konvansiyonel BİA ile ölçülen total yağ kitlesi ve yağ yüzdesinin, LDL-K ve TSH düzeyinin ise 0,7'nin altında olduğu saptanmıştır. Vücut kitle indeksi için 27,7 kg/m2, boyun çevresi için 33,8 cm, bel çevresi için 91,5 cm, visseral yağ miktarı için 10,8 birim, gövde yağ yüzdesi için %43,1, HOMAIR indeksi için 2,14 ve insülin düzeyi için 8,7 µU/mL değerlerinin metabolik sendromu predikte etmedeki duyarlılıkları %80 ve üstünde bulunmuştur. Sonuç: Vücut kitle indeksi, boyun çevresi, visseral yağ miktarı, gövde yağ yüzdesi, HOMAIR indeksi ve insülin düzeyleri metabolik sendromun alternatif prediktörü olarak kullanılabilecek pratik ve değerli ölçütlerdir.

Anahtar Kelimeler:

Metabolik Sendrom, Antropometri, Adipoz Doku, İnsülin Direnci

Alternative predictors of metabolic syndrome in women.

Purpose: The aim of this study is to determine whether the parameters that are not included in the criteria of metabolic syndrome and which are considered as indicators of adiposity, differ between women with and without metabolic syndrome and to determine the cut-off points predicting metabolic syndrome. Materials and Methods: A total of 393 adult women without glucose metabolism disorder were included. After determining the participants with and without metabolic syndrome, anthropometric measurements and body fat distribution of all participants were measured and laboratory parameters were examined. The ROC curves were plotted and the areas under the curve were calculated. The cut-off points predicting metabolic syndrome and the sensitivity and specificity ratios of these cut-off points were determined. Results: While the area under the curve for body mass index, neck circumference, visceral fat level, body fat percentage, HOMAIR index and insulin levels was found to be over 0.7, area under the curve for hip circumference, waist-hip ratio, total fat mass and total fat percentage, and LDL-C and TSH levels were below 0.7. The cut-off points of parameters that predict metabolic syndrome for women were found to be 27.7 kg/m2 for body mass index, 33.8 cm for neck circumference, 91.5 cm for waist circumference, 10.8 for visceral fat, 43.1% for trunk fat percentage, 2.14 for HOMAIR index and 8.7 µU/mL for insulin levels. Conclusion: Body mass index, neck circumference, visceral fat level, body fat percentage, HOMAIR index and insulin levels are valuable criteria to predict metabolic syndrome.

Keywords:

Metabolic Syndrome, Anthropometry, Adipose Tissue, Insulin Resistance,

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1. Global status report on noncommunicable diseases 2010. Geneva, World Health Organization, 2011
2. 2008-2013 Action plan for the global strategy for the prevention and control of non communicable diseases, World Health Organization, 2008
3. WHO. WHO consultation, deﬁnition, diagnosis and classiﬁcation of diabetes mellitus and its complications. Part 1: diagnosis and classiﬁcation of diabetes mellitus e Geneva. World Health Organisation; 1999.
4. Balkau B, Charles MA. Comment on the provisional report from the WHO consultation. European Group for the Study of Insulin Resistance (EGIR). Diabet Med 1999;16:442e3.
5. Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults. Executive summary of the third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol ın adults (Adult Treatment Panel III). JAMA 2001;285:2486.
6. Grundy SM, Cleeman JI, Daniels SR, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Circulation 2005;112:2735.
7. Bloomgarden ZT. American Association of Clinical Endocrinologists (AACE) consensus conference on the insulin resistance syndrome: 25e26 August 2002, Washington, DC. Diabetes Care 2003;26:933e9.
8. Alberti KG, Zimmet P, Shaw J. Metabolic syndrome--a new world-wide definition. A Consensus Statement from the International Diabetes Federation. Diabet Med 2006;23:469-480.
9. Ford ES, Giles WH, Dietz WH. Prevalence of the metabolic syndrome among US adults: ﬁndings from the third National Health and Nutrition Examination Survey. JAMA 2002;287:356e9.
10. Ford ES. Prevalence of the metabolic syndrome deﬁned by the International Diabetes Federation among adults in the U.S. Diabetes Care 2005;28:2745e9.
11. Ritchie SA, Connell JM. The link between abdominal obesity, metabolic syndrome and cardiovascular disease. Nutr Metab Cardiovasc Dis 2007;17:319–326.
12. American Diabetes Association. Standards of Medical Care in Diabetes-2013- Position Statement. Diabetes Care 2013;36(Suppl. 1):11-66.
13. Chobanian AV, Bakris GL, Black HR, et al. The seventh report of the joint national committee on prevention, detection, evaluation, and treatment of high blood pressure: the JNC 7 report. JAMA 2003;289:2560.
14. Wallace TM, Levy JC, Matthews DR. Use and abuse of homa modeling. Diabetes Care 2004;27:1487–1495.
15. Ilhan Satman, Nevin Dinccag, Fulya Turker, et al. Twelve-year trends in the prevalence and risk factors of diabetes and prediabetes in Turkish adults (TURDEP-2). Eur J Epidemiol 2013;28:169–180.
16. Kvist H, Chowdhury B, Grangard U, Tylen U, Sjostrom L. Total and visceral adipose-tissue volumes derived from measurements with computed tomography in adult men and women: predictive equations. Am J Clin Nutr 1988;48:1351–1361.
17. Seidell JC, Oosterlee A, Deurenberg P, Hautvast JG, Ruijs JH. Abdominal fat depots measured with computed tomography: effects of degree of obesity, sex, and age. Eur J Clin Nutr 1988;42:805–815.
18. Unno M, Furusyo N, Mukae H, Koga T, Eiraku K, Hayashi J. The utility of visceral fat level by bioelectrical ımpedance analysis in the screening of metabolic syndrome-the results of the Kyushu and Okinawa population study (KOPS). J Atheroscler Thromb 2012;19:462-470.
19. Stabe C, Vasques AC, Lima MM, et al. Neck circumference as a simple tool for identifying the metabolic syndrome and insulin resistance: results from the Brazilian Metabolic Syndrome Study. Clin Endocrinol (Oxf) 2013;78:874-881.
20. Onat A, Hergenç G, Yüksel H, et al. Neck circumference as a measure of central obesity: associations with metabolic syndrome and obstructive sleep apnea syndrome beyond waist circumference. Clinical Nutrition 2009;28:46–51.
21. Nadas J, Putz Z, Kolev G, Nagy S, Jermendy G. Intraobserver and interobserver variability of measuring waist circumference. Med Sci Monit 2008;14:CR15–18.
22. Mason C, Katzmarzyk PT. Variability in waist circumference measurements according to anatomic measurement site. Obesity (Silver Spring) 2009;doi:10.1038/oby.2009.87
23. WHO. Waist circumference and waist–hip ratio: report of a WHO expert consultation. Geneva, 2008.
24. Ashwell M, Cole TJ, Dixon AK. New insight into the anthropometric classification of fat distribution shown by computed tomography. Br Med J Clin Res Ed 1985;290:1692–1694.
25. B.L. Wajchenberg, D. Gianella-Neto, M.E.R. da Silva, R.F. Santos. Depot-spresific hormonal characterstics of subcutaneous and visceral adipose tissue and their relation to the meatbolic syndrome. Horm Metab Res 2002;34:616-621.
26. Wajchenberg BL. Subcutaneous and visceral adipose tissue: their relation to the metabolic syndrome. Endocr Rev 2000;21:697-738.
27. Britton KA and Fox CS. Ectopic fat depots and cardiovascular disease. Circulation 2011;124:e837-e841.
28. Ryo M, Maeda K, Onda T, et al. A new simple method for the measurement of visceral fat accumulation by bioelectrical impedance. Diabetes Care 2005;28:451-453.
29. Odegaard JI, Chawla A. Pleiotropic actions of ınsulin resistance and ınflammation in metabolic homeostasis. Science 2013;Vol.339.
30. Bozkuş Y, Mousa U, Demir CC et al. Abdomınal bıoelectrıc ımpedance for follow-up of dıeters: a prospectıve study. Acta Endocrinol (Buchar) 2019;15:145-152.doi:10.4183/aeb.2019.145.
31. Mousa U, Kut A, Bozkuş Y, Demir Çicek C, Anil C, Tütüncü Bascil N. Performance of abdominal bioelectrical ımpedance analysis and comparison with other known parameters in predicting the metabolic syndrome. Exp Clin Endocrinol Diabetes 2013;121:391-396.
32. Onat A, Hergenç G, Can G. İki metabolik sendrom tanımının kardiyometabolik risk öngörüsünün aynı kohortta prospektif yolla değerlendirilmesi ve halkımız için en uygun tanımın seçilmesi. Anadolu Kardiyol Derg 2007;7:29-34.
33. Ascaso JF, Pardo S, Real JT, Lorente RI, Prıego A, Carmena R. Diagnosing ınsulin resistance by simple quantitative methods in subjects with normal glucose metabolism. Diabetes Care 2003;26:3320-3325.
34. Lee S, Choi S, Kim HJ, et al. Cutoff values of surrogate measures of insulin resistance for metabolic syndrome in Korean non-diabetic adults. J Korean Med Sci 2006;21:695-700.
35. Esteghamati A, Ashraf H, Khalilzadeh O, et al. Optimal cut-off of homeostasis model assessment of insulin resistance (HOMA-IR) for the diagnosis of metabolic syndrome: third national surveillance of risk factors of noncommunicable diseases in Iran (SuRFNCD-2007). Nutrition & Metabolism 2010;7:26.
36. Esteghamati A, Ashraf H, Esteghamati AR, et al. Optimal threshold of homeostasis model assessment for insulin resistance in an Iranian population: the implication of metabolic syndrome to detect insulin resistance. Diabetes Res Clin Pract 2009;84:279-287.
37. Onat A, Hergenç G, Türkmen S, Yazıcı M, Sarı İ, Can G. Discordance between insulin resistance and metabolic syndrome: features and associated cardiovascular risk in adults with normal glucose regulation. Metabolism Clinical and Experimental 2006;55:445– 452.
38. UKPDS Group: U.K. Prospective Diabetes Study 16: Overview of 6 years’ therapy of type II diabetes: a progressive disease. Diabetes 1995;44:1249–1258.