Yetişkin bireylerde metabolik sendromun belirlenmesinde obeziteyle ilişkili geleneksel ve yeni indekslerin karşılaştırılması

Yıl 2022, Cilt: 47 Sayı: 1, 62 - 70, 31.03.2022

Sevil Karahan Yılmaz , Fatih Özçiçek , Cuma Mertoğlu

https://doi.org/10.17826/cumj.1002607

Öz

Amaç: Yetişkin bireylerde metabolik sendromun belirlenmesinde obeziteyle ilişkili geleneksel ve yeni indekslerin değerlendirilmesi ve hangi belirtecin daha iyi öngördürücü olduğunu belirleme amacı ile planlanmıştır. Gereç ve Yöntem: Bu çalışmaya 18-65 yaş arası toplam 419 yetişkin birey dahil edildi. Vücut ağırlığı, boy uzunluğu, bel ve kalça çevresi ile kan basıncı ölçüldü; açlık kan şekeri, total kolesterol, trigliserit, düşük dansiteli lipoprotein kolesterol ve yüksek dansiteli lipoprotein kolesterol değerleri analiz edildi. Metabolik sendrom (MetS) Uluslararası Diabet Federasyonu kriterleri kullanılarak tanımlanmıştır. Obeziteyle ilişkili 23 indeksin değeri hesaplandı.
Bulgular: Metabolik sendrom prevalansı % 58,7 (erkek % 41,2; kadın % 67,7)’dir. Trigliserit-glikoz (TyG) indeksi hem erkeklerde (eğri altında kalan alan (AUC)= 0,894, kesme değeri = 9,3) hemde kadınlar da (AUC = 0,901, kesme değeri = 8,3) en büyük AUC'ye sahiptir. Erkeklerde lipit birikim ürünü (LAP), MetS için ikinci en yüksek belirlemeye sahip iken (AUC = 0,880, kesme değeri = 51,1), ardından TyG-bel/kalça (AUC = 0,876, kesme değeri = 3,7) gelmektedir. Kadınlarda kardiyometabolik indeks (CMI) (AUC = 0,872, kesme değeri = 1,3) ve viseral adipozite indeksi (VAI) (AUC = 0,868, kesme değeri = 4,1) sırasıyla ikinci ve üçüncü en büyük AUC'lere sahiptir.
Sonuç: TyG indeksi MetS belirlemede en iyi öngördürüdür. Bel çevresi kullanışlılığı ve uygun maliyetiyle büyük ölçekli epidemiyolojik çalışmalarda alternatif bir indeks olabilir.

Anahtar Kelimeler

Metabolik sendrom, trigliserit-glikoz indeksi, viseral adipozite indeksi, lipit birikim ürünü, kardiyometabolik indeks.

Comparison of traditional and novel obesity-related indices for identification of metabolic syndrome in adults

Öz

Purpose: The aim of this study was to evaluate the traditional and novel obesity-related indices in the determination of metabolic syndrome in adults and to determine which marker is the better predictor.
Materials and Methods: A total of 419 adults between the ages of 18-65 were included in this study. Body weight, height, waist, hip and waist circumference, and blood pressure were measured; fasting blood glucose, total cholesterol, triglyceride, low-density lipoprotein cholesterol and high-density lipoprotein cholesterol values were analyzed. Metabolic syndrome (MetS) was defined using the International Diabetes Federation criteria. The values of 23 obesity-related indices were calculated.
Results: The prevalence of metabolic syndrome is 58.7% (male 41.2%; female 67.7%). The triglyceride glucose (TyG) index has the largest area under the curve (AUC) in both men (AUC = 0.894, cutoff = 3.9) and women (AUC = 0.901, cutoff = 3.9). In men, lipid accumulation product (LAP) had the second highest determination for MetS (AUC = 0.880, cut-off = 51.1), followed by TyG-waist/hip (AUC = 0.876, cut-off = 3.7). Cardiometabolic index (CMI) (AUC = 0.872, cut-off value = 1.3) and visceral adiposity index VAI (AUC = 0.868, cut-off value = 4.1) had the second and third largest AUCs, respectively, in women.
Conclusion: TyG index is the best predictor of MetS. Waist circumference could be an alternative index in large epidemiology survey due to its convenient and cost-efective characteristics.

Anahtar Kelimeler

Metabolic syndrome, triglyceride-glucose index, visceral adiposity index, lipid accumulation product, cardiometabolic index.

Kaynakça

• Yao MF, He J, Sun X. Gender differences in risks of coronary heart disease and stroke in patients with type 2 diabetes mellitus and their association with metabolic syndrome in China. Int J Endocrinol. 2016;2016:8483405.
• Li W, Wang D, Wang X, Gong Y, Cao S, Yin X et al. The association of metabolic syndrome components and diabetes mellitus: Evidence from china national stroke screening and prevention project. BMC Public Health. 2019;19:192.
• Oğuz A, Telci Çaklılı Ö, Tümerdem Çalık B; PURE Investigators. The Prospective Urban Rural Epidemiology (PURE) study: PURE Turkey. Turk Kardiyol Dern Ars. 2018;46:613-23..
• Onat A, Yuksel M, Koroglu B, Gumrukcuoglu HA, Aydin M, Cakmak HA et al. Turkish Adult Risk Factor Study Survey 2012: Overall and coronary mortality and trends in the prevalence of metabolic syndrome. Turk Kardiyol Dern Ars. 2013;41:373-8.
• Guo X, Ding Q, Liang M. Evaluation of eight anthropometric ındices for identification of metabolic syndrome in adults with diabetes. Diabetes Metab Syndr Obes. 2021;14:1431-44.
• Matsuzawa Y, Funahashi T, Nakamura T. The concept of metabolic syndrome: Contribution of visceral fat accumulation and its molecular mechanism. J Atheroscler Thromb. 2011;18:629-39.
• Rothney MP, Catapano AL, Xia J, Wacker WK, Tidone C, Grigore L et al. Abdominal visceral fat measurement using dual-energy x-ray: Association with cardiometabolic risk factors. Obesity. 2013;21:1798-802.
• Nazare JA, Smith J, Borel AL, Aschner P, Barter P, Van Gaal L et al. Usefulness of measuring both body mass index and waist circumference for the estimation of visceral adiposity and related cardiometabolic risk profile (from the INSPIRE ME IAA study). Am J Cardiol. 2015;115:307-15.
• Kawamoto R, Kikuchi A, Akase T, Ninomiya D, Kumagi T. Usefulness of waist-to-height ratio in screening incident metabolic syndrome among japanese community-dwelling elderly individuals. PLoS ONE. 2019;14:e0216069.
• Gharipour M, Sarrafzadegan N, Sadeghi M, Andalib E, Talaie M, Shafie D et al. Predictors of metabolic syndrome in the iranian population: Waist circumference, body mass index, or waist to hip ratio? Cholesterol. 2013;2013:198384..
• Guerrero-Romero F, Rodriguez-Moran M. Abdominal volume index: An anthropometry-based index for estimation of obesity is strongly related to impaired glucose tolerance and type 2 diabetes mellitus. Arch Med Res. 2003;34:428-32.
• Thomas DM, Bredlau C, Bosy-Westphal A, Mueller M, Shen W, Gallagher D et al. Relationships between body roundness with body fat and visceral adipose tissue emerging from a new geometrical model. Obesity. 2013;21:2264-71.
• Rico-Martin S, Calderon-Garcia JF, Sanchez-Rey P, Franco-Antonio C, Martinez Alvarez M. Sanchez Munoz-Torrero JF. Effectiveness of body roundness index in predicting metabolic syndrome: A systematic review and meta-analysis. Obes Rev. 2020;21:e13023.
• Mantzoros CS, Evagelopoulou K, Georgiadis EI, Katsilambros N. Conicity index as a predictor of blood pressure levels, insulin and triglyceride concentrations of healthy premenopausal women. Horm Metab Res. 1996;28:32–4.
• Bawadi H, Abouwatfa M, Alsaeed S, Kerkadi A, Shi Z. Body shape index is a stronger predictor of diabetes. Nutrients. 2019;11:1018.
• Nevill AM, Duncan MJ, Lahart IM, Sandercock GR. Scaling waist girth for differences in body size reveals a new improved index associated with cardiometabolic risk. Scand J Med Sci Sports. 2017;27:1470-6.
• Nevill AM, Bryant E, Wilkinson K, Gomes TN, Chaves R, Pereira S et al. Can waist circumference provide a new "third" dimension to BMI when predicting percentage body fat in children? Insights using allometric modelling. Pediatr Obes. 2019;14:e12491.
• Huang Y, Gu L, Li N, Fang F, Ding X, Wang Y et al. The product of waist and neck circumference outperforms traditional anthropometric indices in identifying metabolic syndrome in Chinese adults with type 2 diabetes: a cross-sectional study. Diabetol Metab Syndr. 2021;13:35.
• Park Y, Kim NH, Kwon TY, Kim SG. A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. Sci Rep. 2018;8:16753.
• Baveicy K, Mostafaei S, Darbandi M, Hamzeh B, Najafi F, Pasdar Y. Predicting metabolic syndrome by visceral adiposity index, body roundness index and a body shape index in adults: A cross-sectional study from the iranian rancd cohort data. Diabetes Metab Syndr Obes. 2020;13:879-87.
• Guerrero-Romero F, Simental-Mendia LE, Gonzalez-Ortiz M, Martinez-Abundis E, Ramos-Zavala MG, Hernandez-Gonzalez SO et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010;95:3347-51.
• Banik SD, Pacheco-Pantoja E, Lugo R, Gómez-de-Regil L, Aké RC, González RM et al. Evaluation of anthropometric indices and lipid parameters to predict metabolic syndrome among adults in Mexico. Diabetes Metab Syndr Obes. 2021;14:691-701.
• Yu X, Wang L, Zhang W, Ming J, Jia A, Xu S et al. Fasting triglycerides and glucose index is more suitable for the identification of metabolically unhealthy individuals in the chinese adult population: A nationwide study. J Diabetes Investig. 2019;10:1050-8.
• Chiu TH, Huang YC, Chiu H, Wu PY, Chiou HYC, Huang JC et al. Comparison of various obesity-related ındices for ıdentification of metabolic syndrome: a population-based study from Taiwan Biobank. Diagnostics (Basel). 2020;10:1081.
• Saklayen MG. The global epidemic of the metabolic syndrome. Curr Hypertens Rep. 2018;20:12.
• Yu S, Guo X, Li G, Yang H, Zheng L, Sun Y. Lymphocyte to high-density lipoprotein ratio but not platelet to lymphocyte ratio effectively predicts metabolic syndrome among subjects from rural China. Front Cardiovasc Med. 2021;8:583320..
• Nauck M, Warnick GR, Rifai N. Methods for measurement of LDL-cholesterol: a critical assessment of direct measurement by homogeneous assays versus calculation. Clin Chem. 2002;48:236-54.
• Domínguez-Reyes T, Quiroz-Vargas I, Salgado-Bernabé AB, Salgado-Goytia L, Muñoz-Valle JF, Parra-Rojas I. Las medidas antropométricas como indicadores predictivos de riesgo metabólico en una población mexicana. Nutr Hosp. 2017;34:96-101.
• Raimi TH, Dele-Ojo BF, Dada SA, Fadare JO, Ajayi DD, Ajayi EA et al. Triglyceride-glucose index and related parameters predicted metabolic syndrome in Nigerians. Metab Syndr Relat Disord. 2021;19:76-82.
• Shin KA, Kim YJ. Usefulness of surrogate markers of body fat distribution for predicting metabolic syndrome in middle-aged and older korean populations. Diabetes Metab Syndr Obes. 2019;12:2251-9.
• Lin HY, Zhang XJ, Liu YM, Geng LY, Guan LY, Li XH. Comparison of the triglyceride glucose index and blood leukocyte indices as predictors of metabolic syndrome in healthy Chinese population. Sci Rep. 2021;11:10036.
• Amato MC, Pizzolanti G, Torregrossa V, Misiano G, Milano S, Giordano C. Visceral adiposity index (vai) is predictive of an altered adipokine profile in patients with type 2 diabetes. PLoS One. 2014;9:e91969.
• Stefanescu A, Revilla L, Lopez T, Sanchez SE, Williams MA, Gelaye B. Using a Body Shape Index (ABSI) and Body Roundness Index (BRI) to predict risk of metabolic syndrome in Peruvian adults. J Int Med Res. 2020;48:300060519848854..
• Wu Y, Li H, Tao X, Fan Y, Gao Q, Yang J et al. Optimised anthropometric indices as predictive screening tools for metabolic syndrome in adults: a cross sectional study. BMJ Open. 2021;11:e043952.