Coalescence of Insulin and Triglyceride/High Density Lipoprotein Cholesterol Ratio for the Derivation of a Laboratory Index to Predict Metabolic Syndrome in Morbid Obese Children
Morbid obesity is a health threatening condition particularly in children. Generally, it leads to the development of metabolic syndrome (MetS) characterized by central obesity, elevated fasting blood glucose (FBG), triglyceride (TRG), blood pressure values and suppressed high density lipoprotein cholesterol (HDL-C) levels. However, some ambiguities exist during the diagnosis of MetS in children below 10 years of age. Therefore, clinicians are in the need of some surrogate markers for the laboratory assessment of pediatric MetS. In this study, the aim is to develop an index, which will be more helpful during the evaluation of further risks detected in morbid obese (MO) children. A total of 235 children with normal body mass index (N-BMI), with varying degrees of obesity; overweight (OW), obese (OB), MO as well as MetS participated in this study. The study was approved by the Institutional Ethical Committee. Informed consent forms were obtained from the parents of the children. Obesity states of the children were classified using BMI percentiles adjusted for age and sex. For the purpose, tabulated data prepared by WHO were used. MetS criteria were defined. Systolic and diastolic blood pressure values were measured. Parameters related to glucose and lipid metabolisms were determined. FBG, insulin (INS), HDL-C, TRG concentrations were determined. Diagnostic Obesity Notation Model Assessment Laboratory (DONMALAB) Index [ln TRG/HDL-C*INS] was introduced. Commonly used insulin resistance (IR) indices such as Homeostatic Model Assessment for IR (HOMA-IR) as well as ratios such as TRG/HDL-C, TRG/HDL-C*INS, HDL-C/TRG*INS, TRG/HDL-C*INS/FBG, log, and ln versions of these ratios were calculated. Results were interpreted using statistical package program (SPSS Version 16.0) for Windows. The data were evaluated using appropriate statistical tests. The degree for statistical significance was defined as 0.05. 35 N, 20 OW, 47 OB, 97 MO children and 36 with MetS were investigated. Mean ± SD values of TRG/HDL-C were 1.27 ± 0.69, 1.86 ± 1.08, 2.15 ± 1.22, 2.48 ± 2.35 and 4.61 ± 3.92 for N, OW, OB, MO and MetS children, respectively. Corresponding values for the DONMALAB index were 2.17 ± 1.07, 3.01 ± 0.94, 3.41 ± 0.93, 3.43 ± 1.08 and 4.32 ± 1.00. TRG/HDL-C ratio significantly differed between N and MetS groups. On the other hand, DONMALAB index exhibited statistically significant differences between N and all the other groups except the OW group. This index was capable of discriminating MO children from those with MetS. Statistically significant elevations were detected in MO children with MetS (p < 0.05). Multiple parameters are commonly used during the assessment of MetS. Upon evaluation of the values obtained for N, OW, OB, MO groups and for MO children with MetS, the [ln TRG/HDL-C*INS] value was unique in discriminating children with MetS.
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 V. Higgins, and K. Adeli, “Pediatric metabolic syndrome: pathophysiology and laboratory assessment,” J. Int. Fed. Clin. Chem. Lab. Med., vol. 28, pp. 25-42, March 2017.
 T. T. K. Huang, S. S. Sun, and S. R. Daniels, “Understanding the nature of metabolic syndrome components in children and what they can and cannot do to predict adult disease,” J. Pediatr, vol.155, pp. e13-e14, Sept. 2009.
 T. T. K. Huang, “Finding thresholds of risk for components of the pediatric metabolic syndrome,” J. Pediatr., vol.152, pp.158-159, Feb. 2008.
 E. S. Ford, and C. Li, “Defining the metabolic syndrome in children and adolescents: Will the real definition please stand up?,” J. Pediatr., vol.152, pp. 160-164, Feb. 2008.
 R. Weiss, A. A. Bremer, R. H. Lustig, “What is metabolic syndrome, and why are children getting it?,” Ann. N. Y. Acad. Sci., vol.1281, pp.123-140, Apr. 2013.
 D. R. Matthews, J. P. Hosker, A.S. Rudenski, B. A. Naylor, D. F. Treacher, R. C. Turner, “Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.”, Diabetologia, vol. 28, no. 7, pp. 412–419, Jul. 1985.
 L. Zhao, J. Cheng, Y. Chen, Q. Li, B. Han, Y. Chen, F. Xia, C. Chen, D. Lin, X. Yu, N. Wang, and Y. Lu, “Serum alanine aminotransferase/aspartate aminotransferase ratio is one of the best markers of insulin resistance in the Chinese population,” vol.14, pp. 64, Oct.2017.
 M. K. Kim,, C. W. Ahn, S. Kang, J. S. Nam, K. R. Kim, and J. S. Park, Relationship between the triglyceride glucose index and coronary artery calcification in Korean adults. Cardiovasc. Diabetol., 16(1):108, Aug. 2017.
 I. Alías-Hernández, R. Galera-Martínez, E. García-García, F. J. Muñoz-Vico, M. A. Vázquez Lopez, M. C. Olvera-Porcel, and A. Bonillo Perales, “Insulinaemia and insulin resistance in Caucasian general paediatric population aged 2 to 10 years: Associated risk factors,” Pediatr. Diabetes, vol.19, pp. 45-52, Feb. 2018.
 B. Wang, M. Zhang, Y. Liu, X. Sun, L. Zhang, C. Wang, L. Li, Y. Ren, C. Han, Y. Zhao, J. Zhou, C. Pang, L. Yin, T. Feng, J. Zhao, and D. Hu, “Utility of three novel insulin resistance-related lipid indices for predicting type 2 diabetes mellitus among people with normal fasting glucose in rural China,” J. Diabetes, vol.10, pp.641-652, Aug. 2018.
 X. Cao, D. Wang, J. Zhou, and Z. Chen, “Comparison of lipoprotein derived indices for evaluating cardio-metabolic risk factors and subclinical organ damage in middle-aged Chinese adults,” Clin. Chim. Acta, vol.475, pp.22-27, Dec.2017.
 M. Žarković, J. Ćirić, B. Beleslin, M. Stojković, S. Savić, M. Stojanović, and T. Lalić, “Variability of HOMA and QUICKI insulin sensitivity indices,” Scand. J. Clin. Lab. Invest., vol.77, pp. 295-297, Jul 2017.
 Q. Tang, X. Li, P. Song, and L. Xu, “Optimal cut-off values for the homeostasis model assessment of insulin resistance (HOMA-IR) and pre-diabetes screening: Developments in research and prospects for the future,” Drug Discov. Ther., vol.9, pp.380-385, Dec. 2015.
 M. R. Salazar, H. A. Carbajal, W. G. Espeche, M. Aizpurúa, C. A. Dulbecco, and G. M. Reaven, “Comparison of two surrogate estimates of insulin resistance to predict cardiovascular disease in apparently healthy individuals,” Nutr. Metab. Cardiovasc. Dis., vol.27, pp.366-373, Apr. 2017.
 S. B. Lee, C. W. Ahn, B. K. Lee, S. Kang, J. S. Nam, J. H. You, M. J. Kim, M. K. Kim, and J. S. Park, “Association between triglyceride glucose index and arterial stiffness in Korean adults,” Cardiovasc. Diabetol., vol.17, pp.41, Mar.2018.
 World Health Organization (WHO). The WHO Child Growth Standards. Available at: http://www.who.int/childgrowth/en/ Accessed on June 10, 2016.
 P. Zimmet, K. G. Alberti, F. Kaufman, N. Tajima, M. Silink, S. Arslanian, G. Wong, P. Bennett, J. Shaw, S. Caprio, and IDF consensus group, “The metabolic syndrome in children and adolescents- an IDF consensus report”, Pediatr. Diabetes, vol. 8, no. 5, pp. 299 - 306, Oct. 2007.
 M. Tohidi, A. Baghbani-Oskouei, N. S. Ahanchi, F. Azizi, and F. Hadaegh, Fasting plasma glucose is a stronger predictor of diabetes than triglyceride – glucose index, triglycerides/high density lipoprotein cholesterol, and homeostasis model assessment of insulin resistance: Tehran Lipid and Glucose Study. Acta Diabetol., vol.55, pp. 1067-1074, Oct. 2018.