The Relationship between Anthropometric Obesity Indices and Insulin in Children with Metabolic Syndrome
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32919
The Relationship between Anthropometric Obesity Indices and Insulin in Children with Metabolic Syndrome

Authors: Mustafa M. Donma, Orkide Donma


The number of indices developed for the evaluation of obesity and metabolic syndrome (MetS) both in adults and pediatric population is ever increasing. These indices can be weight-dependent or weight–independent. Some are extremely sophisticated equations and their clinical utility is questionable in routine clinical practice. The aim of this study was to compare presently available obesity indices and find the most practical one. Their associations with MetS components were also investigated to determine their capacities in differential diagnosis of morbid obesity with and without MetS. Children with normal body mass index (N-BMI) and morbid obesity were recruited for this study. Three groups were constituted. Age- and sex-dependent BMI percentiles for morbid obese (MO) children were above 99 according to World Health Organization tables. Of them, those with MetS findings were evaluated as MetS group. Children, whose values were between 85 and 15, were included in N-BMI group. The study protocol was approved by the Ethics Committee of Tekirdag Namik Kemal University, Faculty of Medicine. Parents filled out informed consent forms to participate in the study. Anthropometric measurements and blood pressure values were recorded. BMI, hip index (HI), conicity index (CI), triponderal mass index (TPMI), body adiposity index (BAI), body shape index (BSI), body roundness index (BRI), abdominal volume index (AVI), waist-to-hip ratio (WHR) and [waist circumference (WC) + hip circumference (HC)]/2 were the formulas examined in this study. Routine biochemical tests including fasting blood glucose (FBG), insulin (INS), blood lipids were performed. Statistical program SPSS was used for the evaluation of study data; p < 0.05 was accepted as the statistical significance degree. HI did not differ among the groups. A statistically significant difference was noted between N-BMI and MetS groups in terms of ABSI. All the other indices were capable of making discrimination between N-BMI-MO, N-BMI- MetS and MO-MetS groups. No correlation was found between FBG and any obesity indices in any groups. The same was true for INS in N-BMI group. Insulin was correlated with BAI, TPMI, CI, BRI, AVI and (WC+HC)/2 in MO group without MetS findings. In the MetS group, the only index, which was correlated with INS, was (WC+HC)/2. These findings have pointed out that complicated formulas may not be required for the evaluation of the alterations among N-BMI and various obesity groups including MetS. The simple easily computable weight-independent index, (WC+HC)/2, was unique, because it was the only index, which exhibits a valuable association with INS in MetS group. It did not exhibit any correlation with other obesity indices showing associations with INS in MO group. It was concluded that (WC+HC)/2 was pretty valuable practicable index for the discrimination of MO children with and without MetS findings.

Keywords: Fasting blood glucose, insulin, metabolic syndrome, obesity indices.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 238


[1] M. Donma, and O. Donma, “Associations between surrogate insulin resistance indices and the risk of metabolic syndrome in children,” Int. J. Med. Health. Sci., vol. 14, no. 2, pp. 61- 64, 2020.
[2] A. Basit, N. Mustafa, N. Waris, S. Askari, A. Fawwad, and NDSP members, “Predicting the risk of type 2 diabetes through anthropometric indices in Pakistani adults- A sub-analysis of second national diabetes survey of Pakistan 2016-2017 (NDSP-07),” Diabetes Metab. Syndr., vol. 15, no. 2, pp. 543-547, Mar-Apr 2021.
[3] S. N. Darko, K. A. C. Meeks, W. K. B. A. Owiredu, E. F. Laing, D. Boateng, E. Beune, J. Addo, A. de-Graft Aikins, S. Bahendeka, F. Mockenhaupt, J. Spranger, P. Agyei-Baffour, K. Klipstein-Grobusch, L. Smeeth, C. Agyemang, and E. Owusu-Dabo, “Anthropometric indices and their cut-off points in relation to type 2 diabetes among Ghanaian migrants and non-migrants: The RODAM study,” Diabetes Res. Clin. Pract., vol. 173, pp. 108687, Mar. 2021.
[4] J. V. Chávez-Sosa, R. Rojas-Humpire, R. Gutierrez-Ajalcriña, and S. Huancahuire-Vega, “Association between lifestyles, anthropometric measurements and peripheral arterial disease in public sector health workers,” Am. J. Cardiovasc. Dis., vol. 11, no. 2, pp. 194-202, Apr. 2021.
[5] B. Zhang, Y. Fan, Y. Wang, L. Zhang, C. Li, J. He, P. Guo, M. Zhang, and M. Zhang, “Comparison of bioelectrical body and visceral fat indices with anthropometric measures and optimal cutoffs in relation to hypertension by age and gender among Chinese adults,” BMC Cardiovasc. Disord., vol. 21, no. 1, pp. 291, Jun. 2021.
[6] Q. Song, T. Huang, J. Song, X. Meng, C. Li, Y. Wang, and H. Wang, “Causal associations of body mass index and waist-to-hip ratio with cardiometabolic traits among Chinese children: A Mendelian randomization study,” Nutr. Metab. Cardiovasc. Dis., vol. 30, no. 9, pp. 1554-1563, Aug.2020.
[7] W. Albaker, S. El-Ashker, M. A. Baraka, N. El-Tanahi, M. Ahsan, and M. Al-Hariri, “Adiposity and cardiometabolic risk assessment among university students in Saudi Arabia,” Sci. Prog., vol. 104, no. 1, pp. 36850421998532, Jan-Mar 2021.
[8] I. Kishimoto, “Trunk-to-leg fat ratio-An emerging early marker of childhood adiposity, and future cardiometabolic risks,” Circ. J., vol. 80. no. 8, pp. 1707-1709, Jul. 2016.
[9] M. Donma, O. Donma, M. Aydin, M. Demirkol, B. Nalbantoglu, A. Nalbantoglu, and B. Topcu, “Gender differences in morbid obese children: Clinical significance of two diagnostic obesity notation model assessment indices,” Int. J. Med. Health Sci., vol. 10, no. 5, pp. 310 – 316, May 2016.
[10] O. Donma, and M. Donma, “Evaluation of the weight-based and fat-based indices in relation to basal metabolic rate-to-weight ratio,” Int. J. Med. Health Sci., vol. 13, no. 5, pp. 214-218, May 2019.
[11] A. Oliver Olid, L. Martín López, J. M. Moreno Villares, M. Á. Martínez González, V. de la O Pascual, and N. Martín Calvo, “Validation of the anthropometric data reported by parents of participants in the SENDO project,” Nutr. Hosp., 2021 Aug 25. Spanish. Epub ahead of print.
[12] H. K. Tang, C. T. C. Nguyen, and N. H. T. Vo, “Anthropometric indicators to estimate percentage of body fat: A comparison using cross-sectional data of children and adolescents in Ho Chi Minh City, Vietnam,” Indian J. Pediatr., 2021 Sep 13. Epub ahead of print.
[13] M. Mahdavi-Roshan, A. Rezazadeh, F. Joukar, M. Naghipour, S. Hassanipour, and F. Mansour- Ghanaei, “Comparison of anthropometric indices as predictors of the risk factors for cardiovascular disease in Iran: The PERSIAN Guilan Cohort Study,” Anatol. J. Cardiol., vol. 25, no. 2, pp. 120-128, Feb. 2021.
[14] M. Donma, and O. Donma, “An obesity index derived from waist and hip circumferences well-matched with other indices in children with obesity,” Int. J. Med. Health Sci., vol. 16, no. 10, pp. 152 – 155, 2022.
[15] WHO World Health Organization (WHO). The WHO child growth standards. 2016 June. Access:
[16] P. Zimmet, K. G. Alberti, F. Kaufman, N. Tajima, M. Silink, S. Arslanian, G. Wong, P. Bennet, 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, 2007.
[17] M. Litwin, and Z. Kułaga, “Obesity, metabolic syndrome, and primary hypertension,” Pediatr. Nephrol., vol. 36, no.4, pp. 825-837, Apr. 2021.
[18] G. Katamba, A. Musasizi, M. A. Kinene, A. Namaganda, and F. Muzaale, “Relationship of anthropometric indices with rate pressure product, pulse pressure and mean arterial pressure among secondary adolescents of 12-17 years,” BMC Res. Notes., vol. 14, no. 1, pp. 101, Mar. 2021.
[19] S. Weihrauch-Blüher, P. Schwarz, and J. H. Klusmann, “Childhood obesity: increased risk for cardiometabolic disease and cancer in adulthood,” Metabolism., vol. 92, pp.147-152, Mar. 2019.
[20] I. C. Lega, and L. L. Lipscombe, “Review: Diabetes, obesity, and cancer-pathophysiology and clinical implications,” Endocr. Rev., vol. 1, no. 41(1), pp. bnz014, Feb. 2020.
[21] D. Pasanta, K. T. Htun, J. Pan, M. Tungjai, S. Kaewjaeng, S. Chancharunee, S. Tima, H. J. Kim, J. Kæwkhao, and S. Kothan, “Waist circumference and BMI are strongly correlated with MRI-derived fat compartments in young adults,” Life (Basel)., vol. 11, no. 7, pp. 643, Jul. 2021.
[22] X. F. Ye, W. Dong, L. L. Tan, Z. R. Zhang, Y. L. Qiu, and J. Zhang, “Identification of the most appropriate existing anthropometric index for home-based obesity screening in children and adolescents,” Public Health., vol. 189, pp. 20-25, Dec. 2020.
[23] S. Cho, A. Shin, J. Y. Choi, S. M. Park, D. Kang, and J. K. Lee, “Optimal cutoff values for anthropometric indices of obesity as discriminators of metabolic abnormalities in Korea: results from a Health Examinees study,” BMC Public Health., vol. 21, no. 1, pp. 459, Mar. 2021.
[24] F. L. Zhang, J. X. Ren, P. Zhang, H. Jin, Y. Qu, Y. Yu, Z. N. Guo, and Y. Yang, “Strong association of waist circumference (WC), body mass index (BMI), waist-to-height ratio (WHtR), and waist-to-hip ratio (WHR) with diabetes: A population-based cross-sectional study in Jilin Province, China,” J. Diabetes Res., vol. 2021, pp. 8812431, May 2021.
[25] J. Xu, L. Zhang, Q. Wu, Y. Zhou, Z. Jin, Z. Li, and Y. Zhu, “Body roundness index is a superior indicator to associate with the cardio-metabolic risk: evidence from a cross-sectional study with 17,000 Eastern-China adults,” BMC Cardiovasc. Disord., vol. 21, no. 1, pp. 97, Feb. 2021.
[26] A. E. Malavazos, G. Capitanio, V. Milani, F. Ambrogi, I. A. Matelloni, S. Basilico, C. Dubini, F. M. Sironi, E. Stella, S. Castaldi, F. Secchi, L. Menicanti, G. Iacobellis, M. M. Corsi Romanelli, M. O. Carruba, and L. F. Morricone, “Tri-ponderal mass index vs body mass index in discriminating central obesity and hypertension in adolescents with overweight,” Nutr. Metab. Cardiovasc. Dis., vol. 31, no. 5, pp. 1613-1621, May 2021.
[27] S. Christakoudi, K. K. Tsilidis, and D. C. Muller, “A body shape index (absi) achieves better mortality risk stratification than alternative indices of abdominal obesity: results from a large European cohort,” Sci. Rep., vol. 10, pp. 14541, 2020.
[28] Z. Wang, S. He, and X. Chen, “Capacity of different anthropometric measures to predict diabetes in a Chinese population in southwest China: a 15-year prospective study,” Diabet. Med. vol. 36, no. 10, pp. 1261-1267, 2019.
[29] Y. Cui, F. Zhang, H. Wang, L. Zhao, R. Song, M. Han, and X. Shen, “Temporal associations between tri-ponderal mass index and blood pressure in chinese children: a cross- lag analysis,” Nutrients., vol. 24, no. 14(9), pp. 1783, Apr. 2022.
[30] Y. J. Seo, Y. S. Shim, and, H. S. Lee, “Metabolic risk assessment in children and adolescents using the tri-ponderal mass index,” Sci. Rep., vol. 12, pp. 10094, 2022.
[31] F. Mardali, M. Naziri, M. H. Sohouli, S. Fatahi, F. Sadat Hosseini-Baharanchi, M. A. Găman, and F. Shidfar. “Predictors of central and general obesity in iranian preschool children: which anthropometric indices can be used as screening tools?,” BMC Pediatr., vol. 31, no. 22(1), pp. 320, May. 2022.
[32] R. O. Alvim, J. H. Siqueira, D. Zaniqueli, N. S. Paiva, M. P. Baldo, K. V. Bloch, and J. G. Mill, “Reference values for the tri-ponderal mass index and its association with cardiovascular risk factors in Brazilian adolescents aged 12 to 17 years,” Nutrition., vol. 99-100, pp. 111656, Jul-Aug. 2022.
[33] X. C. Liu, Y. S. Liu, H. X. Guan, Y. Q. Feng, and J. Kuang, “Comparison of six anthropometric measures in discriminating diabetes: a cross-sectional study from the national health and nutrition examination survey,” J. Diabetes., vol. 14, no. 7, pp. 465-475, Jul. 2022.