The Link between Anthropometry and Fat-Based Obesity Indices in Pediatric Morbid Obesity

Authors: Mustafa M. Donma, Orkide Donma

Abstract:

Anthropometric measurements are essential for obesity studies. Waist circumference (WC) is the most frequently used measure and along with hip circumference (HC), it is used in most equations derived for the evaluation of obese individuals. Morbid obesity (MO) is the most severe clinical form of obesity and such individuals may also exhibit some clinical findings leading to metabolic syndrome (MetS). Then, it becomes a requirement to discriminate MO children with MetS (MOMetS+) from MO children without MetS (MOMetS-). Almost all obesity indices can differentiate obese (OB) children from children with normal body mass index (N-BMI). However, not all of them are capable of making this distinction. The aim of this study was to find out the clinical availability of (waist circumference + hip circumference)/2 ((WC+HC)/2) for the differential diagnosis of MOMetS+ and MOMetS- and to compare the possible preponderance of it over some other anthropometric or fat-based obesity indices. 45 MOMetS+ and 45 MOMetS- children were included in the study. Participants have submitted informed consent forms. The study protocol was approved by the Non-interventional Clinical Studies Ethics Committee of Tekirdag Namik Kemal University. Anthropometric measurements were performed. BMI, waist-to-hip circumference (WHR), (WC+HC)/2, trunk-to-leg fat ratio (TLFR), trunk-to-appendicular fat ratio (TAFR), trunk fat+leg fat/2 ((trunk+leg fat)/2), diagnostic obesity notation model assessment index-2 (D2I) and fat mass index (FMI) were calculated for both groups. Study data were analyzed statistically and 0.05 for p value was accepted as the statistical significance degree. Statistically higher BMI, WC, (WC+HC)/2, (trunk+leg fat)/2 values were found in MOMetS+ children than MOMetS- children. No statistically significant difference was detected for WHR, TLFR, TAFR, D2I and FMI between two groups. The lack of difference between the groups in terms of FMI and D2I pointed out the fact that the recently developed fat-based index; (trunk+leg fat)/2 gives much more valuable information during the evaluation of MOMetS+ and MOMetS- children. Upon evaluation of the correlations, (WC+HC)/2 was strongly correlated with D2I and FMI in both MOMetS+ and MOMetS- groups. Neither D2I nor FMI was correlated with W/H. Strong correlations were calculated between (WC+HC)/2 and (trunk+leg fat)/2 in both MOMetS- (r = 0.961; p < 0.001) and MOMetS+ (r = 0.936; p < 0.001) groups. Partial correlations between (WC+HC)/2 and (trunk+leg fat)/2 after controlling the effect of basal metabolic rate were r = 0.726; p < 0.001 in MOMetS- group and r = 0.932; p < 0.001 in MOMetS+ group. The correlation in the latter group was higher than the first group. In conclusion, recently developed anthropometric obesity index (WC+HC)/2 and fat-based obesity index (trunk+leg fat)/2 were of preponderance over the previously introduced classical obesity indices such as WHR, D2I and FMI during the differential diagnosis of MOMetS+ and MOMetS- children.

Keywords: Hip circumference, metabolic syndrome, morbid obesity, waist circumference.

References:

[1] A. Issaka, A. J. Cameron, Y. Paradies, J. B. Kiwallo, W. K. Bosu, Y. C. N. Houehanou, C. S. Wesseh, D. S. Houinato, D. J. P. Nazoum, and C. Stevenson, “Associations between obesity indices and both type 2 diabetes and impaired fasting glucose among West African adults: Results from WHO STEPS surveys,” Nutr. Metab. Cardiovasc. Dis., vol. 31, no. 9, pp. 2652-2660, Aug. 2021.
[2] 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.
[3] 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.
[4] S. Weihrauch-Blüher, P. Schwarz, and J. H. Klusmann, “Childhood obesity: increased risk for cardiometabolic disease and cancer in adulthood,” Metabolism., vol: 92, pp147-152, Mar. 2019.
[5] 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.
[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] 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.
[8] M. Donma, and O. Donma, “An obesity ındex derived from waist and hip circumferences well-matched with other ındices in children with obesity,” Int. J. Med. Health Sci., vol. 16, no. 10, pp. 152-155, 2022.
[9] WHO World Health Organization (WHO). The WHO Child Growth Standards. 2016 June. Access: http://www.who.int/childgrowth/en/
[10] 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.
[11] 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.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] K. Kouda, K. Ohara, Y. Fujita, H. Nakamura, and M. Iki, “Trunk-to-peripheral fat ratio predicts subsequent blood pressure levels in pubertal children with relatively low body fat - three-year follow-up study,” Circ. J. vol. 25, no. 80(8), pp. 1838-1845, Jul. 2016.
[17] K. Kouda, Y. Fujita, K. Ohara, T. Tachiki, J. Tamaki, A. Yura, J. S. Moon, E. Kajita, K. Uenishi, and M. Iki, “Associations between trunk-to-peripheral fat ratio and cardiometabolic risk factors in elderly Japanese men: baseline data from the Fujiwara-kyo Osteoporosis Risk in Men (FORMEN) study,” Environ. Health. Prev. Med., vol. 20, no. 26(1), pp. 35, Mar. 2021.
[18] Y. M. Kim, S. Kim, S. H. Kim, and Y. J. Won, “Clinical and body compositional factors associated with metabolic syndrome in obese koreans: a cross-sectional study,” Metab. Syndr. Relat. Disord., vol. 16, no. 6, pp. 290-298, Aug. 2018.
[19] 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.
[20] E. Jequier, “Energy metabolism in human obesity,” Soz. Praventiv. Med., vol. 34, no. 2, pp. 58-62, 1989.
[21] D. P. Córdoba-Rodríguez, I. Iglesia, and A. Gomez-Bruton, “Fat-free/lean body mass in children with insulin resistance or metabolic syndrome: a systematic review and meta-analysis,” BMC Pediatr., vol. 22, pp. 58, 2022.
[22] J. C. Lagacé, A. Marcotte-Chenard, J. Paquin, D. Tremblay, M. Brochu, and I. J. Dionne, “Increased odds of having the metabolic syndrome with greater fat-free mass: counterintuitive results from the National Health and Nutrition Examination Survey database,” J. Cachexia Sarcopenia Muscle., vol. 13, no. 1, pp. 377-385, Feb. 2022.
[23] I. Mahmoud, and N. Sulaiman, “Significance and agreement between obesity anthropometric measurements and indices in adults: a population-based study from the United Arab Emirates,” BMC Public Health, vol. 21, no. 1, pp. 1605, Aug. 2021.
[24] Á. Martín Castellanos, P. Martín Castellanos, E. Martín, and F. J. Barca Durán, “Abdominal obesity and myocardial infarction risk - We demonstrate the anthropometric and mathematical reasons that justify the association bias of the waist-to-hip ratio,” Nutr. Hosp., vol. 38, no. 3, pp. 502-510, Jun. 2021.