Predictive Ability of Waist Circumference and Waist-to-Height Ratio for Cardiometabolic Risk Screening among Spanish Children

Abstract

An excess of fat mass has been associated with adverse cardiometabolic risk factors. Different anthropometric measures have been proposed as alternative non-invasive measures for obesity-related cardiometabolic risk. To evaluate the magnitude of association between waist circumference (WC) and waist-to-height ratio (WtHR) with cardiometabolic risk factors and metabolic syndrome and to determine the WtHR cutoff associated with a more favorable cardiometabolic risk profile in Spanish children, data were taken from a cross-sectional survey conducted in 2010 among 848 schoolchildren aged 8–11 years from 20 public schools in the province of Cuenca (Spain). Anthropometric variables, glucose, insulin, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), systolic (SBP) and diastolic blood pressure (DBP) and metabolic syndrome (MetS) were also analyzed. WtHR and WC had a good accuracy for TG, insulin, and MetS. The diagnostic odds ratio ranged from 2.95 to 9.07 for WtHR and from 5.30 to 27.40 for WC. The main result of the present study suggests that both WtHR and WC could be used as a screening tool to identify children with cardiometabolic abnormalities.

Bio-Electric Impedance Analysis and Anthropometric Obesity Indices as Predictors of Dyslipidaemia Among Adults at A Tertiary Institution in Lusaka, Zambia.

“…Generally, most anthropometric indices were higher in the dyslipidaemic group than the nondyslipidaemic group, re ecting ndings from other studies (13,(28)(29)(30). Our study showed a mean WC to be signi cantly higher among participants who were dyslipidaemic than those who were not and generally in agreement with most studies which favour use of central obesity indices as surrogates in assessing cardiometabolic risk factors across various ethnic, regional and age groups (31)(32)(33)(34). BIA methods of analysis did not show any signi cance association with dyslipidaemia in our study.…”

Accuracy of different cutoffs of the waist‐to‐height ratio as a screening tool for cardiometabolic risk in children and adolescents: A systematic review and meta‐analysis of diagnostic test accuracy studies

“…Forty-one cross-sectional studies were included in the present study, including 138,561 children and adolescents (50% girls) aged between 3 and 19 years. The studies were conducted in the following countries: 11 studies were conducted in countries from the European region (Denmark, Estonia, Portugal, Spain, Scotland, Wales, Finland, Sweden, UK, Greece, Italy, Hungary, Belgium, France, Germany, and Austria) [20][21][22][23][24][25][26][27][28][29][30] ; 10 studies were conducted in countries from the Latin American region (Chile, Brazil, Mexico, and Venezuela) [31][32][33][34][35][36][37][38][39][40] ; 11 studies were conducted in East and Southeast Asian countries (Malaysia, South Korea, China, Taiwan, and Vietnam) [41][42][43][44][45][46][47][48][49][50][51] ; 3 studies were conducted in the USA [52][53][54] ; 1 study was conducted in Algeria 55 ; 1 study was conducted in India 56 ; 1 study was conducted in Iran 57 ; and 3 studies were conducted in South Africa. [58][59][60] Sample sizes ranged from 152 35 to 32,036 49 participants.…”

“…WHtR cutoffs ranged between 0.41 20 and 0.59 35 as follows: 10 studies reported cutoffs of 0.41 to 0.45 20,22,[24][25][26]32,42,48,58,60 ; 23 studies reported cutoffs of 0.46 to 0.50 [23][24][25][26][27][28][29][30][32][33][34]36,[43][44][45][46][47]51,[53][54][55]57,59 ; and 12 studies reported cutoffs ≥0.51. 21,30,31,33,35,[37][38][39]48,52,54,55 The general characteristics of the studies are depicted in Table S1.…”

Usefulness of the waist-to-height ratio for predicting cardiometabolic risk in children and its suggested boundary values

“…Mehta, based on the association between the WHtR and BMI, proposed using a WHtR!0.55 as the cut-off for abdominal obesity in children [28]. Recently, Arellano et al [55] proposed specific cutoffs depending on the risk factors (e.g., a WHtR !0.57 to predict risk of high blood pressure, a WHtR !0.52 to predict elevated triglyceride levels and a WHtR !0.51 to predict metabolic syndrome). In this study, Arellano et al evaluated the association of the WHtR and WC with the risk of metabolic syndrome, which was defined through the sum of internal z scores for various parameters (e.g., insulin, triglycerides, etc.)…”