Obesity and its associated metabolic and cardiovascular complications epidemic is one of the most serious challenges that public health care systems have to take up. It is estimated that in 2008 more than 1.4 billion adults worldwide were overweight, and 500 million was suffering from obesity. According to the WHO statistics, yearly obesity kills almost 3 million people, and the costs of treatment of the obesity-related disorders are inconceivably high. Unfortunately, the problem of obesity is facing not only the adult population, but more and more often it applies to children. In 2011 more than 40 million children under the age of five all over the world were overweight (1). Also, in Poland over 14% of children who reached the age of seven are overweight and more than 5% are obese (2). Overweight and obesity are known risk factors for many diseases: respiratory and musculoskeletal disorders, atherosclerosis, coronary heart disease, stroke, hypertension, non-alcoholic fatty liver disease, type 2 diabetes mellitus, gallbladder disease, obstructive sleep apnea, and some forms of cancer (3). Childhood obesity is associated with greater prevalence of mentioned disorders in adult life, so early-stage diagnosis and taking appropriate efforts towards body mass reduction are indispensable (4). Many different anthropometrical measures of obesity have been proposed, but still there is no agreement which one is the best predictor of metabolic risk. It seems, that the most widely applied body mass index (BMI) does not account body fat distribution and posture. Moreover, the cut-off points for different metabolic risk groups strongly depend on age, gender and ethnicity, which is confounding, especially for parents and other non-healthcare-professionals (5, 6). Waist circumference (WC) was believed to be a stronger predictor of cardiovascular and metabolic risk than BMI, both in adults and children. An important merit of WC is that it diversifies between peripheral and central obesity which is a stronger risk factor for cardio-metabolic disorders (7). Nevertheless likewise BMI, WC cut-off points are race-, age- and sex-dependent, and its appliance to children requires the usage of specific percentile labels. Recently waist-to-height (WHtR) – a new anthropometric obesity indice has emerged. It is less complicated, cheaper, and it does not depend on ethnicity, gender and age (8).

The aim of this study was to estimate the usefulness of waist-to-height ratio (WHtR) in assessing the obesity-related cardio-metabolic risk, and to compare WHtR value to other indices of adiposity.

479 participants (284 girls and 195 boys) aged 17 to 19 were examined. Our subjects were recruited in two secondary schools located in the Żoliborz district, representative for the population of high-school students in Warsaw. The data were obtained between 20th and 30th of May 2013. All participants were provided with information regarding the aim and the design of the study. A written consent for both noninvasive and invasive medical procedures were obtained from each participant or from legal guardians, if individual was under the age of 18. Participation in all procedures was voluntary; a number of 137 individuals did not appear for blood sampling, failing to complete the study. We included them in the initial descriptive characteristics of the group but excluded their data from final statistical analysis.

In all participants we recorded the following parameters: height, weight, waist circumference and hip circumference. Height was measured to the nearest 0.5 cm with the participant standing barefoot, back against a stadiometer. Weight was measured to the nearest 0.1 kg with the use of a physician beam weight. Waist circumference was measured at the middle point between the costal arch and the greater crest. Hip circumference was measured at the level of the greater trochanters. BMI was calculated as body weight (kg) divided by height (m) squared. Subjects with BMI between 25 and 30 kg/m² were considered as overweighed, whereas subjects with BMI between 30 and 40 kg/m² were considered as obese.

WHtR was calculated as waist circumference (cm) divided by height (cm). WHR was calculated as waist circumference (cm) divided by hip circumference (cm). During the same consultation all participants had their blood pressure taken, after having rested in a sitting position for at least 5 minutes. An automatic sphygmomanometer was applied. All measurements were performed by medical students, who had been instructed on the proper technique prior to the beginning of the work. Hypertension was recognized when blood pressure was higher than 130/85. Fasting blood samples for triglycerides, total cholesterol, HDL cholesterol and LDL cholesterol were collected from the intermediate cubital vain by trained nurses in the following day. An enzymatic colorimetric method was used to measure total cholesterol in the presence of cholesterol oxidase and esterase. The sensitivity was 0.116 mg/dL. HDL-cholesterol was measured with enzymatic colorimetric method; sensitivity was 3 mg/dL. Triglycerides were also measured with enzymatic colorimetric method with sensitivity 0.85 mg/dL. All mentioned biochemical measurements were performed using Roche Cobas Integra 400 chemistry analyzer (Roche Diagnostics). Glucose levels was determined with a glucometer in blood drawn from the participant’s little finger. All blood samples were collected between 7:30 and 10 am, the participants were instructed to remain fasting. The norms for blood pressure and laboratory values were derived from the IDF consensus worldwide definition of the metabolic syndrome.

In order to estimate the overall cardio-metabolic risk for the participants we created a continuous Metabolic Risk Score (MRS), primarily based on a similar scores used in several previous studies (9, 10). In the MRS we included the following components of metabolic syndrome: triglycerides, HDL cholesterol, glucose, systolic and diastolic blood pressure. For each of the above variables a z-score was calculated, for the whole group and separately for boys and for girls. An MRS was established for every individual by calculating the mean of all z-scores.

All calculations were performed with the Statistica 7.0 software package (StatSoft Inc, Tulsa, OK, USA).