Longitudinal preventive-screening cutoffs for metabolic syndrome in adolescents



To detect metabolic risk factor cutoff points in adolescence for the diagnosis of metabolic syndrome that develops at the age of 17 years (MS17).


This study adopted a 6-year design incorporating four data collection time points (TPs). Volunteers were assessed prospectively at the ages of 12, 13, 14 and 17.


A total of 210, 204, 198 and 187 schoolchildren volunteered at the first (TP1=12 years old), second (TP2=13 years old), third (TP3=14 years old) and fourth (TP4=17 years old) data collection TP, respectively.


At each data collection TP, anthropometrical, biological and lifestyle data were obtained. Identical protocols were used for each assessment conducted by the same trained investigators.


A total of 12% of the participants were diagnosed with MS17, the majority of them being boys (P<0.05). The prevalence of the syndrome increased directly with the degree of obesity. Using body mass index (BMI), adiposity and/or aerobic fitness levels in both genders, MS17 could be correctly diagnosed as early as TP1. No such cutoff points were found for high-density lipoprotein cholesterol, triglycerides, blood pressure and fasting plasma glucose levels.


With respect to the data presented, it has been established that the calculated longitudinal preventive-screening cutoffs allow successful diagnosis of metabolic syndrome in adolescents using BMI, adiposity or aerobic fitness levels in both sexes. Adoption of such pediatric guidelines may help mitigate future increase in the prevalence of metabolic syndrome.


Voluminous research to date has appeared on metabolic syndrome, the noxious coexistence of several metabolic abnormalities, including obesity, hypertension, atherogenic dyslipidemia, insulin resistance and prothrombotic state.1, 2, 3 The metabolic syndrome is prevalent in approximately 15 and 35% of adults in Europe4 and the United States,5 respectively, and has been recognized6 as a secondary target of risk-reduction therapy by the World Health Organization and the US National Cholesterol Education Program Adult Treatment Panel III,6 because it places individuals at risk of type 2 diabetes7 and cardiovascular disease.8 The pathogenesis of the metabolic syndrome is exceedingly complex and, to date, not entirely understood; yet the interaction of sedentary lifestyle, dietary elements and obesity in childhood and adolescence together with genetic factors have been suggested to contribute to its development.4 As the number of overweight and obese children is increasing9 and as it has become clearly evident that the pathology of the metabolic syndrome begins early in life,3, 10 it is necessary to introduce strong prevention strategies and comprehensive screening in childhood and adolescence. To aid in the development of such pediatric guidelines, our purpose was to generate longitudinal preventive-screening cutoffs for the metabolic syndrome in adolescents by detecting demarcation points in key and easy-to-monitor parameters that enable—as early as 5 years in advance—the successful detection of metabolic syndrome that develops at the age of 17 years (MS17). The variables of interest covered metabolic syndrome risk factors as well as fitness, habitual physical activity and dietary elements.


This study adopted a 6-year design incorporating four data collection time points (TP), and it is part of a large project on aspects of health and fitness in Greek schoolchildren. The baseline surveys, which provided the basis for this investigation, have been published elsewhere. Their aims were to examine the association between coronary heart disease risk factors and lifestyle parameters in 12-year-old schoolchildren9 or in periadolescent children as they progressed from age 12 to 14 years.11

Participants and procedures

At the first data collection time point (TP1), 210 schoolboys and girls aged 12.3 years volunteered. Participants were derived from all seven secondary schools in the provincial city of Katerini (50 000 citizens), Greece. This sample represented 29.4% of all 12-year-old schoolchildren living in the city and consisted of Caucasians, the majority of which were middle-class urban dwellers. At the second (TP2), third (TP3) and fourth (TP4) data collection TPs, the sample size was 204, 198 and 187, and the participants' age was 13, 14 and 17 years, respectively. The number of dropouts from TP1 to TP4 was 23 (15 boys and 8 girls), mainly due to illness and family relocation. Written informed consents from both participants and their parents were obtained following full oral and written explanation of the data collection procedures. The research ethics committee of the University of Wolverhampton, UK, approved the procedures, and permission was granted from the Greek Ministry of Education.

At each data collection TP, anthropometrical (body height, weight and skin folds), biological (maturity status, serum lipoprotein levels, blood pressure and aerobic fitness) and lifestyle parameters (daily physical activity and energy intake) were monitored. Identical protocols were used for each assessment conducted by the same trained investigators. All data were collected at the beginning (that is, October) of each school year.

Data collection


Age (accurate to 1 month) was recorded. Standing height was measured (accurate to the nearest 0.5 cm) using a Seca Stadiometer 208, while children's shoes were removed and their head was aligned in the Frankfort horizontal plane. Body mass (accurate to the nearest 0.5 kg) was assessed with a Seca Beam Balance 710. Using an established equation12 previously applied in Greek children,13 percentage of body fat was calculated from two skin folds (that is, triceps and medial calf, mean of two measurements) with a Harpenden (John Bull, England) caliper. Adiposity was defined as the total percentage of body fat reflecting the amount of fat stored in adipose tissue.

Sexual maturation

Sexual maturation was self-assessed by the children using the Tanner scale.14 Girls rated their breast development and boys their genital development from standard descriptions and pictures on scales from 1 (pre-pubertal) to 5 (post-pubertal). Relevant information from each child was collected by a pediatric medical doctor at the state hospital of Katerini, Greece.

Serum lipoprotein

Serum total cholesterol and triglycerides were determined by automated enzymatic techniques (CHOD-PAP, Boehringer Mannheim GmbH, Germany and CPO-PAP-method, Boehringer Mannheim GmbH, Germany, respectively). Serum high-density lipoprotein (HDL)-C concentration was measured in the supernatant after precipitation of very low density and low-density lipoproteins with phosphotungstic acid (Boehringer Mannheim Kit, Indianapolis, USA).

Blood pressure

Blood pressure was measured using a standard mercury sphygmomanometer after each child had been sitting quietly for 5 min. The mean of two measurements of Korotkoff phases I and IV were recorded for systolic and diastolic blood pressures, respectively.

Aerobic fitness

The 20-m multistage shuttle run test was used for the assessment of aerobic fitness. Children completed the test in groups of 10–15 according to published procedures.15 In brief, children were instructed to run between two lines 20 m apart in synchrony with a sound signal emitted from an audiocassette. Testing for each child was terminated when he/she was unable to maintain the prescribed pace for three consecutive signals. The maximal speed (km m−1) attained during the final stage of the test was subsequently used to calculate aerobic fitness in ml kg−1 min−1 (Flouris et al.15). The test was conducted indoors (in the schools' gymnasia) by the same investigator and his assistant who was responsible for recording the individual scores.

Daily physical activity

The Past-Year Physical Activity Recall Questionnaire16 was used to assess participation in physical activity. In this questionnaire, children were encouraged to recall the total time spent during the past year on school physical education, organized sport and other leisure-time activities. The time spent in the various activities was then combined with the metabolic cost of each individual activity17 to estimate a total physical activity score expressed in kcal kg−1 day−1. The questionnaire was administered in the participants' classrooms under the supervision and assistance of the principal investigator.

Energy intake

A 7-day dietary diary was used to obtain information on energy intake, as described previously.11 Each day was divided into five sections (that is, early morning, mid-morning, noon, afternoon and evening). To facilitate keeping the diary, a photographic album illustrating graduated portion sizes (1/1, 1/2, 1/4) of the most commonly consumed food items was used, whereas children and their parents were trained on keeping record of the amount and type of food consumed. Following completion, energy intake was estimated (in kcal kg−1 day−1) using the Greek Food Composition Table.18

Metabolic syndrome diagnosis

We used criteria analogous to the National Cholesterol Education Program—Adult Treatment Panel III definition19, 20 and the American Heart, Lung and Blood Institute.21 According to the adopted criteria, adolescents were classified as having metabolic syndrome if three or more of the following five risk factors were present:

  • Systolic blood pressure 90th percentile (age, height and gender specific)

  • HDL cholesterol for boys: <45 mg per 100 ml and girls: <50 per 100 ml

  • Triglycerides 150 mg per 100 ml

  • Fasting plasma glucose levels 110 mg per 100 ml

  • Body mass index (BMI) 90th percentile (age and gender specific) instead of waist circumference >94 cm in boys and >80 cm in girls (these data were not available in our data set).

Statistical analysis

Preliminary analyses included calculation of mean values for each parameter and prevalence rates for metabolic syndrome at 17 years of age (MS17), overweight, obesity and clinical obesity. The BMI-based prevalence rates for overweight and obesity were calculated according to age- and sex-specific cut-offs proposed by the International Obesity Task Force.22 Clinical obesity was considered at 25 and 30 relative body fat for boys and girls, respectively.20 Thereafter, 95% confidence intervals (CI95%) and Receiver Operating Characteristics (ROC) curve statistics were calculated using Number Cruncher Statistical Systems (version 2000, NCSS Statistical Software, Kaysville, USA) (UT, USA) statistical software. The area under the ROC curve was estimated using the Wilcoxon nonparametric method.23 Calculated sensitivity and specificity with corresponding CI95% were used to determine the efficacy of each parameter in screening for MS17. Sensitivity was defined as the proportion of children diagnosed as diseased positive on the basis of the ROC curve results who demonstrated a positive MS17 diagnosis. Specificity was defined as the proportion of children diagnosed as disease free on the basis of the ROC curve results who demonstrated a negative MS17 diagnosis. Cohen's Kappa statistic was used to evaluate the agreement between each parameter's diagnosis and MS17. The level of statistical significance was set at P<0.05.


Descriptive data of the entire cohort appear in Table 1. Anthropometrical characteristics are in line with recently published pediatric data,24 suggesting that this sample is representative of periadolescent children in industrialized countries. All studied parameters demonstrated changes as participants progressed from TP1 to TP4. Sexual maturation, BMI and arterial blood pressure increased, whereas dietary energy intake and energy expended for physical activity decreased in both sexes (P<0.05). Adiposity and HDL cholesterol decreased in boys and increased in girls, whereas triglycerides and aerobic fitness increased in boys but decreased in girls (P<0.05).

Table 1 Subject population numbers and selected parameters as assessed throughout the study (mean±s.d.)

At TP4, 12% of the participants were diagnosed with metabolic syndrome, the majority of them being boys (P<0.05) (Table 2). It is also noteworthy that 59% of all participants were diagnosed with low HDL cholesterol, whereas 19% of the children—mostly boys (P<0.05)—showed abnormally increased fasting glucose levels. The prevalence of overweight boys and girls was 29.4 and 15.3% in TP1, 28.4 and 20% in TP2, 26.5 and 21.2% in TP3, and 22.5 and 7.1% in TP4, respectively. The prevalence of obese boys and girls was 2.9 and 4.7% in TP1, 5.9 and 3.5% in TP2, 4.9 and 1.2% in TP3, and 5.9 and 2.4% in TP4, respectively. The prevalence of clinically obese boys and girls was 29.4 and 17.6% in TP1, 24.5 and 16.5% in TP2, 23.5 and 27.1% in TP3, and 14.7 and 17.6% in TP4, respectively. Furthermore, it was noteworthy that the prevalence of MS17 increased from 5.3 in normal participants to 31 and 62.5% in overweight and obese participants, respectively.

Table 2 Prevalence±CI95% and χ2 sex comparisons for MS17 and its components in the study population at TP4

Relevant univariate statistics and ROC curve analyses appear in Table 3 and Figure 1. We were unable to detect cutoff points for HDL cholesterol, triglycerides, blood pressure and fasting plasma glucose levels that would allow a correct diagnosis for MS17, on the basis of data from TP1, TP2 and TP3. On the basis of Cohen's Kappa statistic, it was possible to correctly identify MS17 as early as TP1 using BMI, adiposity and/or aerobic fitness levels in both sexes (P<0.05).

Table 3 Preventive-screening cutoffs for MS17 and ROC curve statistics±CI95%
Figure 1

Preventive cutoffs for developing metabolic syndrome at the age of 17 years in boys and girls.


These results demonstrated that it is possible to correctly diagnose metabolic syndrome that develops at the age of 17 years (MS17) in 12-, 13- and 14-year-old boys and girls using BMI, adiposity or aerobic fitness levels. The longitudinal preventive-screening cutoffs for MS17 reported herein are statistically valid and can be adopted as practical pediatric guidelines for mitigating future increases in the prevalence of metabolic syndrome.

The variables chosen as screening indices for MS17 included metabolic syndrome risk factors as well as fitness, habitual physical activity and dietary elements. Interestingly, we were unable to detect appropriate cutoff points for HDL cholesterol, triglycerides, blood pressure, fasting plasma glucose levels, habitual physical activity and dietary factors that would allow a correct diagnosis for MS17 on the basis of data from TP1, TP2 and TP3. On the other hand, BMI, adiposity and aerobic fitness levels were effective screening tools for both sexes. These findings may suggest that obesity and deteriorated aerobic fitness are precursors to the metabolic abnormalities associated with the metabolic syndrome. Given that the development of metabolic syndrome in young adulthood is associated independently with adolescent excess body fatness and deteriorated aerobic fitness,25 metabolic syndrome prevention programmes should most usefully involve measures that enhance fitness and lower fatness and monitor them closely through the present age- and sex-specific cutoffs.

Previous research has confirmed that the key elements of the metabolic syndrome are excess bodyweight, physical inactivity and a genetic predisposition to develop insulin resistance.3 Furthermore, it has become apparent that excess bodyweight is a root cause in the development of this syndrome, owing to its known link with insulin resistance.26 Adipose tissue is also an important endocrinological component expressing adipocytokines, including adiponectin, interleukin-6, angiotensinogen/angiotensin II, plasminogen activator inhibitor type 1 and tumor necrosis factor-alpha, all of which contribute to the development of coronary heart disease.3 In line with these findings, the prevalence of metabolic syndrome in this population increased directly from normal to overweight and to obese children, confirming a recent report in the United States.27 Indeed, published data on children and adults have shown that all elements of the metabolic syndrome deteriorate with increasing obesity independently of sex, age and maturation status.27, 28 On the same issue, the decrease in the prevalence of overweight and obese girls at TP4—despite the overall sample increase in BMI and adiposity across time—suggests that children who are at the ‘unhealthy’ end of the BMI spectrum do not necessarily remain in the same category throughout adolescence. This positively surprising finding may be a recent development due to public awareness raised from publications showing that Greek children and adolescents demonstrate increased prevalence of obesity,11, 24 decreased physical activity levels11, 29 and, compared with their European and North American counterparts, low aerobic fitness levels.11, 30

These practical preventive-screening cutoffs allow for mass MS17 screening with minimal equipment and cost. For instance, the BMI sensitivity and specificity reported here indicate that should the current guidelines be used to screen for MS17 in a population with 1000 boys and 1000 girls aged 12 years, 141 of 160 boys and 47 of 70 girls with MS17 would be identified, on the basis of the current prevalence rates. A total of 235 boys and 279 girls would be misdiagnosed (that is, incorrectly identified as ‘at risk for MS17’) if the BMI cutoff was used alone. Therefore, further confirmatory testing with appropriate criteria such as the National Cholesterol Education Program—Adult Treatment Panel III definition would be required for a total of 702 (35.1%) of the 2000 children being tested. This would reduce the cost of diagnosis from the present situation, which requires screening of all children using criteria such as those of the National Cholesterol Education Program—Adult Treatment Panel III and, as such, is prohibitively expensive. This is noteworthy, considering that this 5-years-in-advance diagnosis is achieved through simple height and weight measurements.

We replaced waist circumference—an indicator of central obesity in adults—with BMI for defining the metabolic syndrome in this study because the former variable was not included in our data set. Despite that waist circumference in children is a good predictor of visceral adiposity,31 its use is limited in detecting differences in body proportions that are related to puberty.32 In addition, BMI correlates well with the visceral lipid depot in adolescents.27 It also correlates with blood pressure better than waist circumference and performs equally for dyslipidemia.33 Given that the prevalence of metabolic syndrome in this population was similar to that recently reported for European populations,4 we believe that the use of BMI in defining this syndrome in this study is warranted.

It is important to note that this sample represented only part of a provincial city's adolescent population and was comprised mainly from middle-class urban dwellers. In addition, the 7-day dietary diary, which we used to obtain information on energy intake, has not been fully validated in Greek adolescents. Given that the dietary energy intake in our study decreased across time, which is contrary to previous reports,34 it is possible that methodological limitations in our assessment may mask a link between dietary factors and metabolic syndrome.

The early identification of children at high risk for developing metabolic syndrome in the years to come may help to develop strategies by focusing on the syndrome's underlying pathophysiology, particularly because the metabolic syndrome phenotype persists over time and tends to progress clinically, with type 2 diabetes being imminent.27 This is especially true for overweight and obese adolescents who are at higher risk for developing the metabolic syndrome and who, generally, become obese adults. In conclusion, this article proposes longitudinal preventive-screening cutoffs that allow for a successful detection of metabolic syndrome that develops at the age of 17 years in 12-, 13- and 14-year-old children using BMI, adiposity or aerobic fitness levels in both sexes. Additional research is needed to confirm the current findings and to further explore the capabilities adopting such practical pediatric guidelines for mitigating a future increase in the prevalence of metabolic syndrome.


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Correspondence to A D Flouris.

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Flouris, A., Bouziotas, C., Christodoulos, A. et al. Longitudinal preventive-screening cutoffs for metabolic syndrome in adolescents. Int J Obes 32, 1506–1512 (2008). https://doi.org/10.1038/ijo.2008.142

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  • adiposity
  • aerobic fitness
  • prevention
  • adolescence
  • ROC curve
  • screening

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