1. Funded by the EC, FP 6, Contract No. 016181 (FOOD) In search for a definition of the metabolic syndrome in pre-adolescent children – a population-based approach Wolfgang Ahrens & Luis A. Moreno Leibniz Institute for Prevention Research and Epidemiology, Bremen, Germany - on behalf of the IDEFICS consortium - Symposium „Reference values for metabolic health indicators from the IDEFICS study“ ECOG Salzburg, 13-15 November 2014

2. Luis A. More no Azna r lmore no@ uniza r.es GEN UD Rese arch Grou p Univ ersid ad de Zara goza Complications of obesity in children Ebbeling CB et al. Lancet 2002; 360: 473-482

3. Luis A. More no Azna r lmore no@ uniza r.es GEN UD Rese arch Grou p Univ ersid ad de Zara goza Metabolic syndrome in children  Cardiovascular risk factors tend to cluster Webber LS et al. Prev Med 1979; 8: 407-418 Chu NF et al. Am J Clin Nutr 1998; 67: 1141-1146.  8.9 % of obese children have the metabolic syndrome Csábi G et al. Eur J Pediatr 2000; 159: 91-94.  Factor analysis of metabolic syndrome components show 2-4 factors Chen W et al. Am J Epidemiol 1999; 150: 667-674 Moreno LA et al. Horm Metab Res 2002; 34: 394-399

4. Hansen BC. Ann N Y Acad Sci 1999; 892: 1-24 The most frequent components of the metabolic syndrome (MetS)  Obesity  Hypertension  Dyslipidaemia (high triglyceride and low HDL-C concentrations)  Insulin resistance – hyperinsulinemia – glucose intolerance and/or type 2 diabetes What are the appropriate physiological parameters to be measured?

5. Luis A. Moreno Aznarlmoreno@unizar.es GENUD Research GroupUniversidad de Zaragoza Brambilla P, Bedogni G, Moreno LA et al. Int J Obes 2006; 30: 23-30 Correlation between waist circumference and visceral fat assessed by magnetic resonance imaging

6. Luis A. Moreno Aznarlmoreno@unizar.es GENUD Research GroupUniversidad de Zaragoza Different reference standards for waist circumference (90 th percentile) Brambilla P et al. Int J Obes 2007; 31: 591-600 GirlsBoys

7. Luis A. More no Azna r lmore no@ uniza r.es GEN UD Rese arch Grou p Univ ersid ad de Zara goza Lipoprotein metabolism Lusis AJ et al. Circulation 2004; 110: 1868-1873

8. Ludwig & Ebbeling. JAMA 2001; 286: 1427-1430 Insulin sensitive Insulin resistance Compensatory hyperinsulinaemia  -cell decompensation Relative insulin deficiency  -cell failure NORMOGLYCEMIA POSTPRANDIAL HYPERGLYCEMIA IMPAIRED FASTING GLUCOSE DIABETES MELLITUS Vascular complications Obesity Diet Sedentary lifestyle Genetics Perinatal factors Natural history of type 2 diabetes

9. Luis A. More no Azna r lmore no@ uniza r.es GEN UD Rese arch Grou p Univ ersid ad de Zara goza Insulin resistance  Homeostatic model assessment (HOMA): the product of the fasting plasma insulin level (  U/ml) and the fasting plasma glucose level (mmol/l), divided by 22.5.  Lower insulin-resistance values indicate a higher insulin sensitivity, whereas higher values indicate a lower insulin sensitivity.

10. Definitions of paediatric metabolic syndrome (MetS) in children DefinitionExcess adiposity Blood pressureBlood lipidsBlood glucose/ insulin Cook et al. WC  90 th percentile SBP or DBP  90 th percentile Triacylglycerols  110 mg/dl or HDL cholesterol ≤ 40 mg/dl Impaired fasting glucose  110 mg/dl Viner et al. BMI  95 th percentile SBP  95 th percentile Triacylglycerols  150 mg/dl or HDL cholesterol <35 mg/dl or High total cholesterol  95 th percentile Hyperinsulinemia  15 mU/l or Impaired fasting glucose  110 mg/dl IDF* WC  90 th percentile SBP  130 mmHg or DBP  85 mmHg Triacylglycerols  150 mg/dl or HDL cholesterol  40 mg/dl Impaired fasting glucose  110 mg/dl *IDF=International Diabetes Federation Cook S et al.. Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988–1994. Arch Pediatr Adolesc Med 2003; 157: 821-827. Viner RM et al.. Prevalence of the insulin resistance syndrome in obesity. Arch Dis Child 2005; 90: 10-14. Zimmet P et al.; IDF Consensus Group: The metabolic syndrome in children and adolescents – an IDF consensus report. Pediatr Diabetes 2007; 8: 299-306.

11. Metabolic syndrome in children? Brambilla P et al. Int J Obes 2007; 31: 591-600 Background: The diagnostic criteria of the metabolic syndrome (MS) have been applied in studies of obese adults to estimate the metabolic risk-associated with obesity, even though no general consensus exists concerning its definition and clinical value. We reviewed the current literature on the MS, focusing on those studies that used the MS diagnostic criteria to analyze children, and we observed extreme heterogeneity for the sets of variables and cutoff values chosen. Objectives: To discuss concerns regarding the use of the existing definition of the MS (as defined in adults) in children and adolescents, analyzing the scientific evidence needed to detect a clustering of cardiovascular risk-factors. Finally, we propose a new methodological approach for estimating metabolic risk-factor clustering in children and adolescents. Results: Major concerns were the lack of information on the background derived from a child's family and personal history; the lack of consensus on insulin levels, lipid parameters, markers of inflammation or steato-hepatitis; the lack of an additive relevant effect of the MS definition to obesity per se. We propose the adoption of 10 evidence-based items from which to quantify metabolic risk-factor clustering, collected in a multilevel Metabolic Individual Risk-factor And CLustering Estimation (MIRACLE) approach, and thus avoiding the use of the current MS term in children. Conclusion: Pediatricians should consider a novel and specific approach to assessing children/adolescents and should not simply derive or adapt definitions from adults. Evaluation of insulin and lipid levels should be included only when specific references for the relation of age, gender, pubertal status and ethnic origin to health risk become available. This new approach could be useful for improving the overall quality of patient evaluation and for optimizing the use of the limited resources available facing to the obesity epidemic. Pediatricians should consider a novel and specific approach to assessing children/adolescents and should not simply derive or adapt definitions from adults. Evaluation of insulin and lipid levels should be included only when specific references for the relation of age, gender, pubertal status and ethnic origin to health risk become available.

12. Prevalence of each component of MetS according to different definitions (1 of 5) Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

13. Prevalence of each component of MetS according to different definitions (2 of 5) Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

14. Prevalence of each component of MetS according to different definitions (3 of 5) Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

15. Definitions of paediatric metabolic syndrome (MetS) DefinitionExcess adiposity Blood pressureBlood lipidsBlood glucose/ insulin Cook et al. WC  90 th percentile SBP or DBP  90 th percentile Triacylglycerols  110 mg/dl or HDL cholesterol ≤ 40 mg/dl Impaired fasting glucose  110 mg/dl Viner et al. BMI  95 th percentile SBP  95 th percentile Triacylglycerols  150 mg/dl or HDL cholesterol <35 mg/dl or High total cholesterol  95 th percentile Hyperinsulinemia  15 mU/l or Impaired fasting glucose  110 mg/dl IDF* WC  90 th percentile SBP  130 mmHg or DBP  85 mmHg Triacylglycerols  150 mg/dl or HDL cholesterol  40 mg/dl Impaired fasting glucose  110 mg/dl IDEFICS- monitoring level WC  90 th percentile SBP  90 th percentile or DBP  90 th percentile Triacylglycerols  90 th percentile or HDL cholesterol ≤ 10 th percentile HOMA-insulin resistance  90 th percentile or Fasting glucose  90 th percentile IDEFICS- action level WC  95 th percentile SBP  95 th percentile or DBP  95 th percentile Triacylglycerols  95 th percentile or HDL cholesterol ≤ 5 th percentile HOMA-insulin resistance  95 th percentile or Fasting glucose  95 th percentile *IDF=International Diabetes Federation

16. Prevalence of each component of MetS according to different definitions (4 of 5) Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

17. Prevalence of each component of MetS according to different definitions (5 of 5) Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

18. Prevalence of MetS in normal weight/ thin, overweight and obese children 18 MetS monitoring level = 5.5%MetS action level = 1.8% Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

19. 19 Are the cut-offs chosen to define the MetS too arbitrary?

20. Luis A. More no Azna r lmore no@ uniza r.es GEN UD Rese arch Grou p Univ ersid ad de Zara goza Number of MetS risk variables and elasticity of common carotid artery Urbina EM et al. Atherosclerosis 2004; 176: 157-164 Bogalusa Study Ep = Peterson's elastic modulus YEM = relative wall thickness-adjusted Young's elastic modulus Increasing stiffness

21. Calculation of a MetS score Continuous MetS score calculated according to Eisenmann as the sum of component z-scores:  Adiposity: WC (waist circumference (cm))  Blood pressure: mean of SBP and DBP (systolic / diastolic blood pressure (mm Hg))  Blood lipids: mean of TRG and negative HDL (triglycerides / HDL cholesterol (mg/dl))  Insulin resistance: HOMA (homeostasis model assessment) 21 Eisenmann JC. Cardiovasc Diabetol 2008; 7: 17 Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

22. Calculation of a MetS score: four examples Measured valuesStandardised residuals (z-scores) SubjectSexAgeWCSBPDBPTRGHDLHOMAz WC z SBP z DBP z TRG z HDL z HOMA MetS score 1M6.559.5109.067.548380.651.660.850.720.29-1.22-0.153.05 2F5.860.492.052.550521.592.20-0.96-1.770.170.101.392.26 3F5.253.094.058.045611.000.58-0.51-0.81-0.730.880.68-0.07 4M5.557.599.066.078450.991.64-0.130.591.15-0.540.713.43 22 Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

23. Age-specific reference curves for MetS in girls and boys 23 Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

24. Conclusions  We propose sex- and age-specific standards for the most frequent cardio-metabolic features associated with obesity in children under 11 years  We propose a new definition of MetS to support clinical decision making  According to different definitions of paediatric MetS, we classify a non-negligible proportion of mostly pre-pubertal children as being affected  NOTE: The proposed cut-offs are based on a statistical definition that does not yet allow to quantify the risk of subsequent disease  We propose a continuous MetS score to improve quantitative risk assessment for various clinical endpoints

25. 25 The IDEFICS Consortium Thank you!

26. 26 Previous achievements

27. Prevalence of MetS in normal weight/ thin, overweight and obese children 27 Ahrens W & Moreno LA et al. Int J Obes 2014; 38: S4-S14

28. Luis A. Moreno Aznarlmoreno@unizar.es GENUD Research GroupUniversidad de Zaragoza International Obesity Task Force (IOTF) body mass index cut-offs for overweight and obesity in youth (Cole TJ et al. BMJ 2000; 320: 1240-1243) GirlsBoys