Abstract
Background: Metabolic syndrome (MS) is an important risk factor in pediatric population for the early onset of type 2 diabetes mellitus and cardiovascular disease. New non-invasive tools are required to identify MS in at risk populations; the aim of this study was to determine an optimal cut-off point for the 13C-glucose breath test (13C-GBT) for the diagnosis of MS in adolescents.
Methods: A total of 136 adolescents between 10 and 16 years old were recruited. MS was defined as: waist circumference >90th percentile and at least two of the following; high density lipoprotein-cholesterol (HDL-C) <50 mg/dL, triglycerides >110 mg/dL, diastolic and/or systolic blood pressure >90th percentile adjusted by age, gender and height, and/or fasting glucose >100 mg/dL. After the ingestion of a glucose load of 1.75 g/kg of body weight (up to 75 g) and an oral dose of 1.5 mg of universally labeled 13C-glucose/kg dissolved in water, breath samples were taken at baseline, 30, 60, 90, 120, 150 and 180 min. Exhaled 13CO2 in breath samples was measured by isotope ratio mass spectrometry.
Results:13C-GBT data, expressed as adjusted cumulative percentage of oxidized dose (A% OD) at 180 min, was significantly higher in the healthy subjects group (17.72%±4.9%) in comparison with subjects with ≥3, 2 or 1 components of the MS (9.95%±4.73%, 14.3%±4.47% and 14.62%±4.62%, respectively). The optimal cut-off point for the A% OD was 16.09, with a sensitivity of 81.5% and a specificity of 66.7%.
Conclusions: Our results demonstrate that the 13C-glucose breath test could be a valid screening method to identify MS in adolescents.
Acknowledgments
We gratefully thank all the volunteers who participated in the study protocol. This study was supported by grants from Consejo Nacional de Ciencia y Tecnología (CONACYT, Mexico: Salud-2010-01-139180).
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Financial support: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
References
1. Zimmet P, Alberti K, Kaufman F, Tajima N, Silink M, Arslanian S, et al. The metabolic syndrome in children and adolescents – an IDF consensus report. Pediatr Diabetes 2007;8:299–306.10.1111/j.1399-5448.2007.00271.xSearch in Google Scholar PubMed
2. Sinaiko A. Metabolic syndrome in children. J Pediatr (Rio J) 2012;88:286–8.10.2223/JPED.2214Search in Google Scholar PubMed
3. Weiss R, Bremer A, Lustig R. What is metabolic syndrome, and why are children getting it? Ann NY Acad Sci 2013;1281:123–40.10.1111/nyas.12030Search in Google Scholar PubMed PubMed Central
4. Magnussen CG, Koskinen J, Chen W, Thomson R, Schmidt MD, Srinivasan SR, et al. Pediatric metabolic syndrome predicts adulthood metabolic syndrome, subclinical atherosclerosis, and type 2 diabetes mellitus but is no better than body mass index alone: the Bogalusa Heart Study and the cardiovascular risk in Young Finns Study. Circulation 2010;122:1604–11.10.1161/CIRCULATIONAHA.110.940809Search in Google Scholar PubMed PubMed Central
5. Morrison J, Friedman-Aronson L, Wang P, Glueck C. Metabolic syndrome in childhood predicts adult metabolic syndrome and type 2 diabetes mellitus 25 to 30 years later. J Pediatr (Rio J) 2008;152:201–6.10.1016/j.jpeds.2007.09.010Search in Google Scholar PubMed
6. Matthews D, Hosker J, Rudenski A, Naylor B, Treacher D, Turner R. Insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412–9.10.1007/BF00280883Search in Google Scholar PubMed
7. Tresaco B, Bueno G, Pineda I, Moreno L, Garagorri J, Bueno M. Homeostatic model assessment (HOMA) index cut-off values to identify the metabolic syndrome in children. J Physiol Biochem 2005;61:381–8.10.1007/BF03167055Search in Google Scholar PubMed
8. Madeira I, Carvalho C, Gazolla F, De Matos H, Borges M, Bordallo M. Cut-off point for homeostatic model assessment for insulin resistance (HOMA-IR) index established from receiver operating characteristic (ROC) curve in the detection of metabolic syndrome in overweight pre-pubertal children. Arq Bras Endocrinol Metabol 2008;52:1466–73.10.1590/S0004-27302008000900010Search in Google Scholar
9. Burrows R, Leiva L, Weisstaub G, Lera L, Albala C, Blanco E, et al. High HOMA-IR, adjusted for puberty, relates to the metabolic syndrome in overweight and obese Chilean youths. Pediatr Diabetes 2011;12:212–8.10.1111/j.1399-5448.2010.00685.xSearch in Google Scholar PubMed
10. Yin J, Li M, Xu L, Wang Y, Cheng H, Zhao X, et al. Insulin resistance determined by homeostasis model assessment (HOMA) and associations with metabolic syndrome among Chinese children and teenagers. Diabetol Metab Syndr 2013;5:1–9.10.1186/1758-5996-5-71Search in Google Scholar PubMed PubMed Central
11. Lewanczuk R, Paty B, Toth E. Comparison of the [13C] glucose breath test to the hyperinsulinemic-euglycemic clamp when determining insulin resistance. Diabetes Care 2004;27:441–7.10.2337/diacare.27.2.441Search in Google Scholar PubMed
12. Dillon E, Janghorbani M, Angel J, Casperson S, Grady J, Urban R, et al. Novel noninvasive breath test method for screening individuals at risk for diabetes. Diabetes Care 2009;32:430–5.10.2337/dc08-1578Search in Google Scholar PubMed PubMed Central
13. Jetha M, Nzekwu U, Lewanczuk R, Ball G. A novel, non-invasive 13C-glucose breath test to estimate insulin resistance in obese prepubertal children. J Pediatr Endocrinol Metab 2009;22: 1051–9.10.1515/JPEM.2009.22.11.1051Search in Google Scholar PubMed
14. Singal P, Janghorbani M, Schuette S, Chisholm R, Mather K. Intra-individual variability of CO2 breath isotope enrichment compared to blood glucose in the oral glucose tolerance test. Diabetes Technol Therapeut 2010;12:947–53.10.1089/dia.2010.0109Search in Google Scholar PubMed PubMed Central
15. Ferranini E. Is insulin resistance the cause of the metabolic syndrome? Ann Med 2006;38:42–51.10.1080/07853890500415358Search in Google Scholar PubMed
16. Kahn R, Buse J, Ferranini E, Stern M. The metbolic syndrome: time for a critical appraisal. Diabetes Care 2005;28:2289–304.10.2337/diacare.28.9.2289Search in Google Scholar PubMed
17. Barazzoni R, Silva V, Singer P. Clinical biomarkers in metabolic syndrome. Nutr Clin Pract 2014;29:215–21.10.1177/0884533613516168Search in Google Scholar PubMed
18. Braden B, Lembcke B, Kuker W, Caspary W. 13C-breath tests: current state of the art and future directions. Dig Liver Dis 2007;39:795–805.10.1016/j.dld.2007.06.012Search in Google Scholar PubMed
19. Marshall WA, Tanner JM. Variation in pattern of pubertal changes in girls. Arch Dis Child 1969;44:291–303.10.1136/adc.44.235.291Search in Google Scholar PubMed PubMed Central
20. Marshall WA, Tanner JM. Variations in the pattern of pubertal changes in boys. Arch Dis Child 1970;45:13–23.10.1136/adc.45.239.13Search in Google Scholar PubMed PubMed Central
21. The fourth report on the diagnosis, evaluation, and treatment of high blood pressure in children and adolescents. Pediatrics 2004;113:555–76.10.1542/peds.114.2.S2.555Search in Google Scholar
22. Expert panel on integrated guidelines for cardiovascular health and risk reduction in children and adole scents: summary report. Pediatrics 2011;128:S213–56.10.1542/peds.2009-2107CSearch in Google Scholar PubMed PubMed Central
23. Fernández JR, Redden DT, Angelo P, Allison DB. Waist circumference percentiles in nationally representative samples of African-American, European-American, and Mexican-American children and adolescents. J Pediatr 2004;145:439–44.10.1016/j.jpeds.2004.06.044Search in Google Scholar PubMed
24. Martínez G, Martínez A, Salas A, Maldonado-Hernández J. Association between metabolic syndrome and erythrocyte fatty acid profile in Mexican Adolescents: a trans fatty acid approach. Food Nutr Sci 2013;4:51–8.Search in Google Scholar
25. Maldonado J, Martínez A, Matute G, López M. The 13C-glucose breath test is a reliable method to identify insulin resistance in Mexican adults without diabetes: comparison with other insulin resistance surrogates. Diabetics Technol Ther 2014;16:385–91.10.1089/dia.2013.0263Search in Google Scholar PubMed
26. Miller AB. Screening. In: Ahrens W, Pigeot I, editors. Handbook of epidemiology. Berlin, Germany: Springer Berlin Heidelberg, 2005.Search in Google Scholar
©2015 by De Gruyter
