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Metabolomics allows the discrimination of the pathophysiological relevance of hyperinsulinism in obese prepubertal children

International Journal of Obesity volume 41, pages 1473–1480 (2017)Cite this article

Subjects

Abstract

Background/Objectives:

Insulin resistance (IR) is the cornerstone of the obesity-associated metabolic derangements observed in obese children. Targeted metabolomics was employed to explore the pathophysiological relevance of hyperinsulinemia in childhood obesity in order to identify biomarkers of IR with potential clinical application.

Subjects/Methods:

One hundred prepubertal obese children (50 girls/50 boys, 50% IR and 50% non-IR in each group), underwent an oral glucose tolerance test for usual carbohydrate and lipid metabolism determinations. Fasting serum leptin, total and high molecular weight-adiponectin and high-sensitivity C-reactive protein (CRP) levels were measured and the metabolites showing significant differences between IR and non-IR groups in a previous metabolomics study were quantified. Enrichment of metabolic pathways (quantitative enrichment analysis) and the correlations between lipid and carbohydrate metabolism parameters, adipokines and serum metabolites were investigated, with their discriminatory capacity being evaluated by receiver operating characteristic (ROC) analysis.

Results:

Twenty-three metabolite sets were enriched in the serum metabolome of IR obese children (P<0.05, false discovery rate (FDR)<5%). The urea cycle, alanine metabolism and glucose-alanine cycle were the most significantly enriched pathways (PFDR<0.00005). The high correlation between metabolites related to fatty acid oxidation and amino acids (mainly branched chain and aromatic amino acids) pointed to the possible contribution of mitochondrial dysfunction in IR. The degree of body mass index-standard deviation score (BMI-SDS) excess did not correlate with any of the metabolomic components studied. In the ROC analysis, the combination of leptin and alanine showed a high IR discrimination value in the whole cohort (area under curve, AUCALL=0.87), as well as in boys (AUCM=0.84) and girls (AUCF=0.91) when considered separately. However, the specific metabolite/adipokine combinations with highest sensitivity were different between the sexes.

Conclusions:

Combined sets of metabolic, adipokine and metabolomic parameters can identify pathophysiological relevant IR in a single fasting sample, suggesting a potential application of metabolomic analysis in clinical practice to better identify children at risk without using invasive protocols.

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References

  1. Reaven GM . Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1988; 37: 1595–1607.

    Article  CAS  Google Scholar 

  2. Eyzaguirre F, Mericq V, Insulin resistance markers in children. Horm Res 71 Switzerland: 2009S. Karger AG: Basel, 2009; p 65–74.

    Google Scholar 

  3. Martos-Moreno GA, Barrios V, Chowen JA, Argente J . Adipokines in childhood obesity. Vitam Horm 2013; 91: 107–142.

    Article  CAS  Google Scholar 

  4. Martos-Moreno GA, Barrios V, Martinez G, Hawkins F, Argente J . Effect of weight loss on high-molecular weight adiponectin in obese children. Obesity (Silver Spring) 2010; 18: 2288–2294.

    Article  CAS  Google Scholar 

  5. Martos-Moreno GA, Kratzsch J, Korner A, Barrios V, Hawkins F, Kiess W et al. Serum visfatin and vaspin levels in prepubertal children: effect of obesity and weight loss after behavior modifications on their secretion and relationship with glucose metabolism. Int J Obes (Lond) 2011; 35: 1355–1362.

    Article  CAS  Google Scholar 

  6. Zhang A, Sun H, Wang X . Power of metabolomics in biomarker discovery and mining mechanisms of obesity. Obes Rev 2013; 14: 344–349.

    Article  CAS  Google Scholar 

  7. Guasch-Ferre M, Hruby A, Toledo E, Clish CB, Martinez-Gonzalez MA, Salas-Salvado J et al. Metabolomics in Prediabetes and Diabetes: A Systematic Review and Meta-analysis. Diabetes Care 2016; 39: 833–846.

    Article  CAS  Google Scholar 

  8. Mastrangelo A, Barbas C, Chronic Diseases and Lifestyle Biomarkers Identification by Metabolomics. In: Sussulini A ed. Metabolomics: From Fundamentals to Clinical Applications Advances in Experimental Medicine and Biology 965. Springer International Publishing: New York, NY, 2017; p 235–263.

    Chapter  Google Scholar 

  9. Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C et al. Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS One 2010; 5: e13953.

    Article  Google Scholar 

  10. Frohnert BI, Rewers MJ . Metabolomics in childhood diabetes. Pediatr Diabetes 2016; 17: 3–14.

    Article  Google Scholar 

  11. Oresic M . Metabolomics in the studies of islet autoimmunity and type 1 diabetes. Rev Diabet Stud 2012; 9: 236–247.

    Article  Google Scholar 

  12. Butte NF, Liu Y, Zakeri IF, Mohney RP, Mehta N, Voruganti VS et al. Global metabolomic profiling targeting childhood obesity in the Hispanic population. Am J Clin Nutr 2015; 102: 256–267.

    Article  CAS  Google Scholar 

  13. McCormack SE, Shaham O, McCarthy MA, Deik AA, Wang TJ, Gerszten RE et al. Circulating branched-chain amino acid concentrations are associated with obesity and future insulin resistance in children and adolescents. Pediatr Obes 2013; 8: 52–61.

    Article  CAS  Google Scholar 

  14. Mastrangelo A, Martos-Moreno GA, Garcia A, Barrios V, Ruperez FJ, Chowen JA et al. Insulin resistance in prepubertal obese children correlates with sex-dependent early onset metabolomic alterations. Int J Obes (Lond) 2016; 40: 1494–1502.

    Article  CAS  Google Scholar 

  15. Mamas M, Dunn WB, Neyses L, Goodacre R . The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch Toxicol 2011; 85: 5–17.

    Article  CAS  Google Scholar 

  16. Klein MS, Shearer J . Metabolomics and Type 2 Diabetes: Translating Basic Research into Clinical Application. J Diabetes Res 2016; 2016: 3898502.

    Article  Google Scholar 

  17. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH . Establishing a standard definition for child overweight and obesity worldwide: international survey. Bmj 2000; 320: 1240–1243.

    Article  CAS  Google Scholar 

  18. Naz S, Calderon AA, Garcia A, Gallafrio J, Mestre RT, Gonzalez EG et al. Unveiling differences between patients with acute coronary syndrome with and without ST elevation through fingerprinting with CE-MS and HILIC-MS targeted analysis. Electrophoresis 2015; 36: 2303–2313.

    Article  CAS  Google Scholar 

  19. Mastrangelo A, Ferrarini A, Rey-Stolle F, Garcia A, Barbas C . From sample treatment to biomarker discovery: A tutorial for untargeted metabolomics based on GC-(EI)-Q-MS. Anal Chim Acta 2015; 900: 21–35.

    Article  CAS  Google Scholar 

  20. Xia J, Wishart DS . MSEA: a web-based tool to identify biologically meaningful patterns in quantitative metabolomic data. Nucleic Acids Res 2010; 38: W71–W77.

    Article  CAS  Google Scholar 

  21. Xia J, Sinelnikov IV, Han B, Wishart DS . MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res 2015; 43: W251–W257.

    Article  CAS  Google Scholar 

  22. Munoz-Garach A, Cornejo-Pareja I, Tinahones FJ . Does metabolically healthy obesity exist? Nutrients 2016; 8: 6.

    Article  Google Scholar 

  23. Giesbertz P, Daniel H . Branched-chain amino acids as biomarkers in diabetes. Curr Opin Clin Nutr Metab Care 2016; 19: 48–54.

    Article  CAS  Google Scholar 

  24. Goessling W, Massaro JM, Vasan RS, D'Agostino RB Sr, Ellison RC, Fox CS . Aminotransferase levels and 20-year risk of metabolic syndrome, diabetes, and cardiovascular disease. Gastroenterology 2008; 135: 1935–1944 44.e1.

    Article  CAS  Google Scholar 

  25. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E et al. Metabolite profiles and the risk of developing diabetes. Nat Med 2011; 17: 448–453.

    Article  Google Scholar 

  26. Li LO, Hu YF, Wang L, Mitchell M, Berger A, Coleman RA . Early hepatic insulin resistance in mice: a metabolomics analysis. Mol Endocrinol 2010; 24: 657–666.

    Article  CAS  Google Scholar 

  27. Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF et al. A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metab 2009; 9: 311–326.

    Article  CAS  Google Scholar 

  28. Stancakova A, Civelek M, Saleem NK, Soininen P, Kangas AJ, Cederberg H et al. Hyperglycemia and a common variant of GCKR are associated with the levels of eight amino acids in 9,369 Finnish men. Diabetes 2012; 61: 1895–1902.

    Article  CAS  Google Scholar 

  29. Lee A, Jang HB, Ra M, Choi Y, Lee HJ, Park JY et al. Prediction of future risk of insulin resistance and metabolic syndrome based on Korean boy's metabolite profiling. Obes Res Clin Pract 2014; 9: 336–345.

    Article  Google Scholar 

  30. Hellmuth C, Uhl O, Kirchberg FF, Grote V, Weber M, Rzehak P et al. Effects of Early Nutrition on the Infant Metabolome. Nestle Nutr Inst Workshop Ser 2016; 85: 89–100.

    Article  Google Scholar 

  31. Meier U, Gressner AM . Endocrine regulation of energy metabolism: review of pathobiochemical and clinical chemical aspects of leptin, ghrelin, adiponectin, and resistin. Clin Chem 2004; 50: 1511–1525.

    Article  CAS  Google Scholar 

  32. Yamada J, Ujikawa M, Sugimoto Y . Serum leptin levels after central and systemic injection of a serotonin precursor, 5-hydroxytryptophan, in mice. Eur J Pharmacol 2000; 406: 159–162.

    Article  CAS  Google Scholar 

  33. Park HK, Ahima RS . Physiology of leptin: energy homeostasis, neuroendocrine function and metabolism. Metabolism 2015; 64: 24–34.

    Article  CAS  Google Scholar 

  34. Wang CH, Wang CC, Huang HC, Wei YH . Mitochondrial dysfunction leads to impairment of insulin sensitivity and adiponectin secretion in adipocytes. Febs j 2013; 280: 1039–1050.

    Article  CAS  Google Scholar 

  35. Liu Y, Turdi S, Park T, Morris NJ, Deshaies Y, Xu A et al. Adiponectin corrects high-fat diet-induced disturbances in muscle metabolomic profile and whole-body glucose homeostasis. Diabetes 2013; 62: 743–752.

    Article  CAS  Google Scholar 

  36. Blumer RM, van Roomen CP, Meijer AJ, Houben-Weerts JH, Sauerwein HP, Dubbelhuis PF . Regulation of adiponectin secretion by insulin and amino acids in 3T3-L1 adipocytes. Metabolism 2008; 57: 1655–1662.

    Article  Google Scholar 

  37. Zhang HH, Huang J, Duvel K, Boback B, Wu S, Squillace RM et al. Insulin stimulates adipogenesis through the Akt-TSC2-mTORC1 pathway. PLoS One 2009; 4: e6189.

    Article  Google Scholar 

  38. Nakamura H, Jinzu H, Nagao K, Noguchi Y, Shimba N, Miyano H et al. Plasma amino acid profiles are associated with insulin, C-peptide and adiponectin levels in type 2 diabetic patients. Nutr Diabetes 2014; 4: e133.

    Article  CAS  Google Scholar 

  39. Koves TR, Ussher JR, Noland RC, Slentz D, Mosedale M, Ilkayeva O et al. Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance. Cell Metab 2008; 7: 45–56.

    Article  CAS  Google Scholar 

  40. Kelley DE, He J, Menshikova EV, Ritov VB . Dysfunction of mitochondria in human skeletal muscle in type 2 diabetes. Diabetes 2002; 51: 2944–2950.

    Article  CAS  Google Scholar 

  41. Ritov VB, Menshikova EV, He J, Ferrell RE, Goodpaster BH, Kelley DE . Deficiency of subsarcolemmal mitochondria in obesity and type 2 diabetes. Diabetes 2005; 54: 8–14.

    Article  CAS  Google Scholar 

  42. Bogacka I, Xie H, Bray GA, Smith SR . Pioglitazone induces mitochondrial biogenesis in human subcutaneous adipose tissue in vivo. Diabetes 2005; 54: 1392–1399.

    Article  CAS  Google Scholar 

  43. Petersen KF, Dufour S, Befroy D, Garcia R, Shulman GI . Impaired mitochondrial activity in the insulin-resistant offspring of patients with type 2 diabetes. N Engl J Med 2004; 350: 664–671.

    Article  CAS  Google Scholar 

  44. Slattery MJ, Bredella MA, Thakur H, Torriani M, Misra M . Insulin resistance and impaired mitochondrial function in obese adolescent girls. Metab Syndr Relat Disord 2014; 12: 56–61.

    Article  CAS  Google Scholar 

  45. Fleischman A, Kron M, Systrom DM, Hrovat M, Grinspoon SK . Mitochondrial function and insulin resistance in overweight and normal-weight children. J Clin Endocrinol Metab 2009; 94: 4923–4930.

    Article  CAS  Google Scholar 

  46. Chen H, Zhang L, Li X, Sun G, Yuan X, Lei L et al. Adiponectin activates the AMPK signaling pathway to regulate lipid metabolism in bovine hepatocytes. J Steroid Biochem Mol Biol 2013; 138: 445–454.

    Article  CAS  Google Scholar 

  47. Wang CH, Wang CC, Wei YH . Mitochondrial dysfunction in insulin insensitivity: implication of mitochondrial role in type 2 diabetes. Ann N Y Acad Steroid 2010; 1201: 157–165.

    Article  CAS  Google Scholar 

  48. Kerner J, Hoppel C . Fatty acid import into mitochondria. Biochim Biophys Acta 2000; 1486: 1–17.

    Article  CAS  Google Scholar 

  49. Bougneres P, Stunff CL, Pecqueur C, Pinglier E, Adnot P, Ricquier D . In vivo resistance of lipolysis to epinephrine. A new feature of childhood onset obesity. J Clin Invest 1997; 99: 2568–2573.

    Article  CAS  Google Scholar 

  50. Jacobsson B, Smith U . Effect of cell size on lipolysis and antilipolytic action of insulin in human fat cells. J Lipid Res 1972; 13: 651–656.

    CAS  PubMed  Google Scholar 

  51. Skurk T, Alberti-Huber C, Herder C, Hauner H . Relationship between adipocyte size and adipokine expression and secretion. J Clin Endocrinol Metab 2007; 92: 1023–1033.

    Article  CAS  Google Scholar 

  52. Yang C, Aye CC, Li X, Diaz Ramos A, Zorzano A, Mora S . Mitochondrial dysfunction in insulin resistance: differential contributions of chronic insulin and saturated fatty acid exposure in muscle cells. Biosci Rep 2012; 32: 465–478.

    Article  CAS  Google Scholar 

  53. Jung UJ, Choi MS . Obesity and its metabolic complications: the role of adipokines and the relationship between obesity, inflammation, insulin resistance, dyslipidemia and nonalcoholic fatty liver disease. Int J Mol Sci 2014; 15: 6184–6223.

    Article  Google Scholar 

  54. Newbern D, Gumus Balikcioglu P, Balikcioglu M, Bain J, Muehlbauer M, Stevens R et al. Sex differences in biomarkers associated with insulin resistance in obese adolescents: metabolomic profiling and principal components analysis. J Clin Endocrinol Metab 2014; 99: 4730–4739.

    Article  CAS  Google Scholar 

  55. Wurtz P, Makinen VP, Soininen P, Kangas AJ, Tukiainen T, Kettunen J et al. Metabolic signatures of insulin resistance in 7,098 young adults. Diabetes 2012; 61: 1372–1380.

    Article  CAS  Google Scholar 

  56. Adams SH . Emerging perspectives on essential amino acid metabolism in obesity and the insulin-resistant state. Adv Nutr 2011; 2: 445–456.

    Article  CAS  Google Scholar 

  57. Plaisance EP, Greenway FL, Boudreau A, Hill KL, Johnson WD, Krajcik RA et al. Dietary methionine restriction increases fat oxidation in obese adults with metabolic syndrome. J Clin Endocrinol Metab 2011; 96: E836–E840.

    Article  CAS  Google Scholar 

  58. Stone KP, Wanders D, Orgeron M, Cortez CC, Gettys TW . Mechanisms of increased in vivo insulin sensitivity by dietary methionine restriction in mice. Diabetes 2014; 63: 3721–3733.

    Article  CAS  Google Scholar 

  59. Masuda Y, Kubo A, Kokaze A, Yoshida M, Fukuhara N, Takashima Y . Factors associated with serum total homocysteine level in type 2 diabetes. Environ Health Prev Med 2008; 13: 148–155.

    Article  CAS  Google Scholar 

  60. Demarest TG, Schuh RA, Waite EL, Waddell J, McKenna MC, Fiskum G . Sex dependent alterations in mitochondrial electron transport chain proteins following neonatal rat cerebral hypoxic-ischemia. J Bioenerg Biomembr 2016; 48: 591–598.

    Article  CAS  Google Scholar 

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Acknowledgements

AM received a PhD grant from the Spanish Ministry of Economy and Competitiveness (AP-2012-1385). We express our gratitude to the financial support received from the Spanish Ministry of Economy and Competitiveness MINECO CTQ2014-55279-R (CB) and BFU2014-51836-C2-2R (JAC); Fondo de Investigación Sanitaria with Fondos FEDER [FIS: PI13/01295 and PI16/00485 (JA)] and CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN) (JA and JAC). Instituto de Salud Carlos III. Madrid, Spain and Fundación Endocrinología y Nutrición.

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Author notes

  1. G Á Martos-Moreno and A Mastrangelo: These authors contributed equally to this work.

  2. C Barbas and J Argente: CB and JA are co-senior authors.

Authors and Affiliations

  1. Department of Pediatrics and Pediatric Endocrinology, Hospital Infantil Universitario Niño Jesús, Instituto de Investigación La Princesa, Madrid, Spain

    G Á Martos-Moreno, V Barrios, J A Chowen & J Argente

  2. Department of Pediatrics, Universidad Autónoma de Madrid, CIBEROBN, Instituto de Salud Carlos III, Madrid, Spain

    G Á Martos-Moreno, V Barrios, J A Chowen & J Argente

  3. Centre for Metabolomics and Bioanalysis (CEMBIO), Faculty of Pharmacy, CEU San Pablo CEU University, Madrid, Spain

    A Mastrangelo, A García, F J Rupérez & C Barbas

  4. IMDEA Food Institute, CEI UAM+CSIC, Madrid, Spain

    J Argente

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  1. G Á Martos-Moreno
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Correspondence to C Barbas or J Argente.

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Martos-Moreno, G., Mastrangelo, A., Barrios, V. et al. Metabolomics allows the discrimination of the pathophysiological relevance of hyperinsulinism in obese prepubertal children. Int J Obes 41, 1473–1480 (2017). https://doi.org/10.1038/ijo.2017.137

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