Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Original Article
  • Published:

Clinical Studies and Practice

Mapping of the circulating metabolome reveals α-ketoglutarate as a predictor of morbid obesity-associated non-alcoholic fatty liver disease

International Journal of Obesity volume 39, pages 279–287 (2015)Cite this article

Subjects

Abstract

Background:

Obesity severely affects human health, and the accompanying non-alcoholic fatty liver disease (NAFLD) is associated with high morbidity and mortality. Rapid and non-invasive methods to detect this condition may substantially improve clinical care.

Methods:

We used liquid and gas chromatography–quadruple time-of-flight–mass spectrometry (LC/GC-QTOF-MS) analysis in a non-targeted metabolomics approach on the plasma from morbidly obese patients undergoing bariatric surgery to gain a comprehensive measure of metabolite levels. On the basis of these findings, we developed a method (GC-QTOF-MS) for the accurate quantification of plasma α-ketoglutarate to explore its potential as a novel biomarker for the detection of NAFLD.

Results:

Plasma biochemical differences were observed between patients with and without NAFLD indicating that the accumulation of lipids in hepatocytes decreased β-oxidation energy production, reduced liver function and altered glucose metabolism. The results obtained from the plasma analysis suggest pathophysiological insights that link lipid and glucose disturbances with α-ketoglutarate. Plasma α-ketoglutarate levels are significantly increased in obese patients compared with lean controls. Among obese patients, the measurement of this metabolite differentiates between those with or without NAFLD. Data from the liver were consistent with data from plasma. Clinical utility was assessed, and the results revealed that plasma α-ketoglutarate is a fair-to-good biomarker in patients (n=230). Other common laboratory liver tests used in routine application did not favourably compare.

Conclusion:

Plasma α-ketoglutarate is superior to common liver function tests in obese patients as a surrogate biomarker of NAFLD. The measurement of this biomarker may potentiate the search for a therapeutic approach, may decrease the need for liver biopsy and may be useful in the assessment of disease progression.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

References

  1. Yanovski SZ, Yanovski JA . Obesity prevalence in the United States—up down or sideways? N Engl J Med 2011; 364: 987–989.

    Article  CAS  Google Scholar 

  2. Liu J, Hay J, Faught BE . The Association of Sleep Disorder, Obesity Status, and Diabetes Mellitus among US adults-The NHANES 2009-2010 Survey Results. Int J Endocrinol 2013; 2013: 234129.

    PubMed  PubMed Central  Google Scholar 

  3. Must A, Spadano J, Coakley EH, Field AE, Colditz G, Dietz WH . The disease burden associated with overweight and obesity. JAMA 1999; 282: 1523–1529.

    Article  CAS  Google Scholar 

  4. Joven J, Micol V, Segura-Carretero A, Alonso-Villaverde C, Menendez JA . Polyphenols and the modulation of gene expression pathways: can we eat our way out of the danger of chronic disease? Crit Rev Food Sci Nutr 2013; 54: 985–1001.

    Article  Google Scholar 

  5. Froguel P, Boutin P . Genetics of pathways regulating body weight in the development of obesity in humans. Exp Biol Med 2001; 226: 991–996.

    Article  CAS  Google Scholar 

  6. Mägi R, Manning S, Yousseif A, Pucci A, Santini F, Karra E et al. Contribution of 32 GWAS-identified common variants to severe obesity in European adults referred for bariatric surgery. PLoS One 2013; 8 : e70735.

    Article  Google Scholar 

  7. Korenblat KM, Fabbrini E, Mohammed BS, Klein S . Liver, muscle, and adipose tissue insulin action is directly related to intrahepatic triglyceride content in obese subjects. Gastroenterology 2008; 134: 1369–1375.

    Article  CAS  Google Scholar 

  8. Tiniakos DG, Vos MB, Brunt EM . Non-alcoholic fatty liver disease: pathology and pathogenesis. Annu Rev Pathol 2010; 5: 145–171.

    Article  CAS  Google Scholar 

  9. Cornier MA, Dabelea D, Hernandez TL, Lindstrom RC, Steig AJ, Stob NR et al. The metabolic syndrome. Endocr Rev 2008; 29: 777–822.

    Article  CAS  Google Scholar 

  10. Sharma P, Martin DR, Pineda N, Xu Q, Vos M, Anania F et al. Quantitation analysis of T2 correction in single voxel magnetic resonance spectroscopy of hepatic lipid fraction. J Magn Reson Imaging 2009; 29: 629–635.

    Article  Google Scholar 

  11. Xia J, Broadhurst DI, Wilson M, Wishart DS . Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 2013; 9: 280–299.

    Article  CAS  Google Scholar 

  12. Terra X, Auguet T, Guiu-Jurado E, Berlanga A, Orellana-Gavaldà JM, Hernández M et al. Long-term changes in leptin, chemerin and ghrelin levels following different bariatric surgery procedures: roux-en-Y gastric bypass and sleeve gastrectomy. Obes Surg 2013; 23: 1790–1798.

    Article  Google Scholar 

  13. Joven J, Espinel E, Rull A, Beltrán-Debón R, Aragonès G, Rodríguez-Gallego E et al. Serum fatty acid synthase concentration is increased in patients with hepatitis viral infection and may assist in the prediction of liver steatosis. J Clin Virol 2011; 51: 199–201.

    Article  CAS  Google Scholar 

  14. Simó JM, Castellano I, Ferré N, Joven J, Camps J . Evaluation of a homogeneous assay for high-density lipoprotein cholesterol: limitations in patients with cardiovascular, renal, and hepatic disorders. Clin Chem 1998; 10: 1233–1241.

    Google Scholar 

  15. Joven J, Rull A, Ferré N, Escolà-Gil JC, Marsillach J, Coll B et al. The results in rodent models of atherosclerosis are not interchangeable: the influence of diet and strain. Atherosclerosis 2007; 195: e85–92.

    Article  CAS  Google Scholar 

  16. Evans AM, DeHaven CD, Barrett T, Mitchell M, Milgram E . Integrated, non-targeted ultrahigh performance liquid chromatography/electrospray ionization tandem mass spectrometry platform for the identification and relative quantification of the small-molecule complement of biological systems. Anal Chem 2009; 81: 6656–6667.

    Article  CAS  Google Scholar 

  17. Marai L, Kuksis A . Simultaneous quantitation of Krebs cycle and related acids by mass fragmentography. J Chromatogr 1983; 268: 447–460.

    Article  CAS  Google Scholar 

  18. Roessner U, Wagner C, Kopka J, Trethewey RN, Willmitzer L . Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J 2000; 23: 131–142.

    Article  CAS  Google Scholar 

  19. Storey JD, Tibshirani R . Statistical significance for genome wide studies. Proc Natl Acad Sci USA 2003; 100: 9440–9445.

    Article  CAS  Google Scholar 

  20. Goldstein BA, Hubbard AE, Cutler A, Barcellos LF . An application of Random Forests to a genome-wide association dataset: methodological considerations and new findings. BMC Genet 2010; 11: 49.

    Article  Google Scholar 

  21. Ferré N, Camps J, Marsillach J, Mackness B, Mackness M, Coll B et al. Comparison of paraoxonase 1 measurements in serum and in lithium-heparin-anticoagulated plasma samples. Clin Chem 2005; 51: 922–923.

    Article  Google Scholar 

  22. Mamer O, Gravel SP, Choinière L, Chénard V, St-Pierre J, Avizonis D . The complete targeted profile of the organic acid intermediates of the citric acid cycle using a single stable isotope dilution analysis, sodium borodeuteride reduction and selected ion monitoring GC/MS. Metabolomics 2013; 9: 1019–1030.

    Article  CAS  Google Scholar 

  23. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 2001; 69: 89–95.

    Article  Google Scholar 

  24. Arkin CF, Wachtel MS . How many patients are necessary to assess test performance? JAMA 1990; 263: 275–278.

    Article  CAS  Google Scholar 

  25. Yin P, Peter A, Franken H, Zhao X, Neukamm SS, Rosenbaum L et al. Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clin Chem 2013; 59: 833–845.

    Article  CAS  Google Scholar 

  26. Vinaixa M, Rodríguez MA, Rull A, Beltrán R, Bladé C, Brezmes J et al. Metabolomic assessment of the effect of dietary cholesterol in the progressive development of fatty liver disease. J Proteome Res 2010; 9: 2527–2538.

    Article  CAS  Google Scholar 

  27. Rull A, Rodríguez F, Aragonès G, Marsillach J, Beltrán R, Alonso-Villaverde C et al. Hepatic monocyte chemoattractant protein-1 is upregulated by dietary cholesterol and contributes to liver steatosis. Cytokine 2009; 48: 273–279.

    Article  CAS  Google Scholar 

  28. Tous M, Ferré N, Rull A, Marsillach J, Coll B, Alonso-Villaverde C et al. Dietary cholesterol and differential monocyte chemoattractant protein-1 gene expression in aorta and liver of apoE-deficient mice. Biochem Biophys Res Commun 2006; 340: 1078–1084.

    Article  CAS  Google Scholar 

  29. Coll B, Alonso-Villaverde C, Joven J . Monocyte chemoattractant protein-1 and atherosclerosis: is there room for an additional biomarker? Clin Chim Acta 2007; 383: 21–29.

    Article  CAS  Google Scholar 

  30. Hernández-Aguilera A, Rull A, Rodríguez-Gallego E, Riera-Borrull M, Luciano-Mateo F, Camps J et al. Mitochondrial dysfunction: a basic mechanism in inflammation-related non-communicable diseases and therapeutic opportunities. Mediators Inflamm 2013; 2013: 135698.

    Article  Google Scholar 

  31. Singh R, Kaushik S, Wang Y, Xiang Y, Novak I, Komatsu M et al. Autophagy regulates lipid metabolism. Nature 2009; 458: 1131–1135.

    Article  CAS  Google Scholar 

  32. Hardwick RN, Ferreira DW, More VR, Lake AD, Lu Z, Manautou JE et al. Altered UDP-glucuronosyltransferase and sulfotransferase expression and function during progressive stages of human non-alcoholic fatty liver disease. Drug Metab Dispos 2013; 41: 554–561.

    Article  CAS  Google Scholar 

  33. Patel KR, Andreadi C, Britton RG, Horner-Glister E, Karmokar A, Sale S et al. Sulphate metabolites provide an intracellular pool for resveratrol generation and induce autophagy with senescence. Sci Transl Med 2013; 5: 205ra133.

    Article  Google Scholar 

  34. Gerhard GS, Chu X, Wood GC, Gerhard GM, Benotti P, Petrick AT et al. Next-generation sequence analysis of genes associated with obesity and non-alcoholic Fatty liver disease-related cirrhosis in extreme obesity. Hum Hered 2013; 75: 144–151.

    Article  CAS  Google Scholar 

  35. Pagliassotti MJ . Endoplasmic reticulum stress in non-alcoholic fatty liver disease. Annu Rev Nutr 2012; 32: 17–33.

    Article  CAS  Google Scholar 

  36. García-Heredia A, Kensicki E, Mohney RP, Rull A, Triguero I, Marsillach J et al. Paraoxonase-1 deficiency is associated with severe liver steatosis in mice fed a high-fat high-cholesterol diet: a metabolomic approach. J Proteome Res 2013; 12: 1946–1955.

    Article  Google Scholar 

  37. Tokonami N, Morla L, Centeno G, Mordasini D, Ramakrishnan SK, Nikolaeva S et al. α-Ketoglutarate regulates acid-base balance through an intrarenal paracrine mechanism. J Clin Invest 2013; 123: 3166–3171.

    Article  CAS  Google Scholar 

  38. Tufi R, Gandhi S, de Castro IP, Lehmann S, Angelova PR, Dinsdale D et al. Enhancing nucleotide metabolism protects against mitochondrial dysfunction and neurodegeneration in a PINK1 model of Parkinson's disease. Nat Cell Biol 2014; 16 : 157–166.

    Article  CAS  Google Scholar 

  39. Menendez JA, Alarcón T, Joven J . Gerometabolites. The pseudohypoxic aging side of cancer oncometabolites. Cell Cycle 2014; 13: 1–11.

    Article  Google Scholar 

  40. Menendez JA, Joven J, Cufí S, Corominas-Faja B, Oliveras-Ferraros C, Cuyàs E et al. The Warburg effect version 2.0: metabolic reprogramming of cancer stem cells. Cell Cycle 2013; 12: 1166–79.

    Article  CAS  Google Scholar 

  41. Fiore LD, D'Avolio LW . Detours on the road to personalized medicine: barriers to biomarker validation and implementation. JAMA 2011; 306: 1914–1915.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We acknowledge the contribution of the numerous staff members who assisted in the clinical management, laboratory measurements, statistical assessment and data collection, as well as those who critically read the manuscript and provided helpful suggestions. The Unitat de Recerca Biomèdica is currently being supported by the program of consolidated groups from the Universitat Rovira i Virgili and grants from the Fondo de Investigación Sanitaria (FIS PI08/1032, PI08/1381 and PI11/00130). ER-G is the recipient of a fellowship from the Generalitat de Catalunya (2012FI B 00389) and MR-B is the recipient of a fellowship from the Universitat Rovira i Virgili (2010PFR-URV-B2-58).

Author information

Authors and Affiliations

  1. Unitat de Recerca Biomèdica, Hospital Universitari Sant Joan, Institut d’Investigació Sanitària Pere Virgili, Universitat Rovira i Virgili, Reus, Spain

    E Rodríguez-Gallego, M Guirro, M Riera-Borrull, A Hernández-Aguilera, R Mariné-Casadó, S Fernández-Arroyo, R Beltrán-Debón, J Camps & J Joven

  2. Servei de Cirurgia General i de l’Aparell Digestiu, Hospital Universitari de Sant Joan de Reus, Universitat Rovira i Virgili, Reus, Spain

    F Sabench, M Hernández & D del Castillo

  3. Catalan Institute of Oncology and Girona Biomedical Research Institute, Girona, Spain

    J A Menendez

  4. Center for Omics Sciences, Reus, Spain

    R Ras

  5. Department of Biochemistry and Biotechnology, Universitat Rovira i Virgili. Centre Tecnològic de Nutrició i Salut, Reus, Spain

    L Arola

  6. Campus of International Excellence Southern Catalonia, Tarragona, Spain,

    L Arola & J Joven

Authors
  1. E Rodríguez-Gallego
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M Guirro
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M Riera-Borrull
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A Hernández-Aguilera
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. R Mariné-Casadó
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. S Fernández-Arroyo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. R Beltrán-Debón
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. F Sabench
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. M Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. D del Castillo
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. J A Menendez
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. J Camps
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. R Ras
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. L Arola
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. J Joven
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J Joven.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies this paper on International Journal of Obesity website

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rodríguez-Gallego, E., Guirro, M., Riera-Borrull, M. et al. Mapping of the circulating metabolome reveals α-ketoglutarate as a predictor of morbid obesity-associated non-alcoholic fatty liver disease. Int J Obes 39, 279–287 (2015). https://doi.org/10.1038/ijo.2014.53

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ijo.2014.53

Keywords

This article is cited by

Search

Advanced search

Quick links