Amino Acids

, Volume 50, Issue 11, pp 1539–1548 | Cite as

Plasma profiling of amino acids distinguishes acute gout from asymptomatic hyperuricemia

  • Ying Luo
  • Ling Wang
  • Xin-Ying Liu
  • Xiaolong Chen
  • Ya-Xiang Song
  • Xin-Hua Li
  • Cizong Jiang
  • Ai PengEmail author
  • Jun-Yan LiuEmail author
Original Article


Gout and hyperuricemia are highly prevalent metabolic diseases caused by high level of uric acid. Amino acids (AAs) involve in various biochemical processes including the biosynthesis of uric acid. However, the role of AAs in discriminating gout from hyperuricemia remains unknown. Here, we report that the plasma AAs profile can distinguish acute gout (AG) from asymptomatic hyperuricemia (AHU). We established an LC–MS/MS-based method to measure the plasma AAs without derivatization for the AG and AHU patients, and healthy controls. We found that the plasma profiling of AAs separated the AG patients from AHU patients and controls visually in both principal component analysis and orthogonal partial least-squares discriminant analysis (OPLS-DA) models. In addition, l-isoleucine, l-lysine, and l-alanine were suggested as the key mediators to distinguish the AG patients from AHU and control groups based on the S-plot analysis and variable importance in the projection values in the OPLS-DA models, volcano plot, and the receiver operating characteristic curves. In addition, the saturation of monosodium urate in the AA solutions at physiologically mimic status supported the changes in plasma AAs facilitating the precipitation of monosodium urate. This study suggests that l-isoleucine, l-lysine, and l-alanine could be the potential markers to distinguish the AG from AHU when the patients have similar blood levels of uric acid, providing new strategies for the prevention, treatment, and management of acute gout.


l-Isoleucine l-Lysine l-Alanine Metabolomics Hyperuricemia Gout 



Amino acid




Acute attack of gout, and acute gout


Asymptomatic hyperuricemia


Electrospray ionization




Principal component analysis


Orthogonal partial least-squares discriminant analysis


Monosodium urate


Uric acid


Limit of detection


Limit of quantitation


Declustering potential


Collision energy


Collision cell exit potential


Shared and unique structures


Variable importance in the projection


Receiver operating characteristic



This study was supported NSFC Grant 81470588 (J.-Y. L.). The authors would like to thank all the patients and healthy volunteers for the participation in this study.

Compliance with ethical standards

Conflict of interest

The authors declare no conflict of interest.

Research involving human participants and/or animals

The authors complied with the World Medical Association Declaration of Helsinki regarding the ethical conduct of research involving in human subjects. This study and the associated protocols for sample collection were approved by the Ethics Committee of Shanghai Tenth People’s Hospital (SHSY-IEC-KY-4.0/17–60/01).

Informed consent

Informed consent was obtained from all individual participants included in this study.

Supplementary material

726_2018_2627_MOESM1_ESM.docx (188 kb)
Supplementary material 1 (DOCX 188 kb)


  1. Aung T, Myung G, FitzGerald JD (2017) Treatment approaches and adherence to urate-lowering therapy for patients with gout. Patient Pref Adherence 11:795–800CrossRefGoogle Scholar
  2. Baggott JE, Gorman GS, Tamura T (2007) 13C enrichment of carbons 2 and 8 of purine by folate-dependent reactions after [13C]formate and [2-13C]glycine dosing in adult humans. Metabolism 56:708–715CrossRefPubMedPubMedCentralGoogle Scholar
  3. Bang DH, Xu JF, Keenan RT, Pike VC, Lehmann RA, Tenner C et al (2016) Cardiovascular disease prevalence in patients with osteoarthritis, gout, or both. Bull Hosp Jt Dis 74:113–118Google Scholar
  4. Borghi C, Rodriguez-Artalejo F, De Backer G, Dallongeville J, Medina J, Nuevo J et al (2018) Serum uric acid levels are associated with cardiovascular risk score: a post hoc analysis of the EURIKA study. Int J Cardiol 253:167–173CrossRefPubMedGoogle Scholar
  5. Carpenter J, Bithell J (2000) Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat Med 19:1141–1164CrossRefPubMedGoogle Scholar
  6. Chen YZ, Tang ZZ, Huang ZY, Zhou WW, Li ZY, Li XP et al (2017) The prevalence of gout in mainland China from 2000 to 2016: a systematic review and meta-analysis. J Public Health-Heid 25:521–529CrossRefGoogle Scholar
  7. Chen YY, Kao TW, Yang HF, Chou CW, Wu CJ, Lai CH et al (2018) The association of uric acid with the risk of metabolic syndrome, arterial hypertension or diabetes in young subjects—an observational study. Clin Chim Acta 478:68–73CrossRefPubMedGoogle Scholar
  8. Conijn NFL, Hoorn EJ, Muradin GS, Kok MR, Vis M (2016) Asymptomatic gout in chronic kidney disease: prevalence study using dual energy CT and ultrasound. Ann Rheum Dis 75:369CrossRefGoogle Scholar
  9. Dugelay S, Chauvin MF, Megnin-Chanet F, Martin G, Lareal MC, Lhoste JM et al (1999) Acetate stimulates flux through the tricarboxylic acid cycle in rabbit renal proximal tubules synthesizing glutamine from alanine: a C-13 NMR study. Biochem J 342:555–566CrossRefPubMedPubMedCentralGoogle Scholar
  10. Farres M, Platikanov S, Tsakovski S, Tauler R (2015) Comparison of the variable importance in projection (VIP) and of the selectivity ratio (SR) methods for variable selection and interpretation. J Chemom 29:528–536CrossRefGoogle Scholar
  11. Favilla S, Durante C, Vigni ML, Cocchi M (2013) Assessing feature relevance in NPLS models by VIP. Chemom Intell Lab 129:76–86CrossRefGoogle Scholar
  12. Feig DI, Kang DH, Johnson RJ (2008) Uric acid and cardiovascular risk. N Engl J Med 359:1811–1821CrossRefPubMedPubMedCentralGoogle Scholar
  13. Felig P (1973) The glucose-alanine cycle. Metabolism 22:179–207CrossRefPubMedGoogle Scholar
  14. Fouad M, Fathy H, Zidan A (2016) Serum uric acid and its association with hypertension, early nephropathy and chronic kidney disease in type 2 diabetic patients. J Bras Nefrol 38:403–410CrossRefPubMedGoogle Scholar
  15. George C, Minter DA (2018) Hyperuricemia. StatPearls, Treasure IslandGoogle Scholar
  16. Guasch-Ferre M, Hruby A, Toledo E, Clish CB, Martinez-Gonzalez MA, Salas-Salvado J et al (2016) Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care 39:833–846CrossRefPubMedPubMedCentralGoogle Scholar
  17. Hannawi S, AlSalmi I, Moller I, Naredo E (2017) Uric acid is independent cardiovascular risk factor, as manifested by increased carotid intima-media thickness in rheumatoid arthritis patients. Clin Rheumatol 36:1897–1902CrossRefPubMedGoogle Scholar
  18. Hensgens HE, Meijer AJ (1980) Inhibition of urea-cycle activity by high concentrations of alanine. Biochem J 186:1–4CrossRefPubMedPubMedCentralGoogle Scholar
  19. Holecek M (2001) The BCAA-BCKA cycle: its relation to alanine and glutamine synthesis and protein balance. Nutrition 17:70CrossRefPubMedGoogle Scholar
  20. Hsieh YP, Chang CC, Yang Y, Wen YK, Chiu PF, Lin CC (2017) The role of uric acid in chronic kidney disease patients. Nephrology (Carlton) 22:441–448CrossRefGoogle Scholar
  21. Johnston RB, Henderson L, Henderson MC (1978) Modulation of activity of alanine racemase from B-subtilis by intermediates of citric-acid cycle and their analogs. Fed Proc 37:1426Google Scholar
  22. King C, Lanaspa MA, Jensen T, Tolan DR, Sanchez-Lozada LG, Johnson RJ (2018) Uric acid as a cause of the metabolic syndrome. Contrib Nephrol 192:88–102CrossRefPubMedGoogle Scholar
  23. Kuo CF, Grainge MJ, Zhang W, Doherty M (2015) Global epidemiology of gout: prevalence, incidence and risk factors. Nat Rev Rheumatol 11:649CrossRefPubMedGoogle Scholar
  24. Li X, Song P, Li J, Wang P, Li G (2015) Relationship between hyperuricemia and dietary risk factors in Chinese adults: a cross-sectional study. Rheumatol Int 35:2079–2089CrossRefPubMedGoogle Scholar
  25. Liu H, Zhang XM, Wang YL, Liu BC (2014) Prevalence of hyperuricemia among Chinese adults: a national cross-sectional survey using multistage, stratified sampling. J Nephrol 27:653–658CrossRefPubMedGoogle Scholar
  26. Liu XY, Luo Y, Zhou CY, Peng A, Liu JY (2017) A sensitive and accurate method to simultaneously measure uric acid and creatinine in human saliva by using LC–MS/MS. Bioanalysis 9:1751–1760CrossRefPubMedGoogle Scholar
  27. Mahbub MH, Yamaguchi N, Takahashi H, Hase R, Amano H, Kobayashi-Miura M et al (2017a) Alteration in plasma free amino acid levels and its association with gout. Environ Health Prev 22:7CrossRefGoogle Scholar
  28. Mahbub MH, Yamaguchi N, Takahashi H, Hase R, Ishimaru Y, Sunagawa H et al (2017b) Association of plasma free amino acids with hyperuricemia in relation to diabetes mellitus, dyslipidemia, hypertension and metabolic syndrome. Sci Rep 7:17616CrossRefPubMedPubMedCentralGoogle Scholar
  29. Miao Z, Li C, Chen Y, Zhao S, Wang Y, Wang Z et al (2008) Dietary and lifestyle changes associated with high prevalence of hyperuricemia and gout in the Shandong coastal cities of Eastern China. J Rheumatol 35:1859–1864PubMedGoogle Scholar
  30. Mok Y, Lee SJ, Kim MS, Cui W, Moon YM, Jee SH (2012) Serum uric acid and chronic kidney disease: the Severance cohort study. Nephrol Dial Transplant 27:1831–1835CrossRefPubMedGoogle Scholar
  31. Mook-Kanamori DO, Romisch-Margl W, Kastenmuller G, Prehn C, Petersen AK, Illig T et al (2014) Increased amino acids levels and the risk of developing of hypertriglyceridemia in a 7-year follow-up. J Endocrinol Investig 37:369–374CrossRefGoogle Scholar
  32. Neogi T, Jansen TLTA, Dalbeth N, Fransen J, Schumacher HR, Berendsen D et al (2015) 2015 Gout classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis 74:1789–1798CrossRefPubMedPubMedCentralGoogle Scholar
  33. Qiu L, Cheng XQ, Wu J, Liu JT, Xu T, Ding HT et al (2013) Prevalence of hyperuricemia and its related risk factors in healthy adults from Northern and Northeastern Chinese provinces. BMC Public Health 13:664CrossRefPubMedPubMedCentralGoogle Scholar
  34. Rai SK, Avina-Zubieta JA, McCormick N, De Vera M, Shojania K, Sayre EC et al (2015) Rising incidence and prevalence of gout in the Canadian General Population. Arthritis Rheumatol 67:292Google Scholar
  35. Rai SK, Avina-Zubieta JA, McCormick N, De Vera MA, Shojania K, Sayre EC et al (2017) The rising prevalence and incidence of gout in British Columbia, Canada: population-based trends from 2000 to 2012. Semin Arthritis Rheum 46:451–456CrossRefPubMedGoogle Scholar
  36. Richette P, Bardin T (2010) Gout. Lancet 375:318–328CrossRefPubMedGoogle Scholar
  37. Richette P, Perez-Ruiz F, Doherty M, Jansen TL, Nuki G, Pascual E et al (2014) Improving cardiovascular and renal outcomes in gout: what should we target? Nat Rev Rheumatol 10:654–661CrossRefPubMedGoogle Scholar
  38. Rocha M, Licausi F, Araujo WL, Nunes-Nesi A, Sodek L, Fernie AR et al (2010) Glycolysis and the tricarboxylic acid cycle are linked by alanine aminotransferase during hypoxia induced by waterlogging of Lotus japonicus. Plant Physiol 152:1501–1513CrossRefPubMedPubMedCentralGoogle Scholar
  39. Villegas R, Xiang YB, Cai Q, Fazio S, Linton M, Li H et al (2010) Prevalence and determinants of hyperuricemia in middle-aged, urban Chinese men. Metab Syndr Relat Disord 8:263–270CrossRefPubMedPubMedCentralGoogle Scholar
  40. Wurtz P, Soininen P, Kangas AJ, Ronnemaa T, Lehtimaki T, Kahonen M et al (2013) Branched-chain and aromatic amino acids are predictors of insulin resistance in young adults. Diabetes Care 36:648–655CrossRefPubMedPubMedCentralGoogle Scholar
  41. Zhang M, Chang H, Gao Y, Wang X, Xu W, Liu D et al (2012) Major dietary patterns and risk of asymptomatic hyperuricemia in Chinese adults. J Nutr Sci Vitaminol (Tokyo) 58:339–345CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Nephrology and Metabolomics and Division of Nephrology and RheumatologyShanghai Tenth People’s Hospital, Tongji University School of MedicineShanghaiChina
  2. 2.The School of Life Sciences and Technology, Shanghai Key Laboratory of Signaling and Disease ResearchTongji UniversityShanghaiChina

Personalised recommendations