Skip to main content
Log in

Serum metabolic profile predicts adverse central haemodynamics in patients with type 2 diabetes mellitus

  • Original Article
  • Published:
Acta Diabetologica Aims and scope Submit manuscript

Abstract

Aims

People with type 2 diabetes mellitus (T2DM) have abnormal peripheral and central haemodynamics at rest and during exercise, probably due to metabolic perturbations, but mechanisms are unknown. We used untargeted metabolomics to determine the relationships between metabolic perturbations and haemodynamics (peripheral and central) measured at rest and during exercise.

Methods

Serum samples from 39 participants with T2DM (62 ± 9 years; 46 % male) and 39 controls (52 ± 10 years; 51 % male) were analysed by liquid chromatography–mass spectrometry, nuclear magnetic resonance spectroscopy and principal component analysis. Scores on principal components (PC) were used to assess relationships with haemodynamics including peripheral and central BP, central augmentation index (AIx) and central augmentation pressure (AP).

Results

Participants with T2DM had higher resting and exercise haemodynamics (peripheral and central BP, central AIx and central AP) compared to controls (p < 0.05). PC that comprised of a signature metabolic pattern of T2DM was independently associated with resting and exercise central AIx and central AP (p < 0.05).

Conclusions

Serum metabolic profile was associated with central, but not peripheral, haemodynamics in T2DM participants, suggesting that metabolic irregularities may explain abnormal central haemodynamics in T2DM patients.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. de Vegt F, Dekker JM, Ruhe HG, Stehouwer CD, Nijpels G, Bouter LM, Heine RJ (1999) Hyperglycaemia is associated with all-cause and cardiovascular mortality in the Hoorn population: the Hoorn Study. Diabetologia 42(8):926–931

    Article  PubMed  Google Scholar 

  2. Turner RC, Holman RR, Matthews DR, Bassett PA, Coster R, Stratton IM et al (1993) Hypertension in diabetes study (HDS). 1. Prevalence of hypertension in newly presenting type-2 diabetic-patients and the association with risk-factors for cardiovascular and diabetic complications. J Hypertens 11(3):309–317

    Article  Google Scholar 

  3. Henry RM, Kostense PJ, Spijkerman AM, Dekker JM, Nijpels G, Heine RJ, Kamp O, Westerhof N, Bouter LM, Stehouwer CD, Hoorn S (2003) Arterial stiffness increases with deteriorating glucose tolerance status: the Hoorn Study. Circulation 107(16):2089–2095. doi:10.1161/01.cir.0000065222.34933.fc

    Article  PubMed  Google Scholar 

  4. Vlachopoulos C, Aznaouridis K, O'Rourke MF, Safar ME, Baou K, Stefanadis C (2010) Prediction of cardiovascular events and all-cause mortality with central haemodynamics: a systematic review and meta-analysis. Eur Heart J. doi:10.1093/eurheartj/ehq024

    PubMed  Google Scholar 

  5. Schultz MG, Hare JL, Marwick TH, Stowasser M, Sharman JE (2011) Masked hypertension is “unmasked” by low-intensity exercise blood pressure. Blood Press 20(5):284–289. doi:10.3109/08037051.2011.566251

    Article  PubMed  Google Scholar 

  6. Scott JA, Coombes JS, Prins JB, Leano RL, Marwick TH, Sharman JE (2008) Patients with type 2 diabetes have exaggerated brachial and central exercise blood pressure: relation to left ventricular relative wall thickness. Am J Hypertens 21(6):715–721. doi:10.1038/ajh.2008.166

    Article  PubMed  Google Scholar 

  7. Lu J, Xie G, Jia W, Jia W (2013) Metabolomics in human type 2 diabetes research. Front Med 7(1):4–13. doi:10.1007/s11684-013-0248-4

    Article  PubMed  Google Scholar 

  8. Suhre K, Meisinger C, Doring A, Altmaier E, Belcredi P, Gieger C, Chang D, Milburn MV, Gall WE, Weinberger KM, Mewes HW, Hrabe de Angelis M, Wichmann HE, Kronenberg F, Adamski J, Illig T (2010) Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting. PLoS ONE 5(11):e13953. doi:10.1371/journal.pone.0013953

    Article  PubMed  PubMed Central  Google Scholar 

  9. Welborn T, De Courten M (2001) Case detection and diagnosis expert working group: national evidence based guidelines for the management of type 2 diabetes mellitus; part 3 case detection and diagnosis of type 2 diabetes. National Health and Medical Research Council

  10. Keith LJ, Rattigan S, Keske MA, Jose M, Sharman JE (2013) Exercise aortic stiffness: reproducibility and relation to end-organ damage in men. J Hum Hypertens 27(8):516–522. doi:10.1038/jhh.2013.5

    Article  CAS  PubMed  Google Scholar 

  11. El Assaad MA, Topouchian JA, Darne BM, Asmar RG (2002) Validation of the Omron HEM-907 device for blood pressure measurement. Blood Press Monit 7(4):237–241

    Article  PubMed  Google Scholar 

  12. Pickering TG, Hall JE, Appel LJ, Falkner BE, Graves J, Hill MN, Jones DW, Kurtz T, Sheps SG, Roccella EJ (2005) Recommendations for blood pressure measurement in humans and experimental animals: part 1: blood pressure measurement in humans: a statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation 111(5):697–716. doi:10.1161/01.CIR.0000154900.76284.F6

    Article  PubMed  Google Scholar 

  13. Stergiou GS, Giovas PP, Gkinos CP, Tzamouranis DG (2008) Validation of the A&D UM-101 professional hybrid device for office blood pressure measurement according to the International Protocol. Blood Press Monit 13(1):37–42. doi:10.1097/MBP.0b013e3282c9acb0

    Article  PubMed  Google Scholar 

  14. Schultz MG, Climie RE, Nikolic SB, Ahuja KD, Sharman JE (2012) Reproducibility of cardiac output derived by impedance cardiography during postural changes and exercise. Artery Res 6:78–84

    Article  Google Scholar 

  15. Sharman JE, Lim R, Qasem AM, Coombes JS, Burgess MI, Franco J, Garrahy P, Wilkinson IB, Marwick TH (2006) Validation of a generalized transfer function to noninvasively derive central blood pressure during exercise. Hypertension 47(6):1203–1208

    Article  CAS  PubMed  Google Scholar 

  16. Holland DJ, Sacre JW, McFarlane SJ, Coombes JS, Sharman JE (2008) Pulse wave analysis is a reproducible technique for measuring central blood pressure during hemodynamic perturbations induced by exercise. Am J Hypertens 21(10):1100–1106. doi:10.1038/ajh.2008.253

    Article  PubMed  Google Scholar 

  17. Laurent S, Cockcroft J, Van Bortel L, Boutouyrie P, Giannattasio C, Hayoz D, Pannier B, Vlachopoulos C, Wilkinson I, Struijker-Boudier H, European Network for Non-invasive Investigation of Large A (2006) Expert consensus document on arterial stiffness: methodological issues and clinical applications. Eur Heart J 27(21):2588–2605. doi:10.1093/eurheartj/ehl254

    Article  PubMed  Google Scholar 

  18. Nikolic SB, Wilson R, Hare JL, Adams MJ, Edwards LM, Sharman JE (2014) Spironolactone reduces aortic stiffness via blood pressure-dependent effects of canrenoate. Metabolomics 10(1):105–113. doi:10.1007/s11306-013-0557-2

    Article  CAS  Google Scholar 

  19. Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, Brown M, Knowles JD, Halsall A, Haselden JN, Nicholls AW, Wilson ID, Kell DB, Goodacre R, Human Serum Metabolome C (2011) Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc 6(7):1060–1083. doi:10.1038/nprot.2011.335

    Article  CAS  PubMed  Google Scholar 

  20. Karpievitch YV, Nikolic SB, Wilson R, Sharman JE, Edwards LM (2014) Metabolomics data normalization with EigenMS. PLoS ONE 9(12):e116221. doi:10.1371/journal.pone.0116221

    Article  PubMed  PubMed Central  Google Scholar 

  21. Karpievitch YV, Taverner T, Adkins JN, Callister SJ, Anderson GA, Smith RD, Dabney AR (2009) Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition. Bioinformatics (Oxford, England) 25(19):2573–2580. doi:10.1093/bioinformatics/btp426

    Article  CAS  Google Scholar 

  22. Edwards LM, Lawler NG, Nikolic SB, Peters JM, Horne J, Wilson R, Davies NW, Sharman JE (2012) Metabolomics reveals increased isoleukotoxin diol (12,13-DHOME) in human plasma after acute intralipid infusion. J Lipid Res 53(9):1979–1986. doi:10.1194/jlr.P027706

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. De Meyer T, Sinnaeve D, Van Gasse B, Tsiporkova E, Rietzschel ER, De Buyzere ML, Gillebert TC, Bekaert S, Martins JC, Van Criekinge W (2008) NMR-based characterization of metabolic alterations in hypertension using an adaptive, intelligent binning algorithm. Anal Chem 80(10):3783–3790. doi:10.1021/ac7025964

    Article  PubMed  Google Scholar 

  24. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc 57(1):289–300

    Google Scholar 

  25. Brown M, Wedge DC, Goodacre R, Kell DB, Baker PN, Kenny LC, Mamas MA, Neyses L, Dunn WB (2011) Automated workflows for accurate mass-based putative metabolite identification in LC/MS-derived metabolomic datasets. Bioinformatics (Oxford, England) 27(8):1108–1112. doi:10.1093/bioinformatics/btr079

    Article  CAS  Google Scholar 

  26. Warwick DB, Alexander E, Ralf WJM, Darren CJ, Marie B, Rainer B, Thomas H, Royston G, Steffen N, Joachim K, Mark VR (2013) Mass appeal: metabolite identification in mass spectrometry-focused untargeted metabolomics. Metabolomics 9(1):44–46. doi:10.1007/s11306-012-0434-4

    Article  Google Scholar 

  27. Crockford DJ, Holmes E, Lindon JC, Plumb RS, Zirah S, Bruce SJ, Rainville P, Stumpf CL, Nicholson JK (2006) Statistical heterospectroscopy, an approach to the integrated analysis of NMR and UPLC-MS data sets: application in metabonomic toxicology studies. Anal Chem 78(2):363–371. doi:10.1021/ac051444m

    Article  CAS  PubMed  Google Scholar 

  28. Hjelmesaeth J, Roislien J, Nordstrand N, Hofso D, Hager H, Hartmann A (2010) Low serum creatinine is associated with type 2 diabetes in morbidly obese women and men: a cross-sectional study. BMC Endocr Disord 10:6. doi:10.1186/1472-6823-10-6

    Article  PubMed  PubMed Central  Google Scholar 

  29. Robinson BH (2006) Lactic acidemia and mitochondrial disease. Mol Genet Metab 89(1–2):3–13. doi:10.1016/j.ymgme.2006.05.015

    Article  CAS  PubMed  Google Scholar 

  30. DiGirolamo M, Newby FD, Lovejoy J (1992) Lactate production in adipose tissue: a regulated function with extra-adipose implications. FASEB J 6(7):2405–2412

    CAS  PubMed  Google Scholar 

  31. Mabley JG, Pacher P, Liaudet L, Soriano FG, Hasko G, Marton A, Szabo C, Salzman AL (2003) Inosine reduces inflammation and improves survival in a murine model of colitis. Am J Physiol Gastrointest Liver Physiol 284(1):G138–G144. doi:10.1152/ajpgi.00060.2002

    Article  CAS  PubMed  Google Scholar 

  32. Mabley JG, Rabinovitch A, Suarez-Pinzon W, Hasko G, Pacher P, Power R, Southan G, Salzman A, Szabo C (2003) Inosine protects against the development of diabetes in multiple-low-dose streptozotocin and nonobese diabetic mouse models of type 1 diabetes. Mol Med (Cambridge, Mass) 9(3–4):96–104

    CAS  Google Scholar 

  33. Boos CJ, Lip GY (2006) Is hypertension an inflammatory process? Curr Pharm Des 12(13):1623–1635

    Article  CAS  PubMed  Google Scholar 

  34. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O'Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17(4):448–453. doi:10.1038/nm.2307

    Article  PubMed  PubMed Central  Google Scholar 

  35. Magnusson M, Lewis GD, Ericson U, Orho-Melander M, Hedblad B, Engstrom G, Ostling G, Clish C, Wang TJ, Gerszten RE, Melander O (2013) A diabetes-predictive amino acid score and future cardiovascular disease. Eur Heart J 34(26):1982–1989. doi:10.1093/eurheartj/ehs424

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

The authors thank Ms. Laura J. Keith for significant contribution towards data collection.

Sources of funding

This study was partly supported with a Diabetes Australia Research Grant (Reference Y11SHAJ).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Lindsay M. Edwards or James E. Sharman.

Ethics declarations

Conflicts of interest

The authors declare that they have no conflict of interest.

Human and Animal Rights disclosure

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008 (5).

Informed consent disclosure

Informed consent was obtained from all patients for being included in the study.

Additional information

Managed by Massimo Federici.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 20 kb)

Supplementary material 2 (DOCX 19 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nikolic, S.B., Edwards, L.M., Karpievitch, Y.V. et al. Serum metabolic profile predicts adverse central haemodynamics in patients with type 2 diabetes mellitus. Acta Diabetol 53, 367–375 (2016). https://doi.org/10.1007/s00592-015-0802-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00592-015-0802-4

Keywords

Navigation