Skip to main content

Advertisement

SpringerLink
Log in

Whole-Body MR Imaging Including Angiography: Predicting Recurrent Events in Diabetics

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

Whether whole-body MRI can predict occurrence of recurrent events in patients with diabetes mellitus.

Methods

Whole-body MRI was prospectively applied to 61 diabetics and assessed for arteriosclerosis and ischemic cerebral/myocardial changes. Occurrence of cardiocerebral events and diabetic comorbidites was determined. Patients were stratified whether no, a single or recurrent events arose. As a secondary endpoint, events were stratified into organ system-specific groups.

Results

During a median follow-up of 70 months, 26 diabetics developed a total of 39 events; 18 (30 %) developed one, 8 (13 %) recurrent events. Between diabetics with no, a single and recurrent events, a stepwise higher burden was observed for presence of left ventricular (LV) hypo-/akinesia (3/28/75 %, p < 0.0001), myocardial delayed-contrast-enhancement (17/33/63 %, p = 0.001), carotid artery stenosis (11/17/63 %, p = 0.005), peripheral artery stenosis (26/56/88 %, p = 0.0006) and vessel score (1.00/1.30/1.76, p < 0.0001). After adjusting for clinical characteristics, LV hypo-/akinesia (hazard rate ratio = 6.57, p < 0.0001) and vessel score (hazard rate ratio = 12.29, p < 0.0001) remained independently associated. Assessing organ system risk, cardiac and cerebral MR findings predicted more strongly events in their respective organ system. Vessel-score predicted both cardiac and cerebral, but not non-cardiocerebral, events.

Conclusion

Whole-body MR findings predict occurrence of recurrent events in diabetics independent of clinical characteristics, and may concurrently provide organ system-specific risk.

Key Points

• Patients with long-standing diabetes mellitus are at high risk for recurrent events.

• Whole-body MRI predicts occurrence of recurrent events independently of clinical characteristics.

• The vessel score derived from whole-body angiography is a good general risk-marker.

• Whole-body MRI may also provide organ-specific risk assessment.

• Current findings may indicate benefits of whole-body MRI for risk stratification.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Abbreviations

95 % CI:

95 % confidence interval

AIC:

Akaike information criterion

CHF:

congestive heart failure

DCE:

delayed contrast enhancement

DM:

diabetes mellitus

HbA1c:

glycated haemoglobin

IQR:

interquartile range

LV:

left ventricular

LVEF:

left ventricular ejection fraction

MR:

magnetic resonance

MRI:

magnetic resonance imaging

PAD:

peripheral artery disease

References

  1. National Diabetes Statistics Report (2014) In: Centers for Disease Control and Prevention, ed. Atlanta, 2014

  2. Boyle JP, Thompson TJ, Gregg EW, Barker LE, Williamson DF (2010) Projection of the year 2050 burden of diabetes in the US adult population: dynamic modeling of incidence, mortality, and prediabetes prevalence. Popul Health Metr 8:29

    Article  PubMed  PubMed Central  Google Scholar 

  3. Naslafkih A, Sestier F (2003) Diabetes mellitus related morbidity, risk of hospitalization and disability. J Insur Med 35:102–113

    PubMed  Google Scholar 

  4. Beckman JA, Creager MA, Libby P (2002) Diabetes and atherosclerosis: epidemiology, pathophysiology, and management. JAMA 287:2570–2581

    Article  CAS  PubMed  Google Scholar 

  5. Chuang ML, Gona P, Hautvast GL et al (2014) CMR reference values for left ventricular volumes, mass, and ejection fraction using computer-aided analysis: the Framingham Heart Study. J Magn Reson Imaging 39:895–900

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhang Y, Hu G, Yuan Z, Chen L (2012) Glycosylated hemoglobin in relationship to cardiovascular outcomes and death in patients with type 2 diabetes: a systematic review and meta-analysis. PLoS One 7, e42551

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pradhan AD, Rifai N, Buring JE, Ridker PM (2007) Hemoglobin A1c predicts diabetes but not cardiovascular disease in nondiabetic women. Am J Med 120:720–727

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Di Angelantonio E, Gao P, Khan H et al (2014) Glycated hemoglobin measurement and prediction of cardiovascular disease. JAMA 311:1225–1233

    Article  PubMed  PubMed Central  Google Scholar 

  9. Kilpatrick E, Winocour P (2010) ABCD position statement on haemoglobin A1c for the diagnosis of diabetes. Pract Diab Int 27

  10. Cheong BY, Muthupillai R, Wilson JM et al (2009) Prognostic significance of delayed-enhancement magnetic resonance imaging: survival of 857 patients with and without left ventricular dysfunction. Circulation 120:2069–2076

    Article  PubMed  Google Scholar 

  11. Kwong RY, Chan AK, Brown KA et al (2006) Impact of unrecognized myocardial scar detected by cardiac magnetic resonance imaging on event-free survival in patients presenting with signs or symptoms of coronary artery disease. Circulation 113:2733–2743

    Article  PubMed  Google Scholar 

  12. Mark PB, Doyle A, Blyth KG et al (2008) Vascular function assessed with cardiovascular magnetic resonance predicts survival in patients with advanced chronic kidney disease. J Cardiovasc Magn Reson 10:39

    Article  PubMed  PubMed Central  Google Scholar 

  13. Yoon YE, Kitagawa K, Kato S et al (2013) Prognostic value of unrecognised myocardial infarction detected by late gadolinium-enhanced MRI in diabetic patients with normal global and regional left ventricular systolic function. Eur Radiol 23:2101–2108

    Article  PubMed  Google Scholar 

  14. Uzu T, Kida Y, Shirahashi N et al (2010) Cerebral microvascular disease predicts renal failure in type 2 diabetes. J Am Soc Nephrol 21:520–526

    Article  PubMed  PubMed Central  Google Scholar 

  15. Ladd SC (2009) Whole-body MRI, as a screening tool? Eur J Radiol 70:452–462

    Article  PubMed  Google Scholar 

  16. Heidenreich PA, McClellan M (2001) Trends in treatment and outcomes for acute myocardial infarction: 1975-1995. Am J Med 110:165–174

    Article  CAS  PubMed  Google Scholar 

  17. Roe MT, Messenger JC, Weintraub WS et al (2010) Treatments, trends, and outcomes of acute myocardial infarction and percutaneous coronary intervention. J Am Coll Cardiol 56:254–263

    Article  PubMed  Google Scholar 

  18. Gregg EW, Li YF, Wang J et al (2014) Changes in Diabetes-Related Complications in the United States, 1990-2010. N Engl J Med 370:1514–1523

    Article  CAS  PubMed  Google Scholar 

  19. van der Heijden AA, Van't Riet E, Bot SD et al (2013) Risk of a recurrent cardiovascular event in individuals with type 2 diabetes or intermediate hyperglycemia: the Hoorn Study. Diabetes Care 36:3498–3502

    Article  PubMed  PubMed Central  Google Scholar 

  20. Weckbach S, Findeisen HM, Schoenberg SO et al (2009) Systemic cardiovascular complications in patients with long-standing diabetes mellitus: comprehensive assessment with whole-body magnetic resonance imaging/magnetic resonance angiography. Invest Radiol 44:242–250

    Article  PubMed  Google Scholar 

  21. Bamberg F, Parhofer KG, Lochner E et al (2013) Diabetes Mellitus: Long-term Prognostic Value of Whole-Body MR Imaging for the Occurrence of Cardiac and Cerebrovascular Events. Radiology 269:730–737

    Article  PubMed  Google Scholar 

  22. Findeisen HM, Weckbach S, Stark RG, Reiser MF, Schoenberg SO, Parhofer KG (2010) Metabolic syndrome predicts vascular changes in whole body magnetic resonance imaging in patients with long standing diabetes mellitus. Cardiovasc Diabetol 9:44

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wei LJ, Lin DY, Weissfeld L (1989) Regression Analysis of Multivariate Incomplete Failure Time Data by Modeling Marginal Distribution. J Am Stat Assoc 84:1065–1073

    Article  Google Scholar 

  24. Heinze G, Schemper M (2001) A solution to the problem of monotone likelihood in Cox regression. Biometrics 57:114–119

    Article  CAS  PubMed  Google Scholar 

  25. Abbott RD, Donahue RP, Kannel WB, Wilson PW (1988) The impact of diabetes on survival following myocardial infarction in men vs women. The Framingham Study. JAMA 260:3456–3460

    Article  CAS  PubMed  Google Scholar 

  26. Giorda CB, Avogaro A, Maggini M et al (2008) Recurrence of cardiovascular events in patients with type 2 diabetes: epidemiology and risk factors. Diabetes Care 31:2154–2159

    Article  PubMed  PubMed Central  Google Scholar 

  27. Economic costs of diabetes in the U.S. In 2007 (2008) Diabetes Care 31:596–615

  28. Cardoso CR, Ferreira MT, Leite NC, Salles GF (2013) Prognostic impact of aortic stiffness in high-risk type 2 diabetic patients: the Rio deJaneiro Type 2 Diabetes Cohort Study. Diabetes Care 36:3772–3778

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Gao S, Wong KS, Hansberg T, Lam WW, Droste DW, Ringelstein EB (2004) Microembolic signal predicts recurrent cerebral ischemic events in acute stroke patients with middle cerebral artery stenosis. Stroke 35:2832–2836

    Article  PubMed  Google Scholar 

  30. Altaf N, Daniels L, Morgan PS et al (2008) Detection of intraplaque hemorrhage by magnetic resonance imaging in symptomatic patients with mild to moderate carotid stenosis predicts recurrent neurological events. J Vasc Surg 47:337–342

    Article  PubMed  Google Scholar 

  31. Al-Mallah MH, Qureshi W, Lin FY et al (2014) Does coronary CT angiography improve risk stratification over coronary calcium scoring in symptomatic patients with suspected coronary artery disease? Results from the prospective multicenter international CONFIRM registry. Eur Heart J Cardiovasc Imaging 15:267–274

    Article  PubMed  Google Scholar 

  32. Yang JJ, Hill MD, Morrish WF et al (2002) Comparison of pre- and postcontrast 3D time-of-flight MR angiography for the evaluation of distal intracranial branch occlusions in acute ischemic stroke. AJNR Am J Neuroradiol 23:557–567

    PubMed  Google Scholar 

  33. Baldassarre D, Hamsten A, Veglia F et al (2012) Measurements of carotid intima-media thickness and of interadventitia common carotid diameter improve prediction of cardiovascular events: results of the IMPROVE (Carotid Intima Media Thickness [IMT] and IMT-Progression as Predictors of Vascular Events in a High Risk European Population) study. J Am Coll Cardiol 60:1489–1499

    Article  PubMed  Google Scholar 

  34. Lehrke S, Egenlauf B, Steen H et al (2009) Prediction of coronary artery disease by a systemic atherosclerosis score index derived from whole-body MR angiography. J Cardiovasc Magn Reson 11:36

    Article  PubMed  PubMed Central  Google Scholar 

  35. Yang Y, Zhu XR, Xu QG, Metcalfe H, Wang ZC, Yang JK (2012) Magnetic resonance imaging retinal oximetry: a quantitative physiological biomarker for early diabetic retinopathy? Diabet Med 29:501–505

    Article  CAS  PubMed  Google Scholar 

  36. Trick GL, Edwards PA, Desai U, Morton PE, Latif Z, Berkowitz BA (2008) MRI retinovascular studies in humans: research in patients with diabetes. NMR Biomed 21:1003–1012

    Article  PubMed  Google Scholar 

  37. Gaudiano C, Clementi V, Busato F et al (2013) Diffusion tensor imaging and tractography of the kidneys: assessment of chronic parenchymal diseases. Eur Radiol 23:1678–1685

    Article  PubMed  Google Scholar 

  38. Lu L, Sedor JR, Gulani V et al (2011) Use of diffusion tensor MRI to identify early changes in diabetic nephropathy. Am J Nephrol 34:476–482

    Article  PubMed  PubMed Central  Google Scholar 

  39. Versluis B, Nelemans PJ, Wildberger JE, Schurink GW, Leiner T, Backes WH (2014) Magnetic resonance imaging-derived arterial peak flow in peripheral arterial disease: towards a standardized measurement. Eur J Vasc Endovasc Surg 48:185–192

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We gratefully thank the radiology technicians of the Ludwig Maximilian University, Munich, Germany for their support.

The scientific guarantor of this publication is Sabine Weckbach. The authors of this manuscript declare relationships with the following companies:

Fabian Bamberg: received payment for lectures including service on speakers bureaus from Bayer Healthcare and Siemens Healthcare;

Klaus Parhofer: received payment for lectures including service on speakers bureaus from Merck & Co, Inc, Takeda Pharmaceutical Company, Bayer AG and the Sanofi-Aventis Group; research/grant support from Merck & Co, Inc and was research consultant for Merck & Co, Inc;

Hans-Ulrich Kauczor: received payment for lectures including service on speakers bureaus from Boehringer Ingelheim GmbH, Siemens AG and Novartis AG; research/grant support from Boehringer Ingelheim GmbH, Siemens AG and Bayer AG;

Stefan O Schönberg: the Institute of Clinical Radiology and Nuclear Medicine Mannheim has research agreements with Siemens Healthcare Sector.

The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article:

Sabine Weckbach, Christopher L. Schlett, Elena Lochner, Hannes M. Findeisen

Contrast agent was provided free of charge by GE Healthcare (Milwaukee, WI, USA). The study was supported by the German Center for Cardiovascular Research (GCCR) and the Munich Heart Alliance. One of the authors has significant statistical expertise: Christopher L. Schlett. Institutional review board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Some study subjects or cohorts have been previously reported in Bamberg F, Parhofer KG, Lochner E, et al. Diabetes Mellitus: Long-term Prognostic Value of Whole-Body MR Imaging for the Occurrence of Cardiac and Cerebrovascular Events. Radiology. 2013 Dec;269(3):730-7, and Weckbach S, Findeisen HM, Schoenberg SO et al. Systemic cardiovascular complications in patients with long-standing diabetes: comprehensive assessment with whole-body MRI. Investigative Radiology 2009 Apr;44(4):242-50.

Methodology: prospective, diagnostic or prognostic study, performed at one institution.

Author information

Author notes

    Authors and Affiliations

    1. Department of Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany

      Robert C. Bertheau, Hans-Ulrich Kauczor, Sabine Weckbach & Christopher L. Schlett

    2. Department of Clinical Radiology, Ludwig Maximilians University, Klinikum Grosshadern, Munich, Germany

      Fabian Bamberg & Elena Lochner

    3. Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, Tuebingen, Germany

      Fabian Bamberg

    4. Department of Cardiology and Angiology, University Hospital Münster, Münster, Germany

      Hannes M. Findeisen

    5. Department of Internal Medicine II, Ludwig Maximilians University, Klinikum Grosshadern, Munich, Germany

      Klaus G. Parhofer

    6. Department of Clinical Radiology and Nuclear Medicine, University Medical Center Mannheim, Mannheim, Germany

      Stefan O. Schoenberg

    Authors
    1. Robert C. Bertheau
      View author publications

      You can also search for this author in PubMed Google Scholar

    2. Fabian Bamberg
      View author publications

      You can also search for this author in PubMed Google Scholar

    3. Elena Lochner
      View author publications

      You can also search for this author in PubMed Google Scholar

    4. Hannes M. Findeisen
      View author publications

      You can also search for this author in PubMed Google Scholar

    5. Klaus G. Parhofer
      View author publications

      You can also search for this author in PubMed Google Scholar

    6. Hans-Ulrich Kauczor
      View author publications

      You can also search for this author in PubMed Google Scholar

    7. Stefan O. Schoenberg
      View author publications

      You can also search for this author in PubMed Google Scholar

    8. Sabine Weckbach
      View author publications

      You can also search for this author in PubMed Google Scholar

    9. Christopher L. Schlett
      View author publications

      You can also search for this author in PubMed Google Scholar

    Corresponding author

    Correspondence to Fabian Bamberg.

    Additional information

    Sabine Weckbach and Christopher L. Schlett contributed equally to this work.

    Rights and permissions

    Reprints and permissions

    About this article

    Check for updates. Verify currency and authenticity via CrossMark

    Cite this article

    Bertheau, R.C., Bamberg, F., Lochner, E. et al. Whole-Body MR Imaging Including Angiography: Predicting Recurrent Events in Diabetics. Eur Radiol 26, 1420–1430 (2016). https://doi.org/10.1007/s00330-015-3936-4

    Download citation

    • Received:

    • Revised:

    • Accepted:

    • Published:

    • Issue Date:

    • DOI: https://doi.org/10.1007/s00330-015-3936-4

    Keywords

    Search

    Navigation