Journal of Neurology

, Volume 266, Issue 1, pp 37–45 | Cite as

High-sensitivity cardiac troponin T and severity of cerebral white matter lesions in patients with acute ischemic stroke

  • Regina von RennenbergEmail author
  • Bob Siegerink
  • Ramanan Ganeshan
  • Kersten Villringer
  • Wolfram Doehner
  • Heinrich J. Audebert
  • Matthias Endres
  • Christian H. Nolte
  • Jan F. Scheitz
Original Communication



Cardiac troponin (hs-cTnT) is a sensitive marker of myocardial injury and has been linked to incident dementia. The underlying mechanism of that observation is still unknown. Given that severity of cerebral small vessel disease is a predictor of cognitive decline, we aimed to explore whether there is an association between hs-cTnT and severity of white matter lesions (WML) as a marker of cerebral small vessel disease in patients with ischemic stroke.


We analyzed consecutive acute ischemic stroke patients admitted to Charité-University Hospital, Berlin from 2011 to 2013. Severity of WML was graded on 3T-MRI using the age-related white matter severity score (ARWMS). Patients with hs-cTnT elevation suggestive of acute coronary syndrome (ACS) were excluded (hs-cTnT > 52 ng/l or dynamic change of hs-cTnT > 50%, ESC guideline). We performed unadjusted and adjusted quantile regression models to assess the association between increased hs-cTnT (dichotomized at the 99th percentile, 14 ng/l) and severity of WML.


A total of 860 patients was analyzed (median age 73 years, 44.8% female, median ARWMS 6). Patients with elevated hs-cTnT had more extensive WML than those without (median ARWMS 8 vs. 5, adjusted beta for 50th percentile 1.12, 95% CI 0.41–1.84). The association between WML and hs-cTnT elevation was strongest in patients with severe WML (adjusted beta 1.77, 95% CI 0.26–3.27 for 80th WML percentile).


Elevated hs-cTnT levels were associated with extent of WML in acute stroke patients. Further studies are needed to assess whether hs-cTnT can be used to identify stroke patients at risk for cognitive decline.


Cerebral white matter lesions Cardiac troponin Stroke Cognitive impairment 


Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study protocol conforms to the ethical guidelines of the 1975 Declaration of Helsinki. Since we analysed only anonymised patient data that were obtained during clinical routine no informed consent had to be provided and consultation of the institutional review board was not required.


  1. 1.
    Twerenbold R, Boeddinghaus J, Nestelberger T et al (2018) How to best use high-sensitivity cardiac troponin in patients with suspected myocardial infarction. Clin Biochem 53:143–155 CrossRefGoogle Scholar
  2. 2.
    Lee GR, Jhanji S, Tarrant H, James S, Pearse RM, Fitzgibbon M (2014) Peri-operative troponin monitoring using a prototype high-sensitivity cardiac troponin I (hs-cTnI) assay: comparisons with hs-cTnT and contemporary cTnI assays. Ann Clin Biochem 51(2):258–268CrossRefGoogle Scholar
  3. 3.
    Wijsman LW, de Craen AJ, Trompet S et al (2016) High-sensitivity cardiac troponin T is associated with cognitive decline in older adults at high cardiovascular risk. Eur J Prev Cardiol 23(13):1383–1392CrossRefGoogle Scholar
  4. 4.
    Schneider AL, Rawlings AM, Sharrett AR (2014) High-sensitivity cardiac troponin T and cognitive function and dementia risk: the atherosclerosis risk in communities study. Eur Heart J 35(27):1817–1824CrossRefGoogle Scholar
  5. 5.
    Debette S, Markus HS (2010) The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341:c3666CrossRefGoogle Scholar
  6. 6.
    de Groot M, Verhaaren BF, de Boer R et al (2013) Changes in normal-appearing white matter precede development of white matter lesions. Stroke 44(4):1037–1042CrossRefGoogle Scholar
  7. 7.
    Schmidt R, Ropele S, Enzinger C et al (2005) White matter lesion progression, brain atrophy, and cognitive decline: the Austrian stroke prevention study. Ann Neurol 58(4):610–616CrossRefGoogle Scholar
  8. 8.
    Knopman DS, Griswold ME, Lirette ST et al (2015) Vascular imaging abnormalities and cognition: mediation by cortical volume in nondemented individuals: atherosclerosis risk in communities-neurocognitive study. Stroke 46(2):433–440CrossRefGoogle Scholar
  9. 9.
    Kloppenborg RP, Nederkoorn PJ, Geerlings MI, van den Berg E (2014) Presence and progression of white matter hyperintensities and cognition: a meta-analysis. Neurology 82(23):2127–2138CrossRefGoogle Scholar
  10. 10.
    Schmidt R, Berghold A, Jokinen H et al (2012) White matter lesion progression in LADIS: frequency, clinical effects, and sample size calculations. Stroke 43(10):2643–2647CrossRefGoogle Scholar
  11. 11.
    Kliper E, Ben Assayag E, Tarrasch R et al (2014) Cognitive state following stroke: the predominant role of preexisting white matter lesions. PLoS One 9(8):e105461CrossRefGoogle Scholar
  12. 12.
    Leonards CO, Ipsen N, Malzahn U, Fiebach JB, Endres M, Ebinger M (2012) White matter lesion severity in mild acute ischemic stroke patients and functional outcome after 1 year. Stroke 43(11):3046–3051CrossRefGoogle Scholar
  13. 13.
    van Dijk EJ, Prins ND, Vrooman HA, Hofman A, Koudstaal PJ, Breteler MM (2008) Progression of cerebral small vessel disease in relation to risk factors and cognitive consequences: Rotterdam Scan study. Stroke 39(10):2712–2719CrossRefGoogle Scholar
  14. 14.
    Rozanski M, Richter TB, Grittner U, Endres M, Fiebach JB, Jungehulsing GJ (2014) Elevated levels of hemoglobin A1c are associated with cerebral white matter disease in patients with stroke. Stroke 45(4):1007–1011CrossRefGoogle Scholar
  15. 15.
    Savva GM, Stephan BC, Alzheimer’s Society Vascular Dementia Systematic Review Group (2010) Epidemiological studies of the effect of stroke on incident dementia: a systematic review. Stroke 41(1):e41–e46CrossRefGoogle Scholar
  16. 16.
    Kumar S, Selim MH, Caplan LR (2010) Medical complications after stroke. Lancet Neurol 9(1):105–118CrossRefGoogle Scholar
  17. 17.
    Scheitz JF, Mochmann HC, Nolte CH et al (2011) Troponin elevation in acute ischemic stroke (TRELAS)—protocol of a prospective observational trial. BMC Neurol 11:98CrossRefGoogle Scholar
  18. 18.
    Scheitz JF, Nolte CH, Laufs U, Endres M (2015) Application and interpretation of high-sensitivity cardiac troponin assays in patients with acute ischemic stroke. Stroke 46(4):1132–1140CrossRefGoogle Scholar
  19. 19.
    Wahlund LO, Barkhof F, Fazekas F et al (2001) A new rating scale for age-related white matter changes applicable to MRI and CT. Stroke 32(6):1318–1322CrossRefGoogle Scholar
  20. 20.
    Levey AS. Stevens LA. Schmid CH et al (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150:604–612CrossRefGoogle Scholar
  21. 21.
    Beyerlein A (2014) Quantile regression—opportunities and challenges from a user’s perspective. Am J Epidemiol 180(3):330–331CrossRefGoogle Scholar
  22. 22.
    Hilal S, Chai YL, van Veluw S et al (2017) Association between subclinical cardiac biomarkers and clinically manifest cardiac diseases with cortical cerebral microinfarcts. JAMA Neurol 74(4):403–410CrossRefGoogle Scholar
  23. 23.
    Dadu RT, Fornage M, Virani SS et al (2013) Cardiovascular biomarkers and subclinical brain disease in the atherosclerosis risk in communities study. Stroke 44(7):1803–1808CrossRefGoogle Scholar
  24. 24.
    Zonneveld HI, Ikram MA, Hofman A et al (2017) N-Terminal pro-B-type natriuretic peptide and subclinical brain damage in the general population. Radiology 283(1):205–214CrossRefGoogle Scholar
  25. 25.
    Russo C, Jin Z, Liu R et al (2013) LA volumes and reservoir function are associated with subclinical cerebrovascular disease: the CABL (Cardiovascular Abnormalities and Brain Lesions) study. JACC Cardiovasc Imag 6(3):313–323CrossRefGoogle Scholar
  26. 26.
    Russo C, Jin Z, Homma S et al (2013) Subclinical left ventricular dysfunction and silent cerebrovascular disease: the Cardiovascular Abnormalities and Brain Lesions (CABL) study. Circulation 128(10):1105CrossRefGoogle Scholar
  27. 27.
    Gouw AA, van der Flier WM, van Straaten EC et al (2008) Reliability and sensitivity of visual scales versus volumetry for evaluating white matter hyperintensity progression. Cerebrovasc Dis 25(3):247–253CrossRefGoogle Scholar
  28. 28.
    Nolte CH, Endres M (2014) The heart of the matter: a link between troponin and dementia? Eur Heart J 35(27):1779–1781CrossRefGoogle Scholar
  29. 29.
    Saunders JT, Nambi V, de Lemos JA et al (2011) Cardiac troponin T measured by a highly sensitive assay predicts coronary heart disease, heart failure, and mortality in the Atherosclerosis Risk in Communities Study. Circulation 123(13):1367–1376CrossRefGoogle Scholar
  30. 30.
    Kim BJ, Lee SH, Kim CK et al (2011) Advanced coronary artery calcification and cerebral small vessel diseases in the healthy elderly. Circ J 75(2):451–456CrossRefGoogle Scholar
  31. 31.
    Willeit K (2014) Atherosclerosis and atrial fibrillation—two closely intertwined diseases. Atherosclerosis 233:679–681CrossRefGoogle Scholar
  32. 32.
    Adameova D (2014) Role of microangiopathy in diabetic cardiomyopathy. Heart Fail Rev 19:25–33CrossRefGoogle Scholar
  33. 33.
    de la Torre JC (2012) Cardiovascular risk factors promote brain hypoperfusion leading to cognitive decline and dementia. Cardiovasc Psychiatry Neurol 2012:367516Google Scholar
  34. 34.
    Psaty BM, Manolio TA, Kuller LH et al (1997) Incidence of and risk factors for atrial fibrillation in older adults. Circulation 96(7):2455–2461CrossRefGoogle Scholar
  35. 35.
    Krause T, Werner K, Fiebach JB et al (2017) Stroke in right dorsal anterior insular cortex is related to myocardial injury. Ann Neurol 81(4):502–511CrossRefGoogle Scholar
  36. 36.
    Omland T, Røsjø H, Giannitsis E, Agewall S (2015) Troponins in heart failure. Clin Chim Acta 443:78–84CrossRefGoogle Scholar
  37. 37.
    Cannon JA, Moffitt P, Perez-Moreno AC et al (2017) Cognitive impairment and heart failure: systematic review and meta-analysis. J Card Fail 23(6):464–475CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Regina von Rennenberg
    • 1
    Email author
  • Bob Siegerink
    • 2
  • Ramanan Ganeshan
    • 1
    • 2
  • Kersten Villringer
    • 2
  • Wolfram Doehner
    • 2
    • 3
    • 4
  • Heinrich J. Audebert
    • 1
    • 2
  • Matthias Endres
    • 1
    • 2
    • 5
    • 6
    • 7
  • Christian H. Nolte
    • 1
    • 2
    • 7
  • Jan F. Scheitz
    • 1
    • 2
    • 5
    • 7
  1. 1.Klinik für Neurologie, Klinik und Hochschulambulanz für NeurologieCharité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
  2. 2.Center for Stroke ResearchCharité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
  3. 3.Berlin-Brandenburg Center for Regenerative TherapiesCharite Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
  4. 4.Medizinische Klinik mit Schwerpunkt KardiologieCharité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthBerlinGermany
  5. 5.German Center for Cardiovascular Research (Deutsches Zentrum für Herz-Kreislaufforschung), Partner Site BerlinCharité-Universitätsmedizin BerlinBerlinGermany
  6. 6.German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen), Partner Site BerlinBerlinGermany
  7. 7.Berlin Institute of HealthBerlinGermany

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