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Clinical Tools to Detect and Predict Individuals at Risk of Alzheimer’s

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Alzheimer’s Turning Point

Abstract

The first decade of the twenty-first century has seen a growing recognition that low blood flow to the brain (cerebral hypoperfusion) and abnormal hemodynamics of the aging brain are directly related to the development of cognitive deficits as precursors of Alzheimer’s.

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References

  1. Heinzel S, Metzger FG, Ehlis AC, Korell R, Alboji A, Haeussinger FB, Wurster I, Brockmann K, Suenkel U, Eschweiler GW, Maetzler W, Berg D, Fallgatter AJ. Age and vascular burden determinants of cortical hemodynamics underlying verbal fluency. PLoS ONE. 2015;10(9):e0138863.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Reed BR, Marchant NL, Jagust WJ, DeCarli CC, Mack W, Chui HC. Coronary risk correlates with cerebral amyloid deposition. Neurobiol Aging. 2012;33(9):1979–87.

    Article  CAS  PubMed  Google Scholar 

  3. Villeneuve S, Jagust WJ. Imaging vascular disease and amyloid in the aging brain: implications for treatment. J Prev Alzheimers Dis. 2015;2(1):64–70.

    PubMed  PubMed Central  Google Scholar 

  4. Mosconi L, Berti V, Glodzik L, Pupi A, De Santi S, de Leon MJ. Pre-clinical detection of Alzheimer’s disease using FDG-PET, with or without amyloid imaging. J Alzheimers Dis. 2010;20(3):843–54.

    PubMed  PubMed Central  Google Scholar 

  5. Chetelat G, Desgranges B, de la Sayette V, et al. Mild cognitive impairment: can FDG-PET predict who is to rapidly convert to Alzheimer’s disease? Neurology. 2003;60:1374–7.

    Article  CAS  PubMed  Google Scholar 

  6. Kogure D, Matsuda H, Ohnishi T. Longitudinal evaluation of earlier Alzheimer’s disease using brain perfusion SPECT. J Nucl Med. 2000;41:1155–62.

    Google Scholar 

  7. Mosconi L. Glucose metabolism in normal aging and Alzheimer’s disease: methodological and physiological considerations for PET studies. Clin Transl Imaging. 2013;1(4).

    Google Scholar 

  8. Joubert S, Gour N, Guedj E, Didic M, Guériot C, Koric L, Ranjeva JP, Felician O, Guye M, Ceccaldi M. Early-onset and late-onset Alzheimer’s disease are associated with distinct patterns of memory impairment. Cortex. 2015;17(74):217–32.

    Google Scholar 

  9. Alsop DC, Detre JA, Grossman M. Assessment of cerebral blood flow in Alzheimer’s disease by spin-labeled magnetic resonance imaging. Ann Neurol. 2000;47:93–100.

    Article  CAS  PubMed  Google Scholar 

  10. Johnson NA, Jahng GH, Weiner MW, et al. Pattern of cerebral hypoperfusion in Alzheimer disease and mild cognitive impairment measured with arterial spin-labeling MR imaging: initial experience. Radiology. 2005;234:851–9.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Xekardaki A, Rodriguez C, Montandon ML, Toma S, Tombeur E, Herrmann FR, Zekry D, Lovblad KO, Barkhof 615 F, Giannakopoulos P, Haller S. Arterial spin labeling may contribute to the prediction of cognitive deterioration in healthy elderly individuals. Radiology. 2014;274:490–9.

    Google Scholar 

  12. Chao LL, Buckley S, Kornak, J Schuff N. ASL perfusion MRI predicts cognitive decline and conversion from MCI to dementia. Alzheimer Dis Assoc Disord. 2010;24(1):19–27.

    Google Scholar 

  13. de la Torre JC, Mussivand T. Can disturbed brain microcirculation cause Alzheimer’ disease? Neurol Res. 1993;15:146–53.

    PubMed  Google Scholar 

  14. Gobel FL, Norstrom LA, Nelson RR, Jorgensen CR, Wang Y. The rate-pressure product as an index of myocardial oxygen consumption during exercise in patients with angina pectoris. Circulation. 1978;57(3):549–56.

    Google Scholar 

  15. Otto CM. Principles of echocardiographic image acquisition and doppler analysis. In: Otto CM, editor. Textbook of clinical echocardiography. 2nd ed. Philadelphia: WB Saunders; 2000. p. 1–29.

    Google Scholar 

  16. Hare JL, Jenkins C, Nakatani S, et al. Feasibility and clinical decision-making with 3D echocardiography in routine practice. Heart. 2008;94:440–5.

    Article  CAS  PubMed  Google Scholar 

  17. Ahmed SF, Syed F, Porembka D. Echocardiographic evaluation of hemodynamic parameters. Crit Care Med. 2007;35(suppl):S323–9.

    Article  PubMed  Google Scholar 

  18. Franklin KM, Aurigemma GP. Prognosis in diastolic heart failure. Prog Cardiovasc Dis. 2005;47:333–9.

    Article  PubMed  Google Scholar 

  19. de la Torre JC. Alzheimer disease prevalence can be lowered with non-invasive testing. J Alzheimer Dis. 2008;14:353–9.

    Google Scholar 

  20. Trojano L, Antonelli R, Acanfora D. Cognitive impairment: a key feature of congestive heart failure in the elderly. J Neurol. 2003;250:1456–63.

    Article  PubMed  Google Scholar 

  21. Zuccala G, Cattel C, Manes-Gravina E et al. Left ventricular dysfunction: a clue to cognitive impairment in older patients with heart failure. J Neurol Neurosurg Psychiatry. 1997;63:509–12.

    Google Scholar 

  22. Jefferson AL, Beiser AS, Himali JJ, Seshadri S, O’Donnell CJ, Manning WJ, Wolf PA, Au R, Benjamin EJ. Low cardiac index is associated with incident dementia and Alzheimer disease: the Framingham Heart Study. Circulation. 2015;131(15):1333–9.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lewis TJ, Trempe CL. The End of Alzheimer’s. 2014; pp 145–210.

    Google Scholar 

  24. Ascaso FJ, Cruz N, Modrego PJ, Lopez-Anton R, Santabárbara J, Pascual LF, Lobo A, Cristóbal JA. Retinal alterations in mild cognitive impairment and Alzheimer’s disease: an optical coherence tomography study. Neurol. 2014;261(8):1522–30.

    Google Scholar 

  25. Lesage SR, Mosley TH, Wong TY, Szklo M, Knopman D, Catellier DJ, Cole SR, Klein R, Coresh J, Coker LH, Sharrett AR. Retinal microvascular abnormalities and cognitive decline: the ARIC 14-year follow-up study. Neurology. 2009;15, 73(11):862–8.

    Google Scholar 

  26. Qiu C, Fratiglioni L. A major role for cardiovascular burden in age-related cognitive decline. Nat Rev Cardiol. 2015;12(5):267–77.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. University of Texas at Austin, Austin, TX, USA

    Jack C. de la Torre

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  1. Jack C. de la Torre
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Correspondence to Jack C. de la Torre .

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de la Torre, J.C. (2016). Clinical Tools to Detect and Predict Individuals at Risk of Alzheimer’s. In: Alzheimer’s Turning Point. Springer, Cham. https://doi.org/10.1007/978-3-319-34057-9_15

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  • DOI: https://doi.org/10.1007/978-3-319-34057-9_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-34056-2

  • Online ISBN: 978-3-319-34057-9

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