Neurosonological Markers Predict ing Cognitive Deterioration

Czech version

Authors: A. Tomek; B. Urbanová; H. Magerová; H. Marková; J. Paulasová Schwabová; P. Janský; T. Růžičková; I. Mokrišová; J. Laczó; M. Vyhnálek; J. Hort
Authors‘ workplace: 2. LF UK a FN Motol, Praha ; Neurologická klinika
Published in: Cesk Slov Neurol N 2017; 80/113(4): 409-417
Category: Original Paper
doi: https://doi.org/10.14735/amcsnn2017409

Overview

Introduction:
Vascular brain changes and risk factors play a role in development and progression of Alzheimer‘s disease (AD). The primary aim of our study was to determine the predictive value of neurosonological biomarkers of cerebral microvasculature – resistance index (RI) and breath-holding index (BHI) for the development AD dementia in the older non-demented population. The secondary aim was to compare RI and BHI with other vascular biomarkers.

Methods:
A prospective study with patients with mild cognitive impairment (MCI), subjective memory complaints (SCD) and AD dementia patients as controls. All subjects underwent a detailed neuropsychology examination, brain magnetic resonance imaging and transcranial colour-coded sonography, including the evaluation of BHI and RI in the middle cerebral artery (MCA).

Results:
One hundred and eighty-three patients were enrolled, of which 113 patients with a diagnosis of MCI (n = 38), SCD (n = 49) and AD (n = 26) were included in the analysis. During the follow-up period (mean 40 months), 23 (26.4%) patients converted to dementia. Patients in the conversion group had a significantly lower BHI for both hemispheres; there was no significant difference in the RI values. The ROC analysis showed the cut-off values of BHI = 0.50 for left and BHI = 0.57 for right MCA (Z-score BHI < 0) to be the best predictive factors for dementia conversion. The hazard ratio (HR) of AD conversion for Z-score BHI < 0 was 5.61 (95%CI 1.66– 18.97). The patients with conversion also had a significantly higher age, lower body mass index, higher frequency of ischaemic heart disease, APOE ε4 allele and more severe hippocampal atrophy and vascular white matter lesions.

Conclusion:
BHI measurement seems to be the most useful neurosonological marker of AD conversion. In our study, BHI = 0.50 for left MCA and BHI = 0.57 for right MCA show the best predictive value for conversion to AD dementia.

Key words:
Alzheimer‘s disease – dementia – older nondemented population – vascular risk factors – vascular changes – magnetic resonance imaging – neurosonology – breath-holding index – resistance index – vascular theory of Alzheimer‘s disease

The authors declare they have no potential conflicts of interest concerning drugs, products, or services used in the study.

The Editorial Board declares that the manuscript met the ICMJE “uniform requirements” for biomedical papers.

Chinese summary - 摘要

神经生物学标记预测认知衰退

介绍：

脑血管的变化和风险因素在阿尔茨海默病（AD）的发生和发展中起着重要的作用。本研究的主要目的是确定在老年非痴呆人群中，脑微血管阻力指数（RI）和屏气指数（BHI）作为神经生物学标记对 AD型痴呆发展的预测价值。此外，本研究还将RI和BHI与其他血管生物学标记指标进行比较。

方法：

研究包括轻度认知障碍（MCI），主观记忆障碍（SCD）和AD型痴呆患者。所有受试者均进行详细的神经心理学检查，脑磁共振成像和经颅彩色超声检查，以及评估大脑中动脉（MCA）的BHI和RI值。

结果：

本研究共纳入183例患者，其中有113例患者分别被诊断为MCI（38例），SCD（49例）和AD（26例）。在随访期间（平均40个月），有23例（26.4％）患者发展为痴呆。发展为痴呆的患者在大脑两个半球内BHI值显着降低; RI值没有显着变化。 ROC分析显示，左侧大脑中动脉BHI值为0.5、右侧为0.57时，是预测痴呆发展的最佳指标。发展为AD（Z分数BHI <0）的风险比（HR）为5.61（95％置信区间 1.66-18.97）。症状发展的患者具有如下特点：普遍年龄偏高、体重指数较低、缺血性心脏病发生率较高、伴有APOEε4基因、大脑海马萎缩更为严重、血管白质损伤更为严重。

结论：

BHI测量可能是老年痴呆发展最有价值的神经生物学标记。我们的研究表明，左侧大脑中动脉BHI = 0.50、右侧BHI = 0.57为预测老年痴呆转化的最佳参考值。

关键词：

老年痴呆症 - 老年痴呆症人群 - 血管危险因素 - 血管变化 - 磁共振成像 - 神经超声 - 呼吸指数 - 抵抗指数 - 阿尔茨海默病血管学

Sources

1. de la Torre J, Mussivand T. Can disturbed brain microcirculation cause Alzheimer’s disease? Neurol Res 1993;15(3):146– 53.

2. Snyder H, Corriveau R, Craft S, et al. Vascular contributions to cognitive impairment and dementia including Alzheimer’s disease. Alzheimers Dementia 2015;11(6):710– 7. doi: 10.1016/ j.jalz.2014.10.008.

3. Kume K, Hanyu H, Sato T, et al. Vascular risk factors are associated with faster decline of Alzheimer disease: a longitudinal spect study. J Neurol 2011;258(7):1295– 303. doi: 10.1007/ s00415-011-5927-y.

4. Li J, Wang YJ, Zhang M, et al. Vascular risk factors promote conversion from mild cognitive impairment to Alzheimer disease. Neurology 2011;76(17):1485– 91. doi: 10.1212/ WNL.0b013e318217e7a4.

5. Hort J, O’Brien JT, Gainotti G, et al. Efns guidelines for the diagnosis and management of Alzheimer’s disease. Eur J Neurol 2010;17(10):1236– 48. doi: 10.1111/ j.1468-1331.2010.03040.x.

6. Cao D, Lu H, Lewis TL, et al. Intake of sucrose-sweetened water induces insulin resistance and exacerbates memory deficits and amyloidosis in a transgenic mouse model of Alzheimer disease. J Biol Chem 2007;282(50):36275– 82.

7. Longstreth W, Manolio T, Arnold A, et al. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The cardiovascular health study. Stroke 1996;27(8):1274– 82.

8. Holland C, Smith E, Csapo I, et al. Spatial distribution of white-matter hyperintensities in Alzheimer disease, cerebral amyloid angiopathy, and healthy aging. Stroke 2008;39(4):1127– 33. doi: 10.1161/ STROKEAHA.107.497438.

9. Stenset V, Hofoss D, Johnsen L, et al. White matter lesion load increases the risk of low csf aβ42 in apolipoprotein e-4 carriers attending a memory clinic. J Neuroimaging 2011;21(2):e78– 82. doi: 10.1111/ j.1552-6569.2009.00444.x.

10. Schrag M, McAuley G, Pomakian J, et al. Correlation of hypointensities in susceptibility-weighted images to tissue histology in dementia patients with cerebral amyloid angiopathy: a postmortem mri study. Acta Neuropathol 2010;119(3):291– 302. doi: 10.1007/ s00401-009-0615-z.

11. Goos JDC, Kester MI, Barkhof F, et al. Patients with Alzheimer disease with multiple microbleeds: relation with cerebrospinal fluid biomarkers and cognition. Stroke 2009;40(11):3455– 60. doi: 10.1161/ STROKEAHA.109.558197.

12. Tomek A, Urbanova B, Hort J. Utility of transcranial ultrasound in predicting Alzheimer‘s disease risk. J Alzheimers Dis 2014;42(Suppl 4):S365– 74. doi: 10.3233/ JAD-141803.

13. Stefani A, Sancesario G, Pierantozzi M, et al. CSF biomarkers, impairment of cerebral hemodynamics and degree of cognitive decline in Alzheimer‘s and mixed dementia. J Neurol Sci 2009;283(1– 2):109– 15. doi: 10.1016/ j.jns.2009.02.343.

14. Hofman A, Ott A, Breteler M, et al. Atherosclerosis, apolipoprotein e, and prevalence of dementia and Alzheimer‘s disease in the rotterdam study. Lancet 1997;349(9046):151– 4.

15. Vicenzini E, Ricciardi M, Altieri M, et al. Cerebrovascular reactivity in degenerative and vascular dementia: a transcranial doppler study. Eur Neurol 2007;58(2):84– 9.

16. Maalikjy Akkawi N, Borroni B, Agosti C, et al. Volume reduction in cerebral blood flow in patients with Alzheimer‘s disease: a sonographic study. Dementi Geriatr Cogn Disord 2003;16(3):163– 9.

17. Purandare N, Burns A. Cerebral emboli in the genesis of dementia. J Neurol Sci 2009;283(1– 2):17– 20. doi: 10.1016/ j.jns.2009.02.306.

18. Petersen R. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004;256(3):183– 94.

19. Gauthier S, Reisberg B, Zaudig M, et al. Mild cognitive impairment. Lancet 2006;367(9518):1262– 70.

20. Petersen R, Roberts R, Knopman D, et al. Prevalence of mild cognitive impairment is higher in men. The mayo clinic study of aging. Neurology 2010;75(10):889– 97. doi: 10.1212/ WNL.0b013e3181f11d85.

21. Dubois B, Feldman H, Jacova C, et al. Research criteria for the diagnosis of Alzheimer‘s disease: revising the nincds-adrda criteria. Lancet Neurol 2007;6(8):734– 46.

22. Brinkmann BH, Jones DT, Stead M, et al. Statistical parametric mapping demonstrates asymmetric uptake with tc-99m ecd and tc- 99m hmpao spect in normal brain. J Cereb Blood Flow Metab 2011;32(1):190– 8. doi: 10.1038/ jcbfm.2011.123.

23. Markus H. Estimation of cerebrovascular reactivity using transcranial doppler, including the use of breath-holding as the vasodilatory stimulus. Stroke 1992;23(5):668– 73.

24. Fazekas F, Chawluk J, Alavi A, et al. MR signal abnormalities at 1.5 t in Alzheimer’s dementia and normal aging. AJR Am J Roenthenol 1987;149(2):351– 6.

25. Scheltens P, Leys D, Barkhof F, et al. Atrophy of medial temporal lobes on MRI in „probable“ Alzheimer’s disease and normal ageing: diagnostic value and neuropsychological correlates. J Neurol Neurosurg Psychiatry 1992;55(10):967– 72.

26. Kadlecová A, Vyhnálek M, Laczó J, et al. Interrater variabilita v hodnocení míry atrofie hipokampů pomocí scheltensovy škály. Cesk Slov Neurol N 2013;76/ 109(5):603– 7.

27. Viticchi G, Falsetti L, Vernieri F, et al. Vascular predictors of cognitive decline in patients with mild cognitive impairment. Neurobiol Aging 2012;33(6):1121.e1– 9. doi: 10.1016/ j.neurobiolaging.2011.11.027.

28. Van Laere KJ, Dierckx RA. Brain perfusion spect: age- and sex-related effects correlated with voxel-based morphometric findings in healthy adults1. Radiology 2001;221(3):810– 7.

29. Stefani A, Sancesario G, Pierantozzi M, et al. CSF biomarkers, impairment of cerebral hemodynamics and degree of cognitive decline in Alzheimer‘s and mixed dementia. J Neurol Sci 2009;283(1– 2):109– 15. doi: 10.1016/ j.jns.2009.02.343.

30. Bär KJ, Boettger MK, Seidler N, et al. Influence of galantamine on vasomotor reactivity in Alzheimer’s disease and vascular dementia due to cerebral microangiopathy. Stroke 2007;38(12):3186– 92.

31. Demarin V, Kes VB, Morović S, et al. Evaluation of aging vs dementia by means of neurosonology. J Neurol Sci 2009;283(1– 2):9– 12. doi: 10.1016/ j.jns.2009.02.006.

32. Pimentel-Coelho PM, Rivest S. The early contribution of cerebrovascular factors to the pathogenesis of Alzheimer’s disease. Eur J Neurosci 2012;35(12):1917– 37. doi: 10.1111/ j.1460-9568.2012.08126.x.

33. Weller RO, Subash M, Preston SD, et al. Perivascular drainage of amyloid-β peptides from the brain and its failure in cerebral amyloid angiopathy and Alzheimer‘s disease. Brain Pathology 2007;18(2):253– 66. doi: 10.1111/ j.1750-3639.2008.00133.x.