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当代阿耳茨海默病研究

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ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

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Research Article

与正常衰老相比，阿尔茨海默病（早发和晚发）的局部脑血流、脑葡萄糖摄取和 Aβ-淀粉样蛋白沉积的连通性和模式

卷 18, 期 8, 2021

发表于: 15 November, 2021

页: [646 - 655] 页: 10

弟呕挨: 10.2174/1567205018666211116095035

价格: $65

摘要

目的：本研究的目的是调查阿尔茨海默病早期 (EOAD) 和晚期 (LOAD) 发病的差异，以及葡萄糖摄取、局部脑血流量 (R1)、淀粉样蛋白沉积和功能性脑连接。介于正常年轻 (YC) 和旧控件 (OC) 之间。 方法：该研究包括 22 名 YC（37 ± 5 岁）、22 名 OC（73 ± 5.9 岁）、18 名 EOAD 患者（63 ± 9.5 岁）和 18 名 LOAD 患者（70.6 ± 7.1 岁）。患者接受了FDG和PIB PET/CT。 R1 图像是从动态 PIB 采集的分区分析中获得的。通过体素和基于 VOI 的方法分析图像。从 R1 和葡萄糖摄取图像中研究了功能连接性。 结果：与 YC 相比，OC 的 R1 和葡萄糖摄取显着减少，主要在背外侧和内侧额叶皮层。 EOAD 和 LOAD 对比 OC 显示后顶叶皮质、楔前叶和后扣带回的 R1 和葡萄糖摄取减少。 EOAD 与 LOAD 相比，枕叶和顶叶皮质的葡萄糖摄取和 R1 减少，而额叶和颞叶皮质的葡萄糖摄取增加。 LOAD 与 EOAD 相比，额叶皮质的淀粉样蛋白沉积轻度增加。 YC 在 R1 中呈现出比 OC 更高的连接性，但考虑到葡萄糖摄取，连接性较低。此外，与葡萄糖摄取比 R1 更明显的对照相比，EOAD 和 LOAD 显示连接性降低。 结论：我们的研究结果表明，各组之间淀粉样蛋白沉积和功能成像的差异以及 R1 功能连接和每种临床条件下葡萄糖摄取的差异模式。这些发现为 AD 的病理生理过程提供了新的见解，并可能对患者的诊断评估产生影响。

关键词: 阿尔茨海默病、葡萄糖摄取、局部脑血流、淀粉样蛋白沉积、功能性脑连接、EOAD、负载。

« Previous Next »
[1]
Isik AT. Late onset Alzheimer’s disease in older people. Clin Interv Aging 2010; 5: 307-11.
[http://dx.doi.org/10.2147/CIA.S11718] [PMID: 21103401]
[2]
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 263-9.
[http://dx.doi.org/10.1016/j.jalz.2011.03.005] [PMID: 21514250]
[3]
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 1984; 34(7): 939-44.
[http://dx.doi.org/10.1212/WNL.34.7.939] [PMID: 6610841]
[4]
Kalpouzos G, Eustache F, de la Sayette V, Viader F, Chételat G, Desgranges B. Working memory and FDG-PET dissociate early and late onset Alzheimer disease patients. J Neurol 2005; 252(5): 548-58.
[http://dx.doi.org/10.1007/s00415-005-0685-3] [PMID: 15726251]
[5]
Kim EJ, Cho SS, Jeong Y, et al. Glucose metabolism in early onset versus late onset Alzheimer’s disease: an SPM analysis of 120 patients. Brain 2005; 128(Pt 8): 1790-801.
[http://dx.doi.org/10.1093/brain/awh539] [PMID: 15888536]
[6]
Rabinovici GD, Furst AJ, Alkalay A, et al. Increased metabolic vulnerability in early-onset Alzheimer’s disease is not related to amyloid burden. Brain 2010; 133(Pt 2): 512-28.
[http://dx.doi.org/10.1093/brain/awp326] [PMID: 20080878]
[7]
Arbizu J, García-Ribas G, Carrió I, Garrastachu P, Martínez-Lage P, Molinuevo JL. Recomendaciones para la utilización de biomarcadores de imagen PET en el proceso diagnóstico de las enfermedades neurodegenerativas que cursan con demencia: Documento de consenso SEMNIM y SEN. Rev Esp Med Nucl Imagen Mol 2015; 34(5): 303-13.
[http://dx.doi.org/10.1016/j.remn.2015.03.002] [PMID: 26099942]
[8]
Frisoni GB, Bocchetta M, Chételat G, et al. Imaging markers for Alzheimer disease: which vs how. Neurology 2013; 81(5): 487-500.
[http://dx.doi.org/10.1212/WNL.0b013e31829d86e8] [PMID: 23897875]
[9]
Johnson KA, Minoshima S, Bohnen NI, et al. Appropriate use criteria for amyloid PET: a report of the amyloid imaging task force, the society of nuclear medicine and molecular imaging, and the Alzheimer’s association. Alzheimers Dement 2013; 9(1): e-1-e-16.
[http://dx.doi.org/10.1016/j.jalz.2013.01.002] [PMID: 23360977]
[10]
Schöll M, Damián A, Engler H. Fluorodeoxyglucose PET in neurology and psychiatry. PET Clin 2014; 9(4): 371-390, v.
[http://dx.doi.org/10.1016/j.cpet.2014.07.005] [PMID: 26050943]
[11]
Jack CR Jr, Bennett DA, Blennow K, et al. NIA-AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14(4): 535-62.
[http://dx.doi.org/10.1016/j.jalz.2018.02.018] [PMID: 29653606]
[12]
Blomquist G, Engler H, Nordberg A, et al. Unidirectional influx and net accumulation of PIB. Open Neuroimaging J 2008; 2: 114-25.
[http://dx.doi.org/10.2174/1874440000802010114] [PMID: 19526073]
[13]
Forsberg A, Engler H, Blomquist G, Långström B, Nordberg A. The use of PIB-PET as a dual pathological and functional biomarker in AD. Biochim Biophys Acta 2012; 1822(3): 380-5.
[http://dx.doi.org/10.1016/j.bbadis.2011.11.006] [PMID: 22115832]
[14]
Meyer PT, Hellwig S, Amtage F, et al. Dual-biomarker imaging of regional cerebral amyloid load and neuronal activity in dementia with PET and 11C-labeled Pittsburgh compound B. J Nucl Med 2011; 52(3): 393-400.
[http://dx.doi.org/10.2967/jnumed.110.083683] [PMID: 21321269]
[15]
Sojkova J, Goh J, Bilgel M, et al. Voxelwise relationships between distribution volume ratio and cerebral blood flow: Implications for analysis of β-amyloid images. Jorunal Nucl Med 2015; 56(7): 1042-7.
[http://dx.doi.org/10.2967/jnumed.114.151480] [PMID: 25977462]
[16]
Chen YJ, Rosario BL, Mowrey W, et al. Relative 11C-PiB delivery as a proxy of relative CBF: Quantitative evaluation using single-session 15O-water and 11C-PiB PET. J Nucl Med 2015; 56(8): 1199-205.
[http://dx.doi.org/10.2967/jnumed.114.152405] [PMID: 26045309]
[17]
Peretti DE, Vállez García D, Reesink FE, et al. Diagnostic performance of regional cerebral blood flow images derived from dynamic PIB scans in Alzheimer’s disease. EJNMMI Res 2019; 9(1): 59.
[http://dx.doi.org/10.1186/s13550-019-0528-3] [PMID: 31273465]
[18]
Tiepolt S, Hesse S, Patt M, et al. Early [(18)F]florbetaben and [(11)C]PiB PET images are a surrogate biomarker of neuronal injury in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2016; 43(9): 1700-9.
[http://dx.doi.org/10.1007/s00259-016-3353-1] [PMID: 27026271]
[19]
Rostomian AH, Madison C, Rabinovici GD, Jagust WJ. Early 11C-PIB frames and 18F-FDG PET measures are comparable: a study validated in a cohort of AD and FTLD patients. J Nucl Med 2011; 52(2): 173-9.
[http://dx.doi.org/10.2967/jnumed.110.082057] [PMID: 21233181]
[20]
Oliveira FPM, Moreira AP, de Mendonça A, et al. Can 11C-PiB-PET relative delivery R1 or 11C-PiB-PET perfusion replace 18F-FDG-PET in the assessment of brain neurodegeneration? J Alzheimers Dis 2018; 65(1): 89-97.
[http://dx.doi.org/10.3233/JAD-180274] [PMID: 30056421]
[21]
Lammertsma AA, Hume SP. Simplified reference tissue model for PET receptor studies. Neuroimage 1996; 4(3 Pt 1): 153-8.
[http://dx.doi.org/10.1006/nimg.1996.0066] [PMID: 9345505]
[22]
Marchal G, Rioux P, Petit-Taboué MC, et al. Regional cerebral oxygen consumption, blood flow, and blood volume in healthy human aging. Arch Neurol 1992; 49(10): 1013-20.
[http://dx.doi.org/10.1001/archneur.1992.00530340029014] [PMID: 1417508]
[23]
Gunn RN, Lammertsma AA, Hume SP, Cunningham VJ. Parametric imaging of ligand-receptor binding in PET using a simplified reference region model. Neuroimage 1997; 6(4): 279-87.
[http://dx.doi.org/10.1006/nimg.1997.0303] [PMID: 9417971]
[24]
Matsuda H, Mizumura S, Nemoto K, et al. Automatic voxel-based morphometry of structural MRI by SPM8 plus diffeomorphic anatomic registration through exponentiated lie algebra improves the diagnosis of probable Alzheimer Disease. AJNR Am J Neuroradiol 2012; 33(6): 1109-14.
[http://dx.doi.org/10.3174/ajnr.A2935] [PMID: 22300935]
[25]
Della Rosa PA, Cerami C, Gallivanone F, et al. A standardized [18F]-FDG-PET template for spatial normalization in statistical parametric mapping of dementia. Neuroinformatics 2014; 12(4): 575-93.
[http://dx.doi.org/10.1007/s12021-014-9235-4] [PMID: 24952892]
[26]
Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007; 38(1): 95-113.
[http://dx.doi.org/10.1016/j.neuroimage.2007.07.007] [PMID: 17761438]
[27]
Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS One 2013; 8(7): e68910.
[http://dx.doi.org/10.1371/journal.pone.0068910] [PMID: 23861951]
[28]
Klein A, Tourville J. 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci 2012; 6(DEC): 171.
[http://dx.doi.org/10.3389/fnins.2012.00171] [PMID: 23227001]
[29]
Metter EJ, Riege WH, Kameyama M, Kuhl DE, Phelps ME. Cerebral metabolic relationships for selected brain regions in Alzheimer’s, Huntington’s, and Parkinson’s diseases. J Cereb Blood Flow Metab 1984; 4(4): 500-6.
[http://dx.doi.org/10.1038/jcbfm.1984.74] [PMID: 6238975]
[30]
Arnemann KL, Stöber F, Narayan S, Rabinovici GD, Jagust WJ. Metabolic brain networks in aging and preclinical Alzheimer’s disease. Neuroimage Clin 2017; 17: 987-99.
[http://dx.doi.org/10.1016/j.nicl.2017.12.037] [PMID: 29527500]
[31]
Kakimoto A, Ito S, Okada H, Nishizawa S, Minoshima S, Ouchi Y. Age-related sex-specific changes in brain metabolism and morphology. J Nucl Med 2016; 57(2): 221-5.
[http://dx.doi.org/10.2967/jnumed.115.166439] [PMID: 26609179]
[32]
Yoshizawa H, Gazes Y, Stern Y, Miyata Y, Uchiyama S. Characterizing the normative profile of 18F-FDG PET brain imaging: sex difference, aging effect, and cognitive reserve. Psychiatry Res Neuroimaging 2014; 221(1): 78-85.
[http://dx.doi.org/10.1016/j.pscychresns.2013.10.009] [PMID: 24262800]
[33]
Garraux G, Salmon E, Degueldre C, Lemaire C, Laureys S, Franck G. Comparison of impaired subcortico-frontal metabolic networks in normal aging, subcortico-frontal dementia, and cortical frontal dementia. Neuroimage 1999; 10(2): 149-62.
[http://dx.doi.org/10.1006/nimg.1999.0463] [PMID: 10417247]
[34]
Hsieh TC, Lin WY, Ding HJ, et al. Sex- and age-related differences in brain FDG metabolism of healthy adults: an SPM analysis. J Neuroimaging 2012; 22(1): 21-7.
[http://dx.doi.org/10.1111/j.1552-6569.2010.00543.x] [PMID: 21332873]
[35]
Kalpouzos G, Chételat G, Baron JC, et al. Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging. Neurobiol Aging 2009; 30(1): 112-24.
[http://dx.doi.org/10.1016/j.neurobiolaging.2007.05.019] [PMID: 17630048]
[36]
Kim IJ, Kim SJ, Kim YK. Age- and sex-associated changes in cerebral glucose metabolism in normal healthy subjects: statistical parametric mapping analysis of F-18 fluorodeoxyglucose brain positron emission tomography. Acta Radiol 2009; 50(10): 1169-74.
[http://dx.doi.org/10.3109/02841850903258058] [PMID: 19922315]
[37]
Moeller JR, Ishikawa T, Dhawan V, et al. The metabolic topography of normal aging. J Cereb Blood Flow Metab 1996; 16(3): 385-98.
[http://dx.doi.org/10.1097/00004647-199605000-00005] [PMID: 8621743]
[38]
Willis MW, Ketter TA, Kimbrell TA, et al. Age, sex and laterality effects on cerebral glucose metabolism in healthy adults. Psychiatry Res 2002; 114(1): 23-37.
[http://dx.doi.org/10.1016/S0925-4927(01)00126-3] [PMID: 11864807]
[39]
Yanase D, Matsunari I, Yajima K, et al. Brain FDG PET study of normal aging in Japanese: effect of atrophy correction. Eur J Nucl Med Mol Imaging 2005; 32(7): 794-805.
[http://dx.doi.org/10.1007/s00259-005-1767-2] [PMID: 15759148]
[40]
Zuendorf G, Kerrouche N, Herholz K, Baron JC. Efficient principal component analysis for multivariate 3D voxel-based mapping of brain functional imaging data sets as applied to FDG-PET and normal aging. Hum Brain Mapp 2003; 18(1): 13-21.
[http://dx.doi.org/10.1002/hbm.10069] [PMID: 12454908]
[41]
Jagust W, Reed B, Mungas D, Ellis W, Decarli C. What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia? Neurology 2007; 69(9): 871-7.
[http://dx.doi.org/10.1212/01.wnl.0000269790.05105.16] [PMID: 17724289]
[42]
Silverman DHS, Small GW, Chang CY, et al. Positron emission tomography in evaluation of dementia: regional brain metabolism and long-term outcome. JAMA 2001; 286(17): 2120-7.
[http://dx.doi.org/10.1001/jama.286.17.2120] [PMID: 11694153]
[43]
Hiura M, Nariai T, Ishii K, et al. Changes in cerebral blood flow during steady-state cycling exercise: a study using oxygen-15-labeled water with PET. J Cereb Blood Flow Metab 2014; 34(3): 389-96.
[http://dx.doi.org/10.1038/jcbfm.2013.220] [PMID: 24301294]
[44]
Kemp PM, Holmes C, Hoffmann SMA, et al. Alzheimer’s disease: differences in technetium-99m HMPAO SPECT scan findings between early onset and late onset dementia. J Neurol Neurosurg Psychiatry 2003; 74(6): 715-9.
[http://dx.doi.org/10.1136/jnnp.74.6.715] [PMID: 12754337]
[45]
Sakamoto S, Ishii K, Sasaki M, et al. Differences in cerebral metabolic impairment between early and late onset types of Alzheimer’s disease. J Neurol Sci 2002; 200(1-2): 27-32.
[http://dx.doi.org/10.1016/S0022-510X(02)00114-4] [PMID: 12127672]
[46]
Klunk WE, Engler H, Nordberg A, et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol 2004; 55(3): 306-19.
[http://dx.doi.org/10.1002/ana.20009] [PMID: 14991808]
[47]
Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Braak, H., & Braak, E. (1991). Neuropathological stageing of Alzheimer-related changes. Acta neuropathologica, 82(4), 239–259. Acta Neuropathol 1991; 82(4): 239-59.
[http://dx.doi.org/10.1007/BF00308809] [PMID: 1759558]
[48]
Cho H, Seo SW, Kim JH, et al. Amyloid deposition in early onset versus late onset Alzheimer’s disease. J Alzheimers Dis 2013; 35(4): 813-21.
[http://dx.doi.org/10.3233/JAD-121927] [PMID: 23507771]
[49]
Marshall GA, Fairbanks LA, Tekin S, Vinters HV, Cummings JL. Early-onset Alzheimer’s disease is associated with greater pathologic burden. J Geriatr Psychiatry Neurol 2007; 20(1): 29-33.
[http://dx.doi.org/10.1177/0891988706297086] [PMID: 17341768]
[50]
Ho GJ, Hansen LA, Alford MF, et al. Age at onset is associated with disease severity in Lewy body variant and Alzheimer’s disease. Neuroreport 2002; 13(14): 1825-8.
[http://dx.doi.org/10.1097/00001756-200210070-00028] [PMID: 12395133]
[51]
Ossenkoppele Rik, Jansen Willemijn J, Rabinovici Gil D, et al. Prevalence of amyloid PET positivity in dementia syndromes: a meta-analysis. JAMA 2015; 313(19): 1939-49.
[http://dx.doi.org/10.1001/jama.2015.4669] [PMID: 25988463]
[52]
Pasqualetti G, Harris R, Rinne J, et al. Does cerebral glucose metabolism and blood flow dissociate in early stages of Alzheimer’s Disease? Alzheimers Dement 2014; 10(4): 536.
[http://dx.doi.org/10.1016/j.jalz.2014.05.855]

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