AD诊断标准的范式转变:应用与挑战
Paradigm Shift in AD Diagnostic Criteria: Applications and Challenges
DOI: 10.12677/acm.2024.1482186, PDF,    国家自然科学基金支持
作者: 张春华:同济大学附属东方医院胶州医院神经内科,山东 胶州;巴茂文*:青岛大学医学院附属烟台毓璜顶医院神经内科,山东 烟台
关键词: 阿尔茨海默病诊断标准早期诊断生物标志物Alzheimer’s Disease Diagnostic Criteria Early Diagnosis Biomarkers
摘要: 阿尔茨海默病(AD)是最常见的痴呆类型,影响全球5500万老年人,给医疗卫生系统带来巨大负担。AD的典型病理包括淀粉样蛋白β (Aβ)和Tau蛋白的异常积累,这些变化可能在临床症状出现前数十年即已开始。近年来,AD诊断标准经历了显著演变,从依赖临床症状的传统方法,逐步过渡到生物标志物检测的现代方法。本文综述了AD诊断标准的历史演变,详细探讨了生物标志物在AD早期诊断中的应用及其面临的挑战。
Abstract: Alzheimer’s Disease (AD) is the most common type of dementia, affecting 55 million elderly people worldwide and imposing a significant burden on the healthcare system. The typical pathology of AD includes the abnormal accumulation of amyloid-beta (Aβ) and Tau proteins, which may start decades before the onset of clinical symptoms. In recent years, the diagnostic criteria for AD have undergone significant evolution, transitioning from traditional methods reliant on clinical symptoms to modern methods involving biomarker detection. This article reviews the historical evolution of AD diagnostic criteria, detailing the applications and challenges of biomarkers in the early diagnosis of AD.
文章引用:张春华, 巴茂文. AD诊断标准的范式转变:应用与挑战[J]. 临床医学进展, 2024, 14(8): 87-94. https://doi.org/10.12677/acm.2024.1482186

参考文献

[1] Brookmeyer, R., Corrada, M.M., Curriero, F.C. and Kawas, C. (2002) Survival Following a Diagnosis of Alzheimer Disease. Archives of Neurology, 59, 1764-1767. [Google Scholar] [CrossRef] [PubMed]
[2] Lashley, T., Schott, J.M., Weston, P., Murray, C.E., Wellington, H., Keshavan, A., et al. (2018) Molecular Biomarkers of Alzheimer’s Disease: Progress and Prospects. Disease Models & Mechanisms, 11, dmm031781. [Google Scholar] [CrossRef] [PubMed]
[3] McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R., Kawas, C.H., et al. (2011) 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 & Dementia, 7, 263-269. [Google Scholar] [CrossRef] [PubMed]
[4] Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., et al. (2011) The Diagnosis of Mild Cognitive Impairment Due to Alzheimer’s Disease: Recommendations from the National Institute on Aging‐Alzheimer’s Association Workgroups on Diagnostic Guidelines for Alzheimer’s Disease. Alzheimers & Dementia, 7, 270-279. [Google Scholar] [CrossRef] [PubMed]
[5] Dubois, B., Feldman, H.H., Jacova, C., Hampel, H., Molinuevo, J.L., Blennow, K., et al. (2014) Advancing Research Diagnostic Criteria for Alzheimer’s Disease: The IWG-2 Criteria. The Lancet Neurology, 13, 614-629. [Google Scholar] [CrossRef] [PubMed]
[6] Khachaturian, A.S., Hayden, K.M., Mielke, M.M., Tang, Y., Lutz, M.W., Gustafson, D.R., et al. (2018) Future Prospects and Challenges for Alzheimer’s Disease Drug Development in the Era of the NIA‐AA Research Framework. Alzheimers & Dementia, 14, 532-534. [Google Scholar] [CrossRef] [PubMed]
[7] Dubois, B., Villain, N., Frisoni, G.B., Rabinovici, G.D., Sabbagh, M., Cappa, S., et al. (2021) Clinical Diagnosis of Alzheimer’s Disease: Recommendations of the International Working Group. The Lancet Neurology, 20, 484-496. [Google Scholar] [CrossRef] [PubMed]
[8] Clark, C.M., Pontecorvo, M.J., Beach, T.G., Bedell, B.J., Coleman, R.E., Doraiswamy, P.M., et al. (2012) Cerebral PET with Florbetapir Compared with Neuropathology at Autopsy for Detection of Neuritic Amyloid-β Plaques: A Prospective Cohort Study. The Lancet Neurology, 11, 669-678. [Google Scholar] [CrossRef] [PubMed]
[9] Ossenkoppele, R., Rabinovici, G.D., Smith, R., Cho, H., Schöll, M., Strandberg, O., et al. (2018) Discriminative Accuracy of [18F]flortaucipir Positron Emission Tomography for Alzheimer Disease vs Other Neurodegenerative Disorders. JAMA, 320, 1151-1162. [Google Scholar] [CrossRef] [PubMed]
[10] Morbelli, S., Garibotto, V., Van De Giessen, E., Arbizu, J., Chételat, G., Drezgza, A., et al. (2015) A Cochrane Review on Brain [18F]FDG PET in Dementia: Limitations and Future Perspectives. European Journal of Nuclear Medicine and Molecular Imaging, 42, 1487-1491. [Google Scholar] [CrossRef] [PubMed]
[11] Mosconi, L., Tsui, W.H., Herholz, K., Pupi, A., Drzezga, A., Lucignani, G., et al. (2008) Multicenter Standardized 18F-FDG PET Diagnosis of Mild Cognitive Impairment, Alzheimer’s Disease, and Other Dementias. Journal of Nuclear Medicine, 49, 390-398. [Google Scholar] [CrossRef] [PubMed]
[12] Fleisher, A.S., Pontecorvo, M.J., Devous, M.D., Lu, M., Arora, A.K., Truocchio, S.P., et al. (2020) Positron Emission Tomography Imaging with [18F]flortaucipir and Postmortem Assessment of Alzheimer Disease Neuropathologic Changes. JAMA Neurology, 77, 829-839. [Google Scholar] [CrossRef] [PubMed]
[13] Jelistratova, I., Teipel, S.J. and Grothe, M.J. (2020) Longitudinal Validity of PET‐Based Staging of Regional Amyloid Deposition. Human Brain Mapping, 41, 4219-4231. [Google Scholar] [CrossRef] [PubMed]
[14] Zhang, Y., Chen, H., Li, R., Sterling, K. and Song, W. (2023) Amyloid β-Based Therapy for Alzheimer’s Disease: Challenges, Successes and Future. Signal Transduction and Targeted Therapy, 8, Article No. 248. [Google Scholar] [CrossRef] [PubMed]
[15] Thientunyakit, T., Thongpraparn, T., Sethanandha, C., Yamada, T., Kimura, Y., Muangpaisan, W., et al. (2021) Relationship between F-18 Florbetapir Uptake in Occipital Lobe and Neurocognitive Performance in Alzheimer’s Disease. Japanese Journal of Radiology, 39, 984-993. [Google Scholar] [CrossRef] [PubMed]
[16] Haddad, H.W., Malone, G.W., Comardelle, N.J., Degueure, A.E., Kaye, A.M. and Kaye, A.D. (2022) Aducanumab, a Novel Anti-Amyloid Monoclonal Antibody, for the Treatment of Alzheimer’s Disease: A Comprehensive Review. Health Psychology Research, 10, Article No. 31925. [Google Scholar] [CrossRef] [PubMed]
[17] Sander, K., Lashley, T., Gami, P., Gendron, T., Lythgoe, M.F., Rohrer, J.D., et al. (2016) Characterization of Tau Positron Emission Tomography Tracer [18F]AV‐1451 Binding to Postmortem Tissue in Alzheimer’s Disease, Primary Tauopathies, and Other Dementias. Alzheimers & Dementia, 12, 1116-1124. [Google Scholar] [CrossRef] [PubMed]
[18] Leuzy, A., Chiotis, K., Lemoine, L., Gillberg, P., Almkvist, O., Rodriguez-Vieitez, E., et al. (2019) Tau PET Imaging in Neurodegenerative Tauopathies—Still a Challenge. Molecular Psychiatry, 24, 1112-1134. [Google Scholar] [CrossRef] [PubMed]
[19] Aguero, C., Dhaynaut, M., Normandin, M.D., Amaral, A.C., Guehl, N.J., Neelamegam, R., et al. (2019) Autoradiography Validation of Novel Tau PET Tracer [F-18]-MK-6240 on Human Postmortem Brain Tissue. Acta Neuropathologica Communications, 7, Article No. 37. [Google Scholar] [CrossRef] [PubMed]
[20] Alafuzoff, I., Arzberger, T., Al‐Sarraj, S., Bodi, I., Bogdanovic, N., Braak, H., et al. (2008) Staging of Neurofibrillary Pathology in Alzheimer’s Disease: A Study of the Brainnet Europe Consortium. Brain Pathology, 18, 484-496. [Google Scholar] [CrossRef] [PubMed]
[21] Ossenkoppele, R., Smith, R., Mattsson-Carlgren, N., Groot, C., Leuzy, A., Strandberg, O., et al. (2021) Accuracy of Tau Positron Emission Tomography as a Prognostic Marker in Preclinical and Prodromal Alzheimer Disease: A Head-to-Head Comparison against Amyloid Positron Emission Tomography and Magnetic Resonance Imaging. JAMA Neurology, 78, 961-971. [Google Scholar] [CrossRef] [PubMed]
[22] Gordon, B.A., Blazey, T.M., Su, Y., Hari-Raj, A., Dincer, A., Flores, S., et al. (2018) Spatial Patterns of Neuroimaging Biomarker Change in Individuals from Families with Autosomal Dominant Alzheimer’s Disease: A Longitudinal Study. The Lancet Neurology, 17, 241-250. [Google Scholar] [CrossRef] [PubMed]
[23] Chen, F. (2023) PET Radiomics of White Matter, Can Be Employed as a Biomarker to Identify the Progression of Mild Cognitive Impairment to Alzheimer’s Disease. Academic Radiology, 30, 1885-1886. [Google Scholar] [CrossRef] [PubMed]
[24] Jiang, J., Wang, M., Alberts, I., Sun, X., Li, T., Rominger, A., et al. (2022) Using Radiomics-Based Modelling to Predict Individual Progression from Mild Cognitive Impairment to Alzheimer’s Disease. European Journal of Nuclear Medicine and Molecular Imaging, 49, 2163-2173. [Google Scholar] [CrossRef] [PubMed]
[25] Smailagic, N., Lafortune, L., Kelly, S., Hyde, C. and Brayne, C. (2018) 18F-FDG PET for Prediction of Conversion to Alzheimer’s Disease Dementia in People with Mild Cognitive Impairment: An Updated Systematic Review of Test Accuracy. Journal of Alzheimers Disease, 64, 1175-1194. [Google Scholar] [CrossRef] [PubMed]
[26] Zhou, H., Jiang, J., Lu, J., Wang, M., Zhang, H. and Zuo, C. (2019) Dual-Model Radiomic Biomarkers Predict Development of Mild Cognitive Impairment Progression to Alzheimer’s Disease. Frontiers in Neuroscience, 12, Article No. 1045. [Google Scholar] [CrossRef] [PubMed]
[27] Blazhenets, G., Ma, Y., Sörensen, A., Schiller, F., Rücker, G., Eidelberg, D., et al. (2019) Predictive Value of 18F-Florbetapir and 18F-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia. Journal of Nuclear Medicine, 61, 597-603. [Google Scholar] [CrossRef] [PubMed]
[28] Hansson, O., Lehmann, S., Otto, M., Zetterberg, H. and Lewczuk, P. (2019) Advantages and Disadvantages of the Use of the CSF Amyloid β (aβ) 42/40 Ratio in the Diagnosis of Alzheimer’s Disease. Alzheimers Research & Therapy, 11, Article No. 34. [Google Scholar] [CrossRef] [PubMed]
[29] Janelidze, S., Zetterberg, H., Mattsson, N., Palmqvist, S., Vanderstichele, H., Lindberg, O., et al. (2016) CSF Aβ42/aβ40 and Aβ42/aβ38 Ratios: Better Diagnostic Markers of Alzheimer Disease. Annals of Clinical and Translational Neurology, 3, 154-165. [Google Scholar] [CrossRef] [PubMed]
[30] Leuzy, A., Mattsson‐Carlgren, N., Palmqvist, S., Janelidze, S., Dage, J.L. and Hansson, O. (2021) Blood‐Based Biomarkers for Alzheimer’s Disease. EMBO Molecular Medicine, 14, e14408. [Google Scholar] [CrossRef] [PubMed]
[31] Li, Y., Schindler, S.E., Bollinger, J.G., Ovod, V., Mawuenyega, K.G., Weiner, M.W., et al. (2022) Validation of Plasma Amyloid-β 42/40 for Detecting Alzheimer Disease Amyloid Plaques. Neurology, 98, e688-e699. [Google Scholar] [CrossRef] [PubMed]
[32] Schindler, S.E., Bollinger, J.G., Ovod, V., Mawuenyega, K.G., Li, Y., Gordon, B.A., et al. (2019) High-Precision Plasma β-Amyloid 42/40 Predicts Current and Future Brain Amyloidosis. Neurology, 93, e1647-e1659. [Google Scholar] [CrossRef] [PubMed]
[33] Brickman, A.M., Manly, J.J., Honig, L.S., Sanchez, D., Reyes‐Dumeyer, D., Lantigua, R.A., et al. (2021) Plasma P‐tau181, P‐tau217, and Other Blood‐Based Alzheimer’s Disease Biomarkers in a Multi‐Ethnic, Community Study. Alzheimers & Dementia, 17, 1353-1364. [Google Scholar] [CrossRef] [PubMed]
[34] Ashton, N.J., Pascoal, T.A., Karikari, T.K., Benedet, A.L., Lantero-Rodriguez, J., Brinkmalm, G., et al. (2021) Plasma P-Tau231: A New Biomarker for Incipient Alzheimer’s Disease Pathology. Acta Neuropathologica, 141, 709-724. [Google Scholar] [CrossRef] [PubMed]
[35] Moscoso, A., Grothe, M.J., Ashton, N.J., Karikari, T.K., Rodriguez, J.L., Snellman, A., et al. (2020) Time Course of Phosphorylated-Tau181 in Blood across the Alzheimer’s Disease Spectrum. Brain, 144, 325-339. [Google Scholar] [CrossRef] [PubMed]
[36] Smirnov, D.S., Ashton, N.J., Blennow, K., Zetterberg, H., Simrén, J., Lantero-Rodriguez, J., et al. (2022) Plasma Biomarkers for Alzheimer’s Disease in Relation to Neuropathology and Cognitive Change. Acta Neuropathologica, 143, 487-503. [Google Scholar] [CrossRef] [PubMed]
[37] Jonaitis, E.M., Janelidze, S., Cody, K.A., Langhough, R., Du, L., Chin, N.A., et al. (2023) Plasma Phosphorylated Tau 217 in Preclinical Alzheimer’s Disease. Brain Communications, 5, fcad057. [Google Scholar] [CrossRef] [PubMed]
[38] Preische, O., Schultz, S.A., Apel, A., Kuhle, J., Kaeser, S.A., Barro, C., et al. (2019) Serum Neurofilament Dynamics Predicts Neurodegeneration and Clinical Progression in Presymptomatic Alzheimer’s Disease. Nature Medicine, 25, 277-283. [Google Scholar] [CrossRef] [PubMed]
[39] Mattsson, N., Cullen, N.C., Andreasson, U., Zetterberg, H. and Blennow, K. (2019) Association between Longitudinal Plasma Neurofilament Light and Neurodegeneration in Patients with Alzheimer Disease. JAMA Neurology, 76, 791-799. [Google Scholar] [CrossRef] [PubMed]
[40] Benedet, A.L., Milà-Alomà, M., Vrillon, A., Ashton, N.J., Pascoal, T.A., Lussier, F., et al. (2021) Differences between Plasma and Cerebrospinal Fluid Glial Fibrillary Acidic Protein Levels across the Alzheimer Disease Continuum. JAMA Neurology, 78, 1471-1483. [Google Scholar] [CrossRef] [PubMed]
[41] Zhong, L., Xu, Y., Zhuo, R., Wang, T., Wang, K., Huang, R., et al. (2019) Soluble TREM2 Ameliorates Pathological Phenotypes by Modulating Microglial Functions in an Alzheimer’s Disease Model. Nature Communications, 10, Article No. 1365. [Google Scholar] [CrossRef] [PubMed]
[42] Muszyński, P., Groblewska, M., Kulczyńska-Przybik, A., Kułakowska, A. and Mroczko, B. (2017) YKL-40 as a Potential Biomarker and a Possible Target in Therapeutic Strategies of Alzheimer’s Disease. Current Neuropharmacology, 15, 906-917. [Google Scholar] [CrossRef] [PubMed]
[43] Jack, C.R., Therneau, T.M., Lundt, E.S., Wiste, H.J., Mielke, M.M., Knopman, D.S., et al. (2022) Long-Term Associations between Amyloid Positron Emission Tomography, Sex, Apolipoprotein E and Incident Dementia and Mortality among Individuals without Dementia: Hazard Ratios and Absolute Risk. Brain Communications, 4, fcac017. [Google Scholar] [CrossRef] [PubMed]
[44] Chen, Y., Ma, X., Sundell, K., Alaka, K., Schuh, K., Raskin, J., et al. (2016) Quantile Regression to Characterize Solanezumab Effects in Alzheimer’s Disease Trials. Alzheimers & Dementia: Translational Research & Clinical Interventions, 2, 192-198. [Google Scholar] [CrossRef] [PubMed]
[45] Bucci, M., Chiotis, K. and Nordberg, A. (2021) Alzheimer’s Disease Profiled by Fluid and Imaging Markers: Tau PET Best Predicts Cognitive Decline. Molecular Psychiatry, 26, 5888-5898. [Google Scholar] [CrossRef] [PubMed]