Accuracy of Biomarker Testing for Neuropathologically Defined Alzheimer Disease in Older Adults With Dementia: A Systematic Review

Fink, Howard A.; Linskens, Eric J.; Silverman, Pombie C.; McCarten, J. Riley; Hemmy, Laura S.; Ouellette, Jeannine M.; Greer, Nancy L.; Wilt, Timothy J.; Butler, Mary

doi:10.7326/m19-3888

Cited by 57Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsCerebrospinal fluid and hydrocephalus

Accuracy of Biomarker Testing for Neuropathologically Defined Alzheimer Disease in Older Adults With Dementia: A Systematic Review

Fink, Howard A., Linskens, Eric J., Silverman, Pombie C., McCarten, J. Riley, Hemmy, Laura S., Ouellette, Jeannine M., Greer, Nancy L., Wilt, Timothy J. +1 more·2020·Annals of Internal Medicine

DOI 10.7326/m19-3888

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Abstract

Background: Biomarker accuracy for Alzheimer disease (AD) is uncertain. Purpose: To summarize evidence on biomarker accuracy for classifying AD in older adults with dementia. Data Sources: Electronic bibliographic databases (searched from January 2012 to November 2019 for brain imaging and cerebrospinal fluid [CSF] tests and from inception to November 2019 for blood tests), ClinicalTrials.gov (to November 2019), and systematic review bibliographies. Study Selection: English-language studies evaluating the accuracy of brain imaging, CSF testing, or blood tests for distinguishing neuropathologically defined AD from non-AD among older adults with dementia. Studies with low or medium risk of bias were analyzed. Data Extraction: Two reviewers rated risk of bias. One extracted data; the other verified accuracy. Data Synthesis: Fifteen brain imaging studies and 9 CSF studies met analysis criteria. Median sensitivity and specificity, respectively, were 0.91 and 0.92 for amyloid positron emission tomography (PET), 0.89 and 0.74 for 18F-labeled fluorodeoxyglucose (18F-FDG) PET, 0.64 and 0.83 for single-photon emission computed tomography, and 0.91 and 0.89 for medial temporal lobe atrophy on magnetic resonance imaging (MRI). Individual CSF biomarkers and ratios had moderate sensitivity (range, 0.62 to 0.83) and specificity (range, 0.53 to 0.69); in the few direct comparisons, β-amyloid 42 (Aβ42)/phosphorylated tau (p-tau) ratio, total tau (t-tau)/Aβ42 ratio, and p-tau appeared more accurate than Aβ42 and t-tau alone. Single studies suggested that amyloid PET, 18F-FDG PET, and CSF test combinations may add accuracy to clinical evaluation. Limitations: Studies were small, biomarker cut points and neuropathologic AD were inconsistently defined, and methods with uncertain applicability to typical clinical settings were used. Few studies directly compared biomarkers, assessed test combinations, evaluated whether biomarkers improved classification accuracy when added to clinical evaluation, or reported harms. Conclusion: In methodologically heterogeneous studies of uncertain applicability to typical clinical settings, amyloid PET, 18F-FDG PET, and MRI were highly sensitive for neuropathologic AD. Amyloid PET, 18F-FDG PET, and CSF test combinations may add accuracy to clinical evaluation. Primary Funding Source: Agency for Healthcare Research and Quality. (PROSPERO: CRD42018117897)

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A systematic review by Fink et al. showed FDG-PET comparing AD to non-AD dementias has median sensitivity 0.89 and specificity 0.74.
“A recent systematic review by Fink et al. analysed the accuracy of FDG-PET comparing AD to non-AD dementias and showed a median sensitivity of 0.89 and specificity of 0.74 [111].”
From The use of neuroimaging techniques in the early and differential diagnosis of dementia (2023) · cites this paper as [111]

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
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915 · see citing works
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
McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.03.005 · Cited by 18,657 · see citing works
Neuropathological stageing of Alzheimer-related changes.
Braak, H., & Braak, E. (1991) · Acta Neuropathologica · doi:10.1007/bf00308809 · Cited by 16,123 · see citing works
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199 · see citing works
NIA‐AA Research Framework: Toward a biological definition of Alzheimer's disease
Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Dunn, B., Haeberlein, S. B., Holtzman, D. M., Jagust, W., Jessen, F., Karlawish, J., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.02.018 · Cited by 10,537 · see citing works
The Consortium to Establish a Registry for Alzheimer's Disease (CERAD): Part II. Standardization of the neuropathologic assessment of Alzheimer's disease
Mirra, S. S., Heyman, A., McKeel, D., Sumi, S. M., Crain, B. J., Brownlee, L. M., Vogel, F. S., Hughes, J. P., Belle, G. V., Berg, L., et al. (1991) · Neurology · doi:10.1212/wnl.41.4.479 · Cited by 4,961 · see citing works
Phases of Aβ-deposition in the human brain and its relevance for the development of AD
Thal, D. R., Rüb, U., Orantes, M., & Braak, H. (2002) · Neurology · doi:10.1212/wnl.58.12.1791 · Cited by 3,490 · see citing works
National Institute on Aging–Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease
Hyman, B. T., Phelps, C. H., Beach, T. G., Bigio, E. H., Cairns, N. J., Carrillo, M. C., Dickson, D. W., Duyckaerts, C., Frosch, M. P., Masliah, E., et al. (2012) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.10.007 · Cited by 3,092 · see citing works
National Institute on Aging–Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach
Montine, T. J., Phelps, C. H., Beach, T. G., Bigio, E. H., Cairns, N. J., Dickson, D. W., Duyckaerts, C., Frosch, M. P., Masliah, E., Mirra, S. S., et al. (2012) · Acta Neuropathologica · doi:10.1007/s00401-011-0910-3 · Cited by 2,992 · cited 1× · see citing works
“gov/ct2/show/NCT03901092), CSF, and blood biomarkers with autopsy-confirmed AD pathology on the basis of the most current consensus definitions (16, 20).”
Background
Prevalence of Dementia in the United States: The Aging, Demographics, and Memory Study
Plassman, B. L., Langa, K. M., Fisher, G. G., Heeringa, S. G., Weir, D. R., Ofstedal, M. B., Burke, J. R., Hurd, M. D., Potter, G. G., Rodgers, W. L., et al. (2007) · Neuroepidemiology · doi:10.1159/000109998 · Cited by 1,932 · see citing works
Atrophy of medial temporal lobes on MRI in "probable" Alzheimer's disease and normal ageing: diagnostic value and neuropsychological correlates.
Scheltens, P., Leys, D., Barkhof, F., Huglo, D. G., Weinstein, H. C., Vermersch, P., Kuiper, M. A., Steinling, M., Wolters, E. C., & Valk, J. (1992) · Journal of Neurology Neurosurgery & Psychiatry · doi:10.1136/jnnp.55.10.967 · Cited by 1,781 · see citing works
Practice parameter: Diagnosis of dementia (an evidence-based review): Report of the Quality Standards Subcommittee of the American Academy of Neurology
Knopman, D. S., DeKosky, S. T., Cummings, J. L., Chui, H., Corey–Bloom, J., Relkin, N., Small, G. W., Miller, B., & Stevens, J. C. (2001) · Neurology · doi:10.1212/wnl.56.9.1143 · Cited by 1,410 · see citing works
Neuropathologic diagnostic and nosologic criteria for frontotemporal lobar degeneration: consensus of the Consortium for Frontotemporal Lobar Degeneration
Cairns, N. J., Bigio, E. H., Mackenzie, I. R. A., Neumann, M., Lee, V. M.-Y., Hatanpaa, K. J., White, C. L., Schneider, J. A., Grinberg, L. T., Halliday, G., et al. (2007) · Acta Neuropathologica · doi:10.1007/s00401-007-0237-2 · Cited by 1,110 · see citing works
Nomenclature and nosology for neuropathologic subtypes of frontotemporal lobar degeneration: an update
Mackenzie, I. R. A., Neumann, M., Bigio, E. H., Cairns, N. J., Alafuzoff, I., Kril, J., Kovacs, G. G., Ghetti, B., Halliday, G., Holm, I. E., et al. (2010) · Acta Neuropathologica · doi:10.1007/s00401-009-0612-2 · Cited by 1,012 · see citing works
Biomarkers for Alzheimer's disease: current status and prospects for the future
Blennow, K., & Zetterberg, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12816 · Cited by 1,000 · see citing works
Consensus Recommendations for the Postmortem Diagnosis of Alzheimer’s Disease
(1997) · Neurobiology of Aging · doi:10.1016/s0197-4580(97)00057-2 · Cited by 981 · see citing works
Positron Emission Tomography in Evaluation of Dementia
Silverman, D. H. S., Small, G. W., Chang, C. Y., Lu, C. S., de Aburto, M. A. K., Chen, W., Czernin, J. G., Rapoport, S. I., Pietrini, P., Alexander, G. E., et al. (2001) · JAMA · doi:10.1001/jama.286.17.2120 · Cited by 939 · see citing works
A Comparison of the Prevalence of Dementia in the United States in 2000 and 2012
Langa, K. M., Larson, E. B., Crimmins, E. M., Faul, J. D., Levine, D. A., Kabeto, M. U., & Weir, D. R. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.6807 · Cited by 910 · see citing works
Cerebrospinal Fluid β-Amyloid 42 and Tau Proteins as Biomarkers of Alzheimer-Type Pathologic Changes in the Brain
Tapiola, T., Alafuzoff, I., Herukka, S.-K., Parkkinen, L., Hartikainen, P., Soininen, H., & Pirttilä, T. (2009) · Archives of Neurology · doi:10.1001/archneurol.2008.596 · Cited by 896 · see citing works
Cerebral PET with florbetapir compared with neuropathology at autopsy for detection of neuritic amyloid-β plaques: a prospective cohort study
Clark, C. M., Pontecorvo, M. J., Beach, T. G., Bedell, B. J., Coleman, R. E., Doraiswamy, P. M., Fleisher, A. S., Reiman, E. M., Sabbagh, M. N., Sadowsky, C. H., et al. (2012) · The Lancet Neurology · doi:10.1016/s1474-4422(12)70142-4 · Cited by 855 · see citing works
Association of Amyloid Positron Emission Tomography With Subsequent Change in Clinical Management Among Medicare Beneficiaries With Mild Cognitive Impairment or Dementia
Rabinovici, G. D., Gatsonis, C., Apgar, C., Chaudhary, K., Gareen, I., Hanna, L., Hendrix, J., Hillner, B. E., Olson, C., Lesman-Segev, O. H., et al. (2019) · JAMA · doi:10.1001/jama.2019.2000 · Cited by 674 · see citing works
Strategic roadmap for an early diagnosis of Alzheimer's disease based on biomarkers
Frisoni, G. B., Boccardi, M., Barkhof, F., Blennow, K., Cappa, S., Chiotis, K., Démonet, J.-F., Garibotto, V., Giannakopoulos, P., Gietl, A., et al. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30159-x · Cited by 626 · see citing works
FDG-PET improves accuracy in distinguishing frontotemporal dementia and Alzheimer's disease
Foster, N. L., Heidebrink, J. L., Clark, C. M., Jagust, W. J., Arnold, S. E., Barbas, N. R., DeCarli, C. S., Scott Turner, R., Koeppe, R. A., Higdon, R., et al. (2007) · Brain · doi:10.1093/brain/awm177 · Cited by 608 · see citing works
Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer's disease: Phase 3 study
Sabri, O., Sabbagh, M. N., Seibyl, J., Barthel, H., Akatsu, H., Ouchi, Y., Senda, K., Murayama, S., Ishii, K., Takao, M., et al. (2015) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2015.02.004 · Cited by 602 · see citing works
Existing Pittsburgh Compound-B positron emission tomography thresholds are too high: statistical and pathological evaluation
Villeneuve, S., Rabinovici, G. D., Cohn-Sheehy, B. I., Madison, C., Ayakta, N., Ghosh, P. M., La Joie, R., Arthur-Bentil, S. K., Vogel, J. W., Marks, S. M., et al. (2015) · Brain · doi:10.1093/brain/awv112 · Cited by 408 · see citing works
Medial temporal lobe atrophy on MRI differentiates Alzheimer's disease from dementia with Lewy bodies and vascular cognitive impairment: a prospective study with pathological verification of diagnosis
Burton, E. J., Barber, R., Mukaetova‐Ladinska, E. B., Robson, J., Perry, R. H., Jaros, E., Kalaria, R. N., & O'Brien, J. T. (2008) · Brain · doi:10.1093/brain/awn298 · Cited by 373 · see citing works
Cerebrospinal Fluid Tau and β-Amyloid: How Well Do These Biomarkers Reflect Autopsy-Confirmed Dementia Diagnoses?
Clark, C. M., Xie, S., Chittams, J., Ewbank, D., Peskind, E., Galasko, D., Morris, J. C., McKeel, D. W., Farlow, M., Weitlauf, S. L., et al. (2003) · Archives of Neurology · doi:10.1001/archneur.60.12.1696 · Cited by 357 · see citing works
Phase 3 Trial of Flutemetamol Labeled With Radioactive Fluorine 18 Imaging and Neuritic Plaque Density
Curtis, C., Gamez, J. E., Singh, U., Sadowsky, C. H., Villena, T., Sabbagh, M. N., Beach, T. G., Duara, R., Fleisher, A. S., Frey, K. A., et al. (2015) · JAMA Neurology · doi:10.1001/jamaneurol.2014.4144 · Cited by 332 · see citing works
Multisite study of the relationships between antemortem [ 11 C]PIB‐PET Centiloid values and postmortem measures of Alzheimer's disease neuropathology
La Joie, R., Ayakta, N., Seeley, W. W., Borys, E., Boxer, A. L., DeCarli, C., Doré, V., Grinberg, L. T., Huang, E., Hwang, J., et al. (2019) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.09.001 · Cited by 317 · see citing works
What does fluorodeoxyglucose PET imaging add to a clinical diagnosis of dementia?
Jagust, W. J., Reed, B., Mungas, D., Ellis, W. G., & DeCarli, C. S. (2007) · Neurology · doi:10.1212/01.wnl.0000269790.05105.16 · Cited by 299 · see citing works
Appropriate use criteria for lumbar puncture and cerebrospinal fluid testing in the diagnosis of Alzheimer's disease
Shaw, L. M., Arias, J., Blennow, K., Galasko, D., Molinuevo, J. L., Salloway, S., Schindler, S., Carrillo, M. C., Hendrix, J. A., Ross, A., et al. (2018) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2018.07.220 · Cited by 266 · see citing works
MRI visual rating scales in the diagnosis of dementia: evaluation in 184 post-mortem confirmed cases
Harper, L., Fumagalli, G. G., Barkhof, F., Scheltens, P., O’Brien, J. T., Bouwman, F., Burton, E. J., Rohrer, J. D., Fox, N. C., Ridgway, G. R., et al. (2016) · Brain · doi:10.1093/brain/aww005 · Cited by 264 · see citing works
SPECT perfusion imaging in the diagnosis of Alzheimer’s disease
Jagust, W., Thisted, R. A., Devous, M. D., Heertum, R. V., Mayberg, H. S., Jobst, K., Smith, A. D., & Borys, N. (2001) · Neurology · doi:10.1212/wnl.56.7.950 · Cited by 254 · see citing works
CSF biomarkers in frontotemporal lobar degeneration with known pathology
Bian, H., Van Swieten, J. C., Leight, S., Massimo, L., Wood, E., Forman, M., Moore, P., de Koning, I., Clark, C. M., Rosso, S., et al. (2008) · Neurology · doi:10.1212/01.wnl.0000311445.21321.fc · Cited by 216 · see citing works
The significance of medial temporal lobe atrophy: A postmortem MRI study in the very old
Barkhof, F., Polvikoski, T. M., van Straaten, E. C. W., Kalaria, R. N., Sulkava, R., Aronen, H. J., Niinistö, L., Rastas, S., Oinas, M., Scheltens, P., et al. (2007) · Neurology · doi:10.1212/01.wnl.0000277459.83543.99 · Cited by 204 · see citing works
CSF biomarkers cutoffs: The importance of coincident neuropathological diseases
Toledo, J. B., Brettschneider, J., Grossman, M., Arnold, S. E., Hu, W. T., Xie, S. X., Lee, V. M.-Y., Shaw, L. M., & Trojanowski, J. Q. (2012) · Acta Neuropathologica · doi:10.1007/s00401-012-0983-7 · Cited by 182 · see citing works
Improved discrimination of autopsy-confirmed Alzheimer's disease (AD) from non-AD dementias using CSF P-tau181P
Koopman, K., Bastard, N. L., Martin, J., Nagels, G., Deyn, P. P. D., & Engelborghs, S. (2009) · Neurochemistry International · doi:10.1016/j.neuint.2009.02.017 · Cited by 151 · see citing works
The diagnostic value of FDG and amyloid PET in Alzheimer’s disease—A systematic review
Rice, L., & Bisdas, S. (2017) · European Journal of Radiology · doi:10.1016/j.ejrad.2017.07.014 · Cited by 150 · see citing works
Comparison of Cerebrospinal Fluid Levels of Tau and Aβ 1-42 in Alzheimer Disease and Frontotemporal Degeneration Using 2 Analytical Platforms
Irwin, D. J., McMillan, C. T., Toledo, J. B., Arnold, S. E., Shaw, L. M., Wang, L.-S., Van Deerlin, V., Lee, V. M.-Y., Trojanowski, J. Q., & Grossman, M. (2012) · Archives of Neurology · doi:10.1001/archneurol.2012.26 · Cited by 116 · see citing works
Antemortem differential diagnosis of dementia pathology using structural MRI: Differential-STAND
Vemuri, P., Simon, G., Kantarci, K., Whitwell, J. L., Senjem, M. L., Przybelski, S. A., Gunter, J. L., Josephs, K. A., Knopman, D. S., Boeve, B. F., et al. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.073 · Cited by 108 · see citing works
Performance of [18F]flutemetamol amyloid imaging against the neuritic plaque component of CERAD and the current (2012) NIA‐AA recommendations for the neuropathologic diagnosis of Alzheimer's disease
Salloway, S., Gamez, J. E., Singh, U., Sadowsky, C. H., Villena, T., Sabbagh, M. N., Beach, T. G., Duara, R., Fleisher, A. S., Frey, K. A., et al. (2017) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2017.06.001 · Cited by 87 · see citing works
Cerebrospinal Fluid Aβ 1-40 Improves Differential Dementia Diagnosis in Patients with Intermediate P-tau 181P Levels
Slaets, S., Bastard, N. L., Martin, J., Sleegers, K., Broeckhoven, C. V., Deyn, P. P. D., & Engelborghs, S. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-130107 · Cited by 87 · see citing works
Amyloid Pathology Influences Aβ1-42 Cerebrospinal Fluid Levels in Dementia with Lewy Bodies
Slaets, S., Bastard, N. L., Theuns, J., Sleegers, K., Verstraeten, A., Leenheir, E. D., Luyckx, J., Martin, J., Broeckhoven, C. V., & Engelborghs, S. (2013) · Journal of Alzheimer s Disease · doi:10.3233/jad-122176 · Cited by 54 · see citing works
Cerebrospinal Fluid P-Tau181P: Biomarker for Improved Differential Dementia Diagnosis
Struyfs, H., Niemantsverdriet, E., Goossens, J., Fransén, E., Martin, J., Deyn, P. P. D., & Engelborghs, S. (2015) · Frontiers in Neurology · doi:10.3389/fneur.2015.00138 · Cited by 52 · see citing works
Contribution of Single Photon Emission Computed Tomography to the Differential Diagnosis of Dementia in a Memory Clinic
Rollin‐Sillaire, A., Bombois, S., Deramecourt, V., Emptaz, A., Salleron, J., Morvan, J., Maurage, C., Steinling, M., & Pasquier, F. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-111067 · Cited by 21 · see citing works
TC-99m HMPAO Brain Blood Flow Imaging in the Dementias with Histopathologic Correlation in 73 Patients
Bonte, F. J., Hynan, L. S., Harris, T. S., & White, C. L. (2010) · International Journal of Molecular Imaging · doi:10.1155/2011/409101 · Cited by 21 · see citing works
EEG ANALYSIS AND SPECT IMAGING IN ALZHEIMER’S DISEASE, VASCULAR DEMENTIA AND MILD COGNITIVE IMPAIRMENT
Radić, P., Petrović, R., Golubić, A. T., Bilić, E., & Borovečki, F. (2019) · Psychiatria Danubina · doi:10.24869/psyd.2019.111 · Cited by 16 · see citing works
Use of fuzzy edge single-photon emission computed tomography analysis in definite Alzheimer's disease - a retrospective study
Rusina, R., Kukal, J., Bělíček, T., Buncová, M., & Matěj, R. (2010) · BMC Medical Imaging · doi:10.1186/1471-2342-10-20 · Cited by 9 · see citing works

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The use of neuroimaging techniques in the early and differential diagnosis of dementia
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“A recent systematic review by Fink et al. analysed the accuracy of FDG-PET comparing AD to non-AD dementias and showed a median sensitivity of 0.89 and speci ﬁ city of 0.74 [111].”
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Grothe, M. J., Moscoso, A., Ashton, N. J., Karikari, T. K., Lantero‐Rodriguez, J., Snellman, A., Zetterberg, H., Blennow, K., Schöll, M., & Initiative, A. D. N. (2021) · Neurology · doi:10.1212/wnl.0000000000012513 · Cited by 96 · see citing works
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Park, H. Y., Suh, C. H., Heo, H., Shim, W. H., & Kim, S. J. (2022) · European Radiology · doi:10.1007/s00330-022-08838-9 · Cited by 35 · see citing works
Treatment of Canine Cognitive Dysfunction With Novel Butyrylcholinesterase Inhibitor
Pipan, M. Z., Mihevc, S. P., Štrbenc, M., Košak, U., Ilić, I. G., Trontelj, J., Žakelj, S., Gobec, S., Pavlin, D., & Majdič, G. (2020) · Scientific Reports · doi:10.1038/s41598-021-97404-2 · Cited by 30 · see citing works
Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment
Bergeron, M. F., Landset, S., Zhou, X., Ding, T., Khoshgoftaar, T. M., Zhao, F., Du, B., Chen, X., Wang, X., Zhong, L., et al. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-191340 · Cited by 24 · see citing works
Multimodal fusion diagnosis of the Alzheimer’s disease via lightweight CNN-LSTM model using magnetic resonance imaging (MRI)
Haq, E. U., Qin, Y., Zhou, Y., Xu, H., & Haq, R. U. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107545 · Cited by 24 · see citing works
Association between blood-based protein biomarkers and brain MRI in the Alzheimer’s disease continuum: a systematic review
Mitolo, M., Lombardi, G., Manca, R., Nacmias, B., & Venneri, A. (2024) · Journal of Neurology · doi:10.1007/s00415-024-12674-w · Cited by 20 · see citing works
Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer’s disease using a high-performance interpretable deep learning network
Park, H. Y., Shim, W. H., Suh, C. H., Heo, H., Oh, H., Kim, J., Sung, J., Lim, J., Lee, J., Kim, H. S., et al. (2023) · European Radiology · doi:10.1007/s00330-023-09708-8 · Cited by 20 · see citing works
Demystifying the Role of Neuroinflammatory Mediators as Biomarkers for Diagnosis, Prognosis, and Treatment of Alzheimer’s Disease: A Review
Doke, R. R., Lamkhade, G., Vinchurkar, K., & Singh, S. K. (2024) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.4c00457 · Cited by 23 · see citing works
Matching Clinical Diagnosis and Amyloid Biomarkers in Alzheimer’s Disease and Frontotemporal Dementia
Giacomucci, G., Mazzeo, S., Bagnoli, S., Casini, M., Padiglioni, S., Polito, C., Berti, V., Balestrini, J., Ferrari, C., Lombardi, G., et al. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11010047 · Cited by 16 · see citing works
Lattice 123 pattern for automated Alzheimer’s detection using EEG signal
Doğan, Ş., Barua, P. D., Bayğın, M., Tuncer, T., Tan, R., Ciaccio, E. J., Fujita, H., Devi, A., & Acharya, U. R. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-024-10104-1 · Cited by 15 · see citing works
Development and validation of a deep-broad ensemble model for early detection of Alzheimer's disease
Ma, P., Wang, J., Zhou, Z., Chen, C. L. P., Initiative, T. A. D. N., & Duan, J. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1137557 · Cited by 15 · see citing works
Magneto-assisted enzymatic DNA walkers for simultaneous electrochemical detection of amyloid-beta oligomers and Tau
Cheng, T., Yuan, H., Dong, Y., Xu, S., Wang, G., Zhao, M., Jiao, J., & Jiao, J. (2023) · Journal of Materials Chemistry B · doi:10.1039/d3tb01502e · Cited by 13 · see citing works
Current progress in aptamer-based sensors for the detection of protein biomarkers in neurodegenerative diseases
Cheng, T., Afshan, N., Jiao, J., & Jiao, J. (2024) · Biosensors and Bioelectronics X · doi:10.1016/j.biosx.2024.100528 · Cited by 12 · see citing works
Comparison of the clinical impact of 2-[18F]FDG-PET and cerebrospinal fluid biomarkers in patients suspected of Alzheimer’s disease
Gjerum, L., Andersen, B. B., Bruun, M., Simonsen, A. H., Henriksen, O. M., Law, I. G., Hasselbalch, S. G., & Frederiksen, K. S. (2021) · PLoS ONE · doi:10.1371/journal.pone.0248413 · Cited by 12 · see citing works
Biomarkers for Alzheimer’s disease across diverse biological domains: an umbrella review and evidence map
Kang, J., Son, Y., Yim, Y., Cho, H., Kim, J., Lark, A. R. S., Mir, F. A., Kim, H. J., Park, J., Lee, H., et al. (2025) · Journal of Advanced Research · doi:10.1016/j.jare.2025.07.022 · Cited by 10 · see citing works
Establishing a machine learning dementia progression prediction model with multiple integrated data
Huang, Y.-C., Liu, T.-C., & Lu, C.-J. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02411-2 · Cited by 8 · see citing works
Deep Learning-Based Framework for Predicting Mild Cognitive Impairment Progression in Neurology Using Longitudinal MRI
Zheng, G., Lü, Y., & Chen, H. (2025) · IEEE Access · doi:10.1109/access.2025.3562432 · Cited by 7 · see citing works
Ruling in, ruling out: the clinical utility of plasma biomarkers in diagnosis of Alzheimer’s disease
Wisch, J. K., & Ances, B. M. (2025) · Journal of Clinical Investigation · doi:10.1172/jci198725 · Cited by 7 · see citing works
Aβ42 treatment of the brain side reduced the level of flotillin from endothelial cells on the blood side via FGF-2 signaling in a blood–brain barrier model
Nakamura, T., Hashita, T., Chen, Y., Gao, Y., Sun, Y., Islam, S., Sato, H., Shibuya, Y., Zou, K., Matsunaga, T., et al. (2023) · Molecular Brain · doi:10.1186/s13041-023-01005-1 · Cited by 7 · see citing works
Blood-Based β-Amyloid and Phosphorylated Tau (p-Tau) Biomarkers in Alzheimer’s Disease: A Systematic Review of Their Diagnostic Potential
Dasari, M., Kurian, J. A., Gundraju, S., Raparthi, A., & Medapati, R. V. (2025) · Cureus · doi:10.7759/cureus.79881 · Cited by 6 · see citing works
On the prevention and treatment of Alzheimer’s disease: Control the promoters and look beyond the brain
Goldstein, M. R., & Cheslock, M. (2021) · Medical Hypotheses · doi:10.1016/j.mehy.2021.110645 · Cited by 8 · see citing works
Improvement in depressive symptoms after antiretroviral therapy initiation in people with HIV in Rakai, Uganda
Nakasujja, N., Vecchio, A., Saylor, D. R., Lofgren, S. M., Nakigozi, G. F., Boulware, D. R., Kisakye, A., Batte, J., Mayanja, R., Anok, A., et al. (2021) · Journal of NeuroVirology · doi:10.1007/s13365-020-00920-6 · Cited by 4 · see citing works
Systematic review of differentially abundant proteins in people with Lewy body dementia
Farr, L. M., Thorpe, N., Brinda, E. M., Al‐Balushi, N., Sohail, M., & Rajkumar, A. P. (2025) · Acta Neuropsychiatrica · doi:10.1017/neu.2025.15 · Cited by 5 · see citing works
Cognitive status and its risk factors in patients with hypertension and diabetes in a low‐income rural area of China: A cross‐sectional study
Guo, Y., Liang, R., Ren, J., Cheng, L., Wang, M., Chai, H., Cheng, X., Yang, Y., Sun, Y., Li, J., et al. (2023) · International Journal of Geriatric Psychiatry · doi:10.1002/gps.6010 · Cited by 4 · see citing works
Physicians and Alzheimer Dementia: Past, Present, and Future
Shah, R. C., & Bennett, D. A. (2020) · Annals of Internal Medicine · doi:10.7326/m20-1500 · Cited by 4 · see citing works
Limited utility of imaging-based (N) markers in predicting cerebral spinal fluid Alzheimer’s disease biomarker positivity in lecanemab-eligible mild cognitive impairment
Kuroda, T., Mori, Y., Shoji, D., Kubota, S., Noguchi‐Shinohara, M., Ono, K., & Murakami, H. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1735548 · Cited by 2 · see citing works
Diagnose It Yourself: Will There Be a Home Test Kit for Alzheimer’s disease?
Menne, F., & Schipke, C. G. (2021) · Neurodegenerative Disease Management · doi:10.2217/nmt-2020-0065 · Cited by 2 · see citing works
Treatment of Canine Cognitive Dysfunction With Novel Butyrylcholinesterase InhibitorPreprint
Pipan, M. Z., Mihevc, S. P., Štrbenc, M., Košak, U., Ilić, I. G., Trontelj, J., Žakelj, S., Gobec, S., Pavlin, D., & Majdič, G. (2020) · Research Square · doi:10.21203/rs.3.rs-118291/v1 · Cited by 2 · see citing works
Alzheimer's Disease Classification and Prediction Using Resnet Architecture
Meenal, H., SelvaKumar, K., & Reddy, C. K. K. (2025) · International Conference on Sustainability, Innovation & Technology (ICSIT) · doi:10.1109/icsit65336.2025.11295800 · Cited by 1 · see citing works
Advanced neuroimaging assessment of neurodegenerative dementia syndromes: A framework for comprehensive multimodal FDG-PET, MR-perfusion, and MR-diffusion analysis
Strobel, J., Kassubek, J., Thaiss, W. M., Anderl‐Straub, S., Uzelac, Ž., Jesse, S. M., Michelberger, L., Solbach, C., Beer, A. J., Bee, M., et al. (2026) · NeuroImage Clinical · doi:10.1016/j.nicl.2026.103964 · Cited by 1 · see citing works
Diagnostic Performance of the AlzoSure Predict Assay and Its Association With Alzheimer's Disease Biomarkers and Imaging Findings
Sanati, A. R., Nasiri, H., Khosravi, F., Ghahrieh, F., Asemanrafat, A., Bahaqiqat, A. K., Saberian, P., Bakhshi, R., Rastegari, F., Ahangar‐Sirous, R., et al. (2026) · Health Science Reports · doi:10.1002/hsr2.72914 · Cited by 1 · see citing works
Diffusion bridge networks simulate clinical-grade PET from MRI for dementia diagnostics: a retrospective cohort study
Li, Y., Ralph, B., Schmitz‐Koep, B., Grimmer, T., Ommer, B., Hedderich, D. M., Igor, Y., & Wachinger, C. (2026) · The Lancet Medical Imaging & Theranostics · doi:10.1016/j.lanmit.2026.100021 · Cited by 1 · see citing works
Diagnostic Evaluation of Dementia
Nous, A., Vyver, M. V., Wiels, W. A., & Engelborghs, S. (2021) · Springer eBooks · doi:10.1007/978-3-030-77904-7_3 · Cited by 1 · see citing works
Effectiveness of brain magnetic resonance imaging in the early diagnosis and characterization of dementias: A systematic review
Tornero, A. M. R., García-Carpintero, E., & de Salazar, B. R. O. (2024) · Medicina Clínica (English Edition) · doi:10.1016/j.medcle.2024.05.025 · Cited by 1 · see citing works
Gériatrie
Germann, D., Cochet, C., Apostolova, Y., Fratangelo, L., Gagliano, M., Nguyen, S., & Büla, C. (2021) · JMIG · doi:10.53738/revmed.2021.17.720-21.0033
Aβ42 treatment of the brain side reduced the level of flotillin from endothelial cells on the blood side via FGF-2 signaling in a blood-brain barrier modelPreprint
Nakamura, T., Hashita, T., Chen, Y., Gao, Y., Sun, Y., Islam, S., Sato, H., Shibuya, Y., Zou, K., Matsunaga, T., et al. (2022) · Research Square · doi:10.21203/rs.3.rs-2087678/v1
Efectividad de la resonancia magnética cerebral en el diagnóstico precoz y caracterización de las demencias: revisión sistemática
Tornero, A. M. R., García-Carpintero, E., & de Salazar, B. R. O. (2024) · Medicina Clínica · doi:10.1016/j.medcli.2024.05.028
Mapping knowledge landscapes and emerging trends of artificial intelligence in the early screening of cognitive impairment diseases
Liu, X., Li, S., Shen, S., Zeng, J., & Cao, S. (2025) · International Conference on Health Big Data · doi:10.1145/3733006.3733046
Advanced Neuroimaging Assessment of Neurodegenerative Dementia Syndromes: A Framework for Comprehensive Multimodal FDG-PET, MR-Perfusion, and Diffusion AnalysisPreprint
Strobel, J., Kassubek, J., Thaiss, W. M., Anderl‐Straub, S., Uzelac, Ž., Jesse, S., Michelberger, L., Solbach, C., Beer, A. J., Beer, M., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5351896
Additional file 3 of Aβ42 treatment of the brain side reduced the level of flotillin from endothelial cells on the blood side via FGF-2 signaling in a blood–brain barrier model
Nakamura, T., Hashita, T., Chen, Y., Gao, Y., Sun, Y., Islam, S., Sato, H., Shibuya, Y., Zou, K., Matsunaga, T., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26564322
Additional file 2 of Aβ42 treatment of the brain side reduced the level of flotillin from endothelial cells on the blood side via FGF-2 signaling in a blood–brain barrier model
Nakamura, T., Hashita, T., Chen, Y., Gao, Y., Sun, Y., Islam, S., Sato, H., Shibuya, Y., Zou, K., Matsunaga, T., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26564319
Additional file 4 of Aβ42 treatment of the brain side reduced the level of flotillin from endothelial cells on the blood side via FGF-2 signaling in a blood–brain barrier model
Nakamura, T., Hashita, T., Chen, Y., Gao, Y., Sun, Y., Islam, S., Sato, H., Shibuya, Y., Zou, K., Matsunaga, T., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26564325
Additional file 1 of Aβ42 treatment of the brain side reduced the level of flotillin from endothelial cells on the blood side via FGF-2 signaling in a blood–brain barrier model
Nakamura, T., Hashita, T., Chen, Y., Gao, Y., Sun, Y., Islam, S., Sato, H., Shibuya, Y., Zou, K., Matsunaga, T., et al. (2024) · Figshare · doi:10.6084/m9.figshare.26564316
What content and methods are effective in developing cognitive enhancement programs for older adults with mild cognitive impairment? A cross-sectional studyPreprint
Kim, Y., Kim, S., & Asami, K. (2025) · medRxiv · doi:10.64898/2025.12.20.25342600
What content and methods are effective in developing cognitive enhancement programs for older adults with mild cognitive impairment?: A mixed-methods study
Kim, Y., Kim, S., & Asami, K. (2026) · Educational Gerontology · doi:10.1080/03601277.2026.2712393
Noninvasive diagnosis of Alzheimer’s disease: a review of emerging biomarkers and multimodal approaches
Zhao, J., Nie, C., Gao, R., Hu, Z., & Lin, Y. (2026) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2026.1964029

Increasing precision of clinical diagnosis of Alzheimer’s disease using a combined algorithm incorporating clinical and novel biomarker data
Sabbagh, M. N., Lue, L.-F., Fayard, D., & Shi, J. (2017) · Neurology and Therapy · doi:10.1007/s40120-017-0069-5 · Cited by 107 · see citing works
Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease
Palmqvist, S., Zetterberg, H., Mattsson, N., Johansson, P., Initiative, F. T. A. D. N., Minthon, L., Blennow, K., Olsson, M., For the Swedish BioFINDER study group, Hansson, O., et al. (2015) · Neurology · doi:10.1212/wnl.0000000000001991 · Cited by 375 · see citing works
Blood‐based biomarkers for detecting Alzheimer's disease pathology in cognitively impaired individuals within specialized care settings: A systematic review and meta‐analysis
Pahlke, S. C., Kahale, L. A., Mahinrad, S., Sousa‐Pinto, B., Vieira, R. J., McAteer, M. B., Claessen, T., Contador, J., Hofmann, A., Kuchenbecker, L. A., et al. (2025) · Alzheimer s & Dementia · doi:10.1002/alz.70828 · Cited by 37 · see citing works
For the Swedish
Zetterberg, H., Mattsson, N., Minthon, L., Blennow, K., Phd, M. O., Study, B., & Hansson, O. (2016)
O1‐07‐01: Diagnostic comparison of regional amyloid PET and different CSF biomarker assays for identifying early Alzheimer's disease
Palmqvist, S., Zetterberg, H., Mattsson, N., Minthon, L., Blennow, K., Ohlsson, M., Hansson, O., & the Swedish BioFINDER Study Group and the Alzheimer's Disease Neuroimaging Initiative (2015) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2015.07.063 · Cited by 3 · see citing works

Authors: Fink, H. A., Linskens, E. J., Silverman, P. C., McCarten, J. R., Hemmy, L. S., Ouellette, J. M., Greer, N. L., Wilt, T. J., & Butler, M.
Affiliations: University of Minnesota (US)
Minneapolis VA Health Care System (US)
University of Iowa (US)
ORCID: Fink, Howard A.
Linskens, Eric J.
McCarten, J. Riley
Hemmy, Laura S.
Wilt, Timothy J.
Butler, Mary
Venue: Annals of Internal Medicine
Publisher: American College of Physicians
Published: 2020-05-19
Volume: 172
Issue: 10
Pages: 669-677
ISSN: 0003-4819 (ISSN-L)
1539-3704
DOI: 10.7326/m19-3888
OpenAlex: W3017957646
Cited by: 57
References: 48
Citations by year: 2026: 5 · 2025: 11 · 2024: 14 · 2023: 6 · 2022: 3 · 2021: 14 · 2020: 4
FWCI: 1.12
Type: journal article
Language: en
Open access: closed
Locations: Annals of Internal Medicine (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Cerebrospinal fluid and hydrocephalus
Keywords: Alzheimer's disease, biomarker accuracy, dementia, amyloid PET, 18F-FDG PET, SPECT, medial temporal lobe atrophy, cerebrospinal fluid biomarkers, blood biomarkers, tau protein, amyloid, diagnostic accuracy, older adults, combined biomarker detection, risk of bias, sensitivity and specificity, risk of overdiagnosis
MeSH terms: Aged, Alzheimer Disease, Brain, Dementia, Humans, Magnetic Resonance Imaging, Reproducibility of Results, Biomarkers

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Accuracy of Biomarker Testing for Neuropathologically Defined Alzheimer Disease in Older Adults With Dementia: A Systematic Review

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