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Trojanowski, John Q.

https://orcid.org/0000-0002-9239-8794

Mayo Clinic

University of Pennsylvania

Citations 120

Common Variants Near ZIC1 and ZIC4 in Autopsy‐Confirmed Multiple System Atrophy
Hopfner, Franziska , Tietz, Anja, Ruf, Viktoria , Ross, Owen A. , Koga, Shunsuke , Attems, Johannes , Beach, Thomas, Beller, Allison , Duyckaerts, Charles, Evsyukov, Valentin, Flanagan, Margaret E. , Frosch, Matthew P. , Gearing, Marla , Gelpí, Ellen , Gerpen, Jay A. van, Glass, Jonathan D. , Grinberg, Lea T. , Halliday, Glenda M. , Höllerhage, Matthias , Huitinga, Inge , Keene, Dirk, Kovács, Gábor G. , Lee, Edward B. , Xiong, Chengjie , Martı́, Marı́a José , Mackenzie, Ian R. , Mollenhauer, Brit , Neumann, Manuela , Newell, Kathy L. , Pendziwiat, Manuela , Molina‐Porcel, Laura , Rábano, Alberto , Rajput, Alex, Rajput, Ali H. , Reimann, Regina , Selvackadunco, Sashika , Simuni, Tanya , Svenningsson, Per , Trenkwalder, Claudia , Troakes, Claire , Trojanowski, John Q. , Uitti, Ryan J. , Xie, Tao , Ximelis, Teresa , Yebenes, Justo, Müller, Ulrich , Schellenberg, Gerard D., Herms, Jochen , Kuhlenbäumer, Gregor , Deuschl, Günther , Gelpi, Ellen , Van Gerpen, Jay A, Halliday, Glenda , Irwin, David J , Mollenhauer, Brit , Pantelyat, Alex , Svenningsson, Per , Höglinger, Günter U , Koga, Shunsuke , Ross, Owen A , Kovacs, Gabor G , Xiong, Chengjie , Grinberg, Lea , Molina-Porcel, Laura , Troakes, Claire , Hopfner, Franziska , Lee, Edward B , Simuni, Tanya , Dickson, Dennis W. , Aguzzi, Adriano , Cheshire, William P. , Deerlin, Vivianna M. Van , Desplats, Paula , Deuschl, Günther , Duyckaerts, Charles , Ellinghaus, David , Franke, André , Ghetti, Bernardino , Helbig, Ingo , Irwin, David J. , McKeith, Ian G. , McLean, Catriona , Pantelyat, Alexander , Peters, Annette , Matěj, Radoslav , Scott, William K. , Seeley, William W. , Stadelmann, Christine , Thomas, Alan , White, Charles L. , Wszołek, Zbigniew K. , Schellenberg, Gerard D., Höglinger, Günter U.

Movement Disorders | August 23, 2022 | Volume: 37 | Issue: 10 | Pages: 2110-2121

10.1002/mds.29164 Get It

ABSTRACT Background Multiple System Atrophy is a rare neurodegenerative disease with alpha‐synuclein aggregation in glial cytoplasmic inclusions and either predominant olivopontocerebellar atrophy or striatonigral degeneration, leading to dysautonomia, parkinsonism, and cerebellar ataxia. One prior genome‐wide association study in mainly clinically diagnosed patients with Multiple System Atrophy failed to identify genetic variants predisposing for the disease. Objective Since the clinical diagnosis of Multiple System Atrophy yields a high rate of misdiagnosis when compared to the neuropathological gold standard, we studied only autopsy‐confirmed cases. Methods We studied common genetic variations in Multiple System Atrophy cases (N = 731) and controls (N = 2898). Results The most strongly disease‐associated markers were rs16859966 on chromosome 3, rs7013955 on chromosome 8, and rs116607983 on chromosome 4 with P ‐values below 5 × 10 −6 , all of which were supported by at least one additional genotyped and several imputed single nucleotide polymorphisms. The genes closest to the chromosome 3 locus are ZIC1 and ZIC4 encoding the zinc finger proteins of cerebellum 1 and 4 (ZIC1 and ZIC4). Interpretation Since mutations of ZIC1 and ZIC4 and paraneoplastic autoantibodies directed against ZIC4 are associated with severe cerebellar dysfunction, we conducted immunohistochemical analyses in brain tissue of the frontal cortex and the cerebellum from 24 Multiple System Atrophy patients. Strong immunohistochemical expression of ZIC4 was detected in a subset of neurons of the dentate nucleus in all healthy controls and in patients with striatonigral degeneration, whereas ZIC4‐immunoreactive neurons were significantly reduced inpatients with olivopontocerebellar atrophy. These findings point to a potential ZIC4‐mediated vulnerability of neurons in Multiple System Atrophy. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
Traumatic brain injury and post‐traumatic stress disorder are not associated with Alzheimer's disease pathology measured with biomarkers
Weiner, Michael W. , Harvey, Danielle , Landau, Susan, Veitch, Dallas P. , Aisen, Paul, Petersen, Ronald C. , Tosun, Duygu , Shaw, Leslie M. , Trojanowski, John Q. , Saykin, Andrew J. , Carli, Charles De, DeCarli, Charles , Tosun, Duygu , DeCarli, Charles , Saykin, Andrew J , Harvey, Danielle J , Tosun, Duygu , Neylan, Thomas C. , Grafman, Jordan Henry , Jack, Clifford R. , Hayes, Jacqueline

Alzheimer's & Dementia | June 29, 2022 | Volume: 19 | Issue: 3 | Pages: 884-895

10.1002/alz.12712 Get It

AbstractIntroductionEpidemiological studies report an association between traumatic brain injury (TBI) and post‐traumatic stress disorder (PTSD) and clinically diagnosed Alzheimer's disease (AD). We examined the association between TBI/PTSD and biomarker‐defined AD.MethodsWe identified 289 non‐demented veterans with TBI and/or PTSD and controls who underwent clinical evaluation, cerebrospinal fluid (CSF) collection, magnetic resonance imaging (MRI), amyloid beta (Aβ) and tau positron emission tomography, and apolipoprotein E testing. Participants were followed for up to 5.2 years.ResultsExposure groups (TBI, PTSD, and TBI + PTSD) had higher prevalence of mild cognitive impairment (MCI: P < .0001) and worse Mini‐Mental State Examination scores (PTSD: P = .008; TBI & PTSD: P = .009) than controls. There were no significant differences in other cognitive scores, MRI volumes, Aβ or tau accumulation, or in most longitudinal measures.DiscussionTBI and/or PTSD were not associated with elevated AD biomarkers. The poorer cognitive status of exposed veterans may be due to other comorbid pathologies.
Autosomal dominant and sporadic late onset Alzheimer's disease share a commonin vivopathophysiology
Schofield, Peter R. , Weiner, Michael W. , Xiong, Chengjie , Coble, Dean W. , Saito, Naomi , Aisen, Paul, Berman, Sarah , Cairns, Nigel J., Carrillo, María C. , Chui, Helena C., Chhatwal, Jasmeer P. , Fagan, Anne M. , Farlow, Martin R. , Fox, Nick C. , Goate, Alison , Gordon, Brian A. , Graff‐Radford, Neill R. , Day, Gregory S. , Hassenstab, Jason , Jack, Clifford R. , Jagust, William J. , Massoumzadeh, Parinaz , Masters, Colin L. , Mori, Hiroshi , Noble, James M. , Petersen, Ronald C. , Ringman, John M. , Salloway, Stephen , Saykin, Andrew J. , Shaw, Leslie M. , Trojanowski, John Q. , Vöglein, Jonathan , Weninger, Stacie, Bateman, Randall J. , Buckles, Virginia, Morris, John C. , Schofield, Peter R , Gordon, Brian A , Ikeuchi, T , Jack, Clifford R , Jagust, William J , Masters, Colin L , Vöglein, Jonathan , Saykin, Andrew J , Day, Gregory S , Bateman, Randall J , Xiong, Chengjie , Fox, Nick C , Hassenstab, Jason , Morris, John C , Goate, Alison M , Berman, Sarah , Noble, James M , Xiong, Chengjie , Beckett, Laurel , Allegri, Ricardo , Benzinger, Tammie L.S. , Chui, Helena C. , Cruchaga, Carlos , Ghetti, Bernardino , Ikeuchi, Takeshi , Jucker, Mathias , Martins, Ralph N. , McDade, Eric , Toga, Arthur W.

Brain | May 17, 2022 | Volume: 145 | Issue: 10 | Pages: 3594-3607

10.1093/brain/awac181 Get It

AbstractThe extent to which the pathophysiology of autosomal dominant Alzheimer's disease corresponds to the pathophysiology of ‘sporadic’ late onset Alzheimer's disease is unknown, thus limiting the extrapolation of study findings and clinical trial results in autosomal dominant Alzheimer's disease to late onset Alzheimer's disease.We compared brain MRI and amyloid PET data, as well as CSF concentrations of amyloid-β42, amyloid-β40, tau and tau phosphorylated at position 181, in 292 carriers of pathogenic variants for Alzheimer's disease from the Dominantly Inherited Alzheimer Network, with corresponding data from 559 participants from the Alzheimer’s Disease Neuroimaging Initiative. Imaging data and CSF samples were reprocessed as appropriate to guarantee uniform pipelines and assays. Data analyses yielded rates of change before and after symptomatic onset of Alzheimer's disease, allowing the alignment of the ∼30-year age difference between the cohorts on a clinically meaningful anchor point, namely the participant age at symptomatic onset.Biomarker profiles were similar for both autosomal dominant Alzheimer's disease and late onset Alzheimer's disease. Both groups demonstrated accelerated rates of decline in cognitive performance and in regional brain volume loss after symptomatic onset. Although amyloid burden accumulation as determined by PET was greater after symptomatic onset in autosomal dominant Alzheimer's disease than in late onset Alzheimer's disease participants, CSF assays of amyloid-β42, amyloid-β40, tau and p-tau181 were largely overlapping in both groups. Rates of change in cognitive performance and hippocampal volume loss after symptomatic onset were more aggressive for autosomal dominant Alzheimer's disease participants.These findings suggest a similar pathophysiology of autosomal dominant Alzheimer's disease and late onset Alzheimer's disease, supporting a shared pathobiological construct.
CSF and blood plasma mass spectrometry measures of Aβ, tau, and NfL species and longitudinal relationship to preclinical and clinical staging of amyloid and tau aggregation and clinical stage of Alzheimer’s disease
Barthélemy, Nicolas R. , Bollinger, James G., Fagan, Anne M. , Mawuenyega, Kwasi G., Ovod, Vitaliy , Budelier, Melissa M. , Petersen, Ronald C. , Shaw, Leslie M. , Trojanowski, John Q. , Weiner, Mike, Xiong, Chengjie , Bateman, Randall J. , Benzinger, Tammie L.S. , Gordon, Brian A. , Hansson, Oskar , Holtzman, David M. , Horie, Kanta , Li, Melody , Masters, Colin L. , McDade, Eric , Morris, John C. , He, Chloe , Mielke, Michelle M. , Sato, Chihiro , Schindler, Suzanne E.

Alzheimer's & Dementia | December 31, 2021 | Volume: 17 | Issue: S5 | Pages: No page available

10.1002/alz.050711 Get It

AbstractBackgroundRecent advances in the development of novel Alzheimer’s disease (AD) measures of amyloid, tau, and neurodegeneration in cerebrospinal fluid (CSF) and blood have enabled a better understanding of the links between amyloid‐beta (Aβ), tau species and neurodegeneration in the brain, CSF, and blood. The discoveries of novel tau species in CSF and blood, including specific phospho‐tau (p‐tau) and truncated species containing the microtubule binding region of tau (MTBR‐tau) which aggregates in tau tangles, have greatly expanded our understanding of tau biology and tau target development. The longitudinal Aβ, tau, and neurofilament light chain (NfL) changes previously measured in CSF are now being measured accurately in blood, enabling interpretation and understanding of AD progression in much larger and more robust populations.MethodWe analyzed longitudinal CSF and/or blood plasma mass spectrometry measures of Aβ42/Aβ40, multiple p‐tau species (including p‐tau217, 231, 181, 205, etc.), total tau, MTBR‐tau (e.g., MTBR‐tau243, MTBR‐tau299, and MTBR‐tau354), and NfL in sporadic AD and dominantly inherited AD cohorts. Mass spectrometry results were compared to amyloid and tau aggregation measures by Positron Emission Tomography (PET) scans and clinical and cognitive measures in local and international clinical cohorts including ADNI, AIBL, BioFINDER, Mayo Clinic, the Knight ADRC, and the DIAN.ResultThe longitudinal results indicate that a pathophysiological cascade of events begin with altered CSF and blood plasma Aβ42/Aβ40 ratio, followed by increases in amyloid plaques as measured by amyloid PET, associated with increased phosphorylation of specific CSF tau species (e.g., p‐tau217, p‐tau181, etc.), before increases in p‐tau205, NfL, total tau concentrations, hypometabolism, and atrophy. Finally, some species of MTBR‐tau increase before or with tau aggregation by tau PET and onset of clinical symptoms and correlate with longitudinal tau aggregation and clinical progression.ConclusionThese findings indicate that CSF and blood plasma Aβ, p‐tau, MTBR‐tau and NfL measures are highly precise biomarkers of brain amyloidosis, tauopathy, and neurodegeneration and can accurately identify stages of preclinical and clinical AD.
The Worldwide Alzheimer's Disease Neuroimaging Initiative: ADNI‐3 updates and global perspectives
Weber, Christopher J., Carrillo, María C. , Jagust, William J. , Shaw, Leslie M. , Trojanowski, John Q. , Saykin, Andrew J. , Sur, Cyrille, Méndez, Patricio Chrem, Li, Kuncheng , Iwatsubo, Takeshi , Perrin, Richard J. , Morris, John C. , Healan, Amanda M.B., Weiner, Michael W. , Saykin, Andrew J , Morris, John C , Jack, Clifford R. , Beckett, Laurel , Rao, Naren Prahalada , Black, Sandra E. , Chang, Chiung‐Chih , Sosa, Ana Luisa , Rowe, Christopher Cleon , Hall, Stephen E.

Alzheimer's & Dementia: Translational Research & Clinical Interventions | December 31, 2021 | Volume: 7 | Issue: 1 | Pages: No page available

10.1002/trc2.12226 Get It

Abstract The Worldwide Alzheimer's Disease Neuroimaging Initiative (WW‐ADNI) is a collaborative effort to investigate imaging and biofluid markers that can inform Alzheimer's disease treatment trials. It is a public‐private partnership that spans North America, Argentina, Australia, Canada, China, Japan, Korea, Mexico, and Taiwan. In 2004, ADNI researchers began a naturalistic, longitudinal study that continues today around the globe. Through several successive phases (ADNI‐1, ADNI‐GO, ADNI‐2, and ADNI‐3), the study has fueled amyloid and tau phenotyping and refined neuroimaging methodologies. WW‐ADNI researchers have successfully standardized analyses and openly share data without embargo, providing a rich data set for other investigators. On August 26, 2020, the Alzheimer's Association convened WW‐ADNI researchers who shared updates from ADNI‐3 and their vision for ADNI‐4.
Contribution of Alzheimer's biomarkers and risk factors to cognitive impairment and decline across the Alzheimer's disease continuum
Tosun, Duygu , Demir, Zeynep , Aisen, Paul, Jack, Clifford R. , Jagust, William, Petersen, Ronald C. , Saykin, Andrew J. , Shaw, Leslie M. , Trojanowski, John Q. , Weiner, Michael W. , Initiative, for the Alzheimer's Disease Neuroimaging, Jack, Clifford R , Saykin, Andrew J , Tosun, Duygu , Veitch, Dallas P. , Weintraub, Daniel , Initiative, for the Alzheimer's Disease Neuroimaging

Alzheimer's & Dementia | October 19, 2021 | Volume: 18 | Issue: 7 | Pages: 1370-1382

10.1002/alz.12480 Get It

Abstract Introduction Amyloid beta (Aβ), tau, and neurodegeneration jointly with the Alzheimer's disease (AD) risk factors affect the severity of clinical symptoms and disease progression. Methods Within 248 Aβ‐positive elderly with and without cognitive impairment and dementia, partial least squares structural equation pathway modeling was used to assess the direct and indirect effects of imaging biomarkers (global Aβ‐positron emission tomography [PET] uptake, regional tau‐PET uptake, and regional magnetic resonance imaging–based atrophy) and risk‐factors (age, sex, education, apolipoprotein E [ APOE ], and white‐matter lesions) on cross‐sectional cognitive impairment and longitudinal cognitive decline. Results Sixteen percent of variance in cross‐sectional cognitive impairment was accounted for by Aβ, 46% to 47% by tau, and 25% to 29% by atrophy, although 53% to 58% of total variance in cognitive impairment was explained by incorporating mediated and direct effects of AD risk factors. The Aβ–tau–atrophy pathway accounted for 50% to 56% of variance in longitudinal cognitive decline while Aβ, tau, and atrophy independently explained 16%, 46% to 47%, and 25% to 29% of the variance, respectively. Discussion These findings emphasize that treatments that remove Aβ and completely stop downstream effects on tau and neurodegeneration would only be partially effective in slowing of cognitive decline or reversing cognitive impairment.
Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease
Weiner, Michael W. , Aisen, Paul, DeCarli, Charles , Harvey, Danielle , Jack, Clifford R. , Jagust, William J. , Landau, Susan, Morris, John C. , Okonkwo, Ozioma C. , Perrin, Richard J. , Petersen, Ronald C. , Mindt, Mónica Rivera , Saykin, Andrew J. , Shaw, Leslie M. , Tosun, Duygu , Trojanowski, John Q. , Initiative, Alzheimer's Disease Neuroimaging, DeCarli, Charles , Jack, Clifford R , Saykin, Andrew J , Morris, John C , Harvey, Danielle J , Tosun, Duygu , Veitch, Dallas P. , Beckett, Laurel , Green, Robert C. , Toga, Arthur W. , Initiative, Alzheimer's Disease Neuroimaging

Alzheimer's & Dementia | September 28, 2021 | Volume: 18 | Issue: 4 | Pages: 824-857

10.1002/alz.12422 Get It

AbstractIntroductionThe Alzheimer's Disease Neuroimaging Initiative (ADNI) has accumulated 15 years of clinical, neuroimaging, cognitive, biofluid biomarker and genetic data, and biofluid samples available to researchers, resulting in more than 3500 publications. This review covers studies from 2018 to 2020.MethodsWe identified 1442 publications using ADNI data by conventional search methods and selected impactful studies for inclusion.ResultsDisease progression studies supported pivotal roles for regional amyloid beta (Aβ) and tau deposition, and identified underlying genetic contributions to Alzheimer's disease (AD). Vascular disease, immune response, inflammation, resilience, and sex modulated disease course. Biologically coherent subgroups were identified at all clinical stages. Practical algorithms and methodological changes improved determination of Aβ status. Plasma Aβ, phosphorylated tau181, and neurofilament light were promising noninvasive biomarkers. Prognostic and diagnostic models were externally validated in ADNI but studies are limited by lack of ethnocultural cohort diversity.DiscussionADNI has had a profound impact in improving clinical trials for AD.
Genomewide Association Studies of LRRK2 Modifiers of Parkinson's Disease
Lai, Dongbing , Fontanillas, Pierre , Aasly, Jan , Beecham, Gary W. , Berg, Daniela , Bressman, Susan , Brockmann, Kathrin , Clark, Lorraine N. , Cookson, Mark , Das, Sayantan , Farrer, Matthew J. , Trinh, Joanne , Gasser, Thomas , Gustavsson, Emil K. , Klein, Christine , Langston, J. William, Lynch, Timothy , Marder, Karen, Martin, Eden R. , Mejia‐Santana, Helen, Molho, Eric , Nuytemans, Karen , Ozelius, Laurie J. , Raymond, Deborah , Ross, Owen A. , Samii, Ali, Saunders‐Pullman, Rachel , Schüle, Birgitt , Tolosa, Eduardo , Tomkins, James E. , Vilas, Dolores , Trojanowski, John Q. , Uitti, Ryan J. , Zabetian, Cyrus P. , Urtreger, Avi Orr, Cannon, P. F. , Fiske, Brian , Brockman, Kathrin , Klein, Christine , Ross, Owen A , Uitti, Ryan, Visanji, Naomi P , Zabetian, Cyrus P , Mirelman, Anat , Alipanahi, Babak , Schwantes-An, Tae-Hwi Linus , Alcalay, Roy N. , Brice, Alexis , Deerlin, Vivianna M. Van , Follett, Jordan , Goldwurm, Stefano , Lang, Anthony E. , Latourelle, Jeanne C. , Marras, Connie , McLean, Cory Y. , Myers, Richard H. , Payami, Haydeh , Rogaeva, Ekaterina A. , Rogers, Michael P. , Schulte, Claudia , Scott, William K. , Tanner, Caroline M. , Vance, Jeffery M. , Visanji, Naomi P. , Wszołek, Zbigniew K. , Giladi, Nir , Foroud, Tatiana

Annals of Neurology | May 3, 2021 | Volume: 90 | Issue: 1 | Pages: 76-88

10.1002/ana.26094 Get It

Objective The aim of this study was to search for genes/variants that modify the effect of LRRK2 mutations in terms of penetrance and age‐at‐onset of Parkinson's disease. Methods We performed the first genomewide association study of penetrance and age‐at‐onset of Parkinson's disease in LRRK2 mutation carriers (776 cases and 1,103 non‐cases at their last evaluation). Cox proportional hazard models and linear mixed models were used to identify modifiers of penetrance and age‐at‐onset of LRRK2 mutations, respectively. We also investigated whether a polygenic risk score derived from a published genomewide association study of Parkinson's disease was able to explain variability in penetrance and age‐at‐onset in LRRK2 mutation carriers. Results A variant located in the intronic region of CORO1C on chromosome 12 (rs77395454; p value = 2.5E‐08, beta = 1.27, SE = 0.23, risk allele: C) met genomewide significance for the penetrance model. Co‐immunoprecipitation analyses of LRRK2 and CORO1C supported an interaction between these 2 proteins. A region on chromosome 3, within a previously reported linkage peak for Parkinson's disease susceptibility, showed suggestive associations in both models (penetrance top variant: p value = 1.1E‐07; age‐at‐onset top variant: p value = 9.3E‐07). A polygenic risk score derived from publicly available Parkinson's disease summary statistics was a significant predictor of penetrance, but not of age‐at‐onset. Interpretation This study suggests that variants within or near CORO1C may modify the penetrance of LRRK2 mutations. In addition, common Parkinson's disease associated variants collectively increase the penetrance of LRRK2 mutations. ANN NEUROL 2021;90:82–94
Detection of β-amyloid positivity in Alzheimer’s Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers
Tosun, Duygu , Aisen, Paul , Jack, Clifford R. , Jagust, William J. , Petersen, Ronald C. , Saykin, Andrew J. , Bollinger, James, Ovod, Vitaliy , Mawuenyega, Kwasi G., Bateman, Randall J. , Shaw, Leslie M. , Trojanowski, John Q. , Blennow, Kaj , Weiner, Michael W. , Tosun, Duygu , Jack, Clifford R , Jagust, William J , Zetterberg, Henrik , Saykin, Andrew J , Bateman, Randall J , Tosun, Duygu , Veitch, Dallas P. , Zetterberg, Henrik

Brain Communications | February 3, 2021 | Volume: 3 | Issue: 2 | Pages: No page available

10.1093/braincomms/fcab008 Get It

Abstract In vivo gold standard for the ante-mortem assessment of brain β-amyloid pathology is currently β-amyloid positron emission tomography or cerebrospinal fluid measures of β-amyloid42 or the β-amyloid42/β-amyloid40 ratio. The widespread acceptance of a biomarker classification scheme for the Alzheimer’s disease continuum has ignited interest in more affordable and accessible approaches to detect Alzheimer’s disease β-amyloid pathology, a process that often slows down the recruitment into, and adds to the cost of, clinical trials. Recently, there has been considerable excitement concerning the value of blood biomarkers. Leveraging multidisciplinary data from cognitively unimpaired participants and participants with mild cognitive impairment recruited by the multisite biomarker study of Alzheimer’s Disease Neuroimaging Initiative, here we assessed to what extent plasma β-amyloid42/β-amyloid40, neurofilament light and phosphorylated-tau at threonine-181 biomarkers detect the presence of β-amyloid pathology, and to what extent the addition of clinical information such as demographic data, APOE genotype, cognitive assessments and MRI can assist plasma biomarkers in detecting β-amyloid-positivity. Our results confirm plasma β-amyloid42/β-amyloid40 as a robust biomarker of brain β-amyloid-positivity (area under curve, 0.80–0.87). Plasma phosphorylated-tau at threonine-181 detected β-amyloid-positivity only in the cognitively impaired with a moderate area under curve of 0.67, whereas plasma neurofilament light did not detect β-amyloid-positivity in either group of participants. Clinical information as well as MRI-score independently detected positron emission tomography β-amyloid-positivity in both cognitively unimpaired and impaired (area under curve, 0.69–0.81). Clinical information, particularly APOE ε4 status, enhanced the performance of plasma biomarkers in the detection of positron emission tomography β-amyloid-positivity by 0.06–0.14 units of area under curve for cognitively unimpaired, and by 0.21–0.25 units for cognitively impaired; and further enhancement of these models with an MRI-score of β-amyloid-positivity yielded an additional improvement of 0.04–0.11 units of area under curve for cognitively unimpaired and 0.05–0.09 units for cognitively impaired. Taken together, these multi-disciplinary results suggest that when combined with clinical information, plasma phosphorylated-tau at threonine-181 and neurofilament light biomarkers, and an MRI-score could effectively identify β-amyloid+ cognitively unimpaired and impaired (area under curve, 0.80–0.90). Yet, when the MRI-score is considered in combination with clinical information, plasma phosphorylated-tau at threonine-181 and plasma neurofilament light have minimal added value for detecting β-amyloid-positivity. Our systematic comparison of β-amyloid-positivity detection models identified effective combinations of demographics, APOE, global cognition, MRI and plasma biomarkers. Promising minimally invasive and low-cost predictors such as plasma biomarkers of β-amyloid42/β-amyloid40 may be improved by age and APOE genotype.