Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum
| Autor: | Jansen, Willemijn J, Janssen, Olin, von Arnim, Christine, Marquié, Marta, Martinez-Lage, Pablo, Maserejian, Nancy, Mattsson, Niklas, de Mendonça, Alexandre, Meyer, Philipp T, Miller, Bruce L, Minatani, Shinobu, Mintun, Mark A, Mok, Vincent C T, Baiardi, Simone, Molinuevo, Jose Luis, Morbelli, Silvia Daniela, Morris, John C, Mroczko, Barbara, Na, Duk L, Newberg, Andrew, Nobili, Flavio, Nordberg, Agneta, Olde Rikkert, Marcel G M, de Oliveira, Catarina Resende, Baldeiras, Ines, Olivieri, Pauline, Orellana, Adela, Paraskevas, George, Parchi, Piero, Pardini, Matteo, Parnetti, Lucilla, Peters, Oliver, Poirier, Judes, Popp, Julius, Prabhakar, Sudesh, Barthel, Henryk, Rabinovici, Gil D, Ramakers, Inez H, Rami, Lorena, Reiman, Eric M, Rinne, Juha O, Rodrigue, Karen M, Rodríguez-Rodriguez, Eloy, Roe, Catherine M, Rosa-Neto, Pedro, Rosen, Howard J, Bateman, Randall J, Rot, Uros, Rowe, Christopher C, Rüther, Eckart, Ruiz, Agustín, Sabri, Osama, Sakhardande, Jayant, Sánchez-Juan, Pascual, Sando, Sigrid Botne, Santana, Isabel, Sarazin, Marie, Van Berckel, Bart, Scheltens, Philip, Schröder, Johannes, Selnes, Per, Seo, Sang Won, Silva, Dina, Skoog, Ingmar, Snyder, Peter J, Soininen, Hilkka, Sollberger, Marc, Sperling, Reisa A, Binette, Alexa Pichet, Spiru, Luisa, Stern, Yaakov, Stomrud, Erik, Takeda, Akitoshi, Teichmann, Marc, Teunissen, Charlotte E, Thompson, Louisa I, Tomassen, Jori, Tsolaki, Magda, Vandenberghe, Rik, Blennow, Kaj, Verbeek, Marcel M, Verhey, Frans R J, Villemagne, Victor, Villeneuve, Sylvia, Vogelgsang, Jonathan, Waldemar, Gunhild, Wallin, Anders, Wallin, Åsa K, Wiltfang, Jens, Wolk, David A, Boada, Merce, Yen, Tzu-Chen, Zboch, Marzena, Zetterberg, Henrik, Boecker, Henning, Tijms, Betty M, Bottlaender, Michel, den Braber, Anouk, Brooks, David J, Van Buchem, Mark A, Camus, Vincent, Carill, Jose Manuel, Cerman, Jiri, Chen, Kewei, Chételat, Gaël, Chipi, Elena, Vos, Stephanie J B, Cohen, Ann D, Daniels, Alisha, Delarue, Marion, Didic, Mira, Drzezga, Alexander, Dubois, Bruno, Eckerström, Marie, Ekblad, Laura L, Engelborghs, Sebastiaan, Epelbaum, Stéphane, Ossenkoppele, Rik, Fagan, Anne M, Fan, Yong, Fladby, Tormod, Fleisher, Adam S, Van der Flier, Wiesje M, Förster, Stefan, Fortea, Juan, Frederiksen, Kristian Steen, Freund-Levi, Yvonne, Frings, Lars, Visser, Pieter Jelle, Frisoni, Giovanni B, Fröhlich, Lutz, Gabryelewicz, Tomasz, Gertz, Hermann-Josef, Gill, Kiran Dip, Gkatzima, Olymbia, Gómez-Tortosa, Estrella, Grimmer, Timo, Guedj, Eric, Habeck, Christian G, Group, Amyloid Biomarker Study, Hampel, Harald, Handels, Ron, Hansson, Oskar, Hausner, Lucrezia, Hellwig, Sabine, Heneka, Michael, Herukka, Sanna-Kaisa, Hildebrandt, Helmut, Hodges, John, Hort, Jakub, Aarsland, Dag, Huang, Chin-Chang, Iriondo, Ane Juaristi, Itoh, Yoshiaki, Ivanoiu, Adrian, Jagust, William J, Jessen, Frank, Johannsen, Peter, Johnson, Keith A, Kandimalla, Ramesh, Kapaki, Elisabeth N, Alcolea, Daniel, Kern, Silke, Kilander, Lena, Klimkowicz-Mrowiec, Aleksandra, Klunk, William E, Koglin, Norman, Kornhuber, Johannes, Kramberger, Milica G, Kuo, Hung-Chou, Van Laere, Koen, Landau, Susan M, Altomare, Daniele, Landeau, Brigitte, Lee, Dong Young, de Leon, Mony, Leyton, Cristian E, Lin, Kun-Ju, Lleó, Alberto, Löwenmark, Malin, Madsen, Karine, Maier, Wolfgang, Marcusson, Jan |
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| Přispěvatelé: | Clinical sciences, Neuroprotection & Neuromodulation, Neurology, Amsterdam Neuroscience - Brain Imaging, Amsterdam Neuroscience - Neurodegeneration, Radiology and nuclear medicine, Laboratory Medicine, Amsterdam Neuroscience - Neuroinfection & -inflammation, APH - Personalized Medicine, APH - Methodology, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, Psychiatrie & Neuropsychologie |
| Rok vydání: | 2022 |
| Předmět: |
Male
MILD COGNITIVE IMPAIRMENT epidemiology [Cognitive Dysfunction] positron emission tomography Alzheimer`s disease Donders Center for Medical Neuroscience [Radboudumc 1] epidemiology [Alzheimer Disease] Neuroscience(all) diagnostic imaging [Cognitive Dysfunction] Amyloidogenic Proteins tau Proteins cerebrospinal fluid [Amyloid beta-Peptides] DIAGNOSIS cerebrospinal fluid Apolipoproteins E Alzheimer Disease Prevalence Humans Amyloid Alzheimer PET Cognitive Dysfunction ddc:610 cerebrospinal fluid [Peptide Fragments] Aged Amyloid beta-Peptides neurology DEMENTIA Correction ASSOCIATION Amyloidosis Middle Aged Disorders of movement Donders Center for Medical Neuroscience [Radboudumc 3] health care planning clinical trial design Peptide Fragments cerebrospinal fluid [Alzheimer Disease] PET DRIFT Cross-Sectional Studies cerebrospinal fluid [Biomarkers] cerebrospinal fluid [tau Proteins] Radiology Nuclear Medicine and imaging Positron-Emission Tomography genetics [Apolipoproteins E] Female Neurology (clinical) diagnostic imaging [Alzheimer Disease] cerebral amyloid aggregation Biomarkers |
| Zdroj: | JAMA Neurol Jama Neurology, 79, 3, pp. 228-243 Jama Neurology, 79, 228-243 Amyloid Biomarker Study Group 2022, ' Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum ', JAMA Neurology, vol. 79, no. 3, pp. 228-243 . https://doi.org/10.1001/jamaneurol.2021.5216 JAMA Neurology, 79(3), 228-243. American Medical Association JAMA neurology 79(3), 228-243 (2022). doi:10.1001/jamaneurol.2021.5216 Jansen, W J, Janssen, O, Tijms, B M, Vos, S J B, Ossenkoppele, R, Visser, P J & Amyloid Biomarker Study Group 2022, ' Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum ', JAMA Neurology, vol. 79, no. 3, pp. 228-243 . https://doi.org/10.1001/jamaneurol.2021.5216 |
| ISSN: | 2168-6157 2168-6149 |
| Popis: | Importance: One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design.Objective: To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates.Design, Setting, and Participants: This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria.Exposures: Alzheimer disease biomarkers detected on PET or in CSF.Main Outcomes and Measures: Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations.Results: Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18).Conclusions and Relevance: This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies. |
| Databáze: | OpenAIRE |
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