Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum

Willemijn J. Jansen, Olin Janssen, Betty M. Tijms, Stephanie J.B. Vos, Rik Ossenkoppele, Pieter Jelle Visser, Dag Aarsland, Daniel Alcolea, Daniele Altomare, Christine Von Arnim, Simone Baiardi, Ines Baldeiras, Henryk Barthel, Randall J. Bateman, Bart Van Berckel, Alexa Pichet Binette, Kaj Blennow, Merce Boada, Henning Boecker, Michel BottlaenderAnouk Den Braber, David J. Brooks, Mark A. Van Buchem, Vincent Camus, Jose Manuel Carill, Jiri Cerman, Kewei Chen, Gaël Chételat, Elena Chipi, Ann D. Cohen, Alisha Daniels, Marion Delarue, Mira Didic, Alexander Drzezga, Bruno Dubois, Marie Eckerström, Laura L. Ekblad, Sebastiaan Engelborghs, Stéphane Epelbaum, Anne M. Fagan, Yong Fan, Tormod Fladby, Adam S. Fleisher, Wiesje M. Van Der Flier, Stefan Förster, Juan Fortea, Kristian Steen Frederiksen, Yvonne Freund-Levi, Lars Frings, Giovanni B. Frisoni, Lutz Fröhlich, Tomasz Gabryelewicz, Hermann Josef Gertz, Kiran Dip Gill, Olymbia Gkatzima, Estrella Gómez-Tortosa, Timo Grimmer, Eric Guedj, Christian G. Habeck, Harald Hampel, Ron Handels, Oskar Hansson, Lucrezia Hausner, Sabine Hellwig, Michael T. Heneka, Sanna Kaisa Herukka, Helmut Hildebrandt, John Hodges, Jakub Hort, Chin Chang Huang, Ane Juaristi Iriondo, Yoshiaki Itoh, Adrian Ivanoiu, William J. Jagust, Frank Jessen, Peter Johannsen, Keith A. Johnson, Ramesh Kandimalla, Elisabeth N. Kapaki, Silke Kern, Lena Kilander, Aleksandra Klimkowicz-Mrowiec, William E. Klunk, Norman Koglin, Johannes Kornhuber, Milica G. Kramberger, Hung Chou Kuo, Koen Van Laere, Susan M. Landau, Brigitte Landeau, Dong Young Lee, Mony De Leon, Cristian E. Leyton, Kun Ju Lin, Alberto Lleó, Malin Löwenmark, Karine Madsen, Wolfgang Maier, Jan Marcusson, Marta Marquié, Pablo Martinez-Lage, Nancy Maserejian, Niklas Mattsson, Alexandre De Mendonça, Philipp T. Meyer, Bruce L. Miller, Shinobu Minatani, Mark A. Mintun, Vincent C.T. Mok, Jose Luis Molinuevo, Silvia Daniela Morbelli, John C. Morris, Barbara Mroczko, Duk L. Na, Andrew Newberg, Flavio Nobili, Agneta Nordberg, Marcel G.M. Olde Rikkert, Catarina Resende De Oliveira, Pauline Olivieri, Adela Orellana, George Paraskevas, Piero Parchi, Matteo Pardini, Lucilla Parnetti, Oliver Peters, Judes Poirier, Julius Popp, Sudesh Prabhakar, Gil D. Rabinovici, Inez H. Ramakers, Lorena Rami, Eric M. Reiman, Juha O. Rinne, Karen M. Rodrigue, Eloy Rodríguez-Rodriguez, Catherine M. Roe, Pedro Rosa-Neto, Howard J. Rosen, Uros Rot, Christopher C. Rowe, Eckart Rüther, Agustín Ruiz, Osama Sabri, Jayant Sakhardande, Pascual Sánchez-Juan, Sigrid Botne Sando, Isabel Santana, Marie Sarazin, Philip Scheltens, Johannes Schröder, Per Selnes, Sang Won Seo, Dina Silva, Ingmar Skoog, Peter J. Snyder, Hilkka Soininen, Marc Sollberger, Reisa A. Sperling, Luisa Spiru, Yaakov Stern, Erik Stomrud, Akitoshi Takeda, Marc Teichmann, Charlotte E. Teunissen, Louisa I. Thompson, Jori Tomassen, Magda Tsolaki, Rik Vandenberghe, Marcel M. Verbeek, Frans R.J. Verhey, Victor Villemagne, Sylvia Villeneuve, Jonathan Vogelgsang, Gunhild Waldemar, Anders Wallin, Åsa K. Wallin, Jens Wiltfang, David A. Wolk, Tzu Chen Yen, Marzena Zboch, Henrik Zetterberg

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3 Scopus citations

Abstract

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 19097 participants (mean [SD] age, 69.1 [9.8] years; 10148 women [53.1%]) included, 10139 (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..

Original languageEnglish
Pages (from-to)228-243
Number of pages16
JournalJAMA Neurology
Volume79
Issue number3
DOIs
StatePublished - Mar 2022

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