TY - JOUR

T1 - Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum

AU - Jansen, Willemijn J.

AU - Janssen, Olin

AU - Tijms, Betty M.

AU - Vos, Stephanie J.B.

AU - Ossenkoppele, Rik

AU - Visser, Pieter Jelle

AU - Aarsland, Dag

AU - Alcolea, Daniel

AU - Altomare, Daniele

AU - Von Arnim, Christine

AU - Baiardi, Simone

AU - Baldeiras, Ines

AU - Barthel, Henryk

AU - Bateman, Randall J.

AU - Van Berckel, Bart

AU - Binette, Alexa Pichet

AU - Blennow, Kaj

AU - Boada, Merce

AU - Boecker, Henning

AU - Bottlaender, Michel

AU - Den Braber, Anouk

AU - Brooks, David J.

AU - Van Buchem, Mark A.

AU - Camus, Vincent

AU - Carill, Jose Manuel

AU - Cerman, Jiri

AU - Chen, Kewei

AU - Chételat, Gaël

AU - Chipi, Elena

AU - Cohen, Ann D.

AU - Daniels, Alisha

AU - Delarue, Marion

AU - Didic, Mira

AU - Drzezga, Alexander

AU - Dubois, Bruno

AU - Eckerström, Marie

AU - Ekblad, Laura L.

AU - Engelborghs, Sebastiaan

AU - Epelbaum, Stéphane

AU - Fagan, Anne M.

AU - Fan, Yong

AU - Fladby, Tormod

AU - Fleisher, Adam S.

AU - Van Der Flier, Wiesje M.

AU - Förster, Stefan

AU - Fortea, Juan

AU - Frederiksen, Kristian Steen

AU - Freund-Levi, Yvonne

AU - Frings, Lars

AU - Frisoni, Giovanni B.

AU - Fröhlich, Lutz

AU - Gabryelewicz, Tomasz

AU - Gertz, Hermann Josef

AU - Gill, Kiran Dip

AU - Gkatzima, Olymbia

AU - Gómez-Tortosa, Estrella

AU - Grimmer, Timo

AU - Guedj, Eric

AU - Habeck, Christian G.

AU - Hampel, Harald

AU - Handels, Ron

AU - Hansson, Oskar

AU - Hausner, Lucrezia

AU - Hellwig, Sabine

AU - Heneka, Michael T.

AU - Herukka, Sanna Kaisa

AU - Hildebrandt, Helmut

AU - Hodges, John

AU - Hort, Jakub

AU - Huang, Chin Chang

AU - Iriondo, Ane Juaristi

AU - Itoh, Yoshiaki

AU - Ivanoiu, Adrian

AU - Jagust, William J.

AU - Jessen, Frank

AU - Johannsen, Peter

AU - Johnson, Keith A.

AU - Kandimalla, Ramesh

AU - Kapaki, Elisabeth N.

AU - Kern, Silke

AU - Kilander, Lena

AU - Klimkowicz-Mrowiec, Aleksandra

AU - Klunk, William E.

AU - Koglin, Norman

AU - Kornhuber, Johannes

AU - Kramberger, Milica G.

AU - Kuo, Hung Chou

AU - Van Laere, Koen

AU - Landau, Susan M.

AU - Landeau, Brigitte

AU - Lee, Dong Young

AU - De Leon, Mony

AU - Leyton, Cristian E.

AU - Lin, Kun Ju

AU - Lleó, Alberto

AU - Löwenmark, Malin

AU - Madsen, Karine

AU - Maier, Wolfgang

AU - Marcusson, Jan

AU - Marquié, Marta

AU - Martinez-Lage, Pablo

AU - Maserejian, Nancy

AU - Mattsson, Niklas

AU - De Mendonça, Alexandre

AU - Meyer, Philipp T.

AU - Miller, Bruce L.

AU - Minatani, Shinobu

AU - Mintun, Mark A.

AU - Mok, Vincent C.T.

AU - Molinuevo, Jose Luis

AU - Morbelli, Silvia Daniela

AU - Morris, John C.

AU - Mroczko, Barbara

AU - Na, Duk L.

AU - Newberg, Andrew

AU - Nobili, Flavio

AU - Nordberg, Agneta

AU - Olde Rikkert, Marcel G.M.

AU - De Oliveira, Catarina Resende

AU - Olivieri, Pauline

AU - Orellana, Adela

AU - Paraskevas, George

AU - Parchi, Piero

AU - Pardini, Matteo

AU - Parnetti, Lucilla

AU - Peters, Oliver

AU - Poirier, Judes

AU - Popp, Julius

AU - Prabhakar, Sudesh

AU - Rabinovici, Gil D.

AU - Ramakers, Inez H.

AU - Rami, Lorena

AU - Reiman, Eric M.

AU - Rinne, Juha O.

AU - Rodrigue, Karen M.

AU - Rodríguez-Rodriguez, Eloy

AU - Roe, Catherine M.

AU - Rosa-Neto, Pedro

AU - Rosen, Howard J.

AU - Rot, Uros

AU - Rowe, Christopher C.

AU - Rüther, Eckart

AU - Ruiz, Agustín

AU - Sabri, Osama

AU - Sakhardande, Jayant

AU - Sánchez-Juan, Pascual

AU - Sando, Sigrid Botne

AU - Santana, Isabel

AU - Sarazin, Marie

AU - Scheltens, Philip

AU - Schröder, Johannes

AU - Selnes, Per

AU - Seo, Sang Won

AU - Silva, Dina

AU - Skoog, Ingmar

AU - Snyder, Peter J.

AU - Soininen, Hilkka

AU - Sollberger, Marc

AU - Sperling, Reisa A.

AU - Spiru, Luisa

AU - Stern, Yaakov

AU - Stomrud, Erik

AU - Takeda, Akitoshi

AU - Teichmann, Marc

AU - Teunissen, Charlotte E.

AU - Thompson, Louisa I.

AU - Tomassen, Jori

AU - Tsolaki, Magda

AU - Vandenberghe, Rik

AU - Verbeek, Marcel M.

AU - Verhey, Frans R.J.

AU - Villemagne, Victor

AU - Villeneuve, Sylvia

AU - Vogelgsang, Jonathan

AU - Waldemar, Gunhild

AU - Wallin, Anders

AU - Wallin, Åsa K.

AU - Wiltfang, Jens

AU - Wolk, David A.

AU - Yen, Tzu Chen

AU - Zboch, Marzena

AU - Zetterberg, Henrik

N1 - Publisher Copyright: © 2022 American Medical Association. All rights reserved.

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

U2 - 10.1001/jamaneurol.2021.5216

DO - 10.1001/jamaneurol.2021.5216

M3 - Journal article

C2 - 35099509

AN - SCOPUS:85124123668

VL - 79

SP - 228

EP - 243

JO - JAMA Neurology

JF - JAMA Neurology

SN - 2168-6149

IS - 3

ER -