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Meta-Analysis
. 2015 May 19;313(19):1924-38.
doi: 10.1001/jama.2015.4668.

Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis

Willemijn J Jansen  1 , Rik Ossenkoppele  2 , Dirk L Knol  3 , Betty M Tijms  4 , Philip Scheltens  4 , Frans R J Verhey  1 , Pieter Jelle Visser  5 ; Amyloid Biomarker Study Group; Pauline Aalten  1 , Dag Aarsland  6 , Daniel Alcolea  7 , Myriam Alexander  8 , Ina S Almdahl  9 , Steven E Arnold  10 , Inês Baldeiras  11 , Henryk Barthel  12 , Bart N M van Berckel  13 , Kristen Bibeau  14 , Kaj Blennow  15 , David J Brooks  16 , Mark A van Buchem  17 , Vincent Camus  18 , Enrica Cavedo  19 , Kewei Chen  20 , Gael Chetelat  21 , Ann D Cohen  22 , Alexander Drzezga  23 , Sebastiaan Engelborghs  24 , Anne M Fagan  25 , Tormod Fladby  9 , Adam S Fleisher  26 , Wiesje M van der Flier  27 , Lisa Ford  28 , Stefan Förster  29 , Juan Fortea  7 , Nadia Foskett  8 , Kristian S Frederiksen  30 , Yvonne Freund-Levi  31 , Giovanni B Frisoni  32 , Lutz Froelich  33 , Tomasz Gabryelewicz  34 , Kiran Dip Gill  35 , Olymbia Gkatzima  36 , Estrella Gómez-Tortosa  37 , Mark Forrest Gordon  38 , Timo Grimmer  39 , Harald Hampel  40 , Lucrezia Hausner  33 , Sabine Hellwig  41 , Sanna-Kaisa Herukka  42 , Helmut Hildebrandt  43 , Lianna Ishihara  44 , Adrian Ivanoiu  45 , William J Jagust  46 , Peter Johannsen  47 , Ramesh Kandimalla  48 , Elisabeth Kapaki  49 , Aleksandra Klimkowicz-Mrowiec  50 , William E Klunk  22 , Sebastian Köhler  1 , Norman Koglin  51 , Johannes Kornhuber  52 , Milica G Kramberger  53 , Koen Van Laere  54 , Susan M Landau  46 , Dong Young Lee  55 , Mony de Leon  56 , Viviana Lisetti  57 , Alberto Lleó  7 , Karine Madsen  58 , Wolfgang Maier  59 , Jan Marcusson  60 , Niklas Mattsson  61 , Alexandre de Mendonça  62 , Olga Meulenbroek  63 , Philipp T Meyer  64 , Mark A Mintun  65 , Vincent Mok  66 , José Luis Molinuevo  67 , Hanne M Møllergård  9 , John C Morris  25 , Barbara Mroczko  68 , Stefan Van der Mussele  24 , Duk L Na  69 , Andrew Newberg  70 , Agneta Nordberg  71 , Arto Nordlund  15 , Gerald P Novak  28 , George P Paraskevas  49 , Lucilla Parnetti  57 , Gayan Perera  72 , Oliver Peters  73 , Julius Popp  74 , Sudesh Prabhakar  75 , Gil D Rabinovici  76 , Inez H G B Ramakers  1 , Lorena Rami  67 , Catarina Resende de Oliveira  11 , Juha O Rinne  77 , Karen M Rodrigue  78 , Eloy Rodríguez-Rodríguez  79 , Catherine M Roe  25 , Uros Rot  53 , Christopher C Rowe  80 , Eckart Rüther  81 , Osama Sabri  12 , Páscual Sanchez-Juan  79 , Isabel Santana  11 , Marie Sarazin  82 , Johannes Schröder  83 , Christin Schütte  43 , Sang W Seo  69 , Femke Soetewey  24 , Hilkka Soininen  42 , Luiza Spiru  84 , Hanne Struyfs  24 , Charlotte E Teunissen  85 , Magda Tsolaki  36 , Rik Vandenberghe  86 , Marcel M Verbeek  87 , Victor L Villemagne  80 , Stephanie J B Vos  1 , Linda J C van Waalwijk van Doorn  87 , Gunhild Waldemar  30 , Anders Wallin  15 , Åsa K Wallin  61 , Jens Wiltfang  81 , David A Wolk  10 , Marzena Zboch  88 , Henrik Zetterberg  89
Affiliations
Meta-Analysis

Prevalence of cerebral amyloid pathology in persons without dementia: a meta-analysis

Willemijn J Jansen et al. JAMA. .

Abstract

Importance: Cerebral amyloid-β aggregation is an early pathological event in Alzheimer disease (AD), starting decades before dementia onset. Estimates of the prevalence of amyloid pathology in persons without dementia are needed to understand the development of AD and to design prevention studies.

Objective: To use individual participant data meta-analysis to estimate the prevalence of amyloid pathology as measured with biomarkers in participants with normal cognition, subjective cognitive impairment (SCI), or mild cognitive impairment (MCI).

Data sources: Relevant biomarker studies identified by searching studies published before April 2015 using the MEDLINE and Web of Science databases and through personal communication with investigators.

Study selection: Studies were included if they provided individual participant data for participants without dementia and used an a priori defined cutoff for amyloid positivity.

Data extraction and synthesis: Individual records were provided for 2914 participants with normal cognition, 697 with SCI, and 3972 with MCI aged 18 to 100 years from 55 studies.

Main outcomes and measures: Prevalence of amyloid pathology on positron emission tomography or in cerebrospinal fluid according to AD risk factors (age, apolipoprotein E [APOE] genotype, sex, and education) estimated by generalized estimating equations.

Results: The prevalence of amyloid pathology increased from age 50 to 90 years from 10% (95% CI, 8%-13%) to 44% (95% CI, 37%-51%) among participants with normal cognition; from 12% (95% CI, 8%-18%) to 43% (95% CI, 32%-55%) among patients with SCI; and from 27% (95% CI, 23%-32%) to 71% (95% CI, 66%-76%) among patients with MCI. APOE-ε4 carriers had 2 to 3 times higher prevalence estimates than noncarriers. The age at which 15% of the participants with normal cognition were amyloid positive was approximately 40 years for APOE ε4ε4 carriers, 50 years for ε2ε4 carriers, 55 years for ε3ε4 carriers, 65 years for ε3ε3 carriers, and 95 years for ε2ε3 carriers. Amyloid positivity was more common in highly educated participants but not associated with sex or biomarker modality.

Conclusions and relevance: Among persons without dementia, the prevalence of cerebral amyloid pathology as determined by positron emission tomography or cerebrospinal fluid findings was associated with age, APOE genotype, and presence of cognitive impairment. These findings suggest a 20- to 30-year interval between first development of amyloid positivity and onset of dementia.

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Figures

Figure 1
Figure 1
Flow Diagram of Literature Search and Study Selection a The European Medical Information Framework–Alzheimer Disease (EMIF-AD) and Biomarkers for Alzheimer Disease and Parkinson Disease (BIOMARKAPD) projects.
Figure 2
Figure 2
Association of Age With Prevalence Estimates of Amyloid Positivity According to Cognitive Status The prevalence estimates were generated from generalized estimating equations. The model included age and cognitive status as predictors. Shading indicates 95% CIs; SCI, subjective cognitive impairment; MCI, mild cognitive impairment.
Figure 3
Figure 3
Association of Age With Prevalence Estimates of Amyloid Positivity According to Cognitive Status and Apolipoprotein E (APOE) Genotype The model for the analyses in panels A and B included age, cognitive status, APOE-ε4 status, an interaction between age and cognitive status, and an interaction between age and APOE-ε4 status as predictors. The models for the analyses in panels C and D included age, cognitive status, APOE genotype, an interaction between age and cognitive status, an interaction between age and APOE genotype, and an interaction between cognitive status and APOE genotype as predictors. In panel C, none of the 10 participants with ε2ε2 were amyloid positive, and no 95% confidence interval is provided for this group. In panel D, data of participants with ε2ε2 are not shown because of the small sample size (n = 5). Shading indicates 95% CIs; SCI, subjective cognitive impairment; MCI, mild cognitive impairment.
Figure 4
Figure 4
Comparison of the Prevalence of Amyloid Positivity With the Prevalence of and Lifetime Risks for Alzheimer Disease–Type Dementia The prevalence estimates in panel A were estimated from a meta-analysis of 14 studies (eMethods in the Supplement). The prevalence estimates in panel B of amyloid positivity in participants with normal cognition are plotted against published lifetime risks for Alzheimer disease (AD)–type dementia by APOE genotype (adapted from Genin et al).
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