Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Alzheimer's Research & Therapy
Published in: Alzheimer's Research & Therapy 1/2021

Open Access 01-12-2021 | Alzheimer's Disease | Research

Early detection of amyloid load using 18F-florbetaben PET

Authors: Santiago Bullich, Núria Roé-Vellvé, Marta Marquié, Susan M. Landau, Henryk Barthel, Victor L. Villemagne, Ángela Sanabria, Juan Pablo Tartari, Oscar Sotolongo-Grau, Vincent Doré, Norman Koglin, Andre Müller, Audrey Perrotin, Aleksandar Jovalekic, Susan De Santi, Lluís Tárraga, Andrew W. Stephens, Christopher C. Rowe, Osama Sabri, John P. Seibyl, Mercè Boada

Published in: Alzheimer's Research & Therapy | Issue 1/2021

Login to get access

Abstract

Background

A low amount and extent of Aβ deposition at early stages of Alzheimer’s disease (AD) may limit the use of previously developed pathology-proven composite SUVR cutoffs. This study aims to characterize the population with earliest abnormal Aβ accumulation using 18F-florbetaben PET. Quantitative thresholds for the early (SUVRearly) and established (SUVRestab) Aβ deposition were developed, and the topography of early Aβ deposition was assessed. Subsequently, Aβ accumulation over time, progression from mild cognitive impairment (MCI) to AD dementia, and tau deposition were assessed in subjects with early and established Aβ deposition.

Methods

The study population consisted of 686 subjects (n = 287 (cognitively normal healthy controls), n = 166 (subjects with subjective cognitive decline (SCD)), n = 129 (subjects with MCI), and n = 101 (subjects with AD dementia)). Three categories in the Aβ-deposition continuum were defined based on the developed SUVR cutoffs: Aβ-negative subjects, subjects with early Aβ deposition (“gray zone”), and subjects with established Aβ pathology.

Results

SUVR using the whole cerebellum as the reference region and centiloid (CL) cutoffs for early and established amyloid pathology were 1.10 (13.5 CL) and 1.24 (35.7 CL), respectively. Cingulate cortices and precuneus, frontal, and inferior lateral temporal cortices were the regions showing the initial pathological tracer retention. Subjects in the “gray zone” or with established Aβ pathology accumulated more amyloid over time than Aβ-negative subjects. After a 4-year clinical follow-up, none of the Aβ-negative or the gray zone subjects progressed to AD dementia while 91% of the MCI subjects with established Aβ pathology progressed. Tau deposition was infrequent in those subjects without established Aβ pathology.

Conclusions

This study supports the utility of using two cutoffs for amyloid PET abnormality defining a “gray zone”: a lower cutoff of 13.5 CL indicating emerging Aβ pathology and a higher cutoff of 35.7 CL where amyloid burden levels correspond to established neuropathology findings. These cutoffs define a subset of subjects characterized by pre-AD dementia levels of amyloid burden that precede other biomarkers such as tau deposition or clinical symptoms and accelerated amyloid accumulation. The determination of different amyloid loads, particularly low amyloid levels, is useful in determining who will eventually progress to dementia. Quantitation of amyloid provides a sensitive measure in these low-load cases and may help to identify a group of subjects most likely to benefit from intervention.

Trial registration

Data used in this manuscript belong to clinical trials registered in ClinicalTrials.gov (NCT00928304, NCT00750282, NCT01138111, NCT02854033) and EudraCT (2014-000798-38).
Appendix
Available only for authorised users
Literature
1.
Braak H, Braak E. Frequency of stages of Alzheimer-related lesions in different age categories. Neurobiol Aging. 1997;18(4):351–7.PubMedCrossRef
2.
Villemagne VL, Burnham S, Bourgeat P, Brown B, Ellis KA, Salvado O, et al. Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol. 2013;12(4):357–67.PubMedCrossRef
3.
Blennow K, Zetterberg H, Rinne JO, Salloway S, Wei J, Black R, et al. Effect of immunotherapy with bapineuzumab on cerebrospinal fluid biomarker levels in patients with mild to moderate Alzheimer disease. Arch Neurol. 2012;69(8):1002–10.PubMedCrossRef
4.
Uenaka K, Nakano M, Willis BA, Friedrich S, Ferguson-Sells L, Dean RA, et al. Comparison of pharmacokinetics, pharmacodynamics, safety, and tolerability of the amyloid beta monoclonal antibody solanezumab in Japanese and white patients with mild to moderate Alzheimer disease. Clin Neuropharmacol. 2012;35(1):25–9.PubMedCrossRef
5.
Weninger S, Carrillo MC, Dunn B, Aisen PS, Bateman RJ, Kotz JD, et al. Collaboration for Alzheimer’s prevention: principles to guide data and sample sharing in preclinical Alzheimer’s disease trials. Alzheimers Dement. 2016;12(5):631–2.PubMedPubMedCentralCrossRef
6.
7.
Ritchie CW, Molinuevo JL, Truyen L, Satlin A, Van der Geyten S, Lovestone S. Development of interventions for the secondary prevention of Alzheimer’s dementia: the European Prevention of Alzheimer’s Dementia (EPAD) project. Lancet Psychiatry. 2016;3(2):179–86.PubMedCrossRef
8.
9.
Sabri O, Sabbagh MN, Seibyl J, Barthel H, Akatsu H, Ouchi Y, et al. Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer’s disease: phase 3 study. Alzheimers Dement. 2015;11(8):964–74.PubMedCrossRef
10.
Seibyl J, Catafau AM, Barthel H, Ishii K, Rowe CC, Leverenz JB, et al. Impact of training method on the robustness of the visual assessment of 18F-florbetaben PET scans: results from a phase-3 study. J Nucl Med. 2016;57(6):900–6.PubMedCrossRef
11.
Bullich S, Villemagne VL, Catafau AM, Jovalekic A, Koglin N, Rowe CC, et al. Optimal reference region to measure longitudinal amyloid-beta change with 18F-florbetaben PET. J Nucl Med. 2017;58(8):1300–6.PubMedCrossRef
12.
Bullich S, Seibyl J, Catafau AM, Jovalekic A, Koglin N, Barthel H, et al. Optimized classification of 18F-florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment. Neuroimage Clin. 2017;15:325–32.PubMedPubMedCentralCrossRef
13.
Barthel H, Gertz HJ, Dresel S, Peters O, Bartenstein P, Buerger K, et al. Cerebral amyloid-beta PET with florbetaben (18F) in patients with Alzheimer’s disease and healthy controls: a multicentre phase 2 diagnostic study. Lancet Neurol. 2011;10(5):424–35.PubMedCrossRef
14.
Jennings D, Seibyl J, Sabbagh M, Lai F, Hopkins W, Bullich S, et al. Age dependence of brain beta-amyloid deposition in Down syndrome: an [18F] florbetaben PET study. Neurology. 2015;84(5):500–7.PubMedCrossRef
15.
Tuszynski T, Rullmann M, Luthardt J, Butzke D, Tiepolt S, Gertz HJ, et al. Evaluation of software tools for automated identification of neuroanatomical structures in quantitative beta-amyloid PET imaging to diagnose Alzheimer’s disease. Eur J Nucl Med Mol Imaging. 2016;43(6):1077–87.PubMedCrossRef
16.
Villemagne VL, Ong K, Mulligan RS, Holl G, Pejoska S, Jones G, et al. Amyloid imaging with (18)F-florbetaben in Alzheimer disease and other dementias. J Nucl Med. 2011;52(8):1210–7.PubMedCrossRef
17.
Doré V, Bullich S, Rowe CC, Bourgeat P, Konate S, Sabri O, et al. Comparison of 18F-florbetaben quantification results using the standard centiloid, MR-based, and MR-less CapAIBL((R)) approaches: validation against histopathology. Alzheimers Dement. 2019;15(6):807–16.PubMedCrossRef
18.
Ong K, Villemagne VL, Bahar-Fuchs A, Lamb F, Chételat G, Raniga P, et al. (18)F-florbetaben Abeta imaging in mild cognitive impairment. Alzheimers Res Ther. 2013;5(1):4.PubMedPubMedCentralCrossRef
19.
Bischof GN, Jacobs HIL. Subthreshold amyloid and its biological and clinical meaning: long way ahead. Neurology. 2019;93(2):72–9.PubMedCrossRef
20.
Fantoni E, Collij L, Lopes Alves I, Buckley C, Farrar G. AMYPAD consortium. The spatial-temporal ordering of amyloid pathology and opportunities for PET imaging. J Nucl Med. 2020;61(2):166–71.PubMedCrossRef
21.
22.
Mattsson N, Palmqvist S, Stomrud E, Vogel J, Hansson O. Staging beta-amyloid pathology with amyloid positron emission tomography. JAMA Neurol. 2019;76(11):1319–29.
23.
Collij LE, Heeman F, Salvadó G, Ingala S, Altomare D, de Wilde A, et al. Multitracer model for staging cortical amyloid deposition using PET imaging. Neurology. 2020;95(11):e1538–53.PubMedPubMedCentralCrossRef
24.
Pascoal TA, Therriault J, Mathotaarachchi S, Kang MS, Shin M, Benedet AL, et al. Topographical distribution of Abeta predicts progression to dementia in Abeta positive mild cognitive impairment. Alzheimers Dement (Amst). 2020;12(1):e12037.PubMedCentral
25.
Hanseeuw BJ, Betensky RA, Mormino EC, Schultz AP, Sepulcre J, Becker JA, et al. PET staging of amyloidosis using striatum. Alzheimers Dement. 2018;14(10):1281–92.PubMedPubMedCentralCrossRef
26.
Rodriguez-Gomez O, Sanabria A, Perez-Cordon A, Sanchez-Ruiz D, Abdelnour C, Valero S, et al. FACEHBI: a prospective study of risk factors, biomarkers and cognition in a cohort of individuals with subjective cognitive decline. Study rationale and research protocols. J Prev Alzheimers Dis. 2017;4(2):100–8.PubMed
27.
Joshi A, Koeppe RA, Fessler JA. Reducing between scanner differences in multi-center PET studies. Neuroimage. 2009;46(1):154–9.PubMedCrossRef
28.
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage. 2002;15(1):273–89.PubMedCrossRef
29.
Rowe CC, Ackerman U, Browne W, Mulligan R, Pike KL, O'Keefe G, et al. Imaging of amyloid beta in Alzheimer’s disease with 18F-BAY94-9172, a novel PET tracer: proof of mechanism. Lancet Neurol. 2008;7(2):129–35.PubMedCrossRef
30.
Klunk WE, Koeppe RA, Price JC, Benzinger TL, Devous MD Sr, Jagust WJ, et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. 2015;11(1):1–15 e1–4.PubMedCrossRef
31.
Rowe CC, Doré V, Jones G, Baxendale D, Mulligan RS, Bullich S, et al. 18F-Florbetaben PET beta-amyloid binding expressed in centiloids. Eur J Nucl Med Mol Imaging. 2017;44(12):2053–9.PubMedPubMedCentralCrossRef
32.
Whittington A, Sharp DJ, Gunn RN. Spatiotemporal distribution of β-amyloid in Alzheimer disease is the result of heterogeneous regional carrying capacities. J Nucl Med. 2018;59(5):822–7.PubMedPubMedCentralCrossRef
33.
Mirra SS, Heyman A, McKeel D, Sumi SM, Crain BJ, Brownlee LM, et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology. 1991;41:479–86.PubMedCrossRef
34.
Bullich S. Converging evidence for a “gray-zone” of amyloid burden and its relevance. in AAIC. 2020. Virtual.
35.
Salvadó G, Molinuevo JL, Brugulat-Serrat A, Falcon C, Grau-Rivera O, Suárez-Calvet M, et al. Centiloid cut-off values for optimal agreement between PET and CSF core AD biomarkers. Alzheimers Res Ther. 2019;11(1):27.PubMedPubMedCentralCrossRef
36.
Mormino EC, Brandel MG, Madison CM, Rabinovici GD, Marks S, Baker SL, et al. Not quite PIB-positive, not quite PIB-negative: slight PIB elevations in elderly normal control subjects are biologically relevant. Neuroimage. 2012;59(2):1152–60.PubMedCrossRef
37.
La Joie R, Ayakta N, Seeley WW, Borys E, Boxer AL, DeCarli C, et al. Multisite study of the relationships between antemortem [11C]PIB-PET Centiloid values and postmortem measures of Alzheimer’s disease neuropathology. Alzheimers Dement. 2019;15(2):205–16.PubMedCrossRef
38.
Villeneuve S, Rabinovici GD, Cohn-Sheehy BI, Madison C, Ayakta N, Ghosh PM, et al. Existing Pittsburgh Compound-B positron emission tomography thresholds are too high: statistical and pathological evaluation. Brain. 2015;138(Pt 7):2020–33.PubMedPubMedCentralCrossRef
39.
Cho H, Choi JY, Hwang MS, Kim YJ, Lee HM, Lee HS, et al. In vivo cortical spreading pattern of tau and amyloid in the Alzheimer disease spectrum. Ann Neurol. 2016;80(2):247–58.PubMedCrossRef
40.
Palmqvist S, Schöll M, Strandberg O, Mattsson N, Stomrud E, Zetterberg H, et al. Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun. 2017;8(1):1214.PubMedPubMedCentralCrossRef
41.
Guo T, Landau SM, Jagust WJ. Detecting earlier stages of amyloid deposition using PET in cognitively normal elderly adults. Neurology. 2020;94(14):e1512–24.PubMedPubMedCentralCrossRef
42.
Guo T, Dukart J, Brendel M, Rominger A, Grimmer T, Yakushev I. Rate of beta-amyloid accumulation varies with baseline amyloid burden: implications for anti-amyloid drug trials. Alzheimers Dement. 2018;14(11):1387–96.PubMedCrossRef
43.
Insel PS, Mormino EC, Aisen PS, Thompson WK, Donohue MC. Neuroanatomical spread of amyloid beta and tau in Alzheimer’s disease: implications for primary prevention. Brain Commun. 2020;2(1):fcaa007.PubMedPubMedCentralCrossRef
44.
Lopes Alves I, Collij LE, Altomare D, Frisoni GB, Saint-Aubert L, Payoux P, et al. Quantitative amyloid PET in Alzheimer’s disease: the AMYPAD prognostic and natural history study. Alzheimers Dement. 2020;16(5):750–8.PubMedPubMedCentralCrossRef
45.
Bullich S, Barthel H, Koglin N, Becker GA, De Santi S, Jovalekic A, et al. Validation of noninvasive tracer kinetic analysis of (18)F-Florbetaben PET using a dual-time-window acquisition protocol. J Nucl Med. 2018;59(7):1104–10.PubMedCrossRef
Metadata
Title
Early detection of amyloid load using 18F-florbetaben PET
Authors
Santiago Bullich
Núria Roé-Vellvé
Marta Marquié
Susan M. Landau
Henryk Barthel
Victor L. Villemagne
Ángela Sanabria
Juan Pablo Tartari
Oscar Sotolongo-Grau
Vincent Doré
Norman Koglin
Andre Müller
Audrey Perrotin
Aleksandar Jovalekic
Susan De Santi
Lluís Tárraga
Andrew W. Stephens
Christopher C. Rowe
Osama Sabri
John P. Seibyl
Mercè Boada
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Alzheimer's Research & Therapy / Issue 1/2021
Electronic ISSN: 1758-9193
DOI
https://doi.org/10.1186/s13195-021-00807-6

Other articles of this Issue 1/2021

Alzheimer's Research & Therapy 1/2021 Go to the issue

Research

VAMP-2 is a surrogate cerebrospinal fluid marker of Alzheimer-related cognitive impairment in adults with Down syndrome

Research

High-intensity exercise and cognitive function in cognitively normal older adults: a pilot randomised clinical trial

Research

Boosting the diagnostic power of amyloid-β PET using a data-driven spatially informed classifier for decision support

Correction

Correction to: Exploring the potential of the platelet membrane proteome as a source of peripheral biomarkers for Alzheimer’s disease

Research

P-tau and neurodegeneration mediate the effect of β-amyloid on cognition in non-demented elders

Research

Association of CSF proteins with tau and amyloid β levels in asymptomatic 70-year-olds