Predicting long-term clinical stability in amyloid-positive subjects by FDG-PET.

Aged; Aged, 80 and over; Alzheimer Disease/diagnostic imaging; Amyloid beta-Peptides/metabolism; Biomarkers/metabolism; Brain; Clinical Trials as Topic; Cognitive Dysfunction/diagnostic imaging; Dementia/diagnosis; Disease Progression; Female; Fluorodeoxyglucose F18/metabolism; Healthy Volunteers; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Positron-Emission Tomography/methods

Abstract :

[en] Imaging biomarkers can be used to screen participants for Alzheimer's disease clinical trials. To test the predictive values in clinical progression of neuropathology change (amyloid-PET) or brain metabolism as neurodegeneration biomarker ([18F]FDG-PET), we evaluated data from N = 268 healthy controls and N = 519 mild cognitive impairment subjects. Despite being a significant risk factor, amyloid positivity was not associated with clinical progression in the majority (≥60%) of subjects. Notably, a negative [18F]FDG-PET scan at baseline strongly predicted clinical stability with high negative predictive values (>0.80) for both groups. We suggest [18F]FDG-PET brain metabolism or other neurodegeneration measures should be coupled to amyloid-PET to exclude clinically stable individuals from clinical trials.

Disciplines :

Radiology, nuclear medicine & imaging

Author, co-author :

Iaccarino, Leonardo

Sala, Arianna ; UniSR

Perani, Daniela

Language :

English

Title :

Predicting long-term clinical stability in amyloid-positive subjects by FDG-PET.

Publication date :

2019

Journal title :

Annals of Clinical and Translational Neurology

eISSN :

2328-9503

Publisher :

Wiley, Hoboken, United States - New Jersey

Volume :

Issue :

Pages :

1113-1120

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 02 June 2021

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Bibliography

Cummings J. Lessons learned from alzheimer disease: clinical trials with negative outcomes. Clin Transl Sci 2018;11:147–152.
Weiner MW, Veitch DP, Aisen PS, et al. Recent publications from the Alzheimer's disease neuroimaging initiative: reviewing progress toward improved AD clinical trials. Alzheimer's & Dementia 2017;13:e1–e85.
Brookmeyer R, Abdalla N. Estimation of lifetime risks of Alzheimer's disease dementia using biomarkers for preclinical disease. Alzheimers Dement 2018;14:981–988.
Jansen WJ, Ossenkoppele R, Knol DL, et al. Prevalence of cerebral amyloid pathology in persons without dementia. JAMA 2015;313:1915–1924.
Ossenkoppele R, Jansen WJ, Rabinovici GD, et al. Prevalence of amyloid PET positivity in dementia syndromes. JAMA 2015;313:1911–1939.
Elobeid A, Libard S, Leino M, et al. Altered proteins in the aging brain. J Neuropathol Exp Neurol 2016;75:316–325.
Dubois B, Epelbaum S, Nyasse F, et al. Cognitive and neuroimaging features and brain β-amyloidosis in individuals at risk of Alzheimer's disease (INSIGHT-preAD): a longitudinal observational study. Lancet Neurol 2018;17:335–346.
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement 2011;7:263–269.
Jack CR, Bennett DA, Blennow K, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement 2018;14:535–562.
Perani D, Della Rosa PA, Cerami C, et al. Validation of an optimized SPM procedure for FDG-PET in dementia diagnosis in a clinical setting. Neuroimage Clin 2014;6:445–454.
Jagust WJ, Bandy D, Chen K, et al. The Alzheimer's disease neuroimaging initiative positron emission tomography core. Alzheimers Dement 2010;6:221–229.
Jagust WJ, Landau SM, Koeppe RA, et al. The Alzheimer's disease neuroimaging initiative 2 PET core: 2015. Alzheimers Dement 2015;11:757–771.
Hua X, Ching CRK, Mezher A, et al. MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials. Neurobiol Aging 2016;37:26–37.
Sevigny J, Suhy J, Chiao P, et al. Amyloid PET screening for enrichment of early-stage Alzheimer disease clinical trials: experience in a phase 1b clinical trial. Alzheimer Dis Assoc Disord 2016;30:1–7.
Wolz R, Schwarz AJ, Gray KR, et al. Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration. Neurology 2016;87:1235–1241.
Holland D, McEvoy LK, Desikan RS, et al. Enrichment and stratification for predementia Alzheimer disease clinical trials. PLoS ONE 2012;7:e47739.
Qian J, Hyman BT, Betensky RA. Neurofibrillary tangle stage and the rate of progression of Alzheimer symptoms: modeling using an autopsy cohort and application to clinical trial design. JAMA Neurol 2017;74:540–548.
Kohannim O, Hua X, Hibar DP, et al. Boosting power for clinical trials using classifiers based on multiple biomarkers. Neurobiol Aging 2010;31:1429–1442.
Caroli A, Prestia A, Galluzzi S, et al. Mild cognitive impairment with suspected nonamyloid pathology (SNAP): prediction of progression. Neurology 2015;84:508–515.
Prestia A, Caroli A, Wade SK, et al. Prediction of AD dementia by biomarkers following the NIA-AA and IWG diagnostic criteria in MCI patients from three European memory clinics. Alzheimers Dement 2015;11:1191–1201.
Prestia A, Caroli A, van der Flier WM, et al. Prediction of dementia in MCI patients based on core diagnostic markers for Alzheimer disease. Neurology 2013;80:1048–1056.
Bertens D, Tijms BM, Vermunt L, et al. The effect of diagnostic criteria on outcome measures in preclinical and prodromal Alzheimer's disease: implications for trial design. Alzheimers Dement 2017;3:513–523.
Cerami C, Della Rosa PA, Magnani G, et al. Brain metabolic maps in Mild Cognitive Impairment predict heterogeneity of progression to dementia. Neuroimage Clin 2015;7:187–194.
Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE 2013;8:e68910.

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