000283056 001__ 283056
000283056 005__ 20251230103619.0
000283056 0247_ $$2doi$$a10.1002/alz70856_103008
000283056 0247_ $$2pmid$$apmid:41451762
000283056 0247_ $$2pmc$$apmc:PMC12741810
000283056 0247_ $$2ISSN$$a1552-5260
000283056 0247_ $$2ISSN$$a1552-5279
000283056 037__ $$aDZNE-2025-01463
000283056 041__ $$aEnglish
000283056 082__ $$a610
000283056 1001_ $$aWisch, Julie K$$b0
000283056 1112_ $$aAlzheimer’s Association International Conference$$cToronto$$d2025-07-27 - 2025-07-31$$gAAIC 25$$wCanada
000283056 245__ $$aValidation of Amyloid Chronicity in Autosomal Dominant Alzheimer Disease
000283056 260__ $$c2025
000283056 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1767015600_31204
000283056 3367_ $$033$$2EndNote$$aConference Paper
000283056 3367_ $$2BibTeX$$aINPROCEEDINGS
000283056 3367_ $$2DRIVER$$aconferenceObject
000283056 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$mjournal
000283056 3367_ $$2DataCite$$aOutput Types/Conference Abstract
000283056 3367_ $$2ORCID$$aOTHER
000283056 520__ $$aAlzheimer Disease (AD) pathology evolves over decades, and understanding this progression is critical to the understanding of the disease and timing therapeutic interventions. Since individuals with Autosomal Dominant AD (ADAD) develop symptoms around the same age as their parent, it is possible to predict symptom onset and stage individuals by their estimated years to symptom onset (EYO). This approach does not generalize to other forms of AD, thus there is a pressing need for the timecourse of ADAD to be defined in broadly relevant terms. The objective of this project is to validate the Sampled Iterative Local Approximation (SILA) algorithm in a cohort with a known disease timecourse. SILA generates an estimate of time from amyloid positivity (Atime) based on longitudinal PET data.We evaluated Atime in a longitudinal ADAD sample (N = 316) with PET PiB data in three ways. First, we compared predicted age at amyloid positive (A+) to observed age at A+ for individuals who became A+ during enrollment. Next, using linear regression, we compared estimated age at A+ to estimated age at symptom onset (EYO=0). Finally, we used generalized additive models to compare the amount of variance in concurrent cognitive performance explained both Atime and EYO.We observed a mean average error of 1.15 years between actual age at A+ (N = 26) and the SILA-predicted Atime. Across all participants, SILA-estimated age at A+ explained 39% of the variance in estimated age at symptom onset (β = 0.918, p < 0.0001). Finally, we observed a nonlinear association between cognition and both Atime and EYO. Atime explained 19% of the variance in the general cognitive composite while EYO explained 43% of the variance.SILA produces a valid estimate of time-from-amyloid positivity in ADAD. This work allows for disease stage in ADAD to be compared to staging for broad forms of AD, which was not previously possible using EYO. However, this work also illustrates that there is a high degree of heterogeneity in preclinical disease duration that is not explained by amyloid alone.
000283056 536__ $$0G:(DE-HGF)POF4-352$$a352 - Disease Mechanisms (POF4-352)$$cPOF4-352$$fPOF IV$$x0
000283056 588__ $$aDataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
000283056 650_7 $$2NLM Chemicals$$aBiomarkers
000283056 650_7 $$2NLM Chemicals$$aAmyloid beta-Peptides
000283056 650_2 $$2MeSH$$aHumans
000283056 650_2 $$2MeSH$$aAlzheimer Disease: diagnostic imaging
000283056 650_2 $$2MeSH$$aAlzheimer Disease: diagnosis
000283056 650_2 $$2MeSH$$aAlzheimer Disease: metabolism
000283056 650_2 $$2MeSH$$aPositron-Emission Tomography
000283056 650_2 $$2MeSH$$aMale
000283056 650_2 $$2MeSH$$aFemale
000283056 650_2 $$2MeSH$$aBiomarkers: metabolism
000283056 650_2 $$2MeSH$$aLongitudinal Studies
000283056 650_2 $$2MeSH$$aAged
000283056 650_2 $$2MeSH$$aDisease Progression
000283056 650_2 $$2MeSH$$aMiddle Aged
000283056 650_2 $$2MeSH$$aAlgorithms
000283056 650_2 $$2MeSH$$aAmyloid beta-Peptides: metabolism
000283056 650_2 $$2MeSH$$aBrain: diagnostic imaging
000283056 650_2 $$2MeSH$$aBrain: metabolism
000283056 7001_ $$aMcKay, Nicole S$$b1
000283056 7001_ $$aZammit, Matthew D$$b2
000283056 7001_ $$aChristian, Bradley T$$b3
000283056 7001_ $$aSchultz, Stephanie A$$b4
000283056 7001_ $$aMillar, Peter R$$b5
000283056 7001_ $$aBarthélemy, Nicolas R$$b6
000283056 7001_ $$aRyan, Natalie S$$b7
000283056 7001_ $$aRenton, Alan E$$b8
000283056 7001_ $$aVermunt, Lisa$$b9
000283056 7001_ $$aJoseph-Mathurin, Nelly$$b10
000283056 7001_ $$aShirzadi, Zahra$$b11
000283056 7001_ $$aStrain, Jeremy F$$b12
000283056 7001_ $$aChrem, Patricio$$b13
000283056 7001_ $$aDaniels, Alisha$$b14
000283056 7001_ $$aChhatwal, Jasmeer P$$b15
000283056 7001_ $$aCruchaga, Carlos$$b16
000283056 7001_ $$aIbanez, Laura$$b17
000283056 7001_ $$0P:(DE-2719)2000010$$aJucker, Mathias$$b18$$udzne
000283056 7001_ $$aDay, Gregory S$$b19
000283056 7001_ $$0P:(DE-HGF)0$$aLee, Jae-Hong$$b20
000283056 7001_ $$0P:(DE-2719)2811659$$aLevin, Johannes$$b21$$udzne
000283056 7001_ $$aLlibre-Guerra, Jorge J$$b22
000283056 7001_ $$aAguillon, David$$b23
000283056 7001_ $$aRoh, Jee Hoon$$b24
000283056 7001_ $$aSupnet-Bell, Charlene$$b25
000283056 7001_ $$aXiong, Chengjie$$b26
000283056 7001_ $$aSchindler, Suzanne E$$b27
000283056 7001_ $$aWang, Guoqiao$$b28
000283056 7001_ $$aLi, Yan$$b29
000283056 7001_ $$aKoeppe, Robert$$b30
000283056 7001_ $$aJack, Clifford R$$b31
000283056 7001_ $$aMorris, John C$$b32
000283056 7001_ $$aMcDade, Eric$$b33
000283056 7001_ $$aBateman, Randall J$$b34
000283056 7001_ $$aBenzinger, Tammie L S$$b35
000283056 7001_ $$aAnces, Beau$$b36
000283056 7001_ $$aBetthauser, Tobey J$$b37
000283056 7001_ $$aGordon, Brian A$$b38
000283056 7001_ $$aNetwork, Dominantly Inherited Alzheimer$$b39$$eCollaboration Author
000283056 773__ $$0PERI:(DE-600)2201940-6$$a10.1002/alz70856_103008$$gVol. 21 Suppl 2, no. Suppl 2, p. e103008$$nSuppl 2$$pe103008$$tAlzheimer's and dementia$$v21$$x1552-5260$$y2025
000283056 8564_ $$uhttps://pub.dzne.de/record/283056/files/DZNE-2025-1463.pdf$$yOpenAccess
000283056 8564_ $$uhttps://pub.dzne.de/record/283056/files/DZNE-2025-1463.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000283056 909CO $$ooai:pub.dzne.de:283056$$popenaire$$popen_access$$pVDB$$pdriver$$pdnbdelivery
000283056 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)2000010$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b18$$kDZNE
000283056 9101_ $$0I:(DE-HGF)0$$6P:(DE-2719)2811659$$aExternal Institute$$b21$$kExtern
000283056 9131_ $$0G:(DE-HGF)POF4-352$$1G:(DE-HGF)POF4-350$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lNeurodegenerative Diseases$$vDisease Mechanisms$$x0
000283056 9141_ $$y2025
000283056 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2025-01-06
000283056 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2025-01-06
000283056 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000283056 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bALZHEIMERS DEMENT : 2022$$d2025-01-06
000283056 915__ $$0StatID:(DE-HGF)3001$$2StatID$$aDEAL Wiley$$d2025-01-06$$wger
000283056 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2025-01-06
000283056 915__ $$0StatID:(DE-HGF)9910$$2StatID$$aIF >= 10$$bALZHEIMERS DEMENT : 2022$$d2025-01-06
000283056 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2025-01-06
000283056 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000283056 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2025-01-06
000283056 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2025-01-06
000283056 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2025-01-06
000283056 9201_ $$0I:(DE-2719)1210001$$kAG Jucker$$lCell Biology of Neurological Diseases$$x0
000283056 980__ $$aabstract
000283056 980__ $$aVDB
000283056 980__ $$aUNRESTRICTED
000283056 980__ $$ajournal
000283056 980__ $$aI:(DE-2719)1210001
000283056 9801_ $$aFullTexts