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dc.contributor.authorBenedet, Andréa L.pt_BR
dc.contributor.authorBrum, Wagner Scheerenpt_BR
dc.contributor.authorHansson, Oskarpt_BR
dc.contributor.authorKarikari, Thomas K.pt_BR
dc.contributor.authorZimmer, Eduardo Rigonpt_BR
dc.contributor.authorZetterberg, Henrikpt_BR
dc.contributor.authorBlennow, Kajpt_BR
dc.contributor.authorAshton, Nicholas J.pt_BR
dc.date.accessioned2022-04-13T04:50:21Zpt_BR
dc.date.issued2022pt_BR
dc.identifier.issn1758-9193pt_BR
dc.identifier.urihttp://hdl.handle.net/10183/237067pt_BR
dc.description.abstractBackground: Plasma biomarkers for Alzheimer’s disease (AD) have broad potential as screening tools in primary care and disease-modifying trials. Detecting elevated amyloid-β (Aβ) pathology to support trial recruitment or initiating Aβ-targeting treatments would be of critical value. In this study, we aimed to examine the robustness of plasma biomarkers to detect elevated Aβ pathology at different stages of the AD continuum. Beyond determining the best biomarker—or biomarker combination—for detecting this outcome, we also simulated increases in inter-assay coefficient of variability (CV) to account for external factors not considered by intra-assay variability. With this, we aimed to determine whether plasma biomarkers would maintain their accuracy if applied in a setting which anticipates higher variability (i.e., clinical routine). Methods: We included 118 participants (cognitively unimpaired [CU, n = 50], cognitively impaired [CI, n = 68]) from the ADNI study with a full plasma biomarker profile (Aβ42/40, GFAP, p-tau181, NfL) and matched amyloid imaging. Initially, we investigated how simulated CV variations impacted single-biomarker discriminative performance of amyloid status. Then, we evaluated the predictive performance of models containing different biomarker combinations, based both on original and simulated measurements. Plasma Aβ42/40 was represented by both immunoprecipitation mass spectrometry (IP-MS) and single molecule array (Simoa) methods in separate analyses. Model selection was based on a decision tree which incorporated Akaike information criterion value, likelihood ratio tests between the best-fitting models and, finally, and Schwartz’s Bayesian information criterion. Results: Increasing variation greatly impacted the performance of plasma Aβ42/40 in discriminating Aβ status. In contrast, the performance of plasma GFAP and p-tau181 remained stable with variations >20%. When biomarker models were compared, the models “AG” (Aβ42/40 + GFAP; AUC = 86.5), “A” (Aβ42/40; AUC = 82.3), and “AGP” (Aβ42/40 + GFAP + p-tau181; AUC = 93.5) were superior in determining Aβ burden in all participants, within-CU, and within-CI groups, respectively. In the robustness analyses, when repeating model selection based on simulated measurements, models including IP-MS Aβ42/40 were also most often selected. Simoa Aβ42/40 did not contribute to any selected model when used as an immunoanalytical alternative to IP-MS Aβ42/40. Conclusions: Plasma Aβ42/40, as quantified by IP-MS, shows high performance in determining Aβ positivity at all stages of the AD continuum, with GFAP and p-tau181 further contributing at CI stage. However, between-assay variations greatly impacted the performance of Aβ42/40 but not that of GFAP and p-tau181. Therefore, when dealing with between-assay CVs that exceed 5%, plasma GFAP and p-tau181 should be considered for a more robust determination of Aβ burden in CU and CI participants, respectively.en
dc.format.mimetypeapplication/pdfpt_BR
dc.language.isoengpt_BR
dc.relation.ispartofAlzheimer's research & therapy. [London]. Vol. 14 (2022), 26, 11 p.pt_BR
dc.rightsOpen Accessen
dc.subjectAmyloid,en
dc.subjectBiomarcadorespt_BR
dc.subjectPlasma biomarkeren
dc.subjectDoença de Alzheimerpt_BR
dc.subjectTomografia por emissão de pósitronspt_BR
dc.subjectMass spectrometryen
dc.subjectPlasmapt_BR
dc.subjectImmunoassayen
dc.subjectAlzheimer’s diseaseen
dc.subjectProteínas taupt_BR
dc.subjectPeptídeos beta-amilóidespt_BR
dc.subjectADNIen
dc.subjectP-tau181en
dc.subjectGFAPen
dc.subjectNfLen
dc.titleThe accuracy and robustness of plasma biomarker models for amyloid PET positivitypt_BR
dc.typeArtigo de periódicopt_BR
dc.identifier.nrb001138761pt_BR
dc.type.originEstrangeiropt_BR


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