Plasma Glial Fibrillary Acidic Protein Detects Alzheimer Pathology and Predicts Future Conversion to Alzheimer Dementia in Patients With Mild Cognitive Impairment

Cicognola, Claudia; Bateman, Randall J.; Hertze, Joakim; Zetterberg, Henrik; Blennow, Kaj; Mattsson‐Carlgren, Niklas; Hansson, Oskar

doi:10.21203/rs.3.rs-118286/v1

Cited by 21 publications

(31 citation statements)

References 40 publications

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“…In BioFINDER-2, plasma GFAP was measured with Simoa GFAP Discovery kits for SR-X, as previously described. 32 For plasma GFAP in BioFINDER-1 and CSF GFAP in both cohorts, the Elecsys immunoassay (Roche Diagnostics) was used. 10…”

Section: Gfapmentioning

confidence: 99%

Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance

Binette

Janelidze

Cullen

et al. 2022

Alzheimer's & Dementia

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Introduction Plasma biomarkers will likely revolutionize the diagnostic work‐up of Alzheimer's disease (AD) globally. Before widespread use, we need to determine if confounding factors affect the levels of these biomarkers, and their clinical utility. Methods Participants with plasma and CSF biomarkers, creatinine, body mass index (BMI), and medical history data were included (BioFINDER‐1: n = 748, BioFINDER‐2: n = 421). We measured beta‐amyloid (Aβ42, Aβ40), phosphorylated tau (p‐tau217, p‐tau181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). Results In both cohorts, creatinine and BMI were the main factors associated with NfL, GFAP, and to a lesser extent with p‐tau. However, adjustment for BMI and creatinine had only minor effects in models predicting either the corresponding levels in CSF or subsequent development of dementia. Discussion Creatinine and BMI are related to certain plasma biomarkers levels, but they do not have clinically relevant confounding effects for the vast majority of individuals. Highlights Creatinine and body mass index (BMI) are related to certain plasma biomarker levels. Adjusting for creatinine and BMI has minor influence on plasma‐cerebrospinal fluid (CSF) associations. Adjusting for creatinine and BMI has minor influence on prediction of dementia using plasma biomarkers.

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Section: Gfapmentioning

confidence: 99%

Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance

Binette

Janelidze

Cullen

et al. 2022

Alzheimer's & Dementia

View full text Add to dashboard Cite

show abstract

“…1 In large, independent cohort studies, these biomarkers have been shown consistently to provide useful prognostic information with respect to longitudinal cognitive decline and risk for AD dementia. [2][3][4][5][6] Recent work has even demonstrated the superiority of plasma biomarkers (combined with other accessible measures) compared to clinicians' predictions of AD-related outcome in a population with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). 7 BBMs also show high diagnostic performance, particularly in differentiating individuals based on abnormal amyloid or tau status as measured by cerebrospinal fluid (CSF) or positron emission tomography (PET).…”

Section: Introductionmentioning

confidence: 99%

Test‐retest variability of plasma biomarkers in Alzheimer's disease and its effects on clinical prediction models

Cullen

Bateman

Mattsson‐Carlgren

et al. 2022

Alzheimer's & Dementia

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Introduction:The effect of random error on the performance of blood-based biomarkers for Alzheimer's disease (AD) must be determined before clinical implementation. Methods:We measured test-retest variability of plasma amyloid beta (Aβ)42/Aβ40, neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and phosphorylated tau (p-tau)217 and simulated effects of this variability on biomarker performance when predicting either cerebrospinal fluid (CSF) Aβ status or conversion to AD dementia in 399 non-demented participants with cognitive symptoms.Results: Clinical performance was highest when combining all biomarkers. Among single-biomarkers, p-tau217 performed best. Test-retest variability ranged from 4.1% (Aβ42/Aβ40) to 25% (GFAP). This variability reduced the performance of the biomarkers (≈ΔAUC [area under the curve] −1% to −4%) with the least effects on models withThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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“…to predict the progression of cognitive decline. 5,[19][20][21] It has also been reported that plasma GFAP levels decrease as eGFR levels and body weight increase, whereas plasma GFAP levels increase with age. 22 Here we show that an indirect-response model, where donanemab treatment decreased amyloid load (a relative change from baseline)…”

Section: Discussionmentioning

confidence: 95%

Donanemab exposure and efficacy relationship using modeling in Alzheimer's disease

Gueorguieva

Willis

Chua

et al. 2023

A&D Transl Res & Clin Interv

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INTRODUCTION:Donanemab is an amyloid-targeting therapy that specifically targets brain amyloid plaques. The objective of these analyses was to characterize the relationship of donanemab exposure with plasma biomarkers and clinical efficacy through modeling.METHODS: Data for the analyses were from participants with Alzheimer's disease from the phase 1 and TRAILBLAZER-ALZ studies. Indirect-response models were used to fit plasma phosphorylated tau 217 (p-tau217) and plasma glial fibrillated acidic protein (GFAP) data over time. Disease-progression models were developed using pharmacokinetic/pharmacodynamic modeling. RESULTS:The plasma p-tau217 and plasma GFAP models adequately predicted the change over time, with donanemab resulting in decreased plasma p-tau217 and plasma GFAP concentrations. The disease-progression models confirmed that donanemab significantly reduced the rate of clinical decline. Simulations revealed that donanemab slowed disease progression irrespective of baseline tau positron emission tomography (PET) level within the evaluated population. DISCUSSION:The disease-progression models show a clear treatment effect of donanemab on clinical efficacy regardless of baseline disease severity.

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Plasma Glial Fibrillary Acidic Protein Detects Alzheimer Pathology and Predicts Future Conversion to Alzheimer Dementia in Patients With Mild Cognitive Impairment

Cited by 21 publications

References 40 publications

Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance

Confounding factors of Alzheimer's disease plasma biomarkers and their impact on clinical performance

Test‐retest variability of plasma biomarkers in Alzheimer's disease and its effects on clinical prediction models

Donanemab exposure and efficacy relationship using modeling in Alzheimer's disease

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