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The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease

BACKGROUND: Quantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer’s disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical tri...

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Autores principales: Hadjichrysanthou, Christoforos, Evans, Stephanie, Bajaj, Sumali, Siakallis, Loizos C., McRae-McKee, Kevin, de Wolf, Frank, Anderson, Roy M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293779/
https://www.ncbi.nlm.nih.gov/pubmed/32534594
http://dx.doi.org/10.1186/s13195-020-00636-z
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author Hadjichrysanthou, Christoforos
Evans, Stephanie
Bajaj, Sumali
Siakallis, Loizos C.
McRae-McKee, Kevin
de Wolf, Frank
Anderson, Roy M.
author_facet Hadjichrysanthou, Christoforos
Evans, Stephanie
Bajaj, Sumali
Siakallis, Loizos C.
McRae-McKee, Kevin
de Wolf, Frank
Anderson, Roy M.
author_sort Hadjichrysanthou, Christoforos
collection PubMed
description BACKGROUND: Quantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer’s disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical trial recruitment and treatment assessment. This study aims to characterise continuous changes of such markers and determine their rate of change and temporal order throughout the AD continuum. METHODS: The methodology is founded on the development of stochastic models to estimate the expected time to reach different clinical disease states, for different risk groups, and synchronise short-term individual biomarker data onto a disease progression timeline. Twenty-seven markers are considered, including a range of cognitive scores, cerebrospinal (CSF) and plasma fluid proteins, and brain structural and molecular imaging measures. Data from 2014 participants in the Alzheimer’s Disease Neuroimaging Initiative database is utilised. RESULTS: The model suggests that detectable memory dysfunction could occur up to three decades prior to the onset of dementia due to AD (ADem). This is closely followed by changes in amyloid-β CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-β accumulation. Brain hypometabolism starts later, about 14 years before onset, along with changes in the levels of total and phosphorylated tau proteins. Loss of functional abilities occurs rapidly around ADem onset. Neurofilament light is the only protein with notable early changes in plasma levels. The rate of change varies, with CSF, memory, amyloid PET and brain structural measures exhibiting the highest rate before the onset of ADem, followed by a decline. The probability of progressing to a more severe clinical state increases almost exponentially with age. In accordance with previous studies, the presence of apolipoprotein E4 alleles and amyloid-β accumulation can be associated with an increased risk of developing the disease, but their influence depends on age and clinical state. CONCLUSIONS: Despite the limited longitudinal data at the individual level and the high variability observed in such data, the study elucidates the link between the long asynchronous pathophysiological processes and the preclinical and clinical stages of AD.
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spelling pubmed-72937792020-06-15 The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease Hadjichrysanthou, Christoforos Evans, Stephanie Bajaj, Sumali Siakallis, Loizos C. McRae-McKee, Kevin de Wolf, Frank Anderson, Roy M. Alzheimers Res Ther Research BACKGROUND: Quantifying changes in the levels of biological and cognitive markers prior to the clinical presentation of Alzheimer’s disease (AD) will provide a template for understanding the underlying aetiology of the clinical syndrome and, concomitantly, for improving early diagnosis, clinical trial recruitment and treatment assessment. This study aims to characterise continuous changes of such markers and determine their rate of change and temporal order throughout the AD continuum. METHODS: The methodology is founded on the development of stochastic models to estimate the expected time to reach different clinical disease states, for different risk groups, and synchronise short-term individual biomarker data onto a disease progression timeline. Twenty-seven markers are considered, including a range of cognitive scores, cerebrospinal (CSF) and plasma fluid proteins, and brain structural and molecular imaging measures. Data from 2014 participants in the Alzheimer’s Disease Neuroimaging Initiative database is utilised. RESULTS: The model suggests that detectable memory dysfunction could occur up to three decades prior to the onset of dementia due to AD (ADem). This is closely followed by changes in amyloid-β CSF levels and the first cognitive decline, as assessed by sensitive measures. Hippocampal atrophy could be observed as early as the initial amyloid-β accumulation. Brain hypometabolism starts later, about 14 years before onset, along with changes in the levels of total and phosphorylated tau proteins. Loss of functional abilities occurs rapidly around ADem onset. Neurofilament light is the only protein with notable early changes in plasma levels. The rate of change varies, with CSF, memory, amyloid PET and brain structural measures exhibiting the highest rate before the onset of ADem, followed by a decline. The probability of progressing to a more severe clinical state increases almost exponentially with age. In accordance with previous studies, the presence of apolipoprotein E4 alleles and amyloid-β accumulation can be associated with an increased risk of developing the disease, but their influence depends on age and clinical state. CONCLUSIONS: Despite the limited longitudinal data at the individual level and the high variability observed in such data, the study elucidates the link between the long asynchronous pathophysiological processes and the preclinical and clinical stages of AD. BioMed Central 2020-06-13 /pmc/articles/PMC7293779/ /pubmed/32534594 http://dx.doi.org/10.1186/s13195-020-00636-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Hadjichrysanthou, Christoforos
Evans, Stephanie
Bajaj, Sumali
Siakallis, Loizos C.
McRae-McKee, Kevin
de Wolf, Frank
Anderson, Roy M.
The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease
title The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease
title_full The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease
title_fullStr The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease
title_full_unstemmed The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease
title_short The dynamics of biomarkers across the clinical spectrum of Alzheimer’s disease
title_sort dynamics of biomarkers across the clinical spectrum of alzheimer’s disease
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293779/
https://www.ncbi.nlm.nih.gov/pubmed/32534594
http://dx.doi.org/10.1186/s13195-020-00636-z
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