Evidence map›Paper›PMID 32534594›Full record

ArticleAlzheimer's research & therapy2020

The dynamics of biomarkers across the clinical spectrum of Alzheimer's disease.

Christoforos Hadjichrysanthou, Stephanie Evans, Sumali Bajaj, Loizos C Siakallis, Kevin McRae-McKee, Frank de Wolf, Roy M Anderson, Alzheimer’s Disease Neuroimaging Initiative

Open access · goldAbstract read
In one paragraph

Article in Alzheimer's research & therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
59citing papers in PubMed, 3 pooled it
8.3field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

59 citing papers in PubMed, 3 syntheses or guidelines pooled it, 86 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
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  6. Observational
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  13. Computational Phenotyping of Cognitive Decline With Retest Learning.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025
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  15. Article
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  18. Personalizing progressive changes to brain structure in Alzheimer's disease using normative modeling.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  19. Article
  20. Cognitively defined Alzheimer's dementia subgroups have distinct atrophy patterns.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 1 country.

Christoforos HadjichrysanthouDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK. c.hadjichrysanthou@imperial.ac.uk.ORCID 0000-0001-6166-5899
Stephanie EvansDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
Sumali BajajDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
Loizos C SiakallisLysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK.
Kevin McRae-McKeeDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
Frank de WolfDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
Roy M AndersonDepartment of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, UK.
Alzheimer’s Disease Neuroimaging Initiative
Imperial College London · GBPublic Health England · GBRoyal London Hospital · GB

Funding

Alzheimer's Disease Neuroimaging Initiative - SupplementU01AG024904 · NIA · NORTHERN CALIFORNIA INSTITUTE RES &EDUC · PI WEINER, MICHAEL W · 2004 to 2015
$121.0M
Medical Research Council MR/L023784/2Medical Research Council MR/R015600/1NIA NIH HHS U01 AG024904
6 · The paper itself

Abstract

backgroundQuantifying 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.

methodsThe 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.

resultsThe 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.

conclusionsDespite 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.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAmyloid beta-PeptidesBiomarkersHumansNeuroimagingtau ProteinsAmyloid beta-PeptidesBiomarkerstau ProteinsADNIAlzheimer’s diseaseBiomarker trajectoriesBrainClinical statesCSFDementiaMarkov chainPETPlasmaTime to event

Identifiers

PMID32534594
PMCPMC7293779
OpenAlexW3034479818

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.