Evidence map›Paper›PMID 30987684›Full record

ArticleAlzheimer's research & therapy2019

Personalized risk for clinical progression in cognitively normal subjects-the ABIDE project.

Ingrid S van Maurik, Rosalinde E R Slot, Sander C J Verfaillie, Marissa D Zwan, Femke H Bouwman, Niels D Prins, Charlotte E Teunissen, Philip Scheltens, Frederik Barkhof, Mike P Wattjes and 8 more

Abstract read
In one paragraph

Article in Alzheimer's research & therapy, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 3 pooled it
–field-weighted citation impact
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

25 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Observational
  6. Article
  7. Article
  8. Review
  9. Facing the new diagnostic and treatment options of Alzheimer's disease: The necessity of informed consent.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Article
  10. Counseling and disclosure practices in predictive Alzheimer's disease diagnostics: A scoping review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024
    Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. The missing X factor in Alzheimer disease.Nature reviews. Neurology · 2021
    Article
  19. Review
  20. Review
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

18 authors.

Ingrid S van MaurikAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands. i.vanmaurik@vumc.nl.
Rosalinde E R SlotAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Sander C J VerfaillieAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Marissa D ZwanAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Femke H BouwmanAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Niels D PrinsAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Charlotte E TeunissenNeurochemistry Laboratory and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Philip ScheltensAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Frederik BarkhofDepartment of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Mike P WattjesDepartment of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Jose Luis MolinuevoAlzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
Lorena RamiAlzheimer's Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain.
Steffen WolfsgruberDepartment of Psychiatry and Psychotherapy, University of Bonn, Bonn, Germany.
Oliver PetersDepartment of Psychiatry, Charité Berlin, Campus Benjamin Franklin, Berlin, Germany.
Frank JessenDepartment of Psychiatry, University of Cologne, Cologne, Germany.
Johannes BerkhofDepartment of Epidemiology and Biostatistics, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Wiesje M van der FlierAlzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.
Alzheimer’s Disease Neuroimaging Initiative

Funding

LSH-TKI LSHM16025
6 · The paper itself

Abstract

backgroundBiomarkers such as cerebrospinal fluid (CSF) and magnetic resonance imaging (MRI) have predictive value for progression to dementia in patients with mild cognitive impairment (MCI). The pre-dementia stage takes far longer, and the interpretation of biomarker findings is particular relevant for individuals who present at a memory clinic, but are deemed cognitively normal. The objective of the current study is to construct biomarker-based prognostic models for personalized risk of clinical progression in cognitively normal individuals presenting at a memory clinic.

methodsWe included 481 individuals with subjective cognitive decline (SCD) from the Amsterdam Dementia Cohort. Prognostic models were developed by Cox regression with patient characteristics, MRI, and/or CSF biomarkers to predict clinical progression to MCI or dementia. We estimated 5- and 3-year individualized risks based on patient-specific values. External validation was performed on Alzheimer's Disease Neuroimaging Initiative (ADNI) and an European dataset.

resultsBased on demographics only (Harrell's C = 0.70), 5- and 3-year progression risks varied from 6% [3-11] and 4% [2-8] (age 55, MMSE 30) to 38% [29-49] and 28% [21-37] (age 70, MMSE 27). Normal CSF biomarkers strongly decreased progression probabilities (Harrell's C = 0.82). By contrast, abnormal CSF markedly increased risk (5 years, 96% [56-100]; 3 years, 89% [44-99]). The CSF model could reclassify 58% of the individuals with an "intermediate" risk (35-65%) based on the demographic model. MRI measures were not retained in the models.

conclusionThe current study takes the first steps in a personalized approach for cognitively normal individuals by providing biomarker-based prognostic models.

Indexed as

Disease ProgressionAgedBiomarkersBrainCognitive DysfunctionDementiaFemaleHumansMaleMiddle AgedPrecision MedicineRisk FactorsBiomarkersBiomarkersCerebrospinal fluidMagnetic resonance imagingProgression

Identifiers

PMID30987684
PMCPMC6466790

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.