Evidence map›Paper›PMID 35805894›Full record

ArticleInternational journal of molecular sciences2022

Establishing In-House Cutoffs of CSF Alzheimer's Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort.

Adelina Orellana, Pablo García-González, Sergi Valero, Laura Montrreal, Itziar de Rojas, Isabel Hernández, Maitee Rosende-Roca, Liliana Vargas, Juan Pablo Tartari, Ester Esteban-De Antonio and 12 more

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 4 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

34 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  4. Pooled it
  5. Observational
  6. Human IL-34 Deficiency Primes Microglia Toward Alzheimer's Disease-Associated States.medRxiv : the preprint server for health sciences · 2026
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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

22 authors.

Adelina OrellanaAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Pablo García-GonzálezAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.ORCID 0000-0003-0125-5403
Sergi ValeroAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Laura MontrrealAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Itziar de RojasAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.ORCID 0000-0002-2148-381X
Isabel HernándezAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Maitee Rosende-RocaAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Liliana VargasAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Juan Pablo TartariAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Ester Esteban-De AntonioAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Urszula BojarynAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Leire NarvaizaAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Emilio Alarcón-MartínAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Montserrat AlegretAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Daniel AlcoleaBiomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain.
Alberto LleóBiomedical Research Networking Centre in Neurodegenerative Diseases (CIBERNED), 28031 Madrid, Spain.
Lluís TárragaAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Vanesa PytelAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Amanda CanoAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Marta MarquiéAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Mercè BoadaAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.
Agustín RuizAce Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), 08029 Barcelona, Spain.ORCID 0000-0003-2633-2495

Funding

Government of Spain FJC2018-036012Innovative Medicines Initiative 115975Instituto de Salud Carlos III FI20/00215Instituto de Salud Carlos III PI19/0124Instituto de Salud Carlos III PI19/01301
6 · The paper itself

Abstract

backgroundClinical diagnosis of Alzheimer's disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases.

methodsWe quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST

resultsCutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing-Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland-Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan-Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (

conclusionsWe established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects.

Indexed as

Alzheimer DiseaseCognitive DysfunctionAmyloid beta-PeptidesBiomarkersCross-Sectional StudiesDisease ProgressionHumansPeptide Fragmentstau ProteinsAmyloid beta-PeptidesBiomarkersPeptide Fragmentstau ProteinsAlzheimer’s diseasecerebrospinal fluidchemiluminescent enzyme immunoassayLumipulseMCI

Identifiers

PMID35805894
PMCPMC9266894

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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.