Evidence map›Paper›PMID 35563350›Full record

ReviewInternational journal of molecular sciences2022

Biomarkers for Alzheimer's Disease in the Current State: A Narrative Review.

Serafettin Gunes, Yumi Aizawa, Takuma Sugashi, Masahiro Sugimoto, Pedro Pereira Rodrigues

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review 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 54 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
54citing papers in PubMed, 2 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

54 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Motor performance and its association with Alzheimer's-related biomarkers: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 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

5 authors.

Serafettin GunesPh.D. Program in Health Data Science, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal.
Yumi AizawaCenter for Minimally Invasive Therapies, Institute of Medical Science Research and Development, Tokyo Medical University, Tokyo 160-8402, Japan.ORCID 0000-0002-0760-6561
Takuma SugashiCenter for Minimally Invasive Therapies, Institute of Medical Science Research and Development, Tokyo Medical University, Tokyo 160-8402, Japan.
Masahiro SugimotoCenter for Minimally Invasive Therapies, Institute of Medical Science Research and Development, Tokyo Medical University, Tokyo 160-8402, Japan.ORCID 0000-0003-3316-2543
Pedro Pereira RodriguesRISE & MEDCIDS, Faculty of Medicine, University of Porto, 4200-450 Porto, Portugal.ORCID 0000-0001-7867-6682

Funding

JSPS KAKENHI 19K10427, 19K11202, and 20H05743
6 · The paper itself

Abstract

Alzheimer's disease (AD) has become a problem, owing to its high prevalence in an aging society with no treatment available after onset. However, early diagnosis is essential for preventive intervention to delay disease onset due to its slow progression. The current AD diagnostic methods are typically invasive and expensive, limiting their potential for widespread use. Thus, the development of biomarkers in available biofluids, such as blood, urine, and saliva, which enables low or non-invasive, reasonable, and objective evaluation of AD status, is an urgent task. Here, we reviewed studies that examined biomarker candidates for the early detection of AD. Some of the candidates showed potential biomarkers, but further validation studies are needed. We also reviewed studies for non-invasive biomarkers of AD. Given the complexity of the AD continuum, multiple biomarkers with machine-learning-classification methods have been recently used to enhance diagnostic accuracy and characterize individual AD phenotypes. Artificial intelligence and new body fluid-based biomarkers, in combination with other risk factors, will provide a novel solution that may revolutionize the early diagnosis of AD.

Indexed as

Alzheimer DiseaseArtificial IntelligenceBiomarkersEarly DiagnosisHumansMachine LearningBiomarkersAlzheimer’s disease (AD)biomarkerslow or non-invasivelymachine-learning classification

Identifiers

PMID35563350
PMCPMC9102515

What OpenQuestion holds

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Registered trials

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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.