ReviewThe journal of applied laboratory medicine2020
Detection of Alzheimer Disease Pathology in Patients Using Biochemical Biomarkers: Prospects and Challenges for Use in Clinical Practice.
Review in The journal of applied laboratory medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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.
Who cites it
7 citing papers in PubMed, 18 citations in OpenAlex.
- An optimized hybrid deep learning model to detect Alzheimer disease.Scientific reports · 2025Article
- Machine-learning models for Alzheimer's disease diagnosis using neuroimaging data: survey, reproducibility, and generalizability evaluation.Brain informatics · 2025Review
- Integrative metabolomics science in Alzheimer's disease: Relevance and future perspectives.Ageing research reviews · 2023Review
- State-of-the-art of lumbar puncture and its place in the journey of patients with Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Review
- Article
- Clinical and Paraclinical Measures Associated with Outcome in Cerebral Amyloid Angiopathy with Related Inflammation.Journal of Alzheimer's disease : JAD · 2021Article
- Trends in the public health significance, definitions of disease, and implications for prevention of Alzheimer's disease.Current epidemiology reports · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundThirty-four years ago, amyloid-β 1-42 peptide was identified in amyloid plaques from brain tissue obtained from patients with Alzheimer disease (AD) and Down syndrome. This finding led to development of immunoassays for this marker of amyloid plaque burden in cerebrospinal fluid (CSF) approximately 10 years later. Subsequently, research immunoassays were developed for total τ protein and τ phosphorylated at the threonine 181 position. Subsequent studies documented the clinical utility of these biomarkers of amyloid plaque burden or τ tangle pathology in cohorts of living patients. CONTENT: We describe the following: (a) clinical utility of AD biomarkers; (b) measurement challenges, including development of mass spectrometry-based reference methods and automated immunoassays; (c) development of "appropriate use criteria" (AUC) guidelines for safe/appropriate use of CSF testing for diagnosis of AD developed by neurologists, a neuroethicist, and laboratorians; (d) a framework, sponsored by the National Institute of Aging-Alzheimer's Association (NIA-AA), that defines AD on the basis of CSF and imaging methods for detecting amyloid plaque burden, τ tangle pathology, and neurodegeneration. This framework's purpose was investigative but has important implications for future clinical practice; (e) recognition of copathologies in AD patients and challenges for developing methods to detect these in living patients. SUMMARY: The field can expect availability of validated research tools for detection of AD pathology that support clinical treatment trials of disease-modifying agents and, ultimately, use in clinical practice. Validated methods are becoming available for CSF testing; emergence of validated methods for AD biomarkers in plasma can be expected in the next few years.
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Registered trials
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.