ArticleFrontiers in neuroscience2021
Machine Learning Driven Profiling of Cerebrospinal Fluid Core Biomarkers in Alzheimer's Disease and Other Neurological Disorders.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 61 citations in OpenAlex.
- Artificial Intelligence for Integrated Analysis of Non-Blood Biological Fluids: From Biomarker Discovery to Clinical Decision-Support Systems.Diagnostics (Basel, Switzerland) · 2026Review
- Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach.BMC medical informatics and decision making · 2026Article
- Mild cognitive impairment-to-Alzheimer's dementia progression risk: the contribution of the Interceptor project.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- A quantitative DOPA decarboxylase biomarker for diagnosis in Lewy body disorders.Nature medicine · 2026Article
- Blood-based biomarkers for Alzheimer's disease: influence of kidney function.Alzheimer's research & therapy · 2026Article
- Application of unsupervised and supervised machine learning approaches to blood-based biomarkers of Alzheimer's disease in a memory clinic cohort.Frontiers in aging neuroscience · 2026Article
- Cerebrospinal fluid and plasma biomarkers in idiopathic normal pressure hydrocephalus: comparison with Alzheimer's and Parkinson's diseases.Frontiers in aging neuroscience · 2026Article
- Extracellular RNAs as Messengers and Early Biomarkers in Neurodegeneration.International journal of molecular sciences · 2025Review
- Development of Plasma Protein Classification Models for Alzheimer's Disease Using Multiple Machine Learning Approaches.International journal of molecular sciences · 2025Article
- Implications of the glymphatic system in the diagnostic and surgical workup of normal pressure hydrocephalus.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Article
- Phosphorylated α-synuclein in CSF and plasma does not reflect synucleinopathy.NPJ Parkinson's disease · 2025Article
- Hippocampal Subfields Volume: Another Hint of the Continuum Between CAA and AD?European journal of neurology · 2025Observational
- Association of αS-SAA kinetics with clinical scores in the clinical spectrum of Parkinson's disease.Journal of Parkinson's disease · 2025Article
- Alzheimer's disease plasma biomarkers in the Midwestern Amish.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Diagnostic performance of plasma Aβ42/40 ratio, p-tau181, GFAP, and NfL along the continuum of Alzheimer's disease and non-AD dementias: An international multi-center study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Studying Alzheimer's disease through an integrative serum metabolomic and lipoproteomic approach.Journal of translational medicine · 2025Article
- Exploring the ability of plasma pTau217, pTau181 and beta-amyloid in mirroring cerebrospinal fluid biomarker profile of Mild Cognitive Impairment by the fully automated LumipulseFluids and barriers of the CNS · 2025Article
- Immunoassay detection of multiphosphorylated tau proteoforms as cerebrospinal fluid and plasma Alzheimer's disease biomarkers.Nature communications · 2025Article
- A mini review of transforming dementia care in China with data-driven insights: overcoming diagnostic and time-delayed barriers.Frontiers in aging neuroscience · 2025Review
- Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.JMIR formative research · 2024Article
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Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Amyloid-beta (Aβ) 42/40 ratio, tau phosphorylated at threonine-181 (p-tau), and total-tau (t-tau) are considered core biomarkers for the diagnosis of Alzheimer's disease (AD). The use of fully automated biomarker assays has been shown to reduce the intra- and inter-laboratory variability, which is a critical factor when defining cut-off values. The calculation of cut-off values is often influenced by the composition of AD and control groups. Indeed, the clinically defined AD group may include patients affected by other forms of dementia, while the control group is often very heterogeneous due to the inclusion of subjects diagnosed with other neurological diseases (OND). In this context, unsupervised machine learning approaches may overcome these issues providing unbiased cut-off values and data-driven patient stratification according to the sole distribution of biomarkers. In this work, we took advantage of the reproducibility of automated determination of the CSF core AD biomarkers to compare two large cohorts of patients diagnosed with different neurological disorders and enrolled in two centers with established expertise in AD biomarkers. We applied an unsupervised Gaussian mixture model clustering algorithm and found that our large series of patients could be classified in six clusters according to their CSF biomarker profile, some presenting a typical AD-like profile and some a non-AD profile. By considering the frequencies of clinically defined OND and AD subjects in clusters, we subsequently computed cluster-based cut-off values for Aβ42/Aβ40, p-tau, and t-tau. This approach promises to be useful for large-scale biomarker studies aimed at providing efficient biochemical phenotyping of neurological diseases.
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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.