ArticleScientific reports2023
Discovery of novel CSF biomarkers to predict progression in dementia using machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- 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
- CSF Leukocytes in Intracranial Infections: Diagnostic, Mechanistic, and Therapeutic Insights for Neurocritical Care.Cellular and molecular neurobiology · 2026Review
- Multi-platform integration of brain and CSF proteomes reveals biomarker panels for Alzheimer's disease.Briefings in bioinformatics · 2026Article
- Extracellular RNAs as Messengers and Early Biomarkers in Neurodegeneration.International journal of molecular sciences · 2025Review
- Distinct cerebrospinal fluid proteomic signatures define clinicopathological subtypes of sporadic Creutzfeldt-Jakob disease and predict patient survival.Acta neuropathologica communications · 2025Article
- Development of Plasma Protein Classification Models for Alzheimer's Disease Using Multiple Machine Learning Approaches.International journal of molecular sciences · 2025Article
- Multivariate models using NULISAseq and plasma p-tau217 for staging Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Machine learning for cardio-oncology: predicting global longitudinal strain from conventional echocardiographic measurements in cancer patients.Cardio-oncology (London, England) · 2025Article
- TGF-β Signaling in Microglia: A Key Regulator of Development, Homeostasis and Reactivity.Biomedicines · 2024Review
- Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis.Journal of medical Internet research · 2024Article
- Tracking neuroinflammatory biomarkers in Alzheimer's disease: a strategy for individualized therapeutic approaches?Journal of neuroinflammation · 2024Review
- Precise prediction of cerebrospinal fluid amyloid beta protein for early Alzheimer's disease detection using multimodal data.MedComm · 2024Article
- Risk prediction of inappropriate implantable cardioverter-defibrillator therapy using machine learning.Scientific reports · 2023Article
- Plasma pTau181 enhances the prediction of future clinical decline in amyloid-positive mild cognitive impairment.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
Abstract
Providing an accurate prognosis for individual dementia patients remains a challenge since they greatly differ in rates of cognitive decline. In this study, we used machine learning techniques with the aim to identify cerebrospinal fluid (CSF) biomarkers that predict the rate of cognitive decline within dementia patients. First, longitudinal mini-mental state examination scores (MMSE) of 210 dementia patients were used to create fast and slow progression groups. Second, we trained random forest classifiers on CSF proteomic profiles and obtained a well-performing prediction model for the progression group (ROC-AUC = 0.82). As a third step, Shapley values and Gini feature importance measures were used to interpret the model performance and identify top biomarker candidates for predicting the rate of cognitive decline. Finally, we explored the potential for each of the 20 top candidates in internal sensitivity analyses. TNFRSF4 and TGF [Formula: see text]-1 emerged as the top markers, being lower in fast-progressing patients compared to slow-progressing patients. Proteins of which a low concentration was associated with fast progression were enriched for cell signalling and immune response pathways. None of our top markers stood out as strong individual predictors of subsequent cognitive decline. This could be explained by small effect sizes per protein and biological heterogeneity among dementia patients. Taken together, this study presents a novel progression biomarker identification framework and protein leads for personalised prediction of cognitive decline in dementia.
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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.