ArticleFrontiers in neuroscience2020
Hierarchical Clustering Analyses of Plasma Proteins in Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based on Differential Levels of Angiogenic and Inflammatory Biomarkers.
Article in Frontiers in neuroscience, 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.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- Overlapping risk factors and pathogenic mechanisms in lung cancer and cardiovascular disease.Discover oncology · 2025Review
- CCR3 Expression in Relation to Delayed Microbleeds in a Rat Model of Large Vessel Occlusion.Journal of experimental neurology · 2024Article
- Molecular biomarkers for vascular cognitive impairment and dementia.Nature reviews. Neurology · 2023Review
- Pathologic sequelae of vascular cognitive impairment and dementia sheds light on potential targets for intervention.Cerebral circulation - cognition and behavior · 2021Article
- Inflammatory Biomarkers Aid in Diagnosis of Dementia.Frontiers in aging neuroscience · 2021Article
- An introduction to therapeutic approaches to vascular cognitive impairment.Cerebral circulation - cognition and behavior · 2021Article
- Predicting continuous amyloid PET values with CSF and plasma Aβ42/Aβ40.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Agglomerative hierarchical clustering analysis (HCA) is a commonly used unsupervised machine learning approach for identifying informative natural clusters of observations. HCA is performed by calculating a pairwise dissimilarity matrix and then clustering similar observations until all observations are grouped within a cluster. Verifying the empirical clusters produced by HCA is complex and not well studied in biomedical applications. Here, we demonstrate the comparability of a novel HCA technique with one that was used in previous biomedical applications while applying both techniques to plasma angiogenic (FGF, FLT, PIGF, Tie-2, VEGF, VEGF-D) and inflammatory (MMP1, MMP3, MMP9, IL8, TNFα) protein data to identify informative subsets of individuals. Study subjects were diagnosed with mild cognitive impairment due to cerebrovascular disease (MCI-CVD). Through comparison of the two HCA techniques, we were able to identify subsets of individuals, based on differences in VEGF (
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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.