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 (p < 0.001), MMP1 (p < 0.001), and IL8 (p < 0.001) levels. These profiles provide novel insights into angiogenic and inflammatory pathologies that may contribute to VCID.
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The authors gratefully acknowledge the NIH [NINR: 4R01NR014189-05 (GJ), NIA: 5UH2NS100606-02 (DW and GJ), NCATS: UL1TR001998, NIA: 5P30AG028383].
The datasets generated for this study are available on request to the corresponding author.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnins.2020.00084/full#supplementary-material It is also available as the additional file listed at the end of this record.
Winder, Zachary; Sudduth, Tiffany L.; Fardo, David W.; Cheng, Qiang; Goldstein, Larry B.; Nelson, Peter T.; Schmitt, Frederick A.; Jicha, Gregory A.; and Wilcock, Donna M., "Hierarchical Clustering Analyses of Plasma Proteins in Subjects With Cardiovascular Risk Factors Identify Informative Subsets Based on Differential Levels of Angiogenic and Inflammatory Biomarkers" (2020). Sanders-Brown Center on Aging Faculty Publications. 136.