Dataset Information

Using multidimensional topological data analysis to identify traits of hip osteoarthritis.

ABSTRACT: BACKGROUND:Osteoarthritis (OA) is a multifaceted disease with many variables affecting diagnosis and progression. Topological data analysis (TDA) is a state-of-the-art big data analytics tool that can combine all variables into multidimensional space. TDA is used to simultaneously analyze imaging and gait analysis techniques. PURPOSE:To identify biochemical and biomechanical biomarkers able to classify different disease progression phenotypes in subjects with and without radiographic signs of hip OA. STUDY TYPE:Longitudinal study for comparison of progressive and nonprogressive subjects. POPULATION:In all, 102 subjects with and without radiographic signs of hip osteoarthritis. FIELD STRENGTH/SEQUENCE:3T, SPGR 3D MAPSS T1? /T2 , intermediate-weighted fat-suppressed fast spin-echo (FSE). ASSESSMENT:Multidimensional data analysis including cartilage composition, bone shape, Kellgren-Lawrence (KL) classification of osteoarthritis, scoring hip osteoarthritis with MRI (SHOMRI), hip disability and osteoarthritis outcome score (HOOS). STATISTICAL TESTS:Analysis done using TDA, Kolmogorov-Smirnov (KS) testing, and Benjamini-Hochberg to rank P-value results to correct for multiple comparisons. RESULTS:Subjects in the later stages of the disease had an increased SHOMRI score (P?

SUBMITTER: Rossi-deVries J

PROVIDER: S-EPMC6174097 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Using multidimensional topological data analysis to identify traits of hip osteoarthritis.

Rossi-deVries Jasmine J Pedoia Valentina V Samaan Michael A MA Ferguson Adam R AR Souza Richard B RB Majumdar Sharmila S

Journal of magnetic resonance imaging : JMRI 20180507 4

<h4>Background</h4>Osteoarthritis (OA) is a multifaceted disease with many variables affecting diagnosis and progression. Topological data analysis (TDA) is a state-of-the-art big data analytics tool that can combine all variables into multidimensional space. TDA is used to simultaneously analyze imaging and gait analysis techniques.<h4>Purpose</h4>To identify biochemical and biomechanical biomarkers able to classify different disease progression phenotypes in subjects with and without radiograp ...[more]

PMID: 29734501

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Project description:Osteoarthritis (OA) is a common degenerative disease. Although some biomarkers and drug targets of OA have been discovered and employed, limitations and challenges still exist in the targeted therapy of OA. Mendelian randomization (MR) analysis has been regarded as a reliable analytic method to identify effective therapeutic targets. Thus, we aimed to identify novel therapeutic targets for OA and investigate their potential side effects based on MR analysis. In this study, two-sample MR, colocalization analysis, summary-data-based Mendelian randomization (SMR) and Mendelian randomization phenome-wide association study (MR-PheWAS) were conducted. We firstly analyzed data from 4907 plasma proteins to identify potential therapeutic targets associated with OA. In addition, blood expression quantitative trait loci (eQTLs) data sources were used to perform additional validation. A protein-protein interaction (PPI) network was also constructed to delve into the interactions among identified proteins. Then, MR-PheWASs were utilized to assess the potential side effects of core therapeutic targets. After MR analysis and FDR correction, we identified twelve proteins as potential therapeutic targets for knee OA or hip OA. Colocalization analysis and additional validation supported our findings, and PPI networks revealed the interactions among identified proteins. Finally, we identified MAPK3 (OR = 0.855, 95% CI: 0.791-0.923, p = 6.88 × 10-5) and GZMK (OR = 1.278, 95% CI: 1.131-1.444, p = 8.58 × 10-5) as the core therapeutic targets for knee OA, and ITIH1 (OR = 0.847, 95% CI: 0.784-0.915, p = 2.44 × 10-5) for hip OA. A further MR phenome-wide association study revealed the potential side effects of treatments targeting MAPK3, GZMK, and ITIH1. This comprehensive study indicates twelve plasma proteins with potential roles in knee and hip OA as therapeutic targets. This advancement holds promise for the progression of OA drug development, and paves the way for more efficacious treatments of OA.

Dataset Information

Using multidimensional topological data analysis to identify traits of hip osteoarthritis.

Publications

Using multidimensional topological data analysis to identify traits of hip osteoarthritis.

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