Metabolomics Differential Correlation Network Analysis of Osteoarthritis

Hu, Ting; Zhang, Weidong; Fan, Zhaozhi; Sun, Guang; Sergei, Likhodi; Randell, Edward; Zhai, Guangju

doi:10.1142/9789814749411_0012

Cited by 22 publications

(30 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Osteoarthritis is one of the most prevalent musculoskeletal arthropathies worldwide in the elderly and diagnosis of OA is mainly based on radiological imaging; however, radiography only provides structural information about the impaired bone and cartilage, and cannot provide the pathophysiological information associated with different phenotypes that have been clinically manifested. Metabolites are intermediate or end products of various cellular processes and their levels can serve as a good indicator of the series of biological systems in response to genetic and environmental influences . Identification of special metabolites by metabolomics is of great value in differentiating patients from healthy individuals in clinical settings.…”

Section: Discussionmentioning

confidence: 99%

“…Based on the full data set, all samples from the OA patients and the controls were roughly separated into OA and control groups by multivariate analysis. Metabolites are intermediate or end products of interconnected cellular processes; analyzing their concentrations provides an avenue to capture the relationships of their represented cellular processes and biological reactions associated with OA . Altered joint metabolism may be a contributor to OA pathogenesis and different metabolites may be a new phenotype contributor to OA diagnosis.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Global and targeted metabolomics of synovial fluid discovers special osteoarthritis metabolites

et al. 2017

View full text Add to dashboard Cite

To identify special metabolites in synovial fluid of osteoarthritis (OA) via a metabolomics approach. Synovial fluid of 35 participants (25 OA patients and 10 controls) was detected by GC-TOF/MS and multivariate data analysis was applied to analyze correlation among the observations. Different metabolites were screened by VIP value (VIP > 1), student t-test (p < 0.05), and fold change (fold >1.5), and verified with the standard metabolites in the synovial fluid of 24 OA patients and 11 controls by LC/MS. The classification performance of different metabolites was analyzed by receiver operating characteristic (ROC) analysis. The results showed that six different metabolites (glutamine, 1,5-anhydroglucitol, gluconic lactone, tyramine, threonine, and 8-aminocaprylic acid) were strongly associated with OA in global metabolomics. Verified results of the first three metabolites were the same as the identified results using targeted metabolomics. ROC curve analysis demonstrated that their concentrations in synovial fluid were strongly correlated to OA. In addition, the concentrations of gluconic lactone were significantly different between OA and RA. Metabolites with altered levels may be contributors to OA pathogenesis and can be used as potential diagnosis criteria for OA. Gluconic lactone may prove to be a novel criterion for differential diagnosis of OA from RA. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1973-1981, 2017.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Global and targeted metabolomics of synovial fluid discovers special osteoarthritis metabolites

et al. 2017

View full text Add to dashboard Cite

show abstract

“…We previously developed a differential correlation network algorithm where we first computed the correlations of metabolite pairs in separate phenotypically distinguished groups and then computed the differential correlations through subtraction (Hu et al 2016). This method allows us to identify metabolite pairs that are differentially correlated in distinct phenotype groups.…”

Section: Differential Correlation Network Analysismentioning

confidence: 99%

Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients

Costello

Liu

et al. 2020

Metabolomics

Self Cite

View full text Add to dashboard Cite

Introduction Up to one third of total joint replacement patients (TJR) experience poor surgical outcome. Objectives To identify metabolomic signatures for non-responders to TJR in primary osteoarthritis (OA) patients. Methods A newly developed differential correlation network analysis method was applied to our previously published metabolomic dataset to identify metabolomic network signatures for non-responders to TJR. Results Differential correlation networks involving 12 metabolites and 23 metabolites were identified for pain non-responders and function non-responders, respectively. Conclusion The differential networks suggest that inflammation, muscle breakdown, wound healing, and metabolic syndrome may all play roles in TJR response, warranting further investigation.

show abstract

“…Metabolites are intermediate and end products of various cellular processes and their levels of concentration serve as a good indicator of a sequence of biological systems in response to genetic and environmental influences. A large number of small-molecule metabolites from body fluids or tissues can be quantitatively detected simultaneously, which promises an immense potential for early diagnosis, therapy monitoring and understanding the pathogenesis of complex diseases [ 23 – 25 ].…”

Section: Introductionmentioning

confidence: 99%

An evolutionary learning and network approach to identifying key metabolites for osteoarthritis

Oksanen

Zhang

et al. 2018

PLoS Comput Biol

Self Cite

View full text Add to dashboard Cite

Metabolomics studies use quantitative analyses of metabolites from body fluids or tissues in order to investigate a sequence of cellular processes and biological systems in response to genetic and environmental influences. This promises an immense potential for a better understanding of the pathogenesis of complex diseases. Most conventional metabolomics analysis methods exam one metabolite at a time and may overlook the synergistic effect of combining multiple metabolites. In this article, we proposed a new bioinformatics framework that infers the non-linear synergy among multiple metabolites using a symbolic model and subsequently, identify key metabolites using network analysis. Such a symbolic model is able to represent a complex non-linear relationship among a set of metabolites associated with osteoarthritis (OA) and is automatically learned using an evolutionary algorithm. Applied to the Newfoundland Osteoarthritis Study (NFOAS) dataset, our methodology was able to identify nine key metabolites including some known osteoarthritis-associated metabolites and some novel metabolic markers that have never been reported before. The results demonstrate the effectiveness of our methodology and more importantly, with further investigations, propose new hypotheses that can help better understand the OA disease.

show abstract

Metabolomics Differential Correlation Network Analysis of Osteoarthritis

Cited by 22 publications

References 32 publications

Global and targeted metabolomics of synovial fluid discovers special osteoarthritis metabolites

Global and targeted metabolomics of synovial fluid discovers special osteoarthritis metabolites

Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients

An evolutionary learning and network approach to identifying key metabolites for osteoarthritis

Contact Info

Product

Resources

About