eCite Digital Repository

A classification modeling approach for determining metabolite signatures in osteoarthritis

Citation

Rockel, JS and Zhang, W and Shestopaloff, K and Likhodii, S and Sun, G and Furey, A and Randell, E and Sundararajan, K and Gandhi, R and Zhai, G and Kapoor, M, A classification modeling approach for determining metabolite signatures in osteoarthritis, PLoS ONE, 13, (6) Article e0199618. ISSN 1932-6203 (2018) [Refereed Article]


Preview
PDF
5Mb
  

Copyright Statement

Copyright 2018 The Authors Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

DOI: doi:10.1371/journal.pone.0199618

Abstract

Multiple factors can help predict knee osteoarthritis (OA) patients from healthy individuals, including age, sex, and BMI, and possibly metabolite levels. Using plasma from individuals with primary OA undergoing total knee replacement and healthy volunteers, we measured lysophosphatidylcholine (lysoPC) and phosphatidylcholine (PC) analogues by metabolomics. Populations were stratified on demographic factors and lysoPC and PC analogue signatures were determined by univariate receiver-operator curve (AUC) analysis. Using signatures, multivariate classification modeling was performed using various algorithms to select the most consistent method as measured by AUC differences between resampled training and test sets. Lists of metabolites indicative of OA [AUC > 0.5] were identified for each stratum. The signature from males age > 50 years old encompassed the majority of identified metabolites, suggesting lysoPCs and PCs are dominant indicators of OA in older males. Principal component regression with logistic regression was the most consistent multivariate classification algorithm tested. Using this algorithm, classification of older males had fair power to classify OA patients from healthy individuals. Thus, individual levels of lysoPC and PC analogues may be indicative of individuals with OA in older populations, particularly males. Our metabolite signature modeling method is likely to increase classification power in validation cohorts.

Item Details

Item Type:Refereed Article
Keywords:osteoarthritis, metabolites, knee, lysophosphatidylcholine, lysoPC, phosphatidylcholine, PC
Research Division:Medical and Health Sciences
Research Group:Neurosciences
Research Field:Central Nervous System
Objective Division:Health
Objective Group:Clinical Health (Organs, Diseases and Abnormal Conditions)
Objective Field:Nervous System and Disorders
UTAS Author:Zhai, G (Dr Guangju Zhai)
ID Code:128997
Year Published:2018
Web of Science® Times Cited:2
Deposited By:Menzies Institute for Medical Research
Deposited On:2018-11-01
Last Modified:2018-12-14
Downloads:64 View Download Statistics

Repository Staff Only: item control page