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Multifactorial landscape parses to reveal a predictive model for knee osteoarthritis

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journal contribution
posted on 2021-06-08, 10:11 authored by Monica Singh, Srishti Valecha, Rubanpal Khinda, Nitin Kumar, Surinderpal Singh, Pawan K Juneja, Taranpal Kaur, Mario Di Napoli, Jatinder S Minhas, Puneetpal Singh, Sarabjit MastanaSarabjit Mastana
The present study attempted to investigate whether concerted contributions of significant risk variables, pro-inflammatory markers, and candidate genes translate into a predictive marker for knee osteoarthritis (KOA). The present study comprised 279 confirmed osteoarthritis patients (Kellgren and Lawrence scale >2) and 287 controls. Twenty SNPs within five genes (CRP, COL1A1, IL-6, VDR, and eNOS), four pro-inflammatory markers (interleukin-6 (IL-6), interleuin-1 beta (IL-1β), tumor necrosis factor alpha (TNF-α), and high sensitivity C-reactive protein (hsCRP)), along with significant risk variables were investigated. A receiver operating characteristic (ROC) curve was used to observe the predictive ability of the model for distinguishing patients with KOA. Multivariable logistic regression analysis revealed that higher body mass index (BMI), triglycerides (TG), poor sleep, IL-6, IL-1β, and hsCRP were independent predictors for KOA after adjusting for the confounding from other risk variables. Four susceptibility haplotypes for the risk of KOA, AGT, GGGGCT, AGC, and CTAAAT, were observed within CRP, IL-6, VDR, and eNOS genes, which showed their impact in recessive β(SE): 2.11 (0.76), recessive β(SE): 2.75 (0.59), dominant β(SE): 1.89 (0.52), and multiplicative modes β(SE): 1.89 (0.52), respectively. ROC curve analysis revealed the model comprising higher values of BMI, poor sleep, IL-6, and IL-1β was predictive of KOA (AUC: 0.80, 95%CI: 0.74-0.86, p< 0.001), and the strength of the predictive ability increased when susceptibility haplotypes AGC and GGGGCT were involved (AUC: 0.90, 95%CI: 0.87-0.95, p< 0.001).This study offers a predictive marker for KOA based on the risk scores of some pertinent genes and their genetic variants along with some pro-inflammatory markers and traditional risk variables.

Funding

Women scientist scheme of the Department of Science and Technology, New Delhi (SR/WOS-A/LS-532/2016)

History

School

  • Sport, Exercise and Health Sciences

Published in

International Journal of Environmental Research and Public Health

Volume

18

Issue

11

Publisher

MDPI

Version

  • VoR (Version of Record)

Rights holder

© The authors

Publisher statement

This is an Open Access Article. It is published by MDPI under the Creative Commons Attribution 4.0 Unported Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/

Acceptance date

2021-05-28

Publication date

2021-05-31

Copyright date

2021

eISSN

1660-4601

Language

  • en

Depositor

Dr Sarabjit Mastana. Deposit date: 7 June 2021

Article number

5933

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