Multifactorial Landscape Parses to Reveal a Predictive Model for Knee Osteoarthritis

Monica Singh, Srishti Valecha, Rubanpal Khinda, Nitin Kumar, Surinderpal Singh, Pawan K. Juneja, Taranpal Kaur, Mario Di Napoli, Jatinder S. Minhas, Puneetpal Singh and Sarabjit Mastana
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Monica Singh: Division of Molecular Genetics, Department of Human Genetics, Punjabi University, Patiala 147002, India
Srishti Valecha: Division of Molecular Genetics, Department of Human Genetics, Punjabi University, Patiala 147002, India
Rubanpal Khinda: Division of Molecular Genetics, Department of Human Genetics, Punjabi University, Patiala 147002, India
Nitin Kumar: Division of Molecular Genetics, Department of Human Genetics, Punjabi University, Patiala 147002, India
Surinderpal Singh: Aggarwal Orthopedic Hospital, Ludhiana 141001, India
Pawan K. Juneja: Aggarwal Orthopedic Hospital, Ludhiana 141001, India
Taranpal Kaur: Amrit Sagar Hospital, Ferozepur 152001, India
Mario Di Napoli: Neurological Service, Annunziata Hospital, Sulmona, 67039 L’Aquila, Italy
Jatinder S. Minhas: Department of Cardiovascular Sciences, University of Leicester, Leicester LE1 7RH, UK
Puneetpal Singh: Division of Molecular Genetics, Department of Human Genetics, Punjabi University, Patiala 147002, India
Sarabjit Mastana: Human Genomics Lab, School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough LE11 3TU, UK

IJERPH, 2021, vol. 18, issue 11, 1-14

Abstract: 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-1?), tumor necrosis factor alpha (TNF-?), 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-1?, 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 ?(SE): 2.11 (0.76), recessive ?(SE): 2.75 (0.59), dominant ?(SE): 1.89 (0.52), and multiplicative modes ?(SE): 1.89 (0.52), respectively. ROC curve analysis revealed the model comprising higher values of BMI, poor sleep, IL-6, and IL-1? 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.

Keywords: multifactorial; knee osteoarthritis; genetic models; haplotypes; ROC curve analysis; predictive marker (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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