{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Singh M"],"funding":["Department of Science and Technology"],"pagination":["5933"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8199148"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["18(11)"],"pubmed_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-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"],"journal":["International journal of environmental research and public health"],"pubmed_title":["Multifactorial Landscape Parses to Reveal a Predictive Model for Knee Osteoarthritis."],"pmcid":["PMC8199148"],"funding_grant_id":["SR/WOS-A/LS-532/2016"],"pubmed_authors":["Minhas JS","Mastana S","Di Napoli M","Singh M","Valecha S","Juneja PK","Singh P","Kumar N","Khinda R","Singh S","Kaur T"],"additional_accession":[]},"is_claimable":false,"name":"Multifactorial Landscape Parses to Reveal a Predictive Model for Knee Osteoarthritis.","description":"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","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 May","modification":"2025-05-29T19:01:12.959Z","creation":"2022-02-10T14:37:57.035Z"},"accession":"S-EPMC8199148","cross_references":{"pubmed":["34073132"],"doi":["10.3390/ijerph18115933"]}}