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