Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background: Previous observational studies have indicated a bidirectional association between metabolic syndrome (MetS) and osteoarthritis (OA). However, it remains unclear whether these bidirectional associations reflect causal relationships or shared genetic factors, and the underlying biological mechanisms of this association are not fully understood.
Methods: Leveraging summary statistics from genome-wide association studies (GWASs) conducted by the UK Biobank and the Glucose and Insulin-related Traits Consortium (MAGIC), we performed global genetic correlation analyses, genome-wide cross-trait meta-analyses, and a bidirectional two-sample Mendelian randomization analyses using summary statistics from GWASs to comprehensively assess the relationship of MetS and OA.
Results: We first detected an extensive genetic correlation between MetS and OA (rg=0.393, P=1.52×10-18), which was consistent in four MetS components, including waist circumference, triglycerides, hypertension and high-density lipoprotein cholesterol and OA with rg ranging from -0.229 to 0.490. We then discovered 32 variants jointly associated with MetS and OA through multi-trait Analysis of GWAS. Co-localization analysis founded 12 genes shared between MetS and OA, with functional implications in several biological pathways. Finally, MR analysis suggested genetic liability to MetS significantly increased the risk of OA, but no reverse causality was found.
Conclusion: Our results illustrate a common genetic architecture, pleiotropic loci, as well as causality between MetS and OA, potentially enhancing our knowledge of high comorbidity and genetic processes that overlap between the two disorders.
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Source |
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http://dx.doi.org/10.1210/clinem/dgae169 | DOI Listing |
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