{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Huang Z"],"funding":["SIRG-CityU Strategic Interdisciplinary Research Grant"],"pagination":["1017"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12465795"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["<h4>Background</h4>Hemorrhoidal disease is highly prevalent and associated with significant psychological distress. However, the interplay between depression and anxiety symptoms and their variation across subgroups remains uncharacterized. This study identifies the key symptoms contributing to distress in these patients and explores subgroup differences to optimize mental health interventions.<h4>Methods</h4>We analyzed data from 10,482 hemorrhoid patients in the UK Biobank and constructed Gaussian Graphical Models (GGMs) based on PHQ-9 and GAD-7 symptom scores. Network estimation was carried out using EBIC graphical lasso regularization, and centrality metrics including strength, betweenness, and closeness were calculated using the qgraph package. Subgroup comparisons by gender, smoking "],"journal":["BMC psychology"],"pubmed_title":["Depression and anxiety symptom networks in hemorrhoid patients: evidence from the UK biobank."],"pmcid":["PMC12465795"],"funding_grant_id":["No.7020093"],"pubmed_authors":["Liu X","Yi X","He Y","Huang Z","Huang J","Ming WK","Yu Z","Yiu HHE"],"additional_accession":[]},"is_claimable":false,"name":"Depression and anxiety symptom networks in hemorrhoid patients: evidence from the UK biobank.","description":"<h4>Background</h4>Hemorrhoidal disease is highly prevalent and associated with significant psychological distress. However, the interplay between depression and anxiety symptoms and their variation across subgroups remains uncharacterized. This study identifies the key symptoms contributing to distress in these patients and explores subgroup differences to optimize mental health interventions.<h4>Methods</h4>We analyzed data from 10,482 hemorrhoid patients in the UK Biobank and constructed Gaussian Graphical Models (GGMs) based on PHQ-9 and GAD-7 symptom scores. Network estimation was carried out using EBIC graphical lasso regularization, and centrality metrics including strength, betweenness, and closeness were calculated using the qgraph package. Subgroup comparisons by gender, smoking ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Sep","modification":"2026-06-03T21:09:15.356Z","creation":"2026-05-01T03:10:58.317Z"},"accession":"S-EPMC12465795","cross_references":{"pubmed":["40999535"],"doi":["10.1186/s40359-025-03170-5"]}}