Exploring the Role of Obesity in Dilated Cardiomyopathy Based on Bio-informatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36547458", "kind": "science-record", "revision": 4, "content_hash": "sha256:c0c66430484e7b23c0540fe0ec2f6b0258dfa2110809b813a99766b77072a2e5", "created_at": "2026-11-01T19:01:04Z", "updated_at": "2026-11-01T22:50:53Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the Role of Obesity in Dilated Cardiomyopathy Based on Bio-informatics Analysis.", "doi": "10.3390/jcdd9120462", "pmid": "36547458", "authors": ["Xuehua Wang", "Wei Liu", "Huili Li", "Jiaxing Ding", "Yu Feng", "Zhijian Chen"], "journal": "Journal of cardiovascular development and disease", "year": 2022, "abstract": "(1) Background: Obesity is a major risk factor for cardiovascular disease (CVD), contributing to increasing global disease burdens. Apart from heart failure, coronary artery disease, and arrhythmia, recent research has found that obesity also elevates the risk of dilated cardiomyopathy (DCM). The main purpose of this study was to investigate the underlying biological role of obesity in increasing the risk of DCM. (2) Methods: The datasets GSE120895, GSE19303, and GSE2508 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using GSE120895 for DCM and GSE2508 for obesity, and the findings were compiled to discover the common genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted for the common genes in RStudio. In addition, CIBERSORT was used to obtain the immune cellular composition from DEGs. The key genes were identified in the set of common genes by the least absolute shrinkage and selection operator (LASSO) algorithm, the prognostic risk models of which were verified by receiver operator characteristic (ROC) curves in GSE19303. Finally, Spearman's correlation was used to explore the connections between key genes and immune cells. (3) Results: GO and KEGG pathway enrichment analyses showed that the main enriched terms of the common genes were transforming growth factor-beta (TGF-β), fibrillar collagen, NADPH oxidase activity, and multiple hormone-related signaling pathways. Both obesity and DCM had a disordered immune environment, especially obesity. The key genes NOX4, CCDC80, COL1A2, HTRA1, and KLHL29 may be primarily responsible for the changes. Spearman's correlation analysis performed for key genes and immune cells indicated that KLHL29 closely correlated to T cells and M2 macrophages, and HTRA1 very tightly correlated to plasma cells. (4) Conclusions: Bio-informatics analyses performed for DCM and obesity in our study suggested that obesity disturbed the immune micro-environment, promoted oxidative stress, and increased myocardial fibrosis, resulting in ventricular remodeling and an increased risk of DCM. The key genes KLHL29 and HTRA1 may play critical roles in obesity-related DCM.", "claims": [{"id": "claim-method", "statement": "Differentially expressed genes (DEGs) for both datasets were identified by RStudio with package “limma” [23], filtered by |Log2 fold change| > mean + (twice the standard deviation), and p-value < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36547458", "locator": "sections[4] \"2.2. Identification of Differentially Expressed Genes (DEGs)\" paragraph 2", "quote": "Differentially expressed genes (DEGs) for both datasets were identified by RStudio with package “limma” [23], filtered by |Log2 fold change| > mean + (twice the standard deviation), and p-value < 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data presented in this study are openly available in Gene Expression Omnibus (GEO) (URL: https://www.ncbi.nlm.nih.gov/geo/, accessed on 13 May 2022).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36547458", "locator": "sections[23] \"notes\" paragraph 1", "quote": "The data presented in this study are openly available in Gene Expression Omnibus (GEO) (URL: https://www.ncbi.nlm.nih.gov/geo/, accessed on 13 May 2022)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36547458"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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