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This study aimed to elucidate its molecular mechanisms and identify potential biomarkers for early diagnosis and precision treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We integrated genome\u2010wide association study (GWAS) and transcriptomic data from periodontitis patients and healthy controls. Summary data\u2010based Mendelian randomization (SMR) and the heterogeneity in dependent instruments (HEIDI) test were used to identify genetically associated genes. Differentially expressed genes (DEGs) were identified using LIMMA, and weighted gene co\u2010expression network analysis (WGCNA) revealed disease\u2010related gene modules. Candidate biomarkers were prioritized through intersection analysis and evaluated using five machine learning algorithms. Causal relationships were further validated by two\u2010sample Mendelian randomization (TSMR). Functional enrichment was assessed via gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA), and immune infiltration was analyzed using CIBERSORT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      SMR identified 360 gene\u2010trait associations, with 320 passing the HEIDI test, corresponding to 294 unique genes. DEGs were enriched in immune and neuronal development pathways. WGCNA uncovered nine gene modules associated with periodontitis. Intersection and machine learning analyses identified five key biomarkers\u2014GPX2, IGKV2D\u201030, CD34, GSTA4, and NYNRIN\u2014with strong predictive performance, validated by MR analysis (\n                      <jats:italic>p<\/jats:italic>\n                      &lt; 0.05). Immune infiltration analysis revealed increased regulatory T cells, activated mast cells, and neutrophils, and decreased memory B cells and resting mast cells in periodontitis, with biomarker expression levels showing significant immune correlations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This integrative multiomics analysis uncovers causal genes and robust biomarkers involved in periodontitis pathogenesis, providing new insights for early detection and individualized treatment strategies. Further experimental validation is needed to confirm their functional roles in disease progression and therapeutic potential.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1155\/mi\/6044837","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T15:05:48Z","timestamp":1763996748000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Causal Gene Identification and Biomarker Prioritization in Periodontitis via Integrative Multiomics and Mendelian Randomization"],"prefix":"10.1155","volume":"2025","author":[{"given":"Haokun","family":"Mo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lulu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Ren","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9031-7794","authenticated-orcid":false,"given":"Yue","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1849-0624","authenticated-orcid":false,"given":"Ai","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"e_1_2_15_1_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41577-020-00488-6"},{"key":"e_1_2_15_2_2","doi-asserted-by":"publisher","DOI":"10.1002\/JPER.18-0006"},{"key":"e_1_2_15_3_2","doi-asserted-by":"publisher","DOI":"10.1111\/prd.12221"},{"key":"e_1_2_15_4_2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/2146160"},{"key":"e_1_2_15_5_2","doi-asserted-by":"publisher","DOI":"10.1093\/epirev\/mxx006"},{"key":"e_1_2_15_6_2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.36476"},{"key":"e_1_2_15_7_2","doi-asserted-by":"publisher","DOI":"10.1902\/jop.2016.150597"},{"key":"e_1_2_15_8_2","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.33316"},{"key":"e_1_2_15_9_2","doi-asserted-by":"publisher","DOI":"10.1111\/1759-7714.13828"},{"key":"e_1_2_15_10_2","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.33438"},{"key":"e_1_2_15_11_2","doi-asserted-by":"publisher","DOI":"10.1136\/gutjnl-2020-321949"},{"key":"e_1_2_15_12_2","doi-asserted-by":"publisher","DOI":"10.1111\/jcpe.13189"},{"key":"e_1_2_15_13_2","doi-asserted-by":"publisher","DOI":"10.1111\/jcpe.13478"},{"key":"e_1_2_15_14_2","doi-asserted-by":"publisher","DOI":"10.1111\/prd.12427"},{"key":"e_1_2_15_15_2","doi-asserted-by":"publisher","DOI":"10.1111\/prd.12387"},{"key":"e_1_2_15_16_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijms222111831"},{"key":"e_1_2_15_17_2","doi-asserted-by":"publisher","DOI":"10.1245\/s10434-023-14030-0"},{"key":"e_1_2_15_18_2","doi-asserted-by":"publisher","DOI":"10.1038\/ng.3538"},{"key":"e_1_2_15_19_2","doi-asserted-by":"publisher","DOI":"10.1183\/13993003.03196-2020"},{"key":"e_1_2_15_20_2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2022.951582"},{"key":"e_1_2_15_21_2","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa032"},{"key":"e_1_2_15_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-018-03371-0"},{"key":"e_1_2_15_23_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41588-021-00913-z"},{"key":"e_1_2_15_24_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-05473-8"},{"key":"e_1_2_15_25_2","doi-asserted-by":"publisher","DOI":"10.1902\/jop.2008.080139"},{"key":"e_1_2_15_26_2","doi-asserted-by":"publisher","DOI":"10.1177\/0022034514527288"},{"key":"e_1_2_15_27_2","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv007"},{"key":"e_1_2_15_28_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-9-559"},{"key":"e_1_2_15_29_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v028.i05"},{"key":"e_1_2_15_30_2","first-page":"1","article-title":"DALEX: Explainers for Complex Predictive Models in R","volume":"19","author":"Biecek P.","year":"2018","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_15_31_2","doi-asserted-by":"publisher","DOI":"10.21873\/cgp.20063"},{"key":"e_1_2_15_32_2","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-12-77"},{"key":"e_1_2_15_33_2","first-page":"329","article-title":"Impact on Length of Stay After Introduction of Emergency Department Information System","volume":"11","author":"Mayer P. 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