{"title":"表观遗传学、蛋白质组学和代谢组学的多组学整合确定了假定的药物靶点并改善了糖尿病的早期预测。","authors":"Wenran Li, Yingyu Cheng, Aoyuan Cui, Mengyao Huang, Qingxia Huang, Qi Wang, Mingfeng Xia, Jiange Qiu, Qianqian Peng, Jiarui Li, Huating Li, Yong Wang, Geng Zong, Yan Zheng, Jiucun Wang, Xin Gao, Chen Ding, Huiru Tang, Bing-Hua Jiang, Li Jin, Yu Li, Sijia Wang","doi":"10.2337/db25-0354","DOIUrl":null,"url":null,"abstract":"<p><strong>Article highlights: </strong>A total of 175 CpGs, 29 proteins, and 93 metabolites were identified as associated with diabetes, among which 43 CpGs and 25 metabolites were validated in an independent cohort. Causal and mediation analyses revealed 20 biomarkers and 190 signaling pathways involved in diabetes development. The integrative multiomics prioritization provides the community with an ordered list of diabetes biomarkers. We experimentally validated one of the prioritized proteins, COLEC11, and demonstrated its involvement in lipid metabolism. Our findings prioritize potential therapeutic targets and demonstrate that integrating multiomics biomarkers improves diabetes risk prediction beyond traditional clinical models.</p>","PeriodicalId":93977,"journal":{"name":"Diabetes","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multiomics Integration of Epigenetics, Proteomics, and Metabolomics Identifies Putative Drug Targets and Improves Early Prediction for Diabetes.\",\"authors\":\"Wenran Li, Yingyu Cheng, Aoyuan Cui, Mengyao Huang, Qingxia Huang, Qi Wang, Mingfeng Xia, Jiange Qiu, Qianqian Peng, Jiarui Li, Huating Li, Yong Wang, Geng Zong, Yan Zheng, Jiucun Wang, Xin Gao, Chen Ding, Huiru Tang, Bing-Hua Jiang, Li Jin, Yu Li, Sijia Wang\",\"doi\":\"10.2337/db25-0354\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Article highlights: </strong>A total of 175 CpGs, 29 proteins, and 93 metabolites were identified as associated with diabetes, among which 43 CpGs and 25 metabolites were validated in an independent cohort. Causal and mediation analyses revealed 20 biomarkers and 190 signaling pathways involved in diabetes development. The integrative multiomics prioritization provides the community with an ordered list of diabetes biomarkers. We experimentally validated one of the prioritized proteins, COLEC11, and demonstrated its involvement in lipid metabolism. Our findings prioritize potential therapeutic targets and demonstrate that integrating multiomics biomarkers improves diabetes risk prediction beyond traditional clinical models.</p>\",\"PeriodicalId\":93977,\"journal\":{\"name\":\"Diabetes\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Diabetes\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2337/db25-0354\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Diabetes","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2337/db25-0354","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Multiomics Integration of Epigenetics, Proteomics, and Metabolomics Identifies Putative Drug Targets and Improves Early Prediction for Diabetes.
Article highlights: A total of 175 CpGs, 29 proteins, and 93 metabolites were identified as associated with diabetes, among which 43 CpGs and 25 metabolites were validated in an independent cohort. Causal and mediation analyses revealed 20 biomarkers and 190 signaling pathways involved in diabetes development. The integrative multiomics prioritization provides the community with an ordered list of diabetes biomarkers. We experimentally validated one of the prioritized proteins, COLEC11, and demonstrated its involvement in lipid metabolism. Our findings prioritize potential therapeutic targets and demonstrate that integrating multiomics biomarkers improves diabetes risk prediction beyond traditional clinical models.
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