{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T05:35:43Z","timestamp":1767591343480,"version":"3.48.0"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T00:00:00Z","timestamp":1767571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Traditional Chinese Medicine (TCM) offers multi-target strategies for Type 2 Diabetes Mellitus (T2DM), but its mechanisms are unclear. This study combined a randomized controlled trial (RCT) with a multi-omics approach to evaluate the efficacy of Daixie Decoction granules (DDG) as an add-on therapy to metformin and to generate mechanistic hypotheses using a multi-omics framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a randomized, double-blind, placebo-controlled trial involving 136 randomized and 128 completed with DDG plus metformin or placebo plus metformin for 6 months. Mechanistic prediction was based on network pharmacology, integration of T2DM-related genes from public databases (GeneCards, DisGeNET, OMIM), and transcriptomic differentially expressed genes (DEGs) from GEO. Seven machine learning algorithms were applied to prioritize core targets from the overlapping candidate list. A nested serum proteomics sub-study within the randomized trial, with tissue-specific expression profiling (GTEx), was then used to explore the consistency of these computational predictions at the protein and tissue levels. Statistical analysis was performed using appropriate parametric and nonparametric tests, including ANCOVA where applicable.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>DDG reduced HbA1c compared with placebo (\u22120.32%, P=0.032). Fasting plasma glucose showed a borderline reduction (P=0.050). Network pharmacology identified 617 potential targets intersecting with 2,652 DEGs, yielding 29 candidates. Using machine-learning combined with protein\u2013protein interaction topology and literature support, we further prioritized eight core targets (P2RX7, IL1B, PTPN1, AKT2, CD38, NFE2L2, NOS3, and MERTK). Enrichment analyses of these candidates, together with serum proteomic profiling, implicated PI3K\u2013Akt signaling, inflammatory and oxidative stress responses, and focal adhesion\u2013related pathways.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Clinically, DDG used as add-on therapy to metformin produced a modest but statistically significant improvement in glycemic control in patients with inadequately controlled T2DM. Our findings are consistent with the hypothesis that DDG may act through a multi-target network spanning inflammatory (P2RX7, IL1B), insulin\/metabolic (PTPN1, AKT2, CD38), oxidative\u2013endothelial (NFE2L2, NOS3) and vascular-resolution (MERTK) axes, generating testable mechanistic hypotheses for future experimental studies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fphar.2025.1723584","type":"journal-article","created":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T05:31:32Z","timestamp":1767591092000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Randomized trial and multi-omics, machine learning\u2013based mechanistic exploration of daixie decoction granules in type 2 diabetes"],"prefix":"10.3389","volume":"16","author":[{"given":"Zhong","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kepei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaogang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuezhou","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,1,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"D1038","DOI":"10.1093\/nar\/gky1151","article-title":"OMIM.org: leveraging knowledge across phenotype-gene relationships","volume":"47","author":"Amberger","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"B2","doi-asserted-by":"publisher","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI GEO: archive for functional genomics data sets--update","volume":"41","author":"Barrett","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"B3","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995"},{"key":"B4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/a:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. 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