{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T06:26:59Z","timestamp":1786688819996,"version":"build-2736575974"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T00:00:00Z","timestamp":1786665600000},"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. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Type 2 diabetes (T2D) has been consistently associated with alterations in the gut microbiome, although disease, treatment, diet, and other host factors may contribute to the observed patterns. How these associations are organized across different biological levels of the microbial ecosystem remains incompletely understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed shotgun metagenomic sequencing of fecal samples from 82 individuals, including 41 patients with T2D and 41 age-, sex-, and body mass index-matched healthy controls. Taxonomic profiling, functional pathway analysis, enterotype characterization, ecological network inference, and interpretable machine-learning approaches were integrated to characterize microbiome variation across multiple organizational levels.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Despite clear clinical differences between groups, particularly fasting blood glucose, the overall ecological architecture of the gut microbiome remained broadly preserved. Dominant phylum-level composition and enterotype structure were maintained, whereas variation became apparent at finer biological scales. Species-level analyses identified 42 differentially abundant taxa. Community diversity analysis showed reduced Chao1 richness (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.024), increased Simpson diversity (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.024), unchanged Shannon diversity (\n                      <jats:italic>P<\/jats:italic>\n                      = 0.126), and a modest shift in community composition (PERMANOVA,\n                      <jats:italic>R<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      = 0.040,\n                      <jats:italic>P<\/jats:italic>\n                      = 0.006). Functional profiling showed no pathway-level significance after multiple-testing correction but directional trends across several metabolic modules. Exploratory Spearman-based networks differed in topology between groups; because relative-abundance data are compositional, these differences cannot be interpreted as direct ecological interactions or definitive network rewiring. Machine-learning models achieved a within-cohort cross-validated AUC of up to 0.91, but lacked independent external validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings provide a multi-layer description of T2D-associated gut microbiome variation within this cohort. The data are consistent with preserved higher-order community organization accompanied by finer-scale differences in species composition, functional potential, community-state occupancy, statistical co-occurrence, and within-cohort discriminative features. Medication confounding, compositional effects, technical artifacts, and the absence of external validation limit causal, ecological, and diagnostic interpretation. Larger longitudinal, multi-site, medication-resolved, and independently validated studies are required.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1902019","type":"journal-article","created":{"date-parts":[[2026,8,14]],"date-time":"2026-08-14T05:32:04Z","timestamp":1786685524000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-layer gut microbiome variation in type 2 diabetes despite preserved higher-order community structure"],"prefix":"10.3389","volume":"17","author":[{"given":"Junwei","family":"Zhou","sequence":"first","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Shangqiu Medical College","place":["Shangqiu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peijie","family":"Du","sequence":"additional","affiliation":[{"name":"Department of Endocrinology and Metabolic Diseases, The First Affiliated Hospital of Zhengzhou University","place":["Zhengzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biochemistry and Molecular Biology, Shangqiu Medical College","place":["Shangqiu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Wang","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Shangqiu Medical College","place":["Shangqiu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suping","family":"Liu","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Shangqiu Medical College","place":["Shangqiu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guizhi","family":"Yan","sequence":"additional","affiliation":[{"name":"The Second Affiliated Hospital of Shangqiu Medical College","place":["Shangqiu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,14]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e1002358","DOI":"10.1371\/journal.pcbi.1002358","article-title":"Metabolic reconstruction for metagenomic data and its application to the human microbiome","volume":"8","author":"Abubucker","year":"2012","journal-title":"PLoS Comput. 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