{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T07:16:03Z","timestamp":1788419763809,"version":"build-2803163510"},"reference-count":66,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"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>Major depressive disorder (MDD) exhibits pronounced age-related pathophysiological heterogeneity, yet the distinct signatures of the microbiota-gut-brain (MGB) axis between adolescent and adult patients remain inadequately characterized. This study aimed to elucidate age-specific MGB axis dysregulation by integrating gut microbiota profiling, plasma metabolomics, and their interactive networks in first-episode, drug-na\u00efve MDD patients across these two developmental stages.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We enrolled 62 first-episode, drug-na\u00efve MDD patients (28 adolescents, 34 adults) alongside 43 age- and sex-matched healthy controls (22 adolescents, 21 adults). Fecal samples underwent 16S rRNA gene sequencing, while plasma was analyzed via untargeted metabolomics. Comprehensive bioinformatics and network analyses were applied to map microbial compositional shifts, metabolic perturbations, and microbe-metabolite crosstalks.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Integrated multi-omics analysis revealed distinct age-specific gut microbiota and plasma metabolome signatures in TMDD and AMDD compared with their age-matched controls. \u2460 Microbiota: Beyond shared depletion of Firmicutes and butyrate-producing genera (Anaerobutyricum, Blautia), TMDD exhibited more extensive remodeling. Characterized by increased (Chao1 index,\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.05) Bifidobacterium loss and enrichment of Bacteroidetes, Gemmatimonadetes, and Planctomycetes. \u2461 Metabolome: TMDD perturbations targeted neurodevelopmental and oxidative pathways, such as arginine\/proline metabolism, D-amino acid metabolism, and glutathione metabolism. Whereas AMDD focused on energy dysfunction and excitotoxicity, involving the TCA cycle, purine metabolism, and alanine\/aspartate\/glutamate metabolism. \u2462 Interactome: In AMDD, positive correlations with protective (lipids) were attenuated, while pathological links with pro-inflammatory sphingolipids and polyamines strengthened. The interactome in AMDD remained stable.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings suggest that first-episode, drug-na\u00efve MDD patients display distinct age-specific dysregulation of the microbiota-gut-plasma metabolome axis. The non-overlapping pathological signatures, a \u201cdevelopment-nutrition-metabolic\u201d disturbance pattern in adolescents versus an \u201cenergy metabolism-oxidative stress\u201d pattern in adults, underscore the pronounced pathophysiological heterogeneity of MDD across the lifespan. These preliminary observations indicate that future diagnostic biomarkers and therapeutic interventions, particularly microbiota-targeted strategies, should be precisely tailored to the biological profiles of different age groups. However, given the exploratory nature of this subtyping, larger independent cohort studies are warranted to validate these findings before clinical translation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmicb.2026.1922527","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T06:40:46Z","timestamp":1788417646000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Age-specific alterations in gut microbiota-plasma metabolome networks in first-episode, drug-na\u00efve major depressive disorder: adolescent versus adult patients"],"prefix":"10.3389","volume":"17","author":[{"given":"Hui","family":"Mo","sequence":"first","affiliation":[{"name":"Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Chai","sequence":"additional","affiliation":[{"name":"Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"LeTian","family":"Zhao","sequence":"additional","affiliation":[{"name":"Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Hygienic Analysis and Detection, Center for Global Health, Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation and Intelligent Sensing, Shanghai Jiaotong University","place":["Shanghai, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hu","family":"Miaoyang","sequence":"additional","affiliation":[{"name":"Department of Hygienic Analysis and Detection, Center for Global Health, Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"WeiMin","family":"Lu","sequence":"additional","affiliation":[{"name":"Jiangsu Province Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing University of Chinese Medicine","place":["Nanjing, Jiangsu, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1126\/science.abi6087","article-title":"Signaling inflammation across the gut-brain axis","volume":"374","author":"Agirman","year":"2021","journal-title":"Science"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1037\/1082-989x.4.1.76","article-title":"Comparing squared multiple correlation coefficients: examination of a confidence interval and a test significance","volume":"4","author":"Algina","year":"1999","journal-title":"Psychol. 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