{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T17:09:36Z","timestamp":1789492176198,"version":"build-2803163510"},"reference-count":81,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"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. Nutr."],"abstract":"<jats:p>Unlike previous reviews that primarily summarize obesity-associated microbial alterations, this review reframes the recurrent inconsistency of human obesity microbiome findings as an informative consequence of pre-intervention heterogeneity rather than merely failed replication or analytical noise. We integrate biological and contextual determinants of baseline microbiota variation with methodological and analytical sources of heterogeneity into a unified framework for interpreting why microbial diversity, taxonomic composition, and functional signals differ across individuals, populations, and studies. We critically synthesize evidence on baseline microbial diversity, community structure, host characteristics, regional dietary exposure, metabolic heterogeneity, and the potential relevance of pre-intervention microbiota to subsequent treatment outcomes. We also examine how cohort definition, stool sampling, laboratory processing, bioinformatic reconstruction, and statistical analysis shape the microbiome profiles that are ultimately observed. On this basis, we propose a baseline heterogeneity framework built on three linked principles: obesity-associated microbial signals are context-dependent; observed microbiota profiles are method- and pipeline-dependent; and their interpretation must be temporally anchored to the pre-intervention state. This framework positions baseline microbiota profiling not as descriptive cataloging or a search for a universal obesity-specific signature, but as a prerequisite for identifying confounding and effect modification, improving cross-population interpretation, and establishing the microbial and host context from which intervention begins. Future studies should integrate microbiota data with comprehensive characterization of relevant biological and contextual domains and evaluate the robustness of findings across analytical choices and independent populations. This conceptual shift provides a more rigorous foundation for region-specific baseline profiling and future longitudinal, mechanistic, and precision obesity research.<\/jats:p>","DOI":"10.3389\/fnut.2026.1921813","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T16:47:28Z","timestamp":1789490848000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond a universal obesity microbiome signature: pre-intervention heterogeneity and a framework for baseline profiling"],"prefix":"10.3389","volume":"13","author":[{"given":"Penghui","family":"Liu","sequence":"first","affiliation":[{"name":"The General Surgery Department, Lanzhou University Second Hospital","place":["Lanzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runlin","family":"Mao","sequence":"additional","affiliation":[{"name":"Marianapolis Preparatory School","place":["Thompson, CT, United 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