Evidence map›Paper›PMID 42813110›Full record

ReviewFrontiers in nutrition2026

Beyond a universal obesity microbiome signature: pre-intervention heterogeneity and a framework for baseline profiling.

Penghui Liu, Runlin Mao, Na Li, Jiwu Guo, Jizhen Wang, Jie Mao

Abstract readReview
In one paragraph

Review in Frontiers in nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Penghui Liu *The General Surgery Department, Lanzhou University Second Hospital, Lanzhou, China.
Runlin Mao *Marianapolis Preparatory School, Thompson, CT, United States.
Na LiThe General Surgery Department, Lanzhou University Second Hospital, Lanzhou, China.
Jiwu GuoThe General Surgery Department, Lanzhou University Second Hospital, Lanzhou, China.
Jizhen WangThe General Surgery Department, Lanzhou University Second Hospital, Lanzhou, China.
Jie MaoThe General Surgery Department, Lanzhou University Second Hospital, Lanzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

baseline profilinggut microbiotametabolic phenotypemethodological heterogeneityobesitypre-intervention heterogeneity

Identifiers

PMID42813110
PMCPMC13622497

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.