Microbiome and Metabolomics in Obesity: Advances in Understanding and Interventions Across the Lifespan
- Design
- Prospective cohort · Unknown outcome
- Match to healthy normal-weight adults aged 55–75
- Different population[Auto] Participants had obesity/overweight (age/BMI not reported in abstract).
- Could weight loss explain it?
- Possibly[Auto] Participants had obesity and/or type 2 diabetes and the abstract does not separate direct drug effects from weight loss or glycaemic improvement.
- Study tier
- Study tier 3[Auto] Observational design; confounding by indication and healthy-user effects cannot be excluded (auto-provisional).
- Assessment
- Version 1 · automatic, not yet reviewed by a person · Sep 13, 2026
Study facts come from the paper. Population match, weight-loss explanation and study tier are our judgments, made against the reference group of healthy normal-weight adults aged 55–75.
[Auto, unreviewed; last sentences of abstract] Function-centered metrics outperform phylum-level ratios for translation. Harmonized longitudinal cohorts and explainable ML-derived microbial and metabolomic signatures are now pivotal to identify responder subtypes and actionable microbe-metabolite targets, enabling precision nutrition alongside pharmacotherapy across the lifespan.
01Findings
What the study reported
- Drugs
- Class unspecified
- Primary outcome
- Not extracted
- Effect
- Not extracted
- 95% confidence interval
- Not extracted
- Follow-up
- Not stated
- Adverse events
- Not extracted
- Limitations
- Auto-classified from abstract only; effect estimates, adverse events and limitations not extracted. Requires manual review.
Who was studied
- Obesity status
- obesity/overweight present (all or most)
Study quality details
- Study design
- Prospective cohort
- Sample size
- not extracted
- Randomization
- no
- Blinding
- not stated
- Comparator
- not stated
- Follow up duration
- not stated
- Outcome type
- unknown
- Replication
- not assessed (auto)
- Consistency with other evidence
- not assessed (auto)
- Population applicability
- INDIRECT
- Statistical precision
- not extracted
- Risk of bias
- not assessed (auto)
- Funding conflicts
- no
- Peer review status
- yes
02Funding
Funding and conflicts
- Funding
- European Union's Horizon Europe Research and Innovation programme; UK Research and Innovation; Swiss State Secretariat for Education, Research and Innovation
- Industry funded
- No
- Manufacturer
- None identified
- Sponsor role
- no manufacturer funding identified
- Author conflicts
- not available in metadata
- Independent replication
- unknown
Funding is shown on every study and never used to score it.
04Source
The source, as retrieved
Abstract
Obesity arises from intertwined and reciprocal diet-microbiome-host pathways that reshape energy balance, insulin sensitivity, and inflammation. This review synthesizes mechanistic links between microbial functions and metabolic control, charts lifestyle-related lifecourse dynamics from birth to older age, examines how GLP-1-based therapies may perturb gut ecology and metabolite output and surveys AI/ML frameworks for multi-omics integration. Plant-based, fiber-rich dietary patterns generally enrich saccharolytic guilds, boost SCFAs production, and modulate bile acid signaling, whereas Westernized patterns favor bile-tolerant, amino acid-fermenting consortia and proinflammatory metabolites. Preclinical data suggest that incretin-based therapies remodel the microbiome-metabolome axis, but human causal mediation remains unproven and observed changes may partly reflect weight loss or metabolic improvement. Function-centered metrics outperform phylum-level ratios for translation. Harmonized longitudinal cohorts and explainable ML-derived microbial and metabolomic signatures are now pivotal to identify responder subtypes and actionable microbe-metabolite targets, enabling precision nutrition alongside pharmacotherapy across the lifespan.