We are excited to share our new review article, “Design and analysis strategies for robust microbiome ageing research”. The gut microbiome is one of the promising biomarkers and intervention targets in ageing biology. Yet the field faces a reproducibility problem, and methodology is at its core.
In this review, we present an integrated framework organised around five methodological challenges:
- Confounding & collinearity — Chronological age cannot be varied independently of diet, polypharmacy, or lifestyle. We discuss DAGs, mediation analysis, and staggered cohort designs to disentangle these effects.
- Selection bias — Ageing cohorts are systematically enriched for healthy survivors. Centenarian microbiome profiles may partly reflect selective survival rather than typical ageing. Deliberate inclusion of frail and multimorbid individuals is essential.
- Biological heterogeneity — Microbiomes fluctuate on short timescales and diverge across decades. Single time points capture transient states rather than representative baselines. Mixed-effects models and dispersion testing are necessary to partition these variance components.
- Machine learning & ageing clocks — Batch effects can enable a model to function as a cohort detector rather than a genuine biological clock. Batch-aware, leave-one-cohort-out validation is the minimum standard for demonstrating generalisability.
- Mendelian randomisation — MR offers causal leverage, yet low taxon heritability, compositionality, and age-dependent genetic architecture each pose specific challenges requiring transparent reporting and triangulation with longitudinal evidence.
We conclude with a practical checklist (Box 1) and visual workflow (Fig. 1) spanning study design through to reporting.
The central message: analytical methods can address only what study design makes possible. Robust design is a prerequisite for robust inference.
You can read the review here.