Peer-reviewed article
Early-life microbiome trajectories as biomarkers to predict health outcomes.
Raphaela Joos, Aonghus Lavelle, Eugene Dempsey, Catherine Stanton, R Paul Ross ·
Summary
Written by AI, not yet AICS's view · claude-haiku-4-5-20251001This is a methodological review article about using gut microbiome data from early infancy as potential biomarkers to predict health outcomes. It discusses how microbial colonisation patterns relate to infant development, metabolism, immune function and neurodevelopment. The review compares statistical and machine learning approaches for analysing microbiome data collected in the first two years of life. It addresses technical and analytical challenges in using microbiome data for early detection and prevention of health problems.
Key points
- Early-life gut microbiome composition is linked to infant metabolism, immune function, and neurodevelopment
- Microbiome data from the first two years of life may enable early detection and prevention of adverse health trajectories
- Machine learning methods can predict health outcomes from microbiome features, while statistical models identify associations
- The review examines how data acquisition and analytical methods shape current knowledge about the microbiome
Limitations
This is a methodological review that does not evaluate or measure actual health outcomes in children or test the effectiveness of any microbiome-based intervention. It cannot provide evidence about whether using microbiome biomarkers actually improves child health or clinical decision-making in practice.
Abstract
The early-life gut microbiome is tightly linked to different aspects of infant development. Microbial colonisation patterns have been repeatedly shown to play a role in a variety of paediatric outcomes, ranging from metabolism and immune function to neurodevelopment. Concomitantly, the identification of early-life biomarkers is crucial, especially considering that for various conditions, reliable diagnostic tools only emerge in early childhood. As such, microbiome data collected in the first two years of life may offer valuable prospects for early detection, prevention, quantification or even correction of adverse health trajectories. With the increasing availability of high-resolution microbiome data, researchers are leveraging both traditional statistical approaches and machine learning (ML) methods to analyse the evolution of these complex microbial communities. While statistical models are well-suited for identifying associations between microbiome features and health states, ML methods allow for predicting health outcomes from those features. This review explores the role of the early-life gut microbiome in infant health and development, with a focus on how data acquisition and analytical methods can shape current knowledge. We contrast statistical approaches with ML methods, summarising key findings on microbial succession and factors influencing it. By addressing current challenges and identifying areas for methodological refinement, we aim to discuss the potential of the microbiome in the assessment of current and future health states of an individual and aid in the development of more robust, clinically-relevant models for paediatric care.