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<p>The microbiome has critical roles in human health and disease. Advances in high-throughput sequencing and metabolomics have revolutionized our understanding of human gut microbial communities and identified plausible associations with a variety of disorders. However, microbiome research remains constrained by challenges in establishing causality, an over-reliance on correlative studies, and methodological and analytical limitations. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges; however, the seamless integration of preclinical models and clinical trials is crucial to maximizing the translational impact of microbiome studies. This manuscript critically evaluates best methodological practices and limitations in the field, focusing on how emerging AI tools can bridge the gap between microbial insights and clinical applications. Specifically, we emphasize the necessity of rigorous, reproducible methodologies that integrate multiomics approaches, preclinical models and clinical trials in the AI-driven era. We propose a practical framework for applying AI to microbiome studies, alongside strategic recommendations for clinical trial design, regulatory pathways, and best practices for microbiome-based informed diagnostics, AI training and clinical interventions. By establishing these guidelines, we aim to accelerate the translation of microbiome research into clinical practice, enabling precision medicine approaches informed by the human microbiome.</p>

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Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI

  • 期刊论文

  • 发表日期：

    2025年7月31日

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  • Alberto Caminero,
  • Carolina Tropini,
  • Mireia Valles-Colomer,
  • Dennis L. Shung,
  • Sean M. Gibbons,
  • Michael G. Surette,
  • Harry Sokol,
  • Nicholas J. Tomeo,
  • Theresa Alenghat,
  • Muyiwa Awoniyi,
  • Sara C. Di Rienzi,
  • Ari Grinspan,
  • Diane E. Hoffmann,
  • Lori R. Holtz,
  • Aaron L. Hecht,
  • Purna Kashyup,
  • Rohit Loomba,
  • Ryan Ranallo,
  • Alejandro Reyes Muñoz,
  • Mark Pimentel,
  • Long H. Nguyen,
  • Michelle Clark,
  • Phillip I. Tarr,
  • Elena F. Verdu

摘要

The microbiome has critical roles in human health and disease. Advances in high-throughput sequencing and metabolomics have revolutionized our understanding of human gut microbial communities and identified plausible associations with a variety of disorders. However, microbiome research remains constrained by challenges in establishing causality, an over-reliance on correlative studies, and methodological and analytical limitations. Artificial intelligence (AI) has emerged as a powerful tool to address these challenges; however, the seamless integration of preclinical models and clinical trials is crucial to maximizing the translational impact of microbiome studies. This manuscript critically evaluates best methodological practices and limitations in the field, focusing on how emerging AI tools can bridge the gap between microbial insights and clinical applications. Specifically, we emphasize the necessity of rigorous, reproducible methodologies that integrate multiomics approaches, preclinical models and clinical trials in the AI-driven era. We propose a practical framework for applying AI to microbiome studies, alongside strategic recommendations for clinical trial design, regulatory pathways, and best practices for microbiome-based informed diagnostics, AI training and clinical interventions. By establishing these guidelines, we aim to accelerate the translation of microbiome research into clinical practice, enabling precision medicine approaches informed by the human microbiome.