Nutrition plays a fundamental role in shaping human health across the life course, influencing both host physiology and the composition and function of the gut microbiota. In turn, the gut microbiota modulates the effects of dietary intake, creating complex bidirectional interactions with profound implications for metabolic health. Although the concept of personalized nutrition offering tailored dietary advice based on observable traits, environmental factors, and genotype has gained prominence, growing evidence supports the promise of precision nutrition that also considers individual microbiome profiles. This approach is particularly relevant for addressing diet-related conditions such as obesity and type 2 diabetes, where interindividual variability in response to the same diet is well documented. Advances in high-throughput sequencing, metabolomics, and machine learning are driving predictive models that can forecast personalized dietary outcomes. However, methodological heterogeneity, lack of consistency, and limited representation of diverse populations in current studies present significant barriers. Ethical challenges, including data privacy and equitable access to personalized nutrition tools, also warrant urgent attention. To realize the full potential of microbiome-informed nutrition, greater harmonization of research methods, robust validation across large and diverse cohorts, and an interdisciplinary framework are essential.
A Tue, study studied this question.