Key evidence for personalised nutrition: a review of randomised controlled trials

The field of personalised nutrition is growing and is based on the concept that delivering personalised dietary advice will be more effective than generic healthy eating guidelines for individuals to improve their diet and metabolic health. While there is substantial interest in the field, there is also a need to examine the evidence base. The objective of this review was to examine existing literature on the efficacy of personalised nutrition approaches and to identify research gaps and future needs. A literature search was conducted in PubMed for randomised controlled trials published between 2000 and 2025. Studies investigating the effects of personalised nutrition were included, and relevant papers were identified through the reference lists of existing papers. In total, 24 papers were included, with 12 studies investigating personalised nutrition based on current diet, phenotype, and metabolic biomarkers, five studies examining the effects of genotype-based personalised nutrition, and seven studies exploring approaches based on gut microbiome and machine learning algorithms. Overall, evidence from the included studies indicates that personalised nutrition approaches consistently improved dietary quality and led to significant improvements in metabolic markers, including HbA1c, triglycerides, and insulin sensitivity. However, few studies showed significant between-group differences in weight loss, and most studies did not find significant differences in blood pressure. While the results are promising, there are key challenges and research gaps that remain. Some approaches demonstrated potential for targeted improvements, but further high-quality research is needed to confirm their effectiveness and long-term impact. Future research should prioritise longer-term studies, better stratification of responders and non-responders, and cost-effectiveness evaluations to determine where and for whom personalised nutrition adds the most value.