Observational research, mainly prospective cohort studies (PCS), has represented a long-standing challenge for those attempting to draw up consistent policy recommendations in the area of diet and health. This has been due to the inherent limitations in ascribing causality from observed associations due to problems of confounding of the findings and publication and citation bias. Developments in nutritional epidemiology research over the past 20–30 years have enabled causal criteria to be derived from observational studies and the totality of the primary literature to be reviewed objectively, reducing previous focus on narrative accounts of individual studies. The gold standard approach to assessing causal relationships is via randomised controlled trials (RCT), but neither RCT nor PCS provide direct evidence for biological plausibility, which is a key criterion for assessing causality. Although extensive mechanistic data are available in the literature, a systematic approach to select and assess quality and relevance of published studies has not been available. This limits their use in the development of diet and health policy. Recent studies have investigated a proposed two-step framework and novel methodologies for integrating heterogeneous data from cell, animal and human studies. Pilot and feasibility studies have shown this to be a useful novel approach to studies of diet and cancer, but further refinements are required, including development of appropriate quality criteria which are less dependent on RCT designs. Future studies are needed to fully verify the approach and its potential for use in other diet–disease relationships.

The overall aim of precision nutrition is to replace the ‘one size fits all’ approach to dietary advice with recommendations that are more specific to the individual in order to improve the prevention or management of chronic disease. Interest in precision nutrition has grown with advancements in technologies such as genomics, proteomics, metabolomics and measurement of the gut microbiome. Precision nutrition initiatives have three major applications in precision medicine. First, they aim to provide more ‘precision’ dietary assessments through artificial intelligence, wearable devices or by employing omic technologies to characterise diet more precisely. Secondly, precision nutrition allows us to understand the underlying mechanisms of how diet influences disease risk and identify individuals who are more susceptible to disease due to gene–diet or microbiota–diet interactions. Third, precision nutrition can be used for ‘personalised nutrition’ advice where machine-learning algorithms can integrate data from omic profiles with other personal and clinical measures to improve disease risk. Proteomics and metabolomics especially provide the ability to discover new biomarkers of food or nutrient intake, proteomic or metabolomic signatures of diet and disease, and discover potential mechanisms of diet–disease interactions. Although there are several challenges that must be overcome to improve the reproducibility, cost-effectiveness and efficacy of these approaches, precision nutrition methodologies have great potential for nutrition research and clinical application.

Present food systems threaten population and environmental health. Evidence suggests reduced meat and increased plant-based food consumption would align with climate change and health promotion priorities. Accelerating this transition requires greater understanding of determinants of plant-based food choice. A thriving plant-based food industry has emerged to meet consumer demand and support dietary shift towards plant-based eating. ‘Traditional’ plant-based diets are low-energy density, nutrient dense, low in saturated fat and purportedly associated with health benefits. However, fast-paced contemporary lifestyles continue to fuel growing demand for meat-mimicking plant-based convenience foods which are typically ultra-processed. Processing can improve product safety and palatability and enable fortification and enrichment. However, deleterious health consequences have been associated with ultra-processing, though there is a paucity of equivocal evidence regarding the health value of novel plant-based meat alternatives (PBMAs) and their capacity to replicate the nutritional profile of meat-equivalents. Thus, despite the health halo often associated with plant-based eating, there is a strong rationale to improve consumer literacy of PBMAs. Understanding the impact of extensive processing on health effects may help to justify the use of innovative methods designed to maintain health benefits associated with particular foods and ingredients. Furthering knowledge regarding the nutritional value of novel PBMAs will increase consumer awareness and thus support informed choice. Finally, knowledge of factors influencing engagement of target consumer subgroups with such products may facilitate production of desirable, healthier PBMAs. Such evidence-based food manufacturing practice has the potential to positively influence future individual and planetary health.