Mental ill health is currently one of the leading causes of disease burden worldwide. A growing body of data has emerged supporting the role of diet, especially polyphenols, which have anxiolytic and antidepressant-like properties. The aim of the present study was to assess the effect of a high polyphenol diet (HPD) compared to a low polyphenol diet (LPD) on aspects of psychological well-being in the Polyphenol Intervention Trial (PPhIT). Ninety-nine mildly hypertensive participants aged 40–65 years were enrolled in a four-week LPD washout period and then randomised to either an LPD or an HPD for eight weeks. Both at baseline and the end of intervention, participants’ lifestyle and psychological well-being were assessed. The participants in the HPD group reported a decrease in depressive symptoms, as assessed by the Beck Depression Inventory-II, and an improvement in physical component and mental health component scores as assessed with 36-Item Short Form Survey. No differences in anxiety, stress, self-esteem or body image perception were observed. In summary, the study findings suggest that the adoption of a polyphenol-rich diet could potentially lead to beneficial effects including a reduction in depressive symptoms and improvements in general mental health status and physical health in hypertensive participants.

Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data are generated beyond human scale, there is an opportunity for technological tools to support multifactorial, integrated, scalable approaches to address the complexities of dietary behaviour change. Responsible technology could act as a mechanistic conduit between research, policy, industry and society, enabling timely, informed decision making and action by all stakeholders across the food system. Domain expertise in food, nutrition and health should always be integrated into both the development and continuous deployment of AI‐powered nutritional intelligence (NI) to ensure it is responsible, accurate, safe, useable and effective. Dietary behaviours are complex and improving diet‐related health outcomes requires socio‐cultural‐demographic considerations within the design and deployment of NI tools. This article describes existing examples of NI within the food system and future opportunities. Human‐in‐the‐loop approaches with food, health and nutrition experts involved at all stages including data acquisition, processing, output validation and ongoing quality assurance are essential to ensure evidence‐based practice. The same ethical considerations should apply in this domain as in any other (e.g. privacy, inclusivity, robustness, transparency and accountability) and responsible practice must encompass rigorous standards and alignment with regulatory frameworks. Critical today and in the future is accessibility to appropriate high‐quality food compositional data sets, which include up‐to‐date information on commercially available products that reflect the constantly evolving food landscape to realise the potential of responsible AI to help address the existing food system challenges.

Objective: Impaired insulin secretion and action contribute to the development of type 2 diabetes. Dietary fat modification may improve insulin sensitivity, whereas the effect on insulin secretion is unclear. We investigated the effect of dietary fat modification on insulin secretion in subjects with the metabolic syndrome. Design: In a 12-week pan-European parallel, randomized controlled dietary intervention trial (LIPGENE), 486 subjects were assigned to four isoenergetic diets: high-fat diets rich in saturated fat (HSFA) or monounsaturated fat (HMUFA) or low-fat, high-complex carbohydrate diets with (LFHCC n-3) or without (LFHCC control) 1.2 g/day of n-3 PUFA supplementation. Insulin secretion was estimated as acute insulin response to glucose (AIRg) and disposition index (DI), modeled from an intravenous glucose tolerance test. Results: There were no overall effect of the dietary intervention on AIRg and DI in the total cohort, in neither the high-fat nor LFHCC groups. We observed significant diet*fasting glucose category interactions for AIRg (P = 0.021) and DI (P = 0.001) in the high-fat groups. In subjects with normal fasting glucose and preserved first phase insulin secretion, the HMUFA diet increased, whereas the HSFA diet reduced AIRg (P = 0.015) and DI (P = 0.010). Conclusions: The effects of dietary fat modification on insulin secretion were minor, and only evident in normoglycemic subjects. In this case, the HMUFA diet improved AIRg and DI, as compared to the HSFA diet.

Impaired insulin secretion and action are important for development of type 2 diabetes (T2D) and metabolic syndrome (MetS). Despite recognized heterogeneity of these glucometabolic disorders, few data are available of biological variation in insulin secretion and action among individuals with T2D and MetS. The aim of this study was to explore the inter-individual variations using gold standard methods in a cross-sectional study of two independent cohorts of phenotypically well-characterized subjects. Cohort I included 486 subjects with MetS, and cohort II 62 subjects with established T2D. First phase insulin secretion was defined as the incremental area under the curve 0–8 min (iAUC_0–8 min) during an intravenous glucose tolerance test (IVGTT). Insulin sensitivity was measured as the insulin sensitivity index (S_I) modelled from IVGTT in cohort I, and in II as total glucose disposal (TGD) estimated from a euglycaemic-hyperinsulinaemic clamp. Variation is given as total range and, fold-variation between 5%- and 95%-percentile. The iAUC_0–8 min ranged from −60 to 3397 mUL⁻¹min⁻¹ among subjects with MetS and from −263 to 1194 mUL⁻¹min⁻¹ in subjects with T2D, representing a more than 10-fold variation. Insulin sensitivity ranged from S_I 0.19 to 15.29 (mU/L)⁻¹min⁻¹ among subjects with MetS and TGD 12.9–101.6 μmolkgFFM⁻¹min⁻¹ in subjects with T2D, representing a 6.8 and 5.5-fold variation, respectively. The other components of MetS; BMI, waist-hip ratio, HDL-cholesterol, triglycerides and blood pressure (BP), showed a 1.4–4.7-fold variation. In conclusion, our data demonstrated extensive inter-individual variations in insulin secretion and sensitivity. These variations may be essential to take into account when planning clinical research and treatment in subjects with T2D and MetS.

This review explores opportunities and challenges across data, health metrics, nutrition science, digital technology and policy in the context of healthier food system transformation. Rapid advancements in digital technology and artificial intelligence have reshaped how food is produced, formulated, marketed, purchased and consumed, with vast quantities of data generated across supply chains and retail environments. How individuals access, track and generate food and health information has shifted. Despite unprecedented data availability, fragmentation, diverging definitions, inconsistent health metrics and misaligned timelines across academia, policy, industry and technology limit effective and timely integration. This review develops two interrelated concepts, The Food Data Landscape (FDL) and Nutrition Intelligence (NI). It explores the structure and limitations of the FDL, considers the role of health metrics and describes technological developments and emerging NI applications. A conceptual Food Data-Nutrition Intelligence (FD-NI) Framework is presented providing a shared foundation for advancing more coherent research, policy and practice. This framework positions NI as an integrative layer capable of translating complex, fragmented data into actionable, evidence-based insights that can inform healthier decision making at scale. Transformative potential of AI will not be realised through technological advancement alone. Standardised data infrastructures, transparent assumptions, traceable decision making and collaborative governance frameworks that embed scientific integrity are key. Coordinated collaboration is critical to harness the full potential of NI to deliver meaningful, system-wide, timely transformation towards healthier, more sustainable diets. Future research should define stakeholder roles across the FDL and develop methodological, governance and evaluative frameworks to support responsible and impactful NI.