Project Outline

The food environment in many countries including the UK is characterised by an abundance of palatable, high energy dense foods (often ultra processed and low in fibre), which has been identified as a key driver of diet-related ill-health. In particular, nutrition affects all aspects of brain function and accumulating evidence suggests a role for diet in mental health (Adan, Higgs et al., 2022), which is increasingly important in the era of healthy eating and sustainable food. However, the evidence base is currently limited due to the challenges associated with running large scale food-related clinical trials, including issues such as difficulties in blinding participants to the nature of a nutritional intervention and diet adherence. Moreover, little is known about the mechanisms that link nutrition to brain health. Improved mechanistic understanding of how nutrition affects biology, brain function and ultimately well-being will guide the development of new nutritional interventions and evidence-based advice to promote brain fitness throughout life. A promising approach that we have developed is to assesses the effects of controlled exposure to an intervention on early-stage biomarkers that are predictive of subsequent health outcomes e.g. biological markers of inflammation. This approach provides a bridge between preclinical investigations of mechanisms and large-scale clinical trials. Importantly, an experimental design in a laboratory setting is employed to ensure adherence and clear endpoints. We have previously validated an experimental platform to assess the effects of novel treatments for obesity and eating disorders (BBSRC funded, Schneider et al. 2022).

The aim of the proposed project is to use our established platform to assess the effect of a dietary manipulation on a range of validated biomarkers (e.g. gut hormones, blood glucose/triglycerides, internal carotid blood flow, inflammatory markers, gut microbiome composition) and well-being outcomes (assessed via questionnaires and/or computer assessed neurocognitive measures). In addition, we will use state of the art machine learning techniques (e.g., multivariate classification approaches) to determine which biological changes predict positive outcomes and how this might vary by individual characteristics. The intervention will be developed as part of the project but will likely target fibre supplementation as higher intake of dietary fibre has been associated with improved mental health (Knüppel et al. 2018) and fibre intake in the general population is well below levels recommended by the World Health Organisation. Healthy volunteers (equal numbers of men and women) will be recruited to a randomised between-subjects study in which they will consume the experimental food (or a matched placebo) and the effect on health biomarkers will be assessed before and after the consumption period. We predict that the fibre supplementation will result in improved measures of wellbeing and that our novel computational approach will identify the biological mechanisms that underpin these changes. As part of the project the student will also conduct a systematic review of the relationship between fibre intake and mental health, and we will also use existing data from large scale nutritional surveys (e.g. UK Biobank) to provide complementary data from the real world on the association between diet and health.

References

Adan, R. A., Higgs, S., .. & Dickson, S. L. (2019). Nutritional psychiatry: Towards improving mental health by what you eat. Eur Neuropsychopharm, 29(12), 1321-1332.

Knüppel, A. et al. (2018). Dietary fibre intake and common mental disorder: prospective findings from the Whitehall II study. Proc Nut Soc, 77, E126.

Schneider, E., Dourish, C. T., & Higgs, S. (2022). Utility of an experimental medicine model to evaluate efficacy, side-effects and mechanism of action of novel treatments for obesity and binge-eating disorder. Appetite, 176, 106087

Techniques

Behavioural testing, Phlebotomy, Blood assays, Doppler Ultrasound, gut microbiome assays, Computational modelling, Advanced statistics