Using metabolics for the exploration of diet and health in twins

Abstract

Metabolomics is an exciting area of research obtaining the chemical and metabolic signals from biosamples. Food consumption is strongly linked to metabolism making the metabolome an ideal phenotype for identifying biomarkers and helping refine and explore diet-disease associations. However, the metabolome is highly complex and influenced by many factors, such as age, disease, genetics and gut microbiota. Discordant monozygotic twins may provide a strong model for confirming association findings as they are matched for age, sex and the baseline genetic sequence.
In this thesis, I explored the potential and applicability of the metabolome in nutritional research in two main areas: for identifying biomarkers of food exposure and further investigating the relationship of food intake with indicators of health. Firstly, I examined metabolomics profiles associated with self-reported food intakes and dietary patterns and confirmed these associations using the co-twin control method. I then identified top metabolite markers of food group intakes, and created and tested metabolite scores using multiple-methods. I searched for markers of gut microbiome diversity, an emerging indicator of health, by examining the association of alpha-diversity with blood metabolomics profiles and the relationship with diet and the metabolic syndrome. In the final chapter, I created a dietary score predictive of visceral fat mass, a strong risk factor for cardio-metabolic disease, and examined the degree to which the relationship between diet and visceral fat mass is mediated by associated metabolites and microbiome taxa. Throughout each chapter I used discordant monozygotic twins to validate top results. Overall, my findings show that metabolomics is a highly versatile tool for advancing nutrition research from biomarker identification through to untangling the impact of dietary exposures on indicators of metabolic health and the gut microbiome.

Date of Award	2017
Original language	English
Awarding Institution	King's College London
Supervisor	Tim Spector (Supervisor) & Cristina Menni (Supervisor)