Metabolomic characterisation of adiposity across the life course

Project Background

There is evidence that adiposity, measured by body mass index (BMI), causally influences a range of health outcomes, but little understanding of the mechanisms driving BMI effects. The theme of this work is to dissect the causal effects which have been shown to lie between BMI and health outcomes [1]. The focal point is a new longitudinal collection of untargeted metabolomics data representing five timepoints between the ages of 7 and 30 years in the Avon Longitudinal Study of Parents & Children (ALSPAC) [2]. Whilst studies exist elsewhere charting the metabolome of disease or of adult or mid-to-late age participants, there are few examples of longitudinal metabolomic data in such well characterised individuals. This dataset will be complemented by other in-house collections of metabolomic data including longitudinal proton nuclear magnetic resonance spectroscopy data and data collected under alternative study designs including trials [3], plus shared resources, e.g., UK Biobank. This PhD will be coordinated with a programme of research using the most contemporary and powerful study designs, multiomic data and analytical techniques to explore BMI as a risk factor [4].

Project Aims

This work aims to identify omic intermediates important in the link between BMI and disease. The work sets out to better understand how body mass index (BMI) exerts an effect on human health using longitudinal data collections and other complementary study designs, and through applied genetic epidemiology. Proposed work will aim to characterise the relationship between BMI and circulating metabolites through the life course in efforts to assess potential mediating routes between BMI variation and disease.

Project Methods

The proposed project will focus on a dataset comprising untargeted metabolomics data derived from 1250 samples representing 300 unique ALSPAC participants each with data at four or five timepoints between the ages of 7 and 30 years. Metabolomics data will comprise semi-quantitative data on over 1000 metabolites covering the full spectrum of molecules found in the circulation. This work will require the development of a high throughput quality control and imputation pipeline to deliver an analysis ready dataset [5]. The student will use statistical approaches to model linear and non-linear trajectories of continuous outcomes which may include use of penalised regression splines and latent trajectory models. A range of multivariate methods and data reduction techniques may also be deployed. Existing bioinformatics tools will be used to provide biological context for and aid interpretation of results. Throughout the project, there will be an opportunity to develop new methods across the available data sets and study designs.

How to apply for this project

This project will be based in Bristol Medical School - Population Health Sciences in the Faculty of Health Sciences at the University of Bristol.

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