Macronutrients and Metabolic Health - Understanding how metabolic disease arises at the population level using metabolomics and lipidomics.

点击翻译按钮获取中文摘要

Both obesity and type 2 diabetes (T2DM) are increasing in the UK, placing significant pressure on the National Health Service and impacting on the health of the UK. We know that some of the major causes of these increases are associated with increased dietary consumption of fats and sugars, as well as a general decrease in physical activity. While current public health advice is to exercise more and eat less calorie dense foods, this health advice has been unable to halt the increases in either obesity or T2DM. While there are a number of drugs used to treat T2DM and the increased fat concentrations found in the blood of individuals with obesity, many have side effects which complicate their long term use and are costly to administer. One central question to the field of diabetes research is why on an individual basis certain people are predisposed to developing insulin resistance (a pre-diabetic state) and subsequent T2DM while others stay metabolically healthy. Addressing this question could help treat those at risk of progression and have a significant impact on the costs of treating this disease and its complications.In order to do this we use analytical chemistry techniques, including mass spectrometry and Nuclear Magnetic Resonance (NMR) spectroscopy, to measure the total small molecule complement of tissues, cells and biofluids to develop a fingerprint of those metabolites that are associated with disease using a combination of multivariate statistics and pattern recognition techniques. This approach is termed metabolomics. By modelling changes in these metabolites as disease progresses we build up an 'atlas' of response in terms of the key metabolic perturbations associated with the disease. In particular this approach allows us to look at how food intake influences the metabolism of the body, and we can model these changes to look at diet-genotype interactions induced by over-nutrition (eating too much food).To achieve this aim we have identified four themes to be developed in parallel.1. Fat cells in health and disease: It is well established that fat cells (referred to as white adipose tissue) have numerous important roles in maintaining healthy metabolism in addition to their role as a major site for storage of fats, including roles in regulating hormones, maintaining body temperature and even contributing to the body clock. We will apply metabolomics in conjunction with molecular biology tools to investigate the balance between lipid storage and how we might influence fat metabolism to reduce obesity. 2. Ectopic fat deposition: Once the capability of white adipose tissue to store fat has been exceeded, fat deposition occurs inappropriately (ectopically) in other tissues. While the consequences of raised blood glucose are biochemically well defined, we do not understand what the consequences of raised fat concentrations are. We will use comprehensive metabolomic approaches to profile the impact of excessive fat storage in the liver, heart and skeletal muscle, and in particular focus on the progression of fatty liver disease. 3. Lipidomics at the epidemiology scale: While most animal models are caused by rare errors in single genes which cause T2DM, the most common forms found in patients with diabetes are caused by many genes with a strong environmental interaction, particularly as the result of over nutrition and increased sedentary lifestyles. In order to investigate IR and T2DM development in humans we have developed assays that can be performed on a global scale to allow us to address questions about T2DM and diet, ethnicity and age in epidemiology studies.4. Method development in mass spectrometry and bioinformatics: To be able to conduct these studies we require being at the forefront of developments in both mass spectrometry and mathematical tools for processing the data. We are currently developing tools in mass spectrometry imagining and ion mobility for lipidomics.