UQDI Seminar Series - 'Large-scale systems epidemiology – recent applications and current trends'
Presented by Prof Mika Ala-Korpela, Computational Medicine, Faculty of Medicine, University of Oulu & Biocenter Oulu, Finland & Population Health Science, Bristol Medical School and Medical Research Council Integrative Epidemiology Unit, University of Bristol, UK
Identification of subjects at increased risk for developing a metabolic disease, such as type 2 diabetes or coronary heart disease, plays a central role in worldwide efforts to improve prevention of non-communicable diseases. Extensive omics profiling, incorporating genetics, transcriptomics and metabolomics, is becoming more and more common in the attempts to achieve these goals. This is due to recent developments in quantitative methodologies and various appealing results from their applications on understanding life-course health and disease aetiologies (1-6) as well as drug effects (7,8). Although still in its early days, it is becoming apparent that large-scale omics profiling will transform our understanding of diseases and thereby also impact risk assessment, public health and clinical practices.
Our team has developed an automated high-throughput serum nuclear magnetic resonance (NMR) metabolomics platform that provides quantitative molecular data on 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as on numerous low-molecular-weight metabolites, including amino acids, glycolysis related measures and ketone bodies (1). This platform has already been used to analyse around 500,000 samples from >150 clinical and epidemiological studies and biobanks. The molecular data have been used to study type 1 diabetes, type 2 diabetes and cardiovascular disease aetiology as well as to characterize the molecular reflections of long-term physical activity, diet and lipoprotein metabolism. The results have revealed new biomarkers for early atherosclerosis, type 2 diabetes, diabetic nephropathy, cardiovascular diseases, and various causes of mortality. We have also combined genomics and metabolomics in diverse studies (1,4,5).
Various systems epidemiology applications incorporating extensive data from the abovementioned platform will be presented with a focus on cardiometabolic issues, including a recent application on metabolic profiling of statin use and genetic inhibition of HMG-CoA reductase (7), a “natural” clinical trial of statin effects (8).
