Our mission is to discover better diagnostic markers, predictors, and therapies for cardiometabolic disease
What impact will this research have?
Obesity-related diseases are the major health challenges of our generation. Obesity-driven type 2 diabetes has dramatically increased in prevalence in Australia and other Western countries in the last few decades. Alarmingly, in the last decade alone, highly-populous developing countries like India and China have seen type 2 diabetes reach epidemic proportions. In fact, the WHO described the increase of type 2 diabetes in China as “explosive”, where 114 million people have type 2 diabetes and 500 million people (almost 50% of all adults) have prediabetes.
Clearly, there is an urgent global need for better ways to detect and treat type 2 diabetes. One of the cornerstones of treatment of type 2 diabetes is early intervention. In this context, our discovery of a new molecule that independently predicts diabetes 12 years before diagnosis has huge clinical potential to facilitate intervention well before overt onset of diabetes and its attendant complications such as cardiovascular disease. Furthermore, by detailing the entire pathway controlling levels of this molecule, we can now determine if this pathway can treat type 2 diabetes.
Current projects and goals
Obesity-driven metabolic disease such as insulin resistance, diabetes, fatty liver disease, hyperlipidaemia, and hypertension are the major drivers of atherosclerotic cardiovascular disease in the modern era. This trend is continuing despite the best primary prevention efforts. These complex diseases are the consequence of gene-environment interactions, and to truly understand the various levels of dysregulation, both genomic data and environmental data must be captured. We probe carefully-phenotyped patient cohorts using genome scanning and metabolomic profiling to discover novel disease markers that may have clinical utility, e.g., by providing better diagnostic markers of disease, and allowing earlier intervention by predicting future disease. Furthermore, integration of genetic and metabolomic data allows delineation of disease pathways, which we then study in animal and cell models of disease. This allows us to determine disease-specific functional regulation, and potential for therapeutic intervention.
Uncovering Novel Cardiovascular Disease Pathways
We are utilising robustly-phenotyped patient cohorts in concert with genomic, transcriptomic, and metabolomic profiling to shed new light on gene-environment interaction in cardiovascular disease. In addition to providing new markers and predictors of disease, this approach can uncover new targets for therapy. Furthermore, it is becoming increasingly evident that a ‘one-size-fits-all’ approach to cardiovascular prevention and treatment is sub-optimal. We believe re-stratifying risk and targeting disease based on a patient’s unique profile is a far more effective strategy. We have previously identified novel markers and predictors of cardiometabolic disease, in addition to a new disease pathway linking fatty liver disease with diabetes.
Probing a New Metabolic Disease Pathway
While working with Professor Robert Gerszten at Harvard Medical School, we recently discovered a new pathway linking fatty liver disease and diabetes. Integrating non-targeted metabolomic profiling (which captures thousands of metabolites) with genome-wide association (GWAS), we elucidated the chemical identity of a new marker of fatty liver disease. After synthesis and purification, we developed a new assay that we used to show this same marker independently predicted diabetes over a decade in advance in the Malmo Diet and Cancer Study (a Caucasian cohort) and the Jackson Heart Study (an African-American cohort). We are now focusing on functionally interrogating this pathway in model systems, including genetically-modified mouse models.
Truly novel small molecule biomarker discovery is extremely rare, and non-targeted metabolomic profiling offers this potential. However, success stories using this approach are extremely rare. We have previously identified novel disease metabolites using this approach, both in a clinical cohort and in a cell-model system. We are extending our capability and streamlining our discovery pipeline. We have unique expertise with the latest-generation mass spectrometers, chemometric software, and integrative ‘omics’. Furthermore, we are recruiting bioinformaticians to develop novel algorithm, machine learning, and network analysis approaches that harness vast open-access online databases to inform our own data.