Investigating shared molecular pathways underlying cardiovascular diseases, diabetes and cancer

The incidence rates of cardiovascular diseases, diabetes and certain cancers tend to be correlated across countries, that means that countries with higher incidence rates of one disease tend to have higher rates of the other diseases as well. In addition, population studies have found that this correlation also applies at the individual level, that is cardiovascular diseases, type 2 diabetes and some cancers tend to occur in the same patients more frequently that it would be expected by chance. The correlation of diseases is referred to as comorbidities and it is an increasing problem facing the National Health Service as the population lives longer, along with increases in obesity and reduced physical activity.
Previous studies and recent research conducted by us has found that the co-occurrence of these diseases in the same population and even in the same patients, may be due to the fact that these diseases share several lifestyle and metabolic risk factors, such as smoking, excessive alcohol drinking, some aspects of diet, nutrition, overweight and obesity and lack of physical activity. In addition, it has been postulated that co-morbidity may also be due to the possibility that these diseases share biological and pathological mechanisms. In this project, we plan to take advantage of shared risk factor to discover if these diseases also share pathological mechanisms that lead to their clinical development. These would be powerful targets for treating both diseases.
Technological advances have recently allowed us to measure thousands of small molecules in thousands of human samples at a reasonable cost and in a short period of time. This approach is termed "metabolomics" and will be applied to the stored blood samples provided by a large number of volunteers belonging to studies called "population cohorts". In a population cohort study design, the health of the participants is monitored across time to see who will develop the diseases of interest and how this might be related to various measures already performed in this population at the entry into the study, such as the genes they possess, their diet, physical activity, smoking, drinking and many other measures. We will use the "metabolomics" technology to investigate a large number of small molecules comprising a wide range of normal and abnormal biological pathways in human blood plasma providing us with a unique insight into the common pathways of these very different diseases.
We will study the patterns of biological markers associated with the genetic susceptibility to each of the diseases under study and apply advanced statistical methods to find any high-level structural similarities between them. We will use powerful bioinformatic methods to identify pathological pathways that are characterized by the presence of metabolites and biomarkers that are common between diseases. Such pathways may guide us to uncover some of the biological bases of the comorbidity between different diseases.
This project may also help us understand why increased population prevalence of obesity, type 2 diabetes and some cardiovascular conditions is linked to increased risk of developing certain cancers.
We expect that the results of these investigations, by deepening our knowledge of the causes and mechanisms leading to CVD, Diabetes and cancer, may eventually help to identify and implement new prevention and treatment strategies that could address more than one of the disorders at once.

Epidemiologic studies have shown that cardiovascular diseases (CVD; coronary artery disease (CAD) and stroke), type 2 diabetes (T2D) and cancers (e.g. cancers of the colorectum, breast after menopause, endometrium, kidney and prostate) are correlated. We have previously shown that shared risk factors are contributing to the comorbidity across the diseases. Here, we hypothesize that common pathways might contribute to the comorbidity. In this project, we are proposing to use untargeted metabolomics assays (1H NMR spectroscopy and Mass Spectrometry) in epidemiologic studies (AIRWAVE, MESA and Rotterdam) to investigate the common pathways. We will apply our metabolome-wide association study approach on the genetic variants that are identified for each of the disorders (CVD, T2D or cancers) in genome-wide association studies. Multivariate models will be used to identify shared metabolic features across disorders. We will annotate the metabolic features and conduct pathway analysis to highlight pathways that underlie the multimorbidity. This will subsequently be validated in the EPIC cohort. Finally, we will conduct in vitro experimental work to biologically validate the findings using a combination of gene-editing and stable isotope techniques to probe molecular pathways in cancer cell lines. Our findings generate new knowledge about the biological pathways that underlie the disorders and may contribute to the prevention and treatment of the disorders in comprehensive approaches.