This week, we look at how exposome research can transform our understanding of what causes chronic diseases... using cardiovascular disease as a work-example. Cardiovascular disease, especially atherosclerosis, is the leading source of deaths worldwide. Although management of cardiovascular disease has improved, it's worth keeping in mind that the burden of cardiovascular disease... will continue to increase as many countries will become more urban and richer. Why is an exposome approach needed? We have come a long way in understanding the genetic bases of some cardiovascular diseases. A few, like Autosomal Dominant Hypercholesterolemia, are highly heritable. For Coronary Artery Disease, multiple genes have been found to contribute to only a small percentage of the fenotype. From studies of twins, we know that around 20 percent of all Cardiovascular diseases are attributable to our genotype. Only 20 percent. So the environment plays a dominant role in the ideology of most forms of cardiovascular disease. We also know a lot about a few established risk factors, like systolic blood pressure, serum lipid levels and obesity. However, we generally don't know enough about the causes of those risk factors themselves. So what are the causes of the causes? Furthermore, individual prediction risk scores are still performing quite poor. Risk scores, such as the Framingham Risk Score, are generally based on only a few established risk factors... which age is an important driver of, and are derived from populations. To improve our ability to predict and prevent cardiovascular disease, we need to broaden our search for other risk factors... and at the same time account for the timing of these factors along the lifecourse. That is, we need to take an exposome approach. So what should we do? To assess the exposome, we can take advantage of newer technologies, like wearables, sensors and smartphone apps. For example: we can use ecological momentary assessments to assess a study participant's mental... or physical state, when triggered by specific outcomes. Like unusual hart-rate variabilities or arrythmias. Another example is new tools to assess diet. Traditionally, epidemiologists send a food frequency questionnaire once or once every couple of years to study participants. This is highly susceptible to error and also bias, where participants tend to report how they wish they ate. Smartphone apps will allow participants to take photos of their meals and with the rapid development in Artificial Intelligence... like neural networks, we may be able to more accurately classify a person's food intake. And then Big Data. Thanks to advances in computing, cloud access and statistical methods, we can now take advantage of other big data sources. Like electronic health records, registries, biobanks, but also less utilized resources like loyalty cards or barcodes. This allows to use a systems approach to cardiovascular disease, so that we can study higher dimensional interactions between... risk factors, study windows of susceptibility based on lifecourse exposure data... and combine all the data to improve individual risk prediction. Recent efforts to pool cohorts allow us to undertake Big Data approaches. For example: the Big Data at Heart consortium include data on nearly 20 million people and 5 million cases of acute coronary syndrom... artial fibrillation and heart failure. This means that there is sufficiant statistical power to perform Big Data-driven translational research. Gain insights from real-world evidence and advance drug development and personalised medicine through advanced analytics. So what have these newer techniques, exposome approaches, yielded? To list a few examples of recent findings, we start with pollutants. Through a large-scale measurements of the internal exposome... it has been shown that adding chemical pollutants, specifically blood metals... improved risk discrimination and risk reclassification of cardiovascular disease mortality. Researchers at Utrecht University, using mobile sensors... found evidence that it's not the fine particular matter, so-called PM2.5... but the smallest fractions of particular matter, the so-called ultrafine particles... that might be driving cardiovascular disease resulting from air pollution exposure. And then the internal exposome. Using a data-driven, exploratory approach, untargeted high-resolution metabolomics... it was found that trimethylamine N-oxide, TMAO, is related to cardiovascular disease risk. Choline, from for example red meat and cheese, is metabolized by bacteria in our gut to TMAO. Which in experimental studies has been shown to contribute to atherosclerotic plaque formation. This points to the involvement of gut microbiota in the etiology of cardiovascular disease. This last example not only provides an idea of how new biotechnology can be used for identifying new risk factors... but also provides insight how our individual microbiome can change our risk profile. This in turn might provide opportunities for prevention through modification of our microbiome. In conclusion, we focus a lot on biomedical research efforts and spend a lot of resources on treatment of disease. Similar investments in preventing disease haven't yet been made. It is the non-genetic factors that provide the greatest opportunity for prevention and intervention to keep people healthy. To date, we have not made the most of real-world data, advanced statistics and novel tools to characterize the exposome. Advances that could be made towards personalized health and medicine and targeted interventions in communities... precision public health, are large. In this lecture, I have described how capitalizing on exposome science approaches can improve our understanding of... the root causes of cardiovascular diseases and how we can best prevent them.
