May 25, 2016 - Predictive Medicine in the Era of Big Data: ... great opportunities to improve the development and implementation of good predictive models in ...
Predictive Medicine in the Era of Big Data: From Genome to Exposome (UTS Seminar 25/5/2016) Tuan V. Nguyen Garvan Institute of Medical Research Medicine is a science of uncertainty and an art of probability W. Osler
The predisposition to diseases and the response to therapies are highly
variable between individuals. The variability is caused by a complex web of interactions between genetic and non-genetic factors. Each of us has a unique profile of risk factors, and this profile is defined by multiple genetic variants and connected environmental factors (i.e., "environmentome"). Therefore, the goal of predictive medicine is to provide an individual with accurate risk assessment by using the individual's risk profile.
Current challenges of predictive medicine are to identify relevant factors for
developing a risk profile, and to develop good predictive models. Several statistical methods are available for finding relevant factors, that explain rather than predict outcomes. A good predictive model should meet "4R" criteria: reliable, relevant, real-world, and real-time. However, most current predictive models do not meet those criteria, or may be overfitting which partly contributes to the lack of reproducibility. Common genetic variants explain a modest proportion of variance in disease susceptibility. The next frontier of predictive medicine should be focused on environmental exposures to lifestyle, stress, diet, pollution, radiation, and drug, which may collectively be referred to as "exposome". With on-going advance in omics technologies, and mHealth and eHeath technologies, there are great opportunities to improve the development and implementation of good predictive models in medicine. Big datasets resulted from large consortia allow genetic variants with [often] tiny effect sizes to be identified. The advance in mHealth and eHealth technologies will facilitate large scale studies of interactions between genomics, diseasome and exposome to be carried out. These technologies
also
enable
innovative
designs
for
testing
the
effectiveness
of
predictive/personalised medicine.
Medical science is evolving into a more predictive science, where the
development and implementation of predictive models may gradually realise the Osler's vision of medicine. Predictive models in medicine should not just identify high risk individuals for prevention, and identify appropriate patients for treatment with high precision, but also potentially identify optimal therapeutic pathways based on the contributory mechanisms.
