The appeal of precision medicine is the promise that we can understand disease with greater specificity and fashion treatments that are more individualized and more effective.
A core tenet (or “central dogma,” as I wrote in 2015) of precision medicine is the idea that large disease categories – like type 2 diabetes – actually consist of multiple discernable subtypes, each with its own distinct characteristics and genetic drivers. As genetic and phenotypic research advances, the argument goes, diseases like “type 2 diabetes” will go the way of quaint descriptive diagnoses like “dropsy” (edema) and be replaced by more precisely defined subgroups, each ideally associated with a distinct therapy developed for that population.
In 2015, this represented an intuitively appealing idea in search of robust supporting data (at least outside oncology).
In 2017, this represents an intuitively appealing idea in search of robust supporting data (at least outside oncology).
The gap between theory and data has troubled many researchers, and earlier this year, a pair of cardiologists from the Massachusetts General Hospital (MGH) and the Broad Institute, Sek Kathiresan and Amit V. Khera, wrote an important – and I’d suggest underappreciated – commentary in the journal Circulation that examined this very disconnect, through the lens of coronary artery disease (CAD).