In the Era of Precision Medicine and Big Data, Who Is Normal?

The definition of “normal” values for common laboratory tests often governs the diagnosis, treatment, and overall management of tested individuals. Some test results may depend on demographic traits of the tested population including age, race, and sex. Ideally, laboratory test results should be interpreted in reference to a population of“similar” “healthy”individuals. In many settings, however, it is unclear exactly who these individual sare.How much population stratification and what criteria for healthy individuals are optimal? In particular, with the evolution of medicine into fully personalized or “precision” medicine and the availability of large-scale data sets, there may be interest in trying to match each person to an increasingly granular normal reference population. Is this precision feasible to obtain in reliable ways and will it improve practice? are analyses of baseline variation across demographically diverse population strata (including race/ancestry, gender/sex, age, and socioeconomic strata of the population) for even widespread clinical laboratory tests. after of routine use, be that reference standards should be reconsidered for some populations.For example, he 1c (HbA ) underestimate past glycemia in African American patients with the sickle cell trait. of and more granular stratification correlates with clinical studies that assess the outcomes of individuals with laboratory measurements classified as normal with one system vs abnormal with another. include both natural history and treatment benefits and harms. small laboratory incomplete capture of long-term this difficult to