The landmark example of using geographic information systems in medicine is the 1854 Broad Street cholera outbreak in London. John Snow was able to pinpoint the source of the cholera epidmeic by mapping cases of cholera. The cases encircles the Broad Street pump and Snow stopped the epidemic by removing the pump handle.

*Figure: The mapping of cholera cases by John Snow. Source: Wikipedia*

Eric Topol uses the word GIS, but his use of the term ‘human GIS’ has a dramatically different meaning, encompassing demographic, physiologic, biologic, and environmental data. There is a detailed description of some of these concepts in one of his review papers in Cell (freely available here).

Figure 1 from Topol, Cell, 2014

One may be familiar with the terms genome and proteome, and microbiome is very hot right now (see possible #NephJC book club choice for 2017 here),

But the human GIS goes deeper: the physiome and exposome. The phenome is the information from your social graph - and if you think this is not helpful, see this Framingham cohort data suggesting that type 2 diabetes is associated with social contacts. The physiome, comprising various physiologic datapoints such as heart rate, muscle movements etc can be increasingly accessed with smartphones, watches and FitBits. The Anatome, radiologic modeling of the body, is also becoming easier to access with lower costs - again sometimes with smartphone enabled devices, think portable ultrasound connected to a smart phone for interpretation. The exposome refers to environmental factors such as pollution, radiation etc. This is another place where the smartphone maybe asked to monitor, record and communicate.

Topol has coined a neologism, GIS to describe these 10 -omes, which provide a ‘panoromic’ view of an individual relevant to health and medicine: Individualized genomic medicine from pre-womb to tomb.

Figure 4: From Topol in Cell, 2014

The figure shows Topol’s view of the various timepoints in a human life where the use of these GIS systems could be potentially useful. How could all this information be useful? Can one even make sense of all this big data at an individual level? A fascinating example of this is the case of Michael Snyder, who was able to use his GIS to understand how he developed diabetes and then he used that information to adjust his diet and restore normal glucose.

The chapter then explains that the GIS will be useful without being complete. We will add components of the GIS as the cost and ability to interpret the data grow. He describes a number of genes and how we can screen them.

One fascinating story was about the APOE4. This is a gene that was predictive of alzheimers disease: 8% risk without a copy, 24% with one copy, and 75% with two copies of the allele. But the story gets really interesting in that having also makes people very prone to the dementia from sports concussions. Knowing kids genes could be a way better sports physical than listening to their hearts and lungs.

He spends the remainder of the chapter describing the wonders of molecular medicine. It does look like the future of medicine could be very powerful.