Comments on: On gaining Knowledge of Diabetes using Graphs. Interview with Alexander Jarasch Trends and Information on AI, Big Data, New Data Management Technologies, Data Science and Innovation. Sat, 23 Mar 2019 03:15:31 +0000 hourly 1 By: David McComb Fri, 15 Feb 2019 20:20:02 +0000 Yes, very right on. I think graphs are the way to go with complex disease states.

However I think a bit of pruning needs to be done. I have done some research on diabetes myself. I am (was) pre-diabetic with a A1c of 5.8. I am now at 5.3 without medication.

I would prune out animal studies on (mostly) herbivores (rabbits and mice) as the key hormonal pathways are different enough that the conclusions are going to be more distraction than useful.

We know with as much certainty as one gets in medicine that diabetes is based on malfunctioning in the insulin related metabolism. It’s complete absence in type 1 and variations of insulin resistance in type 2. Insulin converts glucose to fat and at the same time inhibits fat metabolism. The western diet gives us a glucose hit every few hours (from sugar, carbohydrates, snacks etc), a healthy body responds by producing insulin. The insulin converts the glucose to fat (that is the relationship between obesity and diabetes by the way, it isn’t that obesity causes diabetes, they are both caused at the same time by the same mechanism which is why they co-occur). Over time adipose fat cells become over stuffed and the deposition gets harder and harder, this is the beginning of insulin resistance. The other path is fat deposition in the liver and especially the pancreas. Large deposits in the pancreas inhibits the islets of islet of Langerhans, and insulin production drops. Both cause glucose levels to remain elevated, which is the classic type 2 diabetes.

The whole cycle is reversible by eliminating sugar and carbohydrate from ones diet.

We should start with what we know and use the graph databases to drill down into the deeper details, instead of getting lost trying to re-invent what is already known.

I know its over stated but let’s not confuse correlation with causation. Especially when we know some of the causation. Let’s look for correlation to find the next level of causation.

(or use the graph database to see if what I just said is crap)