Data Tracking Food

Initially we recorded everything we ate and our symptoms for a month before committing to trying a food chemical elimination diet. This was after we started the gluten-free diet but we were still making mistakes. It was a crude spreadsheet summing up very approximate amounts of food chemicals versus the sum of symptoms. I wanted to see whether the peaks and valleys of food chemical amounts and symptoms seemed to have any correlation. In my case the symptoms graph was noisy and didn’t seem to match up much with food chemicals at all. On a hunch I made another chart with gluten and dairy versus my symptoms and this time it matched up eerily. I was still in denial about this as I didn’t want to give up dairy entirely. But in my partner’s case the peaks and valleys of food chemicals matched up with symptoms with just a slight time delay. It seemed to match up the best with the sum of all food chemicals and also with salicylates in particular.

It was enough to convince me it would be worth it to try the elimination diet.

Turns out, I was the one with salicylate sensitivity and my partner reacted to amines and possibly free glutamate. It can be explained thus. My symptoms were dominated by my allergies. Salicylate sensitivity, while annoying, was a very small subset. That’s why it didn’t show up in my preliminary data tracking attempt. And in my partner’s case, this can be explained by the fact that many foods are high in both salicylates and amines. And also how I weighted those food chemicals in my summation may have been off.

So my crude attempt at using data wasn’t entirely accurate. Yet, it was able to show us likely allergies, and that food chemical sensitivity likely existed. All before actually undergoing an elimination diet. It isn’t too much of a stretch to imagine with refinement and better statistical analysis capabilities the accuracy and speed could improve substantially.

This is big. Data tracking and analysis is a third way that can at least complement the two main options we have at the moment. Medical testing and elimination diets. Testing can be costly and incomplete. Elimination diets are challenging and requires a huge commitment in time and effort.

Food data tracking can help, more than expected to narrow down suspects. To be really thorough, food (quantity and processed form, freshness, ripeness), environmental exposure, menstrual cycle, and any physical or mental symptom out of the norm can be tracked. Although mental symptoms may be better tracked by other means, such as activity level or alternatively reports by a close other, as self awareness might vary with some symptoms. For ultimate confirmation an elimination diet may still be necessary.

Of course, there are limitations we can anticipate. Some exceptional issues, such as silent celiac, may display few symptoms to speak of despite the damage that is still being caused to the body by a certain food (gluten). Also, if you have multiple issues, the data will be really noisy. Still, the fact that it is noisy means you have something going on. Finally, reactions can be masked. The body may have adapted the best it can to constant ingestion of problem foods. 

In conclusion, this was merely an n=2 experiment but there were already some insights that might be generalized. More things could be detected than first expected, such as whether chemical intolerance is likely, and finding likely allergy or sensitivity culprits. Food and symptom tracking and analysis has potential to complement available medical testing and streamline elimination diets by narrowing down the likely suspects in advance.

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