FODMAPs 02 – Exploratory data analysis… Also, I think I have a beef and wedding intolerance

Previous post in this series: FODMAPs 01 – Data collection.

I have been collecting data for five weeks in an attempt to identify what foods cause my symptoms of food intolerances. Using the Memento Database app, I log the intake of each food/ingredient, which is datetime stamped. Here’s a snapshot of the exported CSV. The Fibre column indicates if I took some psyllium husk as recommended by my dietitian. Enzymes indicates when I took a magic out-of-body enzyme pill, which was rare.

My intolerance symptoms post-meal was recorded with datetime stamps. I used four descriptions: "Bloated" was when I was feeling, well, bloated. "Tightening" was when my guts felt uncomfortably tight during digestion. "Fatigue" was when I suddenly felt tired. "Abdominal pain" indicated sharp stabby pains in my gut. Since I’m not concerned about these distinctions, I coded each symptom with “1”. I wish to identify the foods that cause ANY symptoms of intolerance. A good day is when I have no symptoms.

The data was wrangled. Datetimes were coerced to dates. Foods and Symptoms datasets were joined by date. Here’s a look at the merged data in RStudio. It’s terribly simple – Date, Food, Symptoms (flagged with “1” when present on a given date).

I’m no dietician/nutritionist. I assume that when one tries to identify problem foods in one’s diet they look at when the symptoms occur, then look back to see what foods were consumed. With that general approach, I chose some strict parameters to identify the bad foods that led to intolerance symptoms.

Any day of a symptom is considered a bad day. Even one symptom. Thus, a good day was a symptom-free day. To my delighted surprise, I had a string of good days. Setting my diet to low-FODMAPS did make me feel generally better. I was less fatigued, I could concentrate more at work, and I had more nights of decent sleep. Sure, I became a social bore when I limited what food I could eat when dining out. Telling friends I could just go out for tea was met with disappointment. It was easier to stay at home and eat cold cuts by my lonesome. This was all in the name of science, and data, and in the next blog post, some data science (logistic regression).

Consider the good days. The code would look at the previous day and note the foods that were consumed. These foods were all considered “good”. Let’s think about this moving forward in time – I would eat all this good mostly gluten-free food. The following day I would be symptom-free. Therefore, any food the day before a symptom-free day is in my good books.

Consider the bad days. Similar premise – any food consumed the day before a bad day are bad foods. But not all of them. I have a mix of good and bad foods, followed by a bad day. I can’t cast the good foods caught in this net as bad by association. Therefore, any food on my good food list was used to subtract-out from the bad food list. A “really bad” food list became the difference. Drum roll… Here are the really bad foods.

OK, a couple of things stood out. I think I’m allergic to weddings. “wedding beef”, “wedding cake”, “wedding canapes”, “wedding salad”. Guys, I went to a weeding during the diet, OK? I couldn’t not eat the food, it was really really good. Other foods consumed at the wedding included the potato, prawns, pumpkin, oysters. Resolution one: Avoid weddings [1].

There was another grouping I discerned from the really bad foods list. “beef mince”, “beef patties”, “olivo wagyu steak”, “wedding beef”. OMG, I think I have a beef intolerance. No! Stupid, stupid ethnic digestive tract, why?

I Googled – beef intolerance is indeed a thing. As is intolerance towards asparagus, basil and cauliflower. I’m not jumping to conclusions. I have an appointment with my dietitian in several weeks, and I’ll show her the data. She may very well think this approach was a bit much, but, I truly believe that the little data we collect has meaning. Now, it’s easier to collect data, primarily because most data collection occurs in an automated fashion. From Fitbit to Netflix and Google, there’s a spectrum of our personalised data being gathered. Sometimes this data is accessible, such as from Fitbit. Taking those next steps from reported data to insightful and actionable data may take some coding [2].


References and notes
1. I was concerned that my bad days were simply the wedding day. Not the case. I had 16 days when I consumed from the really bad foods list.
2. The code fodmaps_wrangling_exploration.R is on GitHub repo: https://github.com/muhsinkarim/fodmaps

FODMAPs 01 – Data collection

There’s something in my diet that ain’t sitting right. It makes me feel bloated, fatigued and just damn uncomfortable. It’s been like this for years, though it’s been tolerable. Recently I went to a dietitian/nutritionist to learn more about what I should and should not shove down my mouth.

After describing my general diet, I received advice that will sound obvious to most. I need more fruits, vegetables, fibre and water.
“How many fruits and veges am I supposed to eat?”, I asked.
“Two serves of fruit, three serves of vegetables a day.”
“Oh, so the recommendation hasn’t changed since kindergarten?”. I was really hoping that it had been scaled back to two fruits per day. Or one magic fruit pill.

I took the advice as best as I could manage (who has time to eat five serves of vegetables a day? Takes so long to chew). There were marginal improvements. I felt less bloated and fatigued, so my decisions were leading me in the right direction. Similarly, I had stopped drinking coffee back in March and noted improvements. Each dietary change added an improvement.

However, I still feel uncomfortable. Years ago I attempted to rectify my dietary issues with data. I recorded what foods I was eating and what symptoms I felt day to day with the intention to analyse my way to a remedy. I planned to “net” what foods caused upset. I never got around to the analysis.

I’m getting around to it now. I have the right tools.

The nutritionist said I should try a low FODMAP diet. FODMAPs are a group of carbohydrate that are poorly digested. After a FODMAP diet of at least 6 weeks, I’ll gradually reintroduce different FODMAP groups and note my tolerance. I can identify my problem foods then avoid them. But not ice cream. If ice cream is a problem food, I’ll just take lactase beforehand.

I need an app that collects my food intake. I’ve used myfitnesspal in the past. When I Googled for instructions on exporting my data, I couldn’t find a clear guide, or it was a paid option. I can log foods with the Fitbit app, however retrieving the data is also not easy. The Fitbit R scraper I use does not retrieve food data. I would have to access my data via an API.

Instead I’ll use the Memento Database app. Memento Database allows users to customise fields for data capture then easily export the data as CSV. My “Food” library captures the foods or ingredients I consume with the current datetime captured upon entry. I will use short general labels for foods and ingredients as possible since I’d like to group the foods for analysis.

My “Symptoms” library captures a symptom with the datetime. I used to enter detailed symptom descriptions. I want to keep it brief. I'll include feelings of "Fatigue" or feeling "Bloated". The symptoms will be placed in a single-choice list. I expect that these symptoms will decrease as I persist with the lower FODMAP diet. The symptoms will increase when I reintroduce the problem FODMAP groups. Ice cream will totally be fine. Totally.

I will combine this food and symptom data with Fitbit data, namely calories burned, weight and sleep. I’m curious to see if my weight changes with the diet (assuming little change in the calories burned day-to-day) or if my sleep improves. 

In say, 6 weeks’ time, I’ll have data to wrangle then analyse.