The inspiring Richard Sprague joins us again, with a curious finding!
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Having done multiple uBiome tests over the past year, I already have a sense of what my “normal” gut biome looks like. Although there is a fair bit of variation (especially after my various experiments, like sleep-hacking or jungle exploration), my results generally fit the range of “healthy omnivore”. But most of my tests are taken several weeks or even months apart, where it can be hard to understand precisely what’s driving the overall differences. How much variation would I see between samples taken just a week apart?
To find out, I sent two gut samples to uBiome, one on April 21 and the other exactly one week later on April 28th. I received the results last week, less than a month after submitting them. (uBiome turnaround times are getting much faster!) The overall picture looks like this:
That’s more variation than I expected for such a short time period. What’s driving the changes? Fortunately, the new uBiome web site makes it much easier to compare one sample with another. In my case, it shows the following changes over the week:
These charts show changes in the absolute population of various microbes, which uBiome calculates by dividing the newer sample “count_norm” field by the same field in the earlier sample. Since this tends to give extra weight to the smallest populations of microbes, I prefer to calculate by proportion; in other words, which microbes changed most in overall percentage against my entire microbiome. After downloading the raw data and running it against my open source tools, here’s what I found (at the genus level):
| tax_name | count_change |
| Roseburia | 41427 |
| Faecalibacterium | 33862 |
| Bacteroides | 24346 |
| Lachnospira | 13601 |
| Lactobacillus | 9874 |
These are all generally considered “good” bacteria, so I’m glad to see the increases. But why the change at all, especially over such a short time period?
Fortunately, I have some additional data. I regularly track the food I eat using the MyFitnessPal app on my phone. Using a handy data exporter I summarized the macronutrient information like this:
| Calories | Carbs | Fat | Protein | Cholesterol | Sodium | Sugars | Fiber | |
| Average (month) | 1841.7 | 192.2 | 102.7 | 94.9 | 268.0 | 2298.5 | 64.1 | 15.2 |
| Average (Week) | 2242.6 | 241.9 | 124.7 | 108.7 | 262.3 | 2814.3 | 78.3 | 16.9 |
| Difference from Ave | 400.9 | 49.7 | 22.0 | 13.9 | -5.7 | 515.7 | 14.2 | 1.6 |
| % Diff from Ave | 122% | 126% | 121% | 115% | 98% | 122% | 122% | 111% |
Looks like I ate more calories than normal that week (that was when the new Chik Fil A opened near us), which explains the higher-than-average numbers for carbs, fat, sugars and the rest. But there is one unusual result: note that despite my extra appetite (and that Spicy Chicken Deluxe), I ate less dietary cholesterol. Could that explain the increase in those particular microbes?
Of course, this is all extremely speculative, but a quick internet search reveals an intriguing study involving patients with cholesterol gallstones whose microbiomes lost exactly the three microbes that I gained. Is there a link?
Who knows? It was only a week, and it was a pretty small difference. But that’s the fun of experiments like this: “normal” people can make discoveries. And if I did find evidence of a link between cholesterol and the microbiome, this could have huge implications for the treatment and prevention of heart disease.
Alexandra, you may want to give a heads-up to the Nobel Prize Nominating Committee. 
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