Back to blog

One Week Change In My Microbiome

The inspiring Richard Sprague joins us again, with a curious finding!

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. 🙂

6 Thoughts on “One Week Change In My Microbiome”

  • Tim Steele says:

    Another question for all you gut geeks:

    The uBiome raw data is supplied in many R1 and R2 files. I’m told this is due to using a 4 lane Illuma sequencer and providing files for forward and reverse runs.

    To get a true bacterial count/diversity, how can these files be joined to see the whole picture? ie. when using MG-rast.

    • Rae Benedetto says:

      Did you ever get an answer to this? I’m also trying to use mgRast…

  • Tim Steele says:

    Richard – Did you compute an alpha diversity score for the two reports?

    I am not a bit surprised by what you show, similar difference would be seen day-to-day or even within the same 24 hour period of you poop twice a day.

    Here’s an idea for homogenizing your sample:

    Line a clean pail or trash can with a new, small trash bag. Defecate into the bag. Immediately gather up the bag, twist it shut and tie it off. Next, knead the contents until thoroughly mixed. It sounds gross, but it isn’t.

    Poke a small hole in the bag with a sharp knife or disposable razor blade, and dip your uBiome swab into the hole.

    In this manner, I have gotten very consistent results, even between uBiome and AmGut. I think that inter-sample variances are the wild-card when using the “toilet paper swipe” method, but that method is OK for a one-off sample. If continuity is needed, a thoroughly homogenized sample is paramount.

  • […] One Week Change In My Microbiome – Richard Sprague – uBiome blog […]

  • Jon, you have a good point. I know people who use a blender to ensure they have a sample where all the bacteria are evenly distributed, but I’m not that hardcore.

    Still, I think the information from my samples is somewhat useful because (1) the uBiome sequencer detected what it detected — i.e. I trust the info about which organisms it found, even if the quantities aren’t entirely accurate. It’s useful to know something about my overall diversity, for example. (2) All of my samples have some commonalities, even if there are some questions about the accuracy. For example, firmicutes always far outnumbers bacteroidetes in every one of my samples — unlike the samples of many others.

    If you submitted two samples from the same stool on the same day, I’d love to see your results. Please email me and/or push to my github account and let’s compare!

  • Jon half says:

    You are making an assumption that two samples from the same day would produce the same result. I would love to see the data that support that assumption. I think it’s not true. In fact, my own experience with uBiome is that two samples from the same day from the same stool sample can have very different counts.

  • Comments are closed.