Using Gogo and wifi on a plane

After reading an excellent overview of inflight wifi, I decided to try a few experiments on my flight this week to New Orleans.

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Experimenting with a Gut Cleanse

Your gut microbiome changes constantly in response to everything from diet to exercise, so when looking at multiple uBiome test results side-by-side it can be complicated to figure out what caused a particular change. What if you could wipe the slate clean; start over with a completely new biome and just track that, along with precisely what you eat afterwards? What could you learn?

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Upon first seeing a new uBiome result

You just received an email that uBiome has finished processing your sample. Now what do you do?

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Hunter-gatherers sleep like me

Anthropologists Gandhi Yetish, Hillard Kaplan and their colleagues just published the results of some experiments that show hunter gathers get much less sleep than the eight hours we supposedly need. In fact, their sleep patterns closely resemble mine, despite the conventional wisdom that my average 6.5 hours per night is too little. click to continue...

Microbes on my skin

For some reason I haven’t had good luck getting uBiome to process my skin microbe samples. I swab the skin under my ear, just as directed, back and forth for a minute or so. I use their special water. The results always come back later saying they found nothing.

So today, imagine my surprise when they sent me a nice note announcing that they’d re-run my latest skin sample and found something! click to continue...

I'm a secretor

 The Gut Guardians podcast interview with Alanna Collen included an interesting reference to the FUT2 gene, which the podcast hosts says has been linked to response to high fiber diets. One of the alleles, referred to as the non-secretor type, offers a genetic immunity to infection by the Norwalk Norovirus, also known as the “cruise ship virus”. 

Alas, I’m not immune to that particular virus, but if you have to choose I think it’s better to have the “secretor allele”, like me. Overall, secretors seem less susceptible to many influenza strains and pathogenic bacteria, perhaps due to our better response to high fiber. 

What’s especially interesting is that how FUT2 also seems to be associated with your microbiome.  Secretors have noticeably different levels of various bacteria, including missing Bifido species that are known to play a role in health. This is another example of how genes don’t have the final say: you may not get the full benefits of being a secretor if you don’t eat enough fiber.

If you have your 23andme results, you can check your FUT2 status too.

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Measuring my uBiome diversity

While there are few agreed-upon standards for what constitutes a good or bad microbiome, nearly everyone agrees that a diverse microbiome is better than one that is less diverse. How can we measure diversity?

Ecologists have been interested in this question for a long time and they’ve developed several metrics to describe how diverse a particular ecosystem is compared to another. This is my very tentative first step to try to adopt those metrics to my Ubiome results.

You could simply count all the different species (or genera or phyla) in a sample and track that over time. The more unique species, the more diversity. Here’s what that looks like for me:

Diversity Through Time

In this case, all I did was plot the raw number of taxa that uBiome found at each tax_rank. That’s about 60 species in my latest sample.  But since 16S technology doesn’t capture all the species information, or the genera or phyla information for that matter, a simple count of the number of organisms detected is not terribly useful. In my case, uBiome identified between 90-97% of all the phyla in my samples, but only between 49-51% of the species. That makes apples-to-apples comparisons difficult to interpret. 

Ecologists have suffered from this problem for a long time, and they came up with a few metrics to get around the it. They start by considering what it means to say something is more diverse than another. Consider a forest that has 1,000 trees in it. If all 1,000 trees are, say, aspen trees, then that forest is not as diverse as another one that might also have 1,000 individual trees but, say, 1,000 unique species.


There’s a unit of information called the Shannon number, after the information theorist Claude Shannon, who was the first mathematician to systematically try to measure information. To Shannon, whose work was concerned with code breaking in World War II, a radio signal that carries information (i.e. a code) will be slightly different from one that is random noise. He applied a specific formula to tell how different a signal looks compared to random noise, a variation of which can be applied to an ecosystem to tell how different it is from one that is completely dead (0) or has nothing but the same or similar organisms.


I use a slightly more ecologically interesting version of the Shannon number, called the Inverse Simpson number, that looks at the total number of unique life forms in an ecosystem and then weights each by the number of individuals of that type of species. Conveniently, these functions are all available in the R “Vegan” package.  Here’s how I set up my R environment to do the calculations:

allSprague <- read.csv("spragueResultsThruJun2015.csv") 
allGenus <- allSprague[allSprague$tax_rank=="genus",]
allSamples <- allGenus[,-(1:2)]
dV <- sapply(allSamples,fisher.alpha) 


Here is my diversity as an Inverse Shannon number:

#genus diversity
dG <- sapply(allGenus[,-(1:2)],diversity,index="invsimpson")
plot(dG~dPDates,main="Genus Diversity",xlab="",ylab="Inv Simpson Diversity",xaxt="n")


What does it mean?  Apparently, my overall Genus diversity has declined in the past year, though it bumps up and down so much that it’s hard to see a real pattern with so few data points.  It’ll be interesting to compare my diversity to a few other people using this function.

My apologies for the super-technical nature of this post, especially with the un-cleaned R source code. I’m just throwing it on the blog so I discuss it with people who are way more knowledgable than I am and can hopefully guide me to something better. I’ll have much more to say later, after I actually understand what I’m talking about.

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For potato starch, maybe less is more?

A friend who is enthusiastic about the affect potato starch had on his sleep, suggested that maybe my less-than-stellar results were caused by the amount I had been taking. Instead of 3-4 TBS/day, try a much smaller amount, he suggested, maybe only a teaspoon or so.

My results are too preliminary to get excited yet, but at least for my short trial, the smaller amount seems to help. Interestingly, my overall sleep doesn't change much, but I do notice more dreams, and Zeo confirms that my REM sleep is up quite a bit.

Here's the raw data (dumped straight from the R software I use to track everything). The one marked in red below is the most interesting number:

I tried potato starch on 91 days, and I have Zeo sleep data for a total of 45 of those days.

On 19 days I took exactly one tablespoon. On 6 days I took more than 0 but less than 1 tablespoon.

For total sleep (Z):

  • P-value on days when I had any potato starch: 0.3109656
  • P-value on days when I had exactly 1 TBS: 0.2020084
  • P-value on days when I had more than 0 but less than 1 TBS: 0.3041962
For REM Sleep (REM):

  • P-value on days when I had any potato starch: 0.2505854
  • P-value on days when I had exactly 1 TBS: 0.0603005
  • P-value on days when I had more than 0 but less than 1 TBS: 0.0012399
For Deep Sleep (Deep):

  • P-value on days when I had any potato starch: 0.5148044
  • P-value on days when I had exactly 1 TBS: 0.7402774
  • P-value on days when I had more than 0 but less than 1 TBS: 0.3264305

##                   days   Z.Mean REM.Mean Deep.Mean      Z.SD
## 0                  128 6.364245 1.817969  1.048698 0.6971406
## 0.25                 1 7.000000 2.100000  1.133333        NA
## 0.333333333333333    5 6.526667 2.136667  1.096667 0.5198290
## 1                   16 6.609979 1.975000  1.064583 0.7015906
## 1.5                  1 6.283333 1.300000  1.083333        NA
## 2                    7 6.111905 1.676190  1.019048 0.6943365
## 2.5                  1 5.750000 1.983333  1.250000        NA
## 3                    5 6.873333 2.050000  1.036667 0.6796241
## 4                    8 6.402083 1.752083  1.068750 0.7167186
## 8                    1 6.000000 1.433333  1.250000        NA click to continue...

A gut bacteria for caffeine metabolism?

 23andme says I’m a slow caffeine metabolizer because I have the AC genotype at SNP rs762551. You’d think that means I’m extra sensitive to caffeine before bedtime, but that’s not the case: I sleep just fine even if I have a cup of high-octane coffee after dinner. My 99-year-old grandmother drinks coffee by the potful, crediting its warmth as a calming effect to make her drowsy.

This week's Economist points to a new study in Nature by Javier A. Ceja-Navarro et al that names Pseudomonas Fulva as a bacterium active in the guts of a coffee bean pest. Caffeine is normally toxic to insects, but P. Fulva neutralizes the caffeine, apparently using the demethylase ndmA gene. 

I wish I knew enough genetics to test a theory that occurs to me: maybe my own caffeine metabolism is also affecting by a similar gut bacteria that I have in abundance but which is missing in other people. I already checked for Pseudomonas Fulva — I don’t have it in any of my uBiome samples.  I do have abundant levels of the genus Pseudomonas, but that is not the same thing.

It should be possible to screen every one of my gut bacteria demethylizing ndMA gene, but I’m not sure how to do that. If I did find the gene in one of the gut bacteria I harbor, then that would be pretty cool: a microbe that helps me drink coffee.

Coffee Beans



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Seth Roberts Rules for Self-Experimenters

Digging through the blog of the late Seth Roberts, I find many gems. Here is his concise summary of how to do self-experimentation. He mentions intending to write a book about this, but as far as I know it was never finished:
if you want to figure something out via data collection:
1. Do something. Don’t give up before starting.
2. Keep doing something. Science is more drudgery than scientists usually say.
3. Be minimal.
4. Use scientific tools (e.g., graphs), but don’t listen to scientists who say don’t do X or Y.
5. Post your results.
 Worth reading the entire four-part blog post. click to continue...

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