Will statins make me smarter too?

For the next two weeks, I’ll conduct a new test to measure the effect of simvastatin on my Brain Reaction Time.

Two or three Kirkland fish oil pills taken daily make me score higher on Seth Robert’s Brain Reaction Time test. After six months of self-testing, the effect is pretty robust, as you can see from this simple T-test:

## 
##  Welch Two Sample t-test
## 
## data:  rik$ptile[rik$Fish.Oil == 0] and rik$ptile[rik$Fish.Oil > 0]
## t = -2.7736, df = 90.886, p-value = 0.006728
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -14.489218  -2.396073
## sample estimates:
## mean of x mean of y 
##  45.36170  53.80435

What causes the effect? I have some guesses related to role that Omega-3 fats play in brain nutrition, but are there other ways to get similar effects? I’ve already disproven many of the obvious other candidates (sleep, alcohol, vitamin D), but several fellow quantified-selfers have suggested I look also at statin drugs, which besides lowering cholesterol, also seem to benefit the brain. Mark Drangsholt, who studied this extensively on himself, says 2-3 weeks of treatment helped him reduce or eliminate brain fog.

So I’m going to try the same thing: for the next two weeks, I’ll take 20mg of simvastatin daily while I continue to test my BRT for changes. I’ve taken no fish oil for the past two weeks (and as predicted, my BRT averages have declined), so this should be a “clean test”.

I’m posting my trial and methodology in advance to reduce “reporting bias” that happens when people only post their successes. You can follow along with my (mostly) daily updates here: http://rpubs.com/Sprague/SimvastatinBRT

What potato starch really did to my gut

As suspected, there was a glitch in my latest uBiome results. Apparently a server hiccupped, so the scientists there recomputed my sample and sure enough, the new data is much more believable. Here is the single chart summary of all four of the uBiome tests results I’ve received so far:

You can see a number of changes, but what’s exciting about this one is that it’s the first sample where I was deliberately trying to test something: the effect on my gut of taking potato starch to hack my sleep.

A few details about this sample: 

1. Taken on Jan 19th, almost exactly 3 months after my Oct 17th sample.
2. During the 94 days between samples, I had 31 days where I took a dose of potato starch, a total of about 80 tablespoons.
3. I had been taking 1 T daily, an hour before bedtime, for a week before this sample.

To analyze my results, I first popped into my publicly-available uBiome utilities, using the data I had already downloaded from the site. If you want to follow along at home, here are some of the commands I typed.

To answer the question: Which new species appeared in January?

head(uBiome_sample_unique(jan,oct),topN)

##    missing.count_norm                   missing.tax_name
## 1                1257                 Arthrobacter albus
## 2                 573         Bacillus amyloliquefaciens
## 3                 530                Enorma massiliensis
## 4                 376              Ruminococcus lactaris
## 5                 188          Subdoligranulum variabile
## 6                 163        Adlercreutzia equolifaciens
## 7                 145                Oligella urethralis
## 8                 137         Clostridium sp. NML 04A032
## 9                 137 Desulfovibrio sp. oral clone BB161
## 10                111              Streptococcus rubneri

and which went extinct (are no longer in January sample?)

head(uBiome_sample_unique(oct,jan),topN)

##    missing.count_norm                                missing.tax_name
## 1                5929                          bacterium NLAE-zl-H436
## 2                 114                        Dialister micraerophilus
## 3                 101                      Peptoclostridium difficile
## 4                  88            Dehalogenimonas lykanthroporepellens
## 5                  76                     Bifidobacterium catenulatum
## 6                  51                      unidentified bacterium ZF5
## 7                  51 Veillonellaceae bacterium canine oral taxon 211
## 8                  51                           Ruminococcus sp. 25F8
## 9                  51                              Clostridium leptum
## 10                 38                      Peptoniphilus sp. gpac018A

Here’s the overall picture of what changed between Oct and Jan:

Positive numbers indicate something that is more plentiful in January than October:

tail(octVsJan,topN)

##                               tax_name count_change
## 24              Bifidobacterium longum         7713
## 39         Clostridium clostridioforme         8958
## 78                 Ruminococcus bromii        10663
## 20               Bacteroides uniformis        11036
## 23            Bifidobacterium animalis        13578
## 69 Peptostreptococcaceae bacterium TM5        14151
## 47            Coprococcus sp. DJF_CR49        19000
## 36                 Clostridium baratii        24955
## 76                Roseburia sp. 11SE38        35712
## 57        Faecalibacterium prausnitzii        89592

head(octVsJan,topN)

##                              tax_name count_change
## 17               Bacteroides plebeius       -90998
## 30 butyrate-producing bacterium A1-86       -88414
## 38          Clostridium chartatabidum       -41972
## 11             bacterium NLAE-zl-P430       -23659
## 22       Bifidobacterium adolescentis       -18334
## 10              bacterium NLAE-zl-H54        -9970
## 62              Lactobacillus rogosae        -8575
## 27                     Blautia faecis        -7693
## 56                Eubacterium siraeum        -6040
## 5               Alistipes onderdonkii        -5878

See the difference?

Let’s look at the genus level:

(again, positive numbers are more plentiful in January)

tail(octVsJan,topN)

##              tax_name count_change
## 27              Dorea         3396
## 42     Parasutterella         3473
## 48 Pseudobutyrivibrio         5523
## 34        Lachnospira         5895
## 14            Blautia         6612
## 12    Bifidobacterium         7769
## 7        Anaerostipes         8565
## 23        Coprococcus        18317
## 52          Roseburia        34957
## 29   Faecalibacterium       135557

head(octVsJan,topN)

##           tax_name count_change
## 10     Bacteroides       -87903
## 3        Alistipes       -12141
## 41 Parabacteroides        -9820
## 53    Ruminococcus        -9685
## 35   Lactobacillus        -8601
## 28     Eubacterium        -5785
## 21     Clostridium        -5212
## 11     Barnesiella        -3809
## 17   Butyricimonas        -2702
## 26       Dialister        -2410

Summary

Organism	May	Jun	Oct	Jan	Rank
Faecalibacterium prausnitzii	99571	62316	5790	95382	species
Roseburia	13554	11157	7825	42782	genus
Christensenellaceae	82585	39713	40290	40977	family
Christensenella	269	NA	38	17	genus
Akkermansia	30960	19654	7648	6269	genus
Bifidobacteria Longum	32	NA	1858	9571	species
Bifidobacterium	8473	6532	58747	66516	genus
B. Longum as % of total Bifido	0.38%	NA	3.16%	14.39%
Clostridium	35012	41679	71326	66114	genus
C. botulinum	NA	25	x	128	species
C. clostridioforme	28902	35372	15170	24128	species
C. baratii	1223	NA	5588	30543	species

The units are all uBiome’s “count_norm” field, which you can think of as, roughly, a percentage (a fraction of one million). Items in italics are “good”.

I'll have much more to say as I analyze this for a future post, but so far I'm thinking that no, potato starch didn't wreck my gut. The benefits in better sleep appear to come at little or no major cost to the rest of my gut flora. What do you think?

A mistake in my fourth uBiome sample?

[Update: uBiome recomputed my results, which are now much closer to what I expected.  I'll update with a more detailed post soon.]

After all that analysis and discussion with experts about my uBiome results, I had high expectations for the brand new set of answers that arrived today.

Here’s a comparison chart showing all four of my uBiome submissions:

In a word: argh!

If the January 19th sample had been my first and only uBiome test, I’d be tempted to read a lot into this. After all, it appears that my levels of proteobacteria are way outside the norm. That’s not all: look at some other oddities about this one:

• That bifido bloom I saw after sleep-hacking with potato starch: it’s all gone. Not a single bifidobacterium was found in this sample. Hmmm.
• Lots of prevotella (almost 3% of the sample), a species that didn’t appear in any of my previous samples, and a bit worrisome for a meat-eater like me.
• No more Clostridum, either. Commonly thought of as a pathogen, it may be good to get rid of this, but why did it disappear?

All of these massive changes in the span of only three months? Not impossible – the human gut can change pretty quickly under the right circumstances. But you’d expect something different about my environment, eating habits, and certainly my health.

But here’s the thing: I don’t notice a single difference in my health or well-being over this time period. Same sleep, same weight, same general mood. Diet, bowel movements, skin – like everyone, I see minor day-to-day variations, but absolutely nothing about me is different enough to be noteworthy.

On the other hand, there are a few oddities in the sample itself. First, uBiome warned that their first run had too low levels of bacteria; the ones you see above came after they ran the sample again under more amplified settings. Second, I used an older kit, one that had been lying around the house for about a year. Finally, I also ran into trouble with the mail, so it sat around at the post office for several more weeks than normal. Shouldn’t really matter, but still…

Soooo, my bottom line is that I’m just not going to read much into this sample. I’m waiting on my next submission, one that was sent a few weeks after this one, and hopefully that will give me a much better picture.

The takeaway for you? Don’t read much into a single uBiome test. The science is too new, and there are so many other factors that go into the results. My advice: send in multiple kits, spread over several weeks or months, before jumping to conclusions.

Did raw potato starch wreck my gut?

What if you were in New York City and somebody gave you a list of the GPS coordinates for every coffee shop in the area. If you knew nothing else, would that information be useful or distracting? The answer depends on what you’re trying to do. If you just want to know, roughly, where people congregate, then it's probably a nice guide. But if you're lost and you need to get across town, then that information may be worse than useless: without knowing the roads or subways, you may end up in the Hudson River.

That’s how I look at my uBiome test results and the wonderful and incredibly detailed analysis done for me by Dr. Grace Liu, who I consider to be one of the world’s best experts about the gut microbiome. Please read her blog, and listen to her Gut Guardians podcast, where she goes into far more detail, but here’s her conclusion about my results:

Unfortunately after amputating over 1/3 of his gut species, many of the phylogenetic core are depleted. The initial levels were awesome but after a high dose of single source of 'fiber', many on re-testing were gone and dramatically diminished numbers.

She’s referring to my “sleep hacking” experiment using raw potato starch to improve my sleep. Another way to put this (in my own words):

Raw potato starch — which is not in any ancestral diet — is bad for health. It may temporarily improve sleep by feeding gut bacteria responsible for the production of most sleep hormones, but it also crowds out other, more important organisms, and opens the door for pathogens that can cause far more trouble.

Sounds pretty dangerous, and of course I stopped all potato starch experimentation immediately after her warning.

Here’s my summary of the uBiome test results that drove her conclusion (pulled straight from my publicly available data)

Organism	May	Jun	Oct	Rank
Faecalibacterium prausnitzii	99571	62316	5790	species
Roseburia	13554	11157	7825	genus
Christensenellaceae	82585	39713	40290	family
Christensenella	269	NA	38	genus
Akkermansia	30960	19654	7648	genus
Bifidobacteria Longum	32	NA	1858	species
Bifidobacterium	8473	6532	58747	genus
B. Longum as % of total Bifido	0.38%	NA	3.16%
Clostridium	35012	41679	71326	genus
C. botulinum	NA	25		species
C. clostridioforme	28902	35372	15170	species
C. baratii	1223	NA	5588	species

The units are all uBiome’s “count_norm” field, which you can think of as, roughly, a percentage (a fraction of one million). Items in italics are "good".

Her analysis compares only my May and October samples (she dismisses my Jun result as an anomaly) but what if that's not fair? True, there are some issues with the data for June (for some reason, uBiome computed a much smaller sample that time and you can see from the chart that a few items are missing), but it’s still data, and we can’t be sure that the other samples are any more (or less) accurate. To use the New York coffee shop analogy, when you have almost no information to begin with, are you better or worse off when you drop some data points? The answer is that it depends on what you’re trying to accomplish.

Dr. Liu is alarmed at the drop in some key species between May and October, a fact she attributes to my potato starch experiment. But all of those numbers were already dropping in June. The five months pre-potato starch between May and October coincided with seasonal changes (late Spring to Summer to early Fall), lots of travel, multiple camping trips, and of course the normal dietary shifts that happen as I gained access to the freshly-harvested fruits and vegetables of Summer. Was a week of a couple tablespoons of potato starch really the most important change?

In fact, in this table the only species that reversed course after the initial May-June samples and October were the bifidobacterium (often associated with good health and sleep, and up by a LOT) and the clostridioforme (a potential pathogen, down by a little).

I'm pretty healthy, thankfully, and there are no particular disorders I'd like to treat. Like anyone, I want to feel even better, but in my self-experimentation I certainly don't want to risk falling into some terrible dysbiosis or worse. Potato starch appeared on the surface to help -- the improvement in sleep seemed promising -- but I take Dr. Liu's advice and expertise seriously, so I stopped until I can see more uBiome results. I submitted one sample right after my last experiment, and another a few weeks after that. If potato starch really wrecked my gut, then I'll expect the new samples will show considerable worsening across the board. But if not, then, well, maybe it's okay to continue the experiment. Either way, I'll be taking her bionic fiber advice seriously.

To go back to the New York coffee shop analogy, I think we have to respect how very, very little is known about the terrain around us. When you know absolutely nothing about the critically important gut environment, then a tool like uBiome is such a precious gift of information that it's tempting to use it for much more than it is. We'll need much more data, from many more people, before we can use this information to get across town without falling in the river.

The bare minimum

Minimalism is wonderful. Why clutter your life with stuff you don’t need? Here, from the golden sayings of Epictetus are words from the original Stoic himself: 

Take what relates to the body as far as the bare use warrants—as meat, drink, raiment, house and servants. But all that makes for show and luxury reject.

Wait a second…servants ? The ancient Greeks, in the philosophical school that invented minimalism, says you need servants as much as food and shelter?!

Looking into my mouth microbiome

The gut biome is interesting enough, but bacteria colonize just about every part of the body, so recently I’ve been studying my uBiome mouth test results. The simple GitHub RuBiome utilities I use for analyzing my gut will work for that too, so here’s a short example of how I did it:

First I loaded my uBiome data into two variables, one for each sample: June 2014 (junMouth) and the other from October 2014 (OctMouth), after a visit to my dentist.

Let’s see which species are new in the later (October) sample:

octToJunChange <- span=""> uBiome_sample_unique(OctMouth,junMouth)

##   count                        missing.tax_name
## 1  3640                  bacterium NLAE-zl-P562
## 2  2725                 Streptococcus sanguinis
## 3  2075               Capnocytophaga gingivalis
## 4  1969 Peptostreptococcus sp. oral clone FG014
## 5  1618                 Granulicatella adiacens

One of those species, Streptococcus sanguinis looks interesting. Wikipedia says this:

S. sanguinis is a normal inhabitant of the healthy human mouth where it is particularly found in dental plaque, where it modifies the environment to make it less hospitable for other strains of Streptococcus that cause cavities, such as Streptococcus mutans.

No cavities? Nice! More good news: this quick check confirms that I don’t have any S. mutans:

OctMouth[grepl("Streptococcus",OctMouth$tax_name),]$tax_name

## [1] Streptococcus                      Streptococcus pseudopneumoniae    
## [3] Streptococcus sanguinis            Streptococcus constellatus        
## [5] Streptococcus anginosus group      Streptococcus sp. oral clone GM006
## [7] Streptococcus thermophilus         Streptococcus oralis              
## [9] Streptococcus gordonii            
## 250 Levels: [Eubacterium] sulci ... Veillonellaceae

Then I look at the species that disappeared (went extinct) between the two samples:

junToOctChange <- span=""> uBiome_sample_unique(junMouth,OctMouth)

##   count                        missing.tax_name
## 1  6034                Capnocytophaga granulosa
## 2  4153 Peptostreptococcus sp. oral clone FL008
## 3  3375         Prevotella sp. oral clone ID019
## 4  2691                   Streptococcus rubneri
## 5  1571                       Prevotella buccae

Anything in the genus Capnocytophaga is an opportunistic pathogen, so I say good riddance. Usually they’re fine, but if your immune system dips they can turn bad.

Streptococcus rubneri was discovered a couple years ago, so little is known about it.

Prevotella buccae is more interesting. It seems to be implicated in periodonal disease (yuk!) but that genus is involved too in breaking down proteins and carbohydrates.

Hard to say what’s really going on. Meanwhile, here are the biggest changes (increase) since the first sample:

junToOctCompare <- span=""> uBiome_compare_samples(junMouth,OctMouth)

##                                  tax_name count_change
## 64         Streptococcus pseudopneumoniae        62007
## 68         Veillonella sp. oral taxon 780         8065
## 41                       Neisseria oralis         4693
## 2  Abiotrophia sp. oral clone P4PA_155 P1         2308
## 28                 Granulicatella elegans         1987

Whoah! That first one, Streptococcus pseudopneumoniae, looks nasty! Wikipedia says it may cause pneumonia, though a recent medical journal says more hopefully that it “treads the fine line between commensal and pathogen”

...which is a scientific gobbleygook way of saying nobody has a clue. All the more reason to keep testing, submitting, and getting more data. I just sent two more kits to uBiome, and will let you know more as soon as I get back the results.

Fish oil, even when it's not a pill

An interesting result when I measured my BRT today:

Note how today’s result was noticeably higher than for the past few days.

 

 

I've been traveling, and I didn't have any fish oil pills, so it was odd that today's result was so much better than previous days. Was there something unusual in my diet or activity in the past 24-48 hours?

I looked back over my last couple of days eating/exercise/etc and remembered I had salmon last night for dinner.

Bingo.

This is an especially interesting result because it was entirely unexpected. I didn’t know to look for this until after I saw the results of the test.