This month (Feb 2021) there as a major article Structural and Functional Dysbiosis of Fecal Microbiota in Chinese Patients With Alzheimer’s Disease released. I have updated my list of bacteria (with links to source studies),

This post is for caregivers that are interest in low risk treatment that theoretically have a high probability of success and low cost.

Short Summary of Approach

The microbiome produces some 4000+ different chemicals. For many conditions, especially “untreatable”, it appears that imbalances in these chemical mixtures result in cells, including brain cells, malfunctioning.

Some drugs help — and often those drugs were seen to alter the microbiome, correcting some of these shifts. The stupid question is this, if we know the bacteria that are involved — then why not starve or feed to put it into better balance.

IMHO It works! In my 50’s I had a sudden onset of cognitive issues, including memory. A SPECT scan was read as Early Onset Alzheimer’s. I also had another diagnosis. That other diagnosis has a bacteria shift pattern reported in 1998 in Australia. Making changes to alter that pattern caused the cognitive issues to fade and disappear.

Steps

You need to have a microbiome sample (done by taking a little bit of a stool and sending it to a lab). Then the data need to be upload to the free citizen science site, Microbiome Prescription. Not all labs are supported (i.e. they do not make their data available in a suitable format); those that are supported are listed here (with discount codes).

Once the data is uploaded, there are two Quick Suggestions links that generates suggestions using Fuzzy Logic Artificial Intelligence techniques.

There is a demo logic that show all of the features…. BiomeSight Example Login

There are a lot of tools there, depending on you skill sets and devotion to seeking improvement.

There is a YouTube Channel showing how to use this site and discussion of issues.

A reader contacted me about a disagreement and the cause was a bug in the code for Kaltoft-Moltrup — subsequently fixed. This post looks at the bacteria selected by each for similarities and differences — so people can better understand the difference (which is a little abstract).

I am going to use one the demo samples from BiomeSight (BiomeSight:2019-06-10 Self).

• Extreme 3% picked 29 bacteria
• KM picked 24 bacteria

I sorted their selections below in alphabetical order, 13 are in common (just over 50% of the KM choices).

Kaltoft-Moltrup	Extreme 3%
Actinomyces : Too High	Actinomyces : Too High
Actinomyces naturae : Too High
	Anaerofilum : Too High
Actinomycetaceae : Too High
Bacillales Family X. Incertae Sedis : Too High	Bacillales Family X. Incertae Sedis : Too High
Bacteroides cellulosilyticus : Too High
Bacteroides denticanum : Too High
	Bacteroides dorei : Too Low
Bacteroides intestinalis : Too Low	Bacteroides intestinalis : Too Low
Bacteroides rodentium : Too High	Bacteroides rodentium : Too High
Bacteroides sartorii : Too High	Bacteroides sartorii : Too High
	Bacteroides thetaiotaomicron : Too High
Bacteroides vulgatus : Too Low	Bacteroides vulgatus : Too Low
Blautia : Too High
Blautia obeum : Too Low	Blautia obeum : Too Low
	Brochothrix : Too High
	Brochothrix thermosphacta : Too High
	Chitinophagaceae : Too High
Clostridium paradoxum : Too High	Clostridium paradoxum : Too High
Coprobacillus : Too High
Coprococcus : Too High	Coprococcus : Too High
cunicula : Too Low
	Dehalogenimonas : Too High
Desulfovibrio vietnamensis : Too Low
Johnsonella : Too High	Johnsonella : Too High
Johnsonella ignava : Too High	Johnsonella ignava : Too High
	Lachnospira : Too High
Lactococcus : Too High	Lactococcus : Too High
	Leuconostoc : Too High
	Listeriaceae : Too High
	Micrococcaceae : Too High
Oscillospira : Too High
	Prevotellaceae : Too Low
Streptococcaceae : Too High
Streptococcus vestibularis : Too High	Streptococcus vestibularis : Too High
Sutterella : Too Low
	Syntrophobacteraceae : Too High
	Tetragenococcus : Too High
	Thiothrix : Too High
	Turicibacter sanguinis : Too Low

Looking at a chart of Prevotellaceae, we see that KM low is 2.25%, thus be this sample being between 2.25 and 3 resulted it being excluded on one and included on another. For Listeriaceae : Too High, KM used 95.6% instead of 97%.

For Sutterella, KM uses 22% for low, hence it included. This is reasonable because there is a distinctive drop off around that!

For Coprobacillus, it looks like I need to do some adjustments of the KM, a chunk of unusual data caused a “step” that incorrectly triggered the high computation.

Bottom Line

We have good overlap with the differences being due to the curves being different. With the extreme 3% approach, we are insensitive to the difference of shapes. With KM we are sensitive (and some parameters to the algorithm needs a little adjustment).