Brain Fog – Recap and what is known

A reader contacted me over a new post on Biomesight – How to reduce brain fog. He was concerned over the content (knowing that I have often researched and posted on brain fog), so I am doing this post to provide some clarity on brain fog. (Bad pun: Remove some fog from brain fog)

What is Brain Fog?

  • The term brain fog is a vague term that has been defined in the literature as a combination of the following more accurate (and measurable by tests) conditions. A better term is executive dysfunction [2015] or Cognitive Fatigue [2014]. The literature goes back to at least 1989. I know from personal experience, I have taken them from professional psychologist, and other in the family has too.
  • Some people will perform badly on all tests, other will perform poorly on certain tests only.
Tests used to evaluate objectively brain fog, Executive Dysfunction [2015]

Related forms include

IMHO, if you do not have the majority of the above, the term may be misapplied.

For different diseases, what constitues brain fog can vary, for example:

The clinical picture typically affects visuo-spatial immediate memory (g = − 0.55, p = 0.007), reading speed (g = − 0.82, p = 0.0001) and graphics gesture (g = − 0.59, p = 0.0001). Analysis also revealed difficulties in several processes inherent in episodic verbal memory (storage, retrieval, recognition) and visual memory (recovery) and a low efficiency in attentional abilities.

Systematic review and meta-analysis of cognitive impairment in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) [2022]

Common Conditions Having it

The list of conditions having issues somewhere in the executive function space is large, just a few are listed below.

Many of the above have distinct microbiome signatures and thus the hope of getting a universal microbiome signature for brain fog is an ideological belief. This appears to be confirmed in the analysis from Special Studies … Brain fog strongest z-score is just 5.2. This is lowest significance level of 26 items evaluated, the next lowest is General Fatigue. IMHO, there may be no true significance, the z-scores numbers were not adjusted for False discovery rate and incidence of reporting.

Most people will agree that there is no magic cause or microbiome signature for general fatigue — it could be an issue with iron levels, excessive lactic acid (impairment in clearing it), blood circulation issues, respiratory issues etc.

Brain fog could be described as mental fatigue and thus the same wide variety of issues can be involved. For ME/CFS, the dominant causes for brain fog, according to the literature are mentioned in some of my prior posts:

For Long COVID, we should also consider damage to the lungs impacting oxygen levels.

Concerns about Biomesight – How to reduce brain fog

My first concern is simple, the belief of there being a common microbiome pattern is very questionable. There are likely patterns, for example, a microbiome pattern that results in higher d-lactic acid production; a pattern that results in lower d-lactic acid production; a pattern that inhibits one of the many steps in the coagulation cascade; a pattern that overloads one of the many steps in the coagulation cascade; a pattern that causes vascular constriction; a pattern that cause inflammation; a pattern that inhibits the absorption of iron…and on and on. There is not a single pattern that applies to all.

My second concern is a failure to cross validate/document. D-lactic acid is well associated with brain fog in the literature, ” Dlactic acidosis is characterized by brain fogginess (BF) and elevated D-lactate“[2018], Recent research indicates that dlactic acid may inhibit the supply of energy from astrocytes to neurons involved with memory formation.  [2010]. It is not mentioned once in the post (as of this time).

When a statement like this is made “Unsurprisingly, high ethanol producers in the gut based on research findings (separate from what we are seeing from the Biomesight dataset) is associated with brain fog.” I would expect links to these research papers to be included for the reader to follow up. Ethanol is drinking alcohol, booze – which has me very curious about the links and especially if they are seen with many of the conditions cited above.

I am very curious because I use KEGG data to see if any compound production/consumption was statistically significant in Special Study: Neurocognitive: Brain Fog, and ethanol was not found to be statistically significant. Searching for ethanol on Pub Med, we find “Lactic acidosis and acute ethanol intoxication [1994]” and “SEVERE LACTIC ACIDOSIS SECONDARY TO ACUTE ALCOHOL INTOXICATION” [2021] but that was from explicit ethanol (alcohol) consumption.

This does lead me to a model for alcohol intolerance developing with ME/CFS, which I posted here [Alcohol Intolerance in ME/CFS – A Model].

How does special studies compared to Biomesight post

The table below shows no agreement between my special studies and their findings. We used different statistical process, but finding not a single agreement should be a red flag on relying on the data. While I have a smaller sample (approximately 1/3), the data processing to get the microbiome data was identical.

Special Studies
Escherichia coli (species)5.3
Lactiplantibacillus pentosus (species) 5.1
Shuttleworthia (genus) 5.1
Escherichia (genus) 4.5
Veillonella (genus) 4.4
Veillonella dispar (species) 4.4
Staphylococcus pseudolugdunensis (species) 4.2
Clostridium cellulovorans (species) 4.1
Class DeltaproteobacteriaX
Species Bacteroides uniformisX
Species Bacteroides cellulosilyticusX
Species Phascolarctobacterium faeciumX
Genus BacteroidesX
Species Anaerotruncus colihominisX
Species Faecalibacterium prausnitziiX
Genus PrevotellaX

Bottom Line

I do not believe that we can aggregate all microbiome samples reporting brain fog into a single set and find a universal pattern to address a priori. The numbers from Special Study: Neurocognitive: Brain Fog were the weakest of all special studies and, based on some other recent work in progressed, results may be adversely affected by sampling bias, sample quality, and false detection rate.

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