Time to Refresh What we know

A reader request with severe MCS motivated me to revisit Multiple Chemical Sensitivity (MCS) and literature on it. I am first going to recap my memory of what I know, a literature review, Last, analysis using data contributed to Microbiome Prescription – which did not find anything major beyond bacteria shifts that are statistically significant. We explore enzymes, products and substrates with no smoking guns found

There was a processing error — This post was revised

Long weekend tracing issue, made short. Some revisions to reduce running cost had a typo pointing one set of data (Substrate data for Samples) to the wrong lookup table to determine percentile. This has been corrected and the data audited to insure correctness.

I had mild MCS during one flare. It disappeared with remission.

Multiple Chemical Sensitivity (MCS) is not bronchial (breathing). There was a series of studies where the person was wearing a half-face mask breathing compressed air. Bringing a triggered close to the person (blind testing), trigger the response. My own wife lab results leaves a significant scent. She had a complete workup with Dave Berg, Hemex Lab and two weeks later she had a bad MCS reaction. About a week later, new blood work was sent. Everything was the same, except for a marker indicating very active coagulation. The marker had a short half-life around 2 hours. After she fully recovered, same test returned to normal. This hints at coagulation being part of the process.
We did find one effective treatment for shortening the duration of a MCS episode: Olestra (as in Olestra chips). This compound sucks chemicals out of tissues [2015] [2014]

– Ken Lassesen

I attended a talk today with the AAAS (I’m a member) and Memory Cells was discuss for some autoimmune conditions. It occurs to me that these are likely a factor. These cells become uber-sensitive to specific chemical compounds and have a lasting memory. In time they may die off, but not quickly.

Caution: this post is subject to revision. The findings to date are so significant that I am posting early.

The Current Literature

First a summary from a 2022 review – it covers a lot of territory… many quotes are below

Multiple Chemical Sensitivity (MCS), a condition also known as Chemical Sensitivity (CS), Chemical Intolerance (CI), Idiopathic Environmental Illness (IEI) and Toxicant Induced Loss of Tolerance (TILT), is an acquired multifactorial syndrome characterized by a recurrent set of debilitating symptoms. The symptoms of this controversial disorder are reported to be induced by environmental chemicals at doses far below those usually harmful to most persons. They involve a large spectrum of organ systems and typically disappear when the environmental chemicals are removed. However, no clear link has emerged among self-reported MCS symptoms and widely accepted objective measures of physiological dysfunction, and no clear dose-response relationship between exposure and symptom reactions has been observed. In addition, the underlying etiology and pathogenic processes of the disorder remain unknown and disputed, although biologic and psychologic hypotheses abound. It is currently debated whether MCS should be considered a clinical entity at all.

Multiple Chemical Sensitivity [2022]

• “Not surprising, MCS is often juxtaposed to, and sometimes combined with, other diseases for which definitive diagnoses and explanations are also found wanting, including fibromyalgia (FM), Gulf War syndrome (GWS), chronic fatigue syndrome (CFS), sick building syndrome (SBS), and electromagnetic radiation exposure (ERE) [18,23,24,25,26,27,28,29,30,31,32].”
• “An association was documented, however, between MCS and increased nasal airflow resistance, respiration rate, and heart rate.” [2021] Volatile chemicals can not only affect respiration, but can cause laryngeal symptoms which, in the extreme, can induce vocal cord dysfunction [82]. 
• Changes in skin conductance were also seen in MCS patients but not in controls [193].
• Compared to younger persons, those 65 years of age and older are less likely to identify themselves as chemically sensitive [88].
• In accord with this concept, McKeown-Eyssen et al. [219] found, in a female cohort of 203 MCS cases and 162 controls, that the cases had higher levels of cytochrome P450 CYP2D6 (i.e., one of the most important enzymes involved in the metabolism of xenobiotics in the body) and n-acetyl transferase 2 (NAT2). More possible genes listed here.
• Like the sick building syndrome [89], there is a remarkably higher prevalence in women than in men [16,18,21,90,91], with the percentage of women ranging from 60% to 88% [18,57,92,93,94]. 

• More than half (59%) of the mast cell activation syndrome group met criteria for chemical intolerance. [2021]
• Moist and Mold Exposure is Associated With High Prevalence of Neurological Symptoms and MCS in a Finnish Hospital Workers Cohort [2020]

Microbiome and MCS

Almost every condition in the above chart has microbiome shifts associated with it. Exposure to mold also is known to cause microbiome shifts. There appears to be no published studies exploring this possibility. At this time we have 128 people (7.3%) who have annotated their samples with Multiple Chemical Sensitivity [uBiome: 55, Ombre:39, Biomesight 25] to Microbiome Prescription.A

Executing analysis by lab, we found only one taxon reported significant in two labs. Bacteroides clarus where people with MCS had 3x the count of people not reporting MCS [BiomeSight, uBiome]. The top items of significance are shown below. All of them have much higher counts in people with MCS.

Source	probability	Tax_Name	tax_rank	with MCS	without MCS	Obs
BiomeSight	P < 0.001	Anaerotruncus	genus	3356	1834	683
BiomeSight	P < 0.001	Anaerotruncus colihominis	species	3208	1743	683
BiomeSight	P < 0.001	Bacteroides intestinalis	species	28980	2430	396
Thryve	P < 0.001	Prevotella maculosa	species	27399	5733	404

These have been added to the [Research] tab, as shown below.

Looking at KEGG Enzymes we have the strongest candidates below. Looking for any enzyme that is significant for 2 or more labs, we had only one: 5.3.1.15 D-lyxose aldose-ketose-isomerase (Biomesight and Thryve).

EnzymeName	ECKEY	Src	WithMean	WithoutMean	TScore	DF
7alpha-hydroxysteroid:NAD+ 7-oxidoreductase	1.1.1.159	BiomeSight	14681	2479	4.01	657
acetyl-CoA:glycerone phosphate C-acetyltransferase	2.3.1.245	BiomeSight	49551	27444	3.56	701
acetyl-CoA:heparan-alpha-D-glucosaminide N-acetyltransferase	2.3.1.78	BiomeSight	26909	9394	4.02	671
alginate beta-D-mannuronate—uronate lyase	4.2.2.3	BiomeSight	26933	9472	4.02	681
CTP:phosphoglutamine cytidylyltransferase	2.7.7.103	BiomeSight	27471	13156	3.97	702
L-alanine:glyoxylate aminotransferase	2.6.1.44	uBiome	16083	2029	3.40	309
NAD+:glycine ADP-D-ribosyltransferase (sulfide-adding)	2.4.2.59	BiomeSight	48398	26181	3.60	699
phosphoenolpyruvate:glycerone phosphotransferase	2.7.1.121	BiomeSight	68289	41808	3.60	702
sn-glycerol-1-phosphate:NAD(P)+ 2-oxidoreductase	1.1.1.261	BiomeSight	29070	11440	3.55	700

Dropping by Lab and using Percentiles Only

Trying a different approach aggregating all of the data and using percentile ranking. This masks most of the differences between labs and gives us large sample size, We have a very actionable item: increase Bifidobacterium by taking Bifidobacterium probiotics.

tax_name	tax_rank	percentile	Obs
Alistipes indistinctus	species	61.7	61
Anaerobutyricum hallii	species	39.8	41
Bifidobacteriales	order	37.7	106
Bifidobacterium	genus	37.1	122
Oscillospiraceae incertae sedis	norank	63.1	41
Acidobacteria	phylum	33.2	48
Butyricimonas faecihominis	species	61.2	43
Bifidobacteriaceae	family	37.4	122
Collinsella aerofaciens	species	39.1	83
Phascolarctobacterium succinatutens	species	37.3	52
Hungateiclostridiaceae	family	61.4	45

KEGG Enzymes

Applying this to KEGG Enzymes, we have just one of concern that is high: EC3.2.1.46  galactosylceramidase, and a big 39 that are low indicating a deficiency of enzymes is likely

EnzymeName	percentile	obs
[2.7.1.85] ATP:cellobiose 6-phosphotransferase	34.6	64
[4.1.1.31] phosphate:oxaloacetate carboxy-lyase (adding phosphate, phosphoenolpyruvate-forming)	35.2	122
[3.2.1.199] 6-sulfo-alpha-D-quinovosyl diacylglycerol 6-sulfo-D-quinovohydrolase	36.1	61
[1.17.2.1] nicotinate:cytochrome 6-oxidoreductase (hydroxylating)	36.7	63
[3.5.1.18] N-succinyl-LL-2,6-diaminoheptanedioate amidohydrolase	37.9	122
[2.7.1.211] protein-Npi-phospho-L-histidine:sucrose Npi-phosphotransferase	38	122
[1.2.1.99] 4-(gamma-L-glutamylamino)butanal:NAD(P)+ oxidoreductase	38.1	63
[1.17.98.4] formate:[oxidized hydrogenase] oxidoreductase	38.3	121
[3.4.23.51]	38.4	117
[2.7.1.195] protein-Npi-phospho-L-histidine:2-O-alpha-mannopyranosyl-D-glycerate Npi-phosphotransferase	38.4	94
[1.1.1.65] pyridoxine:NADP+ 4-oxidoreductase	38.4	98
[2.1.1.172] S-adenosyl-L-methionine:16S rRNA (guanine1207-N2)-methyltransferase	38.5	120
[2.1.1.264] S-adenosyl-L-methionine:23S rRNA (guanine2069-N7)-methyltransferase	38.6	121
[3.5.1.32] N-benzoylamino-acid amidohydrolase	38.7	63
[2.7.7.12] UDP-alpha-D-glucose:alpha-D-galactose-1-phosphate uridylyltransferase	38.7	122
[2.4.1.342] ADP-alpha-D-glucose:alpha-D-glucose-1-phosphate 4-alpha-D-glucosyltransferase (configuration-retaining)	38.9	121
[4.1.2.22] D-fructose-6-phosphate D-erythrose-4-phosphate-lyase (adding phosphate; acetyl-phosphate-forming)	39	122
[4.1.2.9] D-xylulose-5-phosphate D-glyceraldehyde-3-phosphate-lyase (adding phosphate; acetyl-phosphate-forming)	39	122
[3.2.1.122] alpha-maltose-6′-phosphate 6-phosphoglucohydrolase	39	122
[1.1.1.9] xylitol:NAD+ 2-oxidoreductase (D-xylulose-forming)	39.1	83
[6.3.1.12] D-aspartate:[beta-GlcNAc-(1->4)-Mur2Ac(oyl-L-Ala-gamma-D-Glu-L-Lys-D-Ala-D-Ala)]n ligase (ADP-forming)	39.2	122
[4.1.2.40] D-tagatose 1,6-bisphosphate D-glyceraldehyde-3-phosphate-lyase (glycerone-phosphate-forming)	39.2	122
[3.7.1.12] cobalt-precorrin 5A acylhydrolase	39.2	122
[1.3.1.28] (2S,3S)-2,3-dihydro-2,3-dihydroxybenzoate:NAD+ oxidoreductase	39.2	73
[5.3.1.26] D-galactose-6-phosphate aldose-ketose-isomerase	39.3	115
[2.7.1.19] ATP:D-ribulose-5-phosphate 1-phosphotransferase	39.4	62
[2.7.1.208] protein-Npi-phospho-L-histidine:maltose Npi-phosphotransferase	39.4	122
[2.7.1.207] protein-Npi-phospho-L-histidine:lactose Npi-phosphotransferase	39.4	118
[3.2.1.185] beta-L-arabinofuranoside non-reducing end beta-L-arabinofuranosidase	39.4	72
[1.1.1.130] 3-dehydro-L-gulonate:NAD(P)+ 2-oxidoreductase	39.5	72
[3.2.1.85] 6-phospho-beta-D-galactoside 6-phosphogalactohydrolase	39.5	117
[5.1.1.10] amino-acid racemase	39.7	108
[3.2.1.10] oligosaccharide 6-alpha-glucohydrolase	39.7	122
[1.1.1.203] uronate:NAD+ 1-oxidoreductase	39.7	61
[3.5.2.9] 5-oxo-L-proline amidohydrolase (ATP-hydrolysing)	39.7	120
[2.7.1.191] protein-Npi-phospho-L-histidine:D-mannose Npi-phosphotransferase	39.8	122
[4.2.1.30] glycerol hydro-lyase (3-hydroxypropanal-forming)	39.8	64
[3.4.17.19] Carboxypeptidase Taq	39.9	122
[2.7.1.189] ATP:(S)-4,5-dihydroxypentane-2,3-dione 5-phosphotransferase	39.9	72
[3.2.1.46] D-galactosyl-N-acylsphingosine galactohydrolase	63.3	65

Compound Produced

This is reflected in compound produced. With D-Galactose being the only high – matching enzyme [3.2.1.46] D-galactosyl-N-acylsphingosine galactohydrolase above,

CPID	Compound	percentile	obs
924	Ferrocytochrome	36.9	63
22336	Reduced hydrogenase	38.3	121
250	Pyridoxal	38.6	98
15767	4-(L-gamma-Glutamylamino)butanoate	38.7	63
16699	2-O-(6-Phospho-alpha-mannosyl)-D-glycerate	38.7	94
1113	D-Galactose 6-phosphate	39.2	118
196	2,3-Dihydroxybenzoate	39.3	73
4575	(4R,5S)-4,5,6-Trihydroxy-2,3-dioxohexanoate	39.5	72
615	Protein histidine	39.6	122
666	LL-2,6-Diaminoheptanedioate	39.7	122
1097	D-Tagatose 6-phosphate	39.8	115
20890	D-Glucaro-1,5-lactone	39.9	61
20889	D-Galactaro-1,5-lactone	39.9	61
124	D-Galactose	63.5	65

Compounds Consumed

As above for production and for bacteria, we have just one item with a high percentile and many with a low percentile.

CompoundName	percentile	Obs
Galactosylceramide	63.9	65
5-Oxoproline	39.9	120
Hydrogen selenide	39.9	122
3-Dehydro-L-gulonate	39.9	72
Reduced riboflavin	39.8	50
Protein N(pi)-phospho-L-histidine	39.6	122
5-Aminopentanoate	39.6	106
[SoxY protein]-S-disulfanyl-L-cysteine	39.6	43
(L-Seryl)adenylate	39.6	60
(2S,3S)-2,3-Dihydro-2,3-dihydroxybenzoate	39.6	73
6-Phospho-beta-D-galactoside	39.5	117
Sucrose	39.4	122
Hippurate	39.4	63
alpha-D-Galactose 1-phosphate	39.4	122
2-Hydroxy-3-keto-5-methylthiopentenyl-1-phosphate	39.4	52
D-Galactose 6-phosphate	39.3	115
4-Aminobutyraldehyde	39.3	47
D-Xylulose 5-phosphate	39.1	122
Benzene-1,2,4-triol	39	43
L-Arabinonate	38.6	52
Pyridoxine	38.6	98
Salicylate	38.3	44
dTDP-4-acetamido-4,6-dideoxy-alpha-D-galactose	38.3	60
Oxidized hydrogenase	38.3	121
3,4-Dihydroxyphenylacetate	38.1	47
Thymine	37.8	45
FMN-N5-peroxide	37.8	45
FMN-N5-oxide	37.8	45
Ferricytochrome	37.4	63
Hexadecenoyl-[acyl-carrier protein]	37.2	47
Protein lysine	37.1	120
2-O-(alpha-D-Glucopyranosyl)-D-glycerate	37.1	54
L-Rhamnonate	36.7	46
Geraniol	36.4	49
4-(L-gamma-Glutamylamino)butanoate	36.3	44
D-Glyceraldehyde	35.6	60
3-Hydroxybenzoate	35.3	41
(2R)-3-Sulfolactate	34.1	43
D-Glucaro-1,4-lactone	33.3	43
D-Galactaro-1,4-lactone	33.3	43

Bottom Line

The is no obvious imbalance between compounds produced and consumed. Thus the best path is to use the bacteria identified at the start. A quick select has been added to the menus.

• There are a few things that could be factors, for example D-Galactaro-1,4-lactone is 33.3%ile for consumption and 39.9%ile for production. Unfortunately, the microbiome is not a closed system and whether any surplus accumulates would be too speculative.