After the post on histamines, people with Salicylate sensitivity started to contact. “Me too!”. I looked at the data available and was disappointed that only 31 people have annotated their sample with this condition.

Medical Literature

This is known as  salicylate allergy, salicylate hypersensitivity or salicylate intolerance. The traditional medical view is “It’s not clear exactly what causes salicylate allergy. “[WebMD] It was associated with Lyme Disease in 1991, and in other studies to: rheumatic diseases [1961] and systemic lupus erythematosus[1979]. It is seen in up to 7% of all patients with inflammatory bowel syndrome[2004] where as it was been estimated that 2.5%[2015] of the general population has this issue. The incidence in samples uploaded to microbiome prescription with symptom annotations appears to be 1.8%. This implies a significant microbiome contribution to this condition.

In 1980, we have The uselessness of blood salicylate levels in the diagnosis of salicylate hypersensitivity. However, the level may cause issues with blood donations being received by people with this condition, see Salicylate and acetaminophen in donated blood [1986].

Diet changes appear to have minor impact as reported in Effectiveness of Personalized Low Salicylate Diet in the Management of Salicylates Hypersensitive Patients: Interventional Study[2021] and others [2021] [2016]

In dealing with Plants we have an interesting study Overexpression of Arabidopsis NIMIN1 results in salicylate intolerance [2016] with other plant based studies [2010].

Human Trials

Only one trial could be found

After dietary supplementation with 10 g daily of fish oils rich in omega-3 PUFAs for 6-8 weeks all three experienced complete or virtually complete resolution of symptoms allowing discontinuation of systemic corticosteroid therapy. Symptoms relapsed after dose reduction.”

Control of salicylate intolerance with fish oils [2008]

KEGG: Kyoto Encyclopedia of Genes and Genomes Perspective

I am by temperament, a modeler. Look at the surface layer of information, derive some suggestions of how thing work and then, critically, test the model. Failure is information also! My first model is to assume enzymes associated with salicylates are significant.

Salicylate aka o-Hydroxybenzoic acid aka Salicylic acid is compound C00805 with the following enzymes interacting with it. Conceptually, too much of some enzymes and too few of other enzymes may be the likely cause. The list include the followin:

• 1.2.1.65 salicylaldehyde dehydrogenase
• 1.14.13.1 salicylate 1-monooxygenase;      
• 1.14.13.172 salicylate 5-hydroxylase
• 2.1.1.274  salicylate 1-O-methyltransferase
• 3.1.1.55 acetylsalicylate deacetylase  
• 4.1.1.91 salicylate decarboxylase        
• 4.2.99.21 isochorismate lyase
• 6.2.1.61 salicylate—[aryl-carrier protein] ligase    
• 6.2.1.65 salicylate—CoA ligase

Data From Microbiome Prescription Samples

See Special Studies on Symptoms caused by Bacteria – v.2 for technical detail. A related post is Histamine Release – Literature Review And Speculation.

For more information on each enzyme see BRENDA or KEGG

The most significant ones from are shown below. None are a match for the candidate list above

EC	Enzyme Name	With Sal	Without Sal	TScore	DF	Probability
1.1.1.337	(2S)-2-hydroxycarboxylate:NAD+ oxidoreductase	37077	19517	3.38	666	P < 0.001
1.3.7.1	6-oxo-1,4,5,6-tetrahydronicotinate:ferredoxin oxidoreductase	334	91	4.01	222	P < 0.001
2.1.1.245	5-methyltetrahydrosarcinapterin:corrinoid/iron-sulfur protein methyltransferase	36498	18608	3.46	666	P < 0.001
2.1.1.258	5-methyltetrahydrofolate:corrinoid/iron-sulfur protein methyltransferase	36569	18689	3.46	666	P < 0.001
2.6.1.109	8-amino-3,8-dideoxy-alpha-D-manno-octulosonate:2-oxoglutarate aminotransferase	108	35	3.90	124	P < 0.001
2.7.7.39	CTP:sn-glycerol-3-phosphate cytidylyltransferase	60861	32715	3.56	666	P < 0.001
3.4.19.11	gamma-D-glutamyl-meso-diaminopimelate peptidase	39254	21501	3.42	666	P < 0.001
3.5.2.18	6-oxo-1,4,5,6-tetrahydronicotinate amidohydrolase	334	93	3.93	215	P < 0.001
4.2.1.43	2-dehydro-3-deoxy-L-arabinonate hydro-lyase (2,5-dioxopentanoate-forming)	433	103	4.57	214	P < 0.001
5.4.99.21	23S rRNA-uridine2604 uracil mutase	63603	39414	3.33	666	P < 0.001

Looking at the next level of association, we still find none of these candidate enzymes.

EC	Enzyme Name	With Sal	Without Sal	TScore	DF	Probability
1.1.1.135	GDP-6-deoxy-alpha-D-talose:NAD(P)+ 4-oxidoreductase	279	96	2.82	263	P < 0.01
1.1.1.312	4-carboxy-2-hydroxymuconate semialdehyde hemiacetal:NADP+ 2-oxidoreductase	353	105	2.68	229	P < 0.01
1.1.1.410	D-erythronate:NAD+ 2-oxidoreductase	381	117	3.32	300	P < 0.01
1.1.99.31	(S)-mandelate:acceptor 2-oxidoreductase	265	92	2.71	210	P < 0.01
1.13.11.37	hydroxyquinol:oxygen 1,2-oxidoreductase (ring-opening)	268	88	2.66	247	P < 0.01
1.17.1.4	xanthine:NAD+ oxidoreductase	34795	17231	2.81	666	P < 0.01
1.17.9.1	4-methylphenol:oxidized azurin oxidoreductase (methyl-hydroxylating)	198	83	3.00	338	P < 0.01
1.2.1.87	propanal:NAD+ oxidoreductase (CoA-propanoylating)	25104	13840	2.72	666	P < 0.01
1.2.7.4	carbon-monoxide,water:ferredoxin oxidoreductase	51730	29588	3.18	666	P < 0.01
1.2.99.7	aldehyde:acceptor oxidoreductase (FAD-independent)	34901	17791	2.71	666	P < 0.01
1.3.1.32	3-oxoadipate:NAD(P)+ oxidoreductase	268	88	2.65	260	P < 0.01
2.3.1.101	formylmethanofuran:5,6,7,8-tetrahydromethanopterin 5-formyltransferase	289	88	3.25	207	P < 0.01
2.3.1.276	acetyl-CoA:alpha-D-galactosamine-1-phosphate N-acetyltransferase	51215	29509	3.08	666	P < 0.01
2.3.2.13	protein-glutamine:amine gamma-glutamyltransferase	49247	27040	3.22	666	P < 0.01
2.4.1.245	NDP-alpha-D-glucose:D-glucose 1-alpha-D-glucosyltransferase	111	42	3.33	213	P < 0.01
2.7.1.215	ATP:erythritol 1-phosphotransferase	12084	5590	2.73	653	P < 0.01
2.7.4.31	ATP:[5-(aminomethyl)furan-3-yl]methyl-phosphate phosphotransferase	289	88	3.25	207	P < 0.01
2.7.4.33	ADP:polyphosphate phosphotransferase	36369	19252	2.68	666	P < 0.01
2.7.7.83	UTP:N-acetyl-alpha-D-galactosamine-1-phosphate uridylyltransferase	51217	29512	3.08	666	P < 0.01
2.7.9.6	ATP:rifampicin, water 21-O-phosphotransferase	1458	427	2.99	575	P < 0.01
3.1.1.104	5-phospho-D-xylono-1,4-lactone hydrolase	12119	5680	2.70	656	P < 0.01
3.1.1.81	N-acyl-L-homoserine-lactone lactonohydrolase	12121	5591	2.73	653	P < 0.01
3.4.23.43	prepilin peptidase	104657	72966	2.58	666	P < 0.01
3.5.1.110	(Z)-3-ureidoacrylate amidohydrolase	12177	5917	2.63	661	P < 0.01
3.5.4.44	ectoine aminohydrolase	268	85	2.71	234	P < 0.01
3.5.99.5	2-aminomuconate aminohydrolase	309	106	2.72	177	P < 0.01
3.6.1.15	nucleoside-triphosphate phosphohydrolase	26554	15567	3.28	665	P < 0.01
3.6.1.7	acylphosphate phosphohydrolase	60707	38253	2.67	666	P < 0.01
4.2.1.147	5,6,7,8-tetrahydromethanopterin hydro-lyase (formaldehyde-adding, tetrahydromethanopterin-forming)	309	101	2.77	192	P < 0.01
5.3.1.15	D-lyxose aldose-ketose-isomerase	58198	37194	2.64	666	P < 0.01
5.3.1.34	D-erythrulose-4-phosphate ketose-aldose isomerase	12039	5560	2.71	642	P < 0.01
7.5.2.4	ATP phosphohydrolase (ABC-type, teichoic-acid-exporting)	25268	12222	2.91	666	P < 0.01

So, model 1 is a bust — none of the suspected enzymes showed up as being statistically significant. This echoes The uselessness of blood salicylate levels in the diagnosis of salicylate hypersensitivity[1980]. However one thing stands out…. for each of the enzymes estimates above — the amount with salicylate sensitivity is in every case more than those without it. This is unusual.It hints that the issue is over production of enzymes.

Microbiome Significances

Only Biomesight has sufficient data to do an investigation with. The most significant ones all had an interesting characteristic matching that seen with the enzymes — Too MANY.

Tax_name	Tax_rank	With Sal	Without Sal	Tscore	DF	Probability
Fusobacteriia	class	21983	3330	3.52	346	P < 0.001
Chroococcaceae	family	588	165	3.42	286	P < 0.001
Fusobacteriaceae	family	25628	3687	3.63	305	P < 0.001
Hungateiclostridiaceae	family	8534	2649	3.69	201	P < 0.001
Lachnospiraceae	family	297116	203706	4.2	667	P < 0.001
Rhodanobacteraceae	family	2453	656	3.32	478	P < 0.001
Syntrophobacteraceae	family	115	36	3.57	184	P < 0.001
Acetobacterium	genus	3977	1250	3.92	657	P < 0.001
Blautia	genus	141777	87110	4.2	667	P < 0.001
Chroococcus	genus	588	165	3.42	286	P < 0.001
Clostridium	genus	30531	18614	3.45	667	P < 0.001
Furfurilactobacillus	genus	91	38	3.48	270	P < 0.001
Hungateiclostridium	genus	8178	2229	3.94	200	P < 0.001
Luteibacter	genus	3734	764	4.07	387	P < 0.001
Fusobacteriales	order	21983	3329	3.52	346	P < 0.001
Syntrophobacterales	order	135	45	5.06	331	P < 0.001
Fusobacteria	phylum	21983	3330	3.52	346	P < 0.001
Bacteroides fluxus	species	2722	318	3.41	517	P < 0.001
Blautia glucerasea	species	4228	664	3.86	550	P < 0.001
Blautia schinkii	species	1710	265	4.14	558	P < 0.001
Caloramator uzoniensis	species	300	85	4.53	328	P < 0.001
Chroococcus minutus	species	588	165	3.42	286	P < 0.001
Clostridium akagii	species	113	43	4.37	311	P < 0.001
Furfurilactobacillus siliginis	species	91	38	3.51	271	P < 0.001
Luteibacter anthropi	species	3734	764	4.07	387	P < 0.001

Looking at the next level, we have a lot less BUT THE PATTERN OF OVER ABUNDANCE continues:

Tax_name	Tax_rank	WithMean	WithoutMean	Tscore	DF	Probability
Desulfovibrionaceae	family	8811	5037	2.61	641	P < 0.01
Xanthomonadaceae	family	2131	642	3.16	556	P < 0.01
Bilophila	genus	6608	3415	3.25	587	P < 0.01
Caldicellulosiruptor	genus	581	289	2.67	617	P < 0.01
Fusobacterium	genus	2795	683	2.76	289	P < 0.01
Holdemania	genus	572	296	3.04	589	P < 0.01
Lachnospira	genus	49628	28041	3.22	667	P < 0.01
Macrococcus	genus	2425	419	3.28	222	P < 0.01
Thiorhodococcus	genus	64	28	2.67	108	P < 0.01
Desulfovibrionales	order	8840	5039	2.63	643	P < 0.01
Bacteroides finegoldii	species	7458	2163	3.15	552	P < 0.01
Blautia obeum	species	12039	5596	2.68	642	P < 0.01
Fusobacterium gonidiaformans	species	3980	833	3.12	134	P < 0.01
Shewanella upenei	species	92	46	3	157	P < 0.01

Cross Validation of The above

We know of only one thing known to help, Omega-3 / Fish Oil. We have this information in our database and find that it decreases the following (from multiple studies):

• Lachnospira
• Lachnospiraceae
• Furfurilactobacillus siliginis
• Blautia

A single study suggests that it increases Clostridium / Clostridium akagii (Child). This is a 80% validation rate implying that the Artificial Intelligence suggestions on a person’s microbiome is likely to be effective.

Putting this data to Use

The first step is simple, take 10 g daily of fish oils rich in omega-3 for the first 6-8 weeks. Get relief. Warning: the study states that if you stop, you will revert. It is insufficient to correct the bacteria shifts.

If you do not have a BiomeSight sample on hand…

Go to Citizen Science Special Studies, Enter Salicylate in the search box. The page should give you two buttons as shown below.

The results may change as more samples are uploaded and annotated with symptoms. This research is dynamic and live!. Clicking the green button will show a list of item to take or add to your diet.

The second item is likely more important items that feed the bacteria that are too high. You want to reduce or eliminate these.

There is still more information available on actual diet (foods that you may eat), by clicking on

We know the fish oil helps, let us verify that by clicking on [FIsh, shellfish and their products]. We see fish — specifically salmon! And Walrus liver is a to-avoid!

Returning to the red button, we see a list of off-label usage of drugs mixed into the results. The top ones:

• Risperidone is used to treat a certain mental/mood disorder called schizophrenia.
• Acarbose is used with a proper diet and exercise program to control high blood sugar in people with type 2 diabetes.
• meomycin is used to decrease the risk of infection after certain intestinal surgeries.

Step 2 Order a Suitable Test

You will likely need at least 3 tests, the data is based on Biomesight tests. Order some. If you live in the US, you could order an Ombre Test (this gets more technical because you will need to download some big files [FASTQ] from their site and then upload to Biomesight for them to process them).

You will likely be doing the above for 5-6 weeks before you get your data available on BiomeSight.

Log in to Microbiome Prescription

Go to [Research Features]

Then find on that page, the Experimental section, and click v.2 Of Special Symptom Associations

Select your latest sample (if more than one) and then the condition

The bacteria that definitely match the pattern will be automatically checked.

You should scan for any close matches and check those boxes. For example, above the without symptoms slightly.

Now click the [Add to Hand Picked LIst] button at the top

A new window will appear with the items that you have selected.

Just click [Get Suggestions] and then scope the type of suggestions you wish

Then click the Get Suggestions button on the top left

Your suggestions may be similar (the sample that I am using was for someone with this issue)

The food list may also change

WARNING: Many items suggested may trigger a Salicylate reaction. Consider doing the 6-8 weeks of Fish Oil before starting any items that could trigger a reaction.

This reaction could be caused by bacteria resisting change (similar to a Jarisch Herxheimer)

The usual pattern is to implement the changes for 4 week, do a new sample. Keep doing the changes until the results come in. So this is the likely time line for some

• Week 0: Order Kit, start general list
• Week 1: Take test #1 and send in
• Week 4: Get results, upload and get 1st personalized set of suggestions
• Week 8: Take test #2 and send in
• Week 10: Get results, upload and get 2nd personalized set of suggestions
• Week 14: Take test #3 and send in
• Week 16: Get results, upload and get 3rd personalized set of suggestions

Depending on results, repeat. Your desired result is to have NO Matches with the pattern (and hopefully have eliminated the issue).

Postscript – and Reminder

I am not a licensed medical professional and there are strict laws where I live about “appearing to practice medicine”.  I am safe when it is “academic models” and I keep to the language of science, especially statistics. I am not safe when the explanations have possible overtones of advising a patient instead of presenting data to be evaluated by a medical professional before implementing.

I cannot tell people what they should take or not take. I can inform people items that have better odds of improving their microbiome as a results on numeric calculations. I am a trained experienced statistician with appropriate degrees and professional memberships. All suggestions should be reviewed by your medical professional before starting.

The answers above describe my logic and thinking and is not intended to give advice to this person or any one. Always review with your knowledgeable medical professional.