Histamine issues can occur from consuming food high in histamines. This is the typical approach for people dealing with this issue. There is an another route that should not be overlooked — things that do not contain histamine but which triggers histamine release.

Citations

Histamine release caused by reactions to drug product and/or excipients/vehicles is a phenomenon observed in both toxicology and pharmacology studies. This type of reaction is also referred to as pseudoallergic reaction, anaphylactoid reaction or complement activation-related pseudoallergy (CARPA). 

Biomarkers in Toxicology, 2014

• “Codeine and meperidine are examples of other opioids that can induce mast cell activation with the release of histamine” [ 2020]
  • “Quaternary ammonium compounds (e.g., NMBDs) are generally weaker histamine-releasing substances than are tertiary amines such as morphine.”
• “Histamine release can occur with administration of certain opioids” [2015]
• “Histamine release is primarily caused by morphine, followed by hydromorphone,” [2009]
• “Histamine release and the severity of reactions during vancomycin administration are directly dependent on the rate of infusion.” [2007]
• “It is not clear whether histamine levels are altered following hypo– or hyperthermia seen during several clinical or experimental situations.” [2004]
• “Histamine release and non-IgE-mediated anaphylactic (anaphylactoid) reactions occur with alcuronium” [2016]

The above means that care needs to be taken if a herb, spice or supplement causes a histamine reaction. It may not be histamine in the substance, rather the substance causes mast cells to react and dump histamine. It is a significantly different situation. Other items that have been reported to cause histamine release include: Vitamin C(L-Ascorbic acid), Niacin (vitamin B3), Quercetin, Stinging nettle, Licorice root, Ginkgo biloba, Chamomile and Echinacea.

Histamine toxicity is sometimes confused with an allergic reaction to fish. Here is why:
Some kinds of fish contain naturally high levels of the chemical histidine. This chemical can be converted to histamine by bacteria [ the enzyme histidine decarboxylase EC 4.1.1.22]. In an allergic reaction, mast cells release histamine which triggers allergy symptoms. So, if a person eats fish that has a high level of histamine, the response may resemble an allergic reaction to that food.

American Academy of Allergy, Asthma & Immunology

A list of bacteria with this enzyme is here. The data comes from the KEGG: Kyoto Encyclopedia of Genes and Genomes and a collection of several thousand samples from different labs uploaded to the Microbiome Prescription web site. The top producers over all of the sample are shown below

Bacteria	Estimate Occurances	Contribution
Bacteroides fragilis [species]	1	5897.04
Eggerthella lenta [species]	1	627.29
Acinetobacter baumannii [species]	0.9	109.55
Gordonibacter pamelaeae [species]	1	99.28
Fusobacterium varium [species]	1	93.35
Clostridium perfringens [species]	1	64.31
Fusobacterium ulcerans [species]	1	57.3

Hypothesis Testing on Bacteria Conversion

The citizen science site, Microbiome Prescription, allows people to share their microbiome results from many labs and to annotate their samples with symptoms. Over 1000 samples have these annotations as shown below, so we can suggest a hypothesis and test it.

Hypothesis: People with Histamine Issues are likely to have higher counts of bacteria producing histidine decarboxylase

The results was a bit of a surprise. The Hypothesis failed dramatically! Having more bacteria producing this enzyme appears to be associated with less histamine issue!! This pattern persists across all three labs with significant data sample size.

One hypothesis that could be suggested by this data is that the histamine issue is due to the body’s base level of histamine being abnormally low and thus the body is unfamiliar with histamines and thus overreacts.

An Analogy to Consider

Imagine someone whose diet lacks ANY added sugar. After a year, he drops into a friend who makes him his favorite herbal tea. The friend, she, likes sweet tea and adds several teaspoon of sugar. This person drinks it and gets an atypical headache which confuses the friend – he drinks it regularly! The real cause is too low a base level of sugar consumption for this person to tolerate.

The above suggests that the same may be occurring with histamine reactions.

Did you know that both too much sugar and too little of sugar can cause headaches? When you consume too much sugar at once or don’t eat for an extended period of time, you can cause rapid fluctuations in your blood sugar levels which can trigger a headache. Some people are more prone to these sugar-triggered headaches.

Source

If this is a correct speculation, then the treatment approach is to encourage bacteria that produce the enzyme histidine decarboxylase EC 4.1.1.22.

Phrase 2 Checking each bacteria Taxa

While a bacteria has an enzyme, it is not a given that it is active. Our second pass is looking at the frequency of each bacteria taxa appearing in each group. If a specific taxa is significantly more or less frequent, it may be a trail worth following. Drilling down to species causes our observations to drop to the point that many data points cannot be examined for significance. We have three taxa with good sample sizes.

Remember, different labs use different software resulting in different taxa names.

Bacteria	Percent Having	Mean	SD	Subset	Lab
Bacteroides fragilis	79.2	5138	8629	Histamine	BiomeSight
	81.4	6369	16168	No Histamine
	75.6	6736	22788	Histamine	uBiome
	51.8	16628	47841	No Histamine
	94.2	7343	14279	Histamine	Ombre
	94.2	8646	27095	No Histamine
Eggerthella lenta	74	57	611	Histamine	BiomeSight
	69.8	365	346	No Histamine
	74.4	67	1323	Histamine	uBiome
	69.5	235	1502	No Histamine
	77.9	824	2407	Histamine	Ombre
	79.4	1059	4131	No Histamine
Gordonibacter pamelaeae	57.78	52	459	Histamine	uBiome
	61.2	207	413	No Histamine
	51.9	124	177	Histamine	Ombre
	46.1	187	358	No Histamine

Bacteroides fragilis for Ombre(Thryve), uBiome and BiomeSight were the most statistically significant and all have the same pattern: People reporting histamine issues had less than people that did not report histamine issues. Bacteroides fragilis is also the main source of histidine decarboxylase from the microbiome.

uBiome data was very interesting because the detection rate for Bacteroides fragilis was significantly less for samples that did not report histamine issues with the differences of means being much much more than with other labs. This hints that some part of the base pairs collection that uBiome used to determine Bacteroides fragilis in their software may be particularly important for histamine issues. To put it another way, some part of the sequence being used to determine the taxa, also appears to detect histamine issues. This leads to the possibility that specific strains of Bacteroides fragilis may result in better histamine tolerance.

uBiome and others use a reference database.  Because a 16s test only looks at a tiny portion of the microbial genome, of necessity different bioinformatics pipelines will assign slightly different microbial genus/species names for the string of base pairs they sequence.

uBiome, a company that offered microbiome testing services, used a proprietary reference database called the uBiome Microbial Insights Test (MIT) reference database for the taxonomic classification of 16S rRNA gene sequences. This database was specifically designed for the analysis of human microbiome samples, and included over 1,000 microbial taxa that were commonly found in the human gut, oral, and skin microbiomes. The database was built using a combination of publicly available 16S rRNA gene sequences and uBiome’s own sequencing data, and was regularly updated to incorporate new microbial taxa and improve the accuracy of taxonomic assignments.

From Chat_GPT

• Biomesight: SILVA SSU Ref NR 99 database,
• Ombre: GreenGenes 13_8 release
• American Gut: Greengenes 13_8 release,
• Thorne: Their 16s database is built from a combination of publicly available reference databases, including the Greengenes and SILVA databases, as well as Thorne’s own sequencing data.

Unfortunately uBiome reference database disappeared with it going into bankruptcy. Only time will tell if Thorne’s reference database will identify the key base pairs that appears to be connected to histamine issues.

What is very interesting comes from a 1999 study, “Intestinal mucosa-associated bacteria modulate rat mast cell reactivity” which reports a ConA-induced histamine release was diminished up to 71% of maximal histamine release by Bacteroides fragilis!! This supports that increasing Bacteroides fragilis may be the right way to go.

Where do we go from here for bacteria?

The above suggests that there is a bacteria taxa with a specific key base pair that is connected to histamines issues. Let us call it “Bacteroides Histamilis”(BH). This taxa has some overlap with Bacteroides fragilis (BF). They appear to occupy a similar niche in the microbiome world with an increase of BF decreasing BH (or it’s impact). We are shooting in the fog here, but it seems encouraging BF growth may reduce BH and ease histamine issues. We have know items that increases or decrease listed here: Bacteroides fragilis and a link to possible foods. The top food nutrients are iron, zinc and Riboflavin (Vitamin B2).

Going to Chat_GPT for some quick answers, we get a pleasant surprise:

Iron plays an important role in the regulation of histamine levels in the body. It is required for the activity of an enzyme called diamine oxidase (DAO), which is responsible for breaking down histamine in the gut. Low iron levels can lead to reduced DAO activity and increased histamine levels, which can contribute to histamine intolerance or sensitivity.

Zinc is also important for the activity of DAO, as well as for the regulation of histamine receptors in the body. Zinc deficiency has been linked to increased histamine levels and may contribute to histamine intolerance.

Riboflavin (vitamin B2) is required for the synthesis of DAO, and low riboflavin levels have been associated with reduced DAO activity and increased histamine levels.

Answer from Chat_CPT to question: “Is histamine sensitivity connected to iron, zinc or Riboflavin deficiency?“

So, we have an interesting cascade… the 3 key nutrients available concurrently in food that increases Bacteroides fragilis, are all associated with DAO production. Should we speculate that BH is a mutation that thrives better with low levels of Iron, Zinc and Riboflavin and the difference in base pairs is connected with this mutation? Continuing this thought experiment, would this mutation also have reduced (or no) enzyme histidine decarboxylase EC 4.1.1.22 being produced resulting in an abnormally low level of histamine on an ongoing basis and thus increased sensitivity?

A Parallel Thread in Autism?

Antihistamines have been reported to reduce some autism behaviors [2018]. For example “Altered expression of histamine signaling genes in autism spectrum disorder” [2017]. Bacteroides fragilis has been reported to be low with autism. I will leave it to others to explore this further.

One study published in 2013 found that children with autism had lower levels of Bacteroides fragilis in their gut microbiome compared to typically developing children. Another study published in 2017 found that a group of children with autism had higher levels of Bacteroides fragilis in their fecal samples compared to a control group of typically developing children.

Iron plays an important role in brain development and function, and some studies have suggested that iron deficiency during pregnancy or early childhood may increase the risk of autism.

Zinc is also important for brain development and function, and some studies have found that children with autism may have lower levels of zinc in their blood or hair compared to typically developing children.

Riboflavin (vitamin B2) is required for several important metabolic pathways in the body, and some studies have suggested that children with autism may have lower levels of riboflavin compared to typically developing children.

However, other studies have not found a significant association between these and autism.

Chat_GPT.

Bottom Line

There is a scent that specific strains of Bacteroides fragilis may be associated with histamine sensitivity. In an environment deficient of iron, zinc or riboflavin, this strain increases. This strain may not produce histidine decarboxylase EC 4.1.1.22 (epigenetics?) resulting in a much lower level of histamine in the body resulting in “sugar-shock” when a food containing histamines is consumed. We saw above a consistent pattern of having a lower count has an increased probability of histamine issues. A lower count is typically viewed as having less appropriate nutrients available – and the missing nutrients are implied by our analysis.

A common pattern seen by people with Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Irritable Bowel Disease and other gut disturbances is increasing histamine issues. Mal-absorption due to gut disturbances would result in a dropping of iron, zinc and riboflavin absorption causing this “Bacteroides Histamilis” strain to dominant.

With this model, supplementation with iron, zinc and riboflavin to increase the body’s level to at least the 75%ile may result in significant improvement.

A suggestion for a study would be to measure iron, zinc and riboflavin levels of people with histamine issues against an appropriate matched control population. This may determine if deficiency of just one of this trio is sufficient, or do we need multiple deficiency.

Appendix Statistical Significance Table

The following is general data mining. Remember the lab’s software determines the taxa names using probability. Safest conclusions are when multiple labs report significance in the same direction for the same taxa. Taxa cannot be safely be applied to different labs. Labs report on different bacteria, especially at the species level, on occasion they will pick one name and a different lab will pick a different name.

REMEMBER: These may not be the cause, rather bacteria altered by the bacteria that are the cause.

Taxa name	Taxa Rank	Lab	Histamine Mean	Control Mean	T-Score	DF	Probability
Cyanobacteria /Melainabacteria group	clade	uBiome	6444	268	4.338711	12	P < 0.001
Alphaproteobacteria	class	uBiome	26227	15210	3.283924	269	P < 0.01
Chitinophagia	class	Thryve	94	69	2.824838	330	P < 0.01
Clostridia	class	BiomeSight	619159	558435	2.713198	667	P < 0.01
Deltaproteobacteria	class	uBiome	10291	6987	2.794664	401	P < 0.01
Flavobacteriia	class	Thryve	377	93	4.138491	241	P < 0.001
Spirochaetia	class	Thryve	22731	196	2.747053	68	P < 0.01
Bifidobacteriaceae	family	uBiome	30890	14422	2.643228	474	P < 0.01
Carnobacteriaceae	family	uBiome	815	138	2.86021	182	P < 0.01
Chitinophagaceae	family	Thryve	94	69	2.818824	330	P < 0.01
Chromatiaceae	family	BiomeSight	110	74	2.962499	525	P < 0.01
Coprobacillaceae Verbarg et al. 2014	family	BiomeSight	4986	2981	2.905239	662	P < 0.01
Desulfovibrionaceae	family	uBiome	10288	6984	2.797459	401	P < 0.01
Flavobacteriaceae	family	Thryve	376	95	3.766135	215	P < 0.001
Lachnospiraceae	family	BiomeSight	244569	201657	3.461006	667	P < 0.001
Micrococcaceae	family	Thryve	2041	279	2.732836	198	P < 0.01
Odoribacteraceae	family	uBiome	13802	9604	2.953385	386	P < 0.01
Pasteurellaceae	family	uBiome	11259	3343	2.952883	325	P < 0.01
Rhodospirillaceae	family	uBiome	29014	17878	3.045209	229	P < 0.01
Rubritaleaceae	family	BiomeSight	118	51	3.018423	231	P < 0.01
Streptococcaceae	family	uBiome	12988	6609	3.294864	469	P < 0.01
Syntrophaceae	family	BiomeSight	88	34	3.602406	95	P < 0.001
Weeksellaceae	family	Thryve	826	140	2.707921	42	P < 0.01
Adlercreutzia	genus	BiomeSight	856	346	3.933686	493	P < 0.001
Anaerofustis	genus	BiomeSight	85	45	2.957077	132	P < 0.01
Anaerolinea	genus	BiomeSight	31	18	3.145373	49	P < 0.01
Anaerotruncus	genus	BiomeSight	2476	1835	2.82706	650	P < 0.01
Bacteroides	genus	uBiome	283855	247858	2.683065	471	P < 0.01
Bifidobacterium	genus	uBiome	30859	14357	2.650187	474	P < 0.01
Blautia	genus	BiomeSight	109238	86161	3.171679	667	P < 0.01
Butyrivibrio	genus	Thryve	2238	863	2.78185	414	P < 0.01
Chromatium	genus	BiomeSight	54	23	2.899706	89	P < 0.01
Chryseobacterium	genus	Thryve	112	28	5.423269	18	P < 0.001
Clostridium	genus	uBiome	9065	7003	2.64738	469	P < 0.01
Clostridium	genus	BiomeSight	24536	18253	3.27004	667	P < 0.01
Cronobacter	genus	uBiome	7047	250	3.67629	51	P < 0.001
Desulfomonile	genus	BiomeSight	94	34	3.874419	94	P < 0.001
Desulfovibrio	genus	uBiome	8575	4552	2.939696	243	P < 0.01
Granulicatella	genus	uBiome	839	137	2.901195	179	P < 0.01
Haemophilus	genus	uBiome	12156	3087	3.235353	314	P < 0.01
Henriciella	genus	Thryve	386	176	2.642494	155	P < 0.01
Hungateiclostridium	genus	BiomeSight	4457	2097	2.785968	200	P < 0.01
Limosilactobacillus	genus	uBiome	48109	2843	2.818423	44	P < 0.01
Macrococcus	genus	BiomeSight	1558	388	2.673296	222	P < 0.01
Marvinbryantia	genus	uBiome	4529	2564	2.773658	369	P < 0.01
Negativicoccus	genus	BiomeSight	1370	264	3.038088	353	P < 0.01
Odoribacter	genus	uBiome	8724	5405	3.26612	369	P < 0.01
Pelotomaculum	genus	BiomeSight	259	96	2.808263	236	P < 0.01
Rothia	genus	Thryve	2787	326	2.925505	153	P < 0.01
Rubritalea	genus	BiomeSight	118	51	3.025228	232	P < 0.01
Senegalimassilia	genus	Thryve	2823	617	3.184808	143	P < 0.01
Shuttleworthia	genus	Thryve	105	75	2.606498	318	P < 0.01
Slackia	genus	uBiome	3566	1488	4.3098	154	P < 0.001
Streptococcus	genus	uBiome	12882	6211	3.438454	465	P < 0.001
Trabulsiella	genus	BiomeSight	2381	72	2.944962	84	P < 0.01
Chryseobacterium group	norank	Thryve	112	28	5.423269	18	P < 0.001
unclassified Parabacteroides	norank	uBiome	2321	760	3.29071	91	P < 0.01
unclassified Streptococcus	norank	uBiome	9530	3840	3.188989	429	P < 0.01
Bifidobacteriales	order	uBiome	44064	18404	3.118635	363	P < 0.01
Chitinophagales	order	Thryve	94	69	2.824838	330	P < 0.01
Desulfovibrionales	order	uBiome	10291	6987	2.794631	401	P < 0.01
Eubacteriales	order	BiomeSight	614126	553568	2.710308	667	P < 0.01
Flavobacteriales	order	Thryve	377	93	4.138507	241	P < 0.001
Micrococcales	order	Thryve	1469	345	2.755253	326	P < 0.01
Pasteurellales	order	uBiome	11259	3343	2.952883	325	P < 0.01
Rhodocyclales	order	uBiome	888	327	2.799376	44	P < 0.01
Rhodospirillales	order	uBiome	27884	17148	3.002822	241	P < 0.01
Syntrophobacterales	order	BiomeSight	75	45	2.874101	331	P < 0.01
Chloroflexi	phylum	Thryve	1529	170	3.281628	262	P < 0.01
Cyanobacteria	phylum	uBiome	6444	268	4.338711	12	P < 0.001
Fibrobacteres	phylum	Thryve	180078	34	4.876895	83	P < 0.001
Firmicutes	phylum	BiomeSight	646426	582627	2.769104	667	P < 0.01
Spirochaetes	phylum	Thryve	22731	196	2.747053	68	P < 0.01
Adlercreutzia equolifaciens	species	BiomeSight	549	211	3.480457	447	P < 0.001
Alistipes putredinis	species	uBiome	14784	10219	3.567818	346	P < 0.001
Alistipes sp. NML05A004	species	uBiome	2861	1594	3.211406	270	P < 0.01
Anaerofustis stercorihominis	species	BiomeSight	85	45	2.937001	130	P < 0.01
Anaerolinea thermolimosa	species	BiomeSight	30	18	2.73704	47	P < 0.01
Anaerotruncus colihominis	species	BiomeSight	2335	1749	2.696412	650	P < 0.01
Bacteroides finegoldii	species	BiomeSight	4441	2045	2.613824	552	P < 0.01
Bacteroides heparinolyticus	species	BiomeSight	59	33	2.652157	173	P < 0.01
Bacteroides nordii	species	uBiome	5809	1153	3.833993	148	P < 0.001
Bacteroides reticulotermitis	species	Thryve	724	301	4.303933	341	P < 0.001
Bacteroides sp. 35AE37	species	uBiome	24271	10025	3.724252	219	P < 0.001
Bacteroides uniformis	species	Thryve	39266	26321	3.311011	405	P < 0.01
Bifidobacterium dentium	species	uBiome	18963	288	3.177668	33	P < 0.01
Bifidobacterium longum	species	uBiome	21851	6860	4.131341	271	P < 0.001
Bifidobacterium pseudocatenulatum	species	uBiome	34144	8671	3.752826	94	P < 0.001
Blautia glucerasea	species	BiomeSight	2184	606	2.887145	550	P < 0.01
Blautia obeum	species	BiomeSight	11262	5088	4.539525	642	P < 0.001
Chromatium weissei	species	BiomeSight	54	23	2.899706	89	P < 0.01
Corynebacterium spheniscorum	species	uBiome	12700	3296	2.737118	99	P < 0.01
Desulfohalotomaculum peckii	species	Thryve	35	16	3.191543	21	P < 0.01
Desulfomonile tiedjei	species	BiomeSight	94	34	3.874419	94	P < 0.001
Granulicatella adiacens	species	uBiome	626	100	2.637126	176	P < 0.01
Haemophilus parainfluenzae	species	uBiome	10429	3084	2.943034	310	P < 0.01
Klebsiella oxytoca	species	BiomeSight	2975	337	2.885423	113	P < 0.01
Negativicoccus succinicivorans	species	BiomeSight	1381	248	2.970997	330	P < 0.01
Pelotomaculum isophthalicicum	species	BiomeSight	259	95	2.82543	236	P < 0.01
Phocaeicola coprophilus	species	BiomeSight	18667	2437	3.75374	151	P < 0.001
Phocaeicola plebeius	species	uBiome	104385	39611	2.841081	49	P < 0.01
Porphyromonas bennonis	species	uBiome	9563	2189	2.832604	170	P < 0.01
Ruminococcus bromii	species	BiomeSight	15277	7865	2.709478	558	P < 0.01
Shuttleworthia satelles	species	Thryve	102	71	2.89508	304	P < 0.01
Slackia piriformis	species	Thryve	1676	634	2.804074	126	P < 0.01
Slackia piriformis	species	uBiome	5996	1513	4.050816	41	P < 0.001

Comments from Early Reviewers

“Interesting the connection between histamine and iron. I have some Mast Cell issues which have finally been diagnosed. I also have low ferritin [a blood protein that contains iron], although hemoglobin and even serum iron are within range…. BTW, I recall Hawrelak saying once that histamine behavior of bacteria is strain dependent, not species.”