The metabolome: Adding a new layer of precision to treatment selection
Last year, our partner Biovis introduced metabolome analysis. It measures metabolites that are produced by microbes in the gut, which means that it moves the analysis from “who is there?” to “what are they doing?”. This adds a layer of precision that can aid the personalized treatment of patients, because it can show more than is visible when looking at the abundance of microbes only. As an example, it can happen that in the microbiome analysis not a single pathogen is present in high abundance and proteobacteria are also low, while the metabolome analysis shows high levels of toxic metabolites. In such case, this gives a clue that otherwise would have been missed about over-active pathogens that are important treatment targets.
You may have seen the “metabolome” section in the traditional Biovis microbiome analysis. However, this did not measure the actual metabolites, but rather the abundance of bacteria that are capable of producing certain metabolites (e.g. TMA/TMAO or equol). The new metabolome analysis, however, measures metabolites themselves.
The metabolome analysis is available separately, or as so-called Microbolome analysis, which combines the microbiome and metabolome measurements.
Easy translation of metabolome results into treatment decisions
The Biovis metabolome analysis has already provided valuable insights for some time, and Microbiome Center has now simplified the translation of these results into targeted treatment advice. We have implemented the metabolome analysis results in our advice aid and this (1) enables automatic scoring when you import the Biovis data, and (2) integrates the metabolome results in the personalized treatment.
To link the metabolome results to ingredients, we have used the approach that we have developed and used for years, and which we recently published in Beneficial Microbes. That is, we assess all available evidence for all ingredients, and we have now expanded the effects that we scan the literature for to effects on metabolites. For example, scientific research shows that our Bifidobacterium adolescentis SH001 has the genes to produce GABA and this strain has been shown in vitro to excel in GABA production. As a second example, our E. coli MC231 can produce indole and various indole derivates, but also stimulate serotonin production.
Just like we did for complaints, medical background, and fecal analysis results, we have implemented all these effects on metabolites for each ingredient in our advice aid, and the algorithm takes this into account when generating a proposed personalized treatment. This means that the metabolome data is automatically taken into account, and therewith it has become very easy for you as practitioner to use the metabolome data in a targeted treatment of the patient.
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