Data from the National Institute of Diabetes and Digestive and Kidney Diseases shows that between 60 and 70 million people in the United States struggle with digestive health issues, pushing the gut microbiome testing market valuation projection to $3.5 billion by 2034, according to Emergence Research.
While this market niche has typically relied on stool-based microbiome testing, Zinzino is instead leaning into blood-based gut function assessments by measuring tryptophan-derived metabolites.
Zinzino’s R&D specialist, Dr. Martina Torrissen, shared her insights into why the company has moved towards this method, the process for scientific validation and opportunities for this approach to support more targeted or personalized nutrition regimens.
NIU: What scientific and analytical steps were taken to validate that blood-based metabolomics accurately reflects gut microbiome activity compared to traditional stool tests?
Martina Torrissen (MT): The first step was an exhaustive review of the literature on tryptophan metabolism and gut microbiome-host crosstalk. Three themes emerged very clearly:
• Indole-3-propionic acid (IPA): IPA is produced exclusively by gut bacteria from dietary tryptophan, via the indole pathway. It does not come from human cells.
Higher circulating IPA has been linked to higher dietary fiber intake, greater microbial diversity, healthier microbiome profiles and better metabolic outcomes in several human cohorts. Mechanistically, IPA has been demonstrated to act as a potent antioxidant and interacts with receptors such as PXR and AhR, supporting tight junction expression, gut barrier integrity and dampening of excessive inflammatory signaling.
Low IPA has been associated with metabolic stress states and increased risk of conditions like type 2 diabetes and fatty liver in prospective and cross-sectional studies. Together, this makes IPA a very promising “functional readout” of beneficial microbiome activity that is clearly tied to diet and gut health, but also relevant for systemic metabolic and immune resilience.
• Kynurenine (KYN) and the KYN:TRP ratio: KYN is produced via host enzymes (IDO/TDO) when the immune system and stress pathways are activated. The KYN:TRP ratio is widely used as a biomarker of immune activation and inflammatory stress, reflecting how much tryptophan is being divided into this pathway.
Higher KYN:TRP is seen in several conditions, such as in chronic low-grade inflammation, infection, obesity and cardiometabolic risk states.
• Tryptophan (TRP): TRP is the shared substrate that both microbes and host cells compete for. Its level, and how it is partitioned between the different pathways, integrates information about dietary protein/fiber, microbiome capacity and immune tone.
Based on this, we selected TRP, KYN and IPA because they are mechanistically grounded in microbiome-host biochemistry, directly linked in the literature to dietary fiber intake, microbiome composition and diversity, immune activation and metabolic health, and they are modifiable by diet and lifestyle, which is crucial for a test that aims to guide interventions.
Next, we asked a simple question: Do these markers behave in our data the way the literature predicts? Using our large internal database of dried blood spot samples, dietary information and omega-3 status, we see the same patterns reported in stool-blood multi-omics studies and nutrition cohorts.
Individuals with higher fiber intake and more balanced omega-6:omega-3 ratios tend to have higher IPA and higher IPA-based ratios and lower KYN:TRP. Those with low dietary fiber intake and more unbalanced omega-6:omega-3 ratios more often show lower IPA and higher KYN:TRP.
In other words, the biology lines up: When the diet and microbiome environment looks healthier, IPA is higher and KYN:TRP is lower; when the environment is more pro-inflammatory and fiber-poor, IPA drops and KYN rises. That is exactly what you would expect from mechanistic work.
Traditional stool tests are very good at telling you which microbes and genes are present in the gut lumen. But presence doesn’t necessarily mean those pathways are metabolically active or that their products are reaching the rest of the body.
That distinction is very clear in experimental work such as the study by Sinha and colleagues, where changing substrate availability led to pronounced shifts in indole derivatives, including IPA, while high-level community composition changed relatively little. In a traditional stool profile, the microbiome might look broadly similar before and after, whereas the metabolite pattern, and therefore the signal that actually reaches the host, is very different.
By measuring tryptophan-derived metabolites like IPA and KYN in blood, we capture the output of those microbial and immune pathways—the signals and messengers that leave the gut, circulate, bind to receptors and ultimately deliver the benefit or harm to the host. In other words, stool composition is more about “who is there?” while blood metabolites are about “what they are doing and what signal actually reaches your tissues.”
Once we were confident in the biological rationale, we validated the analytical method to ensure that this could be done accurately from a dried blood spot sample. Vitas AS developed a dedicated high-performance liquid chromatography method to quantify TRP, KYN and IPA directly from DBS.
The method has been validated according to ICH Q2(R2) guidelines for linearity, accuracy, precision, limits of detection and robustness, with internal standards and quality controls included in each analytical run. The assay is fully implemented in the Vitas quality system and is being prepared for ISO 17025 accreditation.
So, in summary: We first validated the biology, then validated the analytics. Together, that’s how a simple drop of blood becomes a reliable window into gut microbiome function and its impact on the host.
NIU: How do you ensure consistency and reproducibility in measuring markers like IPA, KYN and TRP from dried blood spot samples?
MT: Consistency and reproducibility come from the analytical method itself. All samples are processed at Vitas using the same validated LC-FLD/UV HPLC method, with calibration curves, internal standards and multi-level quality control samples included in every run.
The assay has been validated according to ICH Q2(R2) guidelines, showing high accuracy, excellent linearity across the physiological range and low coefficients of variation.
In parallel, we standardize how samples are taken by giving instructions to the users to collect it in the fasted morning state after at least 10 hours with water only, using a simple finger-prick. We cannot control perfectly whether every individual follows those instructions, however, they are there so that repeat tests are taken under comparable conditions.
That way, when someone later looks at how their IPA or KYN:TRP changes over time, they are more likely to be seeing the effect of diet and lifestyle rather than short-term fluctuations.
NIU: How quickly do these metabolomic markers respond to dietary or lifestyle changes, and what does that mean for interpreting results over time?
MT: These markers are responsive on different time scales, and it’s important to distinguish short-term, transient shifts from the more stable baseline signal we capture with a fasted morning test.
From controlled human feeding studies, we know that the tryptophan-microbiome axis can respond within days when diet is changed in a clear, structured way. At the same time, we deliberately measure these markers in a fasted morning state to approximate a more stable baseline rather than a post-meal snapshot.
IPA and IPA-based ratios tend to reflect both diet and the presence and activity of specific microbial pathways. In most diet and microbiome interventions, the more stable, directional changes in IPA emerge over weeks to a few months, not day by day.
For interpretation, this means that one fasted test gives you a baseline map of how tryptophan is currently being partitioned between microbial indoles and immune-linked kynurenine, shaped by the past several weeks rather than just your most recent meal.
To understand whether diet or lifestyle changes are really working, it is most informative to look at how results move over time in relation to that baseline.
Practically, we recommend re-testing about every three to four months under similar conditions. That pattern-based, trajectory-focused view is what makes these markers useful for monitoring sustained change.
NIU: What factors most influence variability in tryptophan metabolism between individuals, and how do you account for this in personalized recommendations?
MT: Tryptophan metabolism is shaped by several layers of biology: what people eat, how their microbiome is configured and how much immune and metabolic stress their body is under. Higher BMI, features of metabolic syndrome, gut-related conditions and long-term low-fiber diets are all associated with lower IPA and less favorable tryptophan profiles.
In practical terms, the biggest drivers are dietary pattern, microbial capacity to produce indole metabolites like IPA and immune activation that pulls tryptophan into the kynurenine pathway. There also seems to be a threshold effect for fiber, where intakes closer to 30 grams per day are more clearly associated with higher IPA levels.
We’re careful not to pretend that a single blood test can see everything. We don’t directly measure microbiome composition, medications, BMI or diagnoses, and we do not use disease-based cut-offs, so our recommendations are pattern-based and deliberately modest.
For a preventive, optimization-focused tool, we think it is more honest to show people how their biology looks today and how it can change over time rather than redefining less favorable profiles as “normal.”
The test translates each person’s IPA, KYN and TRP into a profile and provides tiered guidance. In that sense, the test is also a behavioral tool: It translates complex gut-immune chemistry into a simple visual summary and allows people to track whether their efforts are moving things in the right direction over time.
NIU: From your experience validating omega-3 biomarkers, what key lessons have guided your approach to developing and interpreting microbiome-related metabolomic tests?
MT: Designing and interpreting omega-3 studies taught me that you can’t stop at “this molecule is interesting.” You have to build a bridge from mechanistic plausibility to actual nutritional exposure, tissue incorporation and function.
It also taught me the value of robust, standardized methods that work with home sampling, clear instructions to minimize pre-analytical noise and the importance of looking at how markers move over time with intervention, not just at a single static value.
Analyzing large global datasets of home-collected dried blood spots showed how well-chosen biomarkers can reflect diet and supplementation in real life and how clear target zones can be established and used meaningfully in practice. The same logistical model now underpins our microbiome-related metabolomics work.
The key lessons I’ve brought from omega-3 into this field are therefore: Start from molecules with deep biological grounding, translate them into nutritionally modifiable markers, build assays robust enough for repeated home sampling, and interpret results as patterns and trajectories within a complex system, not as simple yes/no judgements.
NIU: How can supplement manufacturers and ingredient suppliers leverage insights from this test?
MT: For manufacturers, this test is most useful as a directional R&D tool rather than a pass/fail certificate for single ingredients. In well-designed pilots, it allows you to see whether a formulation is nudging the gut–immune biology in the desired direction or not.
Where it becomes especially powerful is in revealing heterogeneity of response. Looking at patterns in relation to baseline diet quality, fiber intake or inflammatory tone helps manufacturers ask who a formulation really serves best and how to refine it.
On the personalized nutrition side, the same information can be used to build profile-based programs instead of one-size-fits-all bundles. By re-testing under similar conditions after several months, brands and practitioners can see whether a given regimen is actually shifting that individual’s profile in a more favorable direction and adjust the strategy accordingly.
