A research pipeline that turns raw organic-acid panels into evidence-linked, prioritised nutritional hypotheses, as decision support for clinicians, never a diagnosis or a prescription.
Organic-acid (OAT) profiles are widely used to probe mitochondrial, oxidative, gut-microbial and detoxification pathways. Yet their interpretation is still largely manual and unstandardised, and clinicians lack objective tools to turn a raw panel into evidence-linked, testable nutritional hypotheses.
MetaPlantAI is a Python/FastAPI research pipeline that reads a metabolic panel, maps altered metabolites to the biological pathways they imply, and matches those inferred needs to plant secondary metabolites, ranked by their measured concentration in specific plant parts and cross-checked against the scientific literature (PubMed). Every output carries severity grading, safety flags and a mandatory clinician-review disclaimer.
Reads organic-acid (OAT) panels from a reference laboratory, with multi-laboratory support in progress.
Links altered metabolites to the metabolic pathways and biological activities they imply, with severity grading.
Matches inferred needs to plant metabolites, concentration-ranked and cross-referenced against PubMed evidence.
Outputs explicitly separate well-supported associations from low-confidence ones, flag weak or extrapolated links, and are drafted as clinician-facing interpretive summaries, decision support, not prescription.
A substantial share of OAT markers are microbial in origin, and several are the gut-bacterial transformation of dietary plant secondary metabolites (for example, hippuric acid from polyphenols). Polyphenols, in turn, reshape the microbiota. MetaPlantAI exploits this axis in both directions:
The metabolic signature reveals the state of the microbiota, a snapshot of an otherwise hidden ecosystem.
Concentration-ranked plant metabolites become candidate levers to modulate that state, with a measurable readout on the same panel.
To our knowledge, no other tool bridges the human and plant secondary metabolomes quantitatively, via the microbiome, in this way. Single microbial markers are always used within patterns, never in isolation.
A core research aim is to integrate the individual's genotype (nutrigenetic and pharmacogenetic polymorphisms, from a companion genetic-report pipeline) with the metabolomic phenotype, so that nutritional hypotheses account for personal variants. Examples include MTHFR/MTRR for folate and B12 markers, COMT for catecholamine markers, DAO/HNMT for histamine markers, and detoxification genes (GST, SOD2, NQO1) for oxidative markers.
This genotype-informed calibration is evidence-graded (documented / high-plausibility / hypothesized / speculative) and clinician-validated, never auto-prescriptive.
In an initial set of seven anonymised OAT panels (adults and children), one marker was elevated in every single panel: indican (7/7), a marker of gut-microbiome protein putrefaction. Other recurrences appeared in tryptophan and serotonin turnover, B-vitamin cofactors and mitochondrial energy metabolism.
The sample is small and uncontrolled, so these recurrences are strictly hypothesis-generating, but the fully penetrant indican signal is notable, and it points to a single testable question linking the project's microbial and immune axes. This is a hypothesis to be tested, not a conclusion to be assumed.
The current phase is methodological and computational: pattern discovery on anonymised, existing panels plus evidence synthesis. It involves no intervention on patients, and none on minors.
All outputs are decision support for licensed clinicians. The tool does not diagnose, prescribe, or replace medical care, and states this in every report. Recommendations are confined to nutrition and micronutrients within established safety margins, and remain subordinate to conventional care. Any future prospective phase would run under confirmed clinical supervision. All patient data are anonymised.
Biostatistical support is being sought as the cohort scales. All patient data referenced are anonymised (Panel 1…N); no identifying information is used.
MetaPlantAI is at an early, self-funded stage, submitted to Anthropic's AI for Science program (currently under evaluation). If you are a clinician, laboratory or researcher interested in the method, we would be glad to talk.