Self-Experiment Protocol¶

A generic monitoring framework for n=1 self-experiments — applicable to any intervention an Open Enzyme user chooses to track on themselves: oral compounds, peptides, prescription dose changes, engineered organisms, injectable agents, dietary changes, sleep / training interventions, anything.

The protocol's job is to make sure (a) risks are surfaced before exposure, (b) data is collected prospectively rather than reconstructed afterward, © halt conditions are pre-defined, and (d) results are structured well enough to be useful to anyone — including a clinician you eventually share them with.

This page is the framework. Specific experiments (gout, EPI, autoimmune, sleep, performance) substitute their own metrics, timelines, and red-flag criteria into the structure below.

Evidence level for this page. The framework is Mechanistic Extrapolation — it applies standard clinical-monitoring principles to the n=1 setting. It does not establish efficacy of any intervention.

1. Scope¶

This protocol covers any single-variable intervention the experimenter chooses to track:

Oral supplements / nutraceuticals
Peptides (oral, intranasal, sublingual, injected, transdermal)
Prescription medications and dose changes (with prescriber's knowledge)
Engineered organisms
Diet / lifestyle changes if framed as an experiment

It is intended for personal self-experimentation. It is not a clinical trial protocol and does not establish efficacy against a control. It is a framework for collecting your own data well.

2. Designing the experiment¶

Before starting any new intervention, define and write down:

Element	Question to answer
Hypothesis	What change is expected? Why? Mechanism of action?
Intervention	Specific dose / form / timing / route
Metrics	Which markers / symptoms / outcomes change if the hypothesis is right?
Cadence	How often to retest, based on the intervention's pharmacokinetics and the marker's biology
Timeline	Total experiment window — from start to primary endpoint
Halt criteria	What outcomes mean stop
Single-variable rule	What other things you commit to not change during the window

Write these down before starting. A retrospective hypothesis is hindsight, not data. Single-variable change is what makes the result interpretable.

3. Cadence — pick by mechanism¶

Different interventions need different retest cadences. Picking the wrong window produces noise, not signal — premature retests miss the effect; late retests miss safety problems.

Mechanism class	Typical retest window
Drug at steady-state (most prescriptions, T-modulators, hormones)	4–6 weeks after dose change (to reach new equilibrium)
Vitamin / mineral correction with stored forms (D3, iron, B12)	8–12 weeks
Antibody-mediated effect (autoimmune-targeting interventions)	3–6 months
Microbiota-mediated (probiotics, dietary fibre interventions)	6–8 weeks (community shifts stabilize)
Acute / pharmacodynamic (caffeine, single-dose peptides)	hours to days
Tissue-level adaptation (training, structured exercise)	weeks to months depending on outcome
Cancer-prevention / risk-modifying (long-latency outcomes)	annual or longer; surrogate markers monthly

Match the half-life of the intervention and the kinetics of the affected marker. Don't average them — pick whichever is longer.

4. Metrics — pick by what the intervention affects¶

Standard elements common to most experiments:

Primary efficacy marker — the thing the hypothesis says should change
Safety markers — organ-function and systemic markers relevant to the intervention's known risk profile
Symptom diary — daily entries with domain-relevant fields, designed to take <60 seconds/day to enter
Compliance / adherence log — did you take the intervention as planned, and when?

Common safety baseline for blood-panel-based experiments: - CBC with differential — catches infection, anemia, WBC shifts (especially relevant for any immunomodulator) - CMP — liver enzymes, kidney function, electrolytes (catches the most common organ-toxicity signals) - hs-CRP — systemic inflammation; useful even when not the primary endpoint

Beyond that, the panel is intervention-specific: - T-axis intervention → Total T, Free T, Estradiol, SHBG, Hematocrit, lipids - Thyroid intervention → TSH, Free T4, Free T3, antibodies if autoimmune - Microbiota intervention → stool sequencing (16S minimum) baseline + endpoint - Glucose/metabolic intervention → fasting glucose, fasting insulin, A1C, lipids - Specialty markers (complement, leukotrienes, etc.) → as the mechanism dictates

For symptom-only experiments without labs, structured daily tracking + a clear primary outcome is enough.

Chokepoint-biomarker map¶

For NLRP3-targeted experiments specifically (the gout / autoimmune-flare track), a four-biomarker specialty panel — serum C5a, urinary LTE4, plasma SPMs (RvD1, MaR1), hs-CRP — maps onto distinct chokepoints in the NLRP3 exploit map v1.2. The point of this map is operational, not descriptive: the panel converts a binary "inflammation present / absent" readout into "which mechanism is currently active," and the decision rules below convert that into "which compound to add next if the stack underperforms." Evidence level is Mechanistic Extrapolation grounded in In Vitro and Animal Model evidence from the cited pages — not Clinical Trial. Specific cutoffs vary by lab; defer numeric thresholds to the ordering physician and the lab's reference range. The contingent NET panel (rule 5 below) is the only entry on this map that costs additional money per flare — keep it contingent.

Biomarker	Reads out chokepoint	What "elevated" means	What "normal/low" means	Suggested next action if at variance with stack expectation
Serum C5a (+ desArg)	CP0 — complement priming (MSU → C1/CRP → C5 convertase → C5a)	Active complement-driven priming; the upstream non-transcriptional "Signal 1" the stack does not currently cover (see complement-c5a-gout.md §11)	Complement priming is not the rate-limiter for this flare phenotype	Persistently elevated C5a + clinical flares despite stack → discuss avacopan (FDA-approved C5aR1 antagonist) with prescriber. Pre-analytics matter: cold-chain EDTA, spun within 30 min, –80 °C — warm transit generates spurious C5a in vitro. Optional flare-trajectory addition (added 2026-05-19, source: spm-resolution-pathway.md §7.3 falsifiable prediction): measure C5a at flare onset (within 24 h) AND at flare resolution (7–14 days post-onset). Compute decline slope = (onset − resolution) / days. Pair with concurrent blood omega-3 index (EPA + DHA % of total RBC fatty acids; OmegaQuant or equivalent). Per the SPM → aggNET → C5a-degradation loop, decline slope should be steeper in DHA-loaded subjects (omega-3 index ≥8%) than DHA-deficient subjects (omega-3 index <4%) — falsifiable prediction that converts SPM/omega-3 supplementation from generic anti-inflammatory framing into a CP0-coverage hypothesis testable on existing flare events. Adds ~$100–200/flare in lab cost (two extra C5a draws); doesn't change the intervention protocol.
Urinary LTE4	CP6a — 5-LOX / LTB4 neutrophil amplification (on-target PD readout for 5-LOX engagement)	Active leukotriene amplification arm; neutrophil chemotaxis loop is open (see zileuton.md)	5-LOX flux is low — quercetin / AKBA / EPA substrate competition is engaging target, or this patient's gout is not CP6a-dominant	LTE4 fails to drop on quercetin/AKBA/zileuton → "non-absorber, not non-responder" — PK / bioavailability problem, not target-engagement problem; check formulation (Phytosome quercetin, Boswellin AKBA) before escalating dose. Clean LTE4 drop + unchanged flare frequency → CP6a is not rate-limiting; redirect attention to CP0 or CP2
Plasma SPMs (RvD1, MaR1) by LC-MS/MS	CP5b — active resolution via ALX/FPR2 (see spm-resolution-pathway.md §12)	Resolution program is engaged; omega-3 substrate is being converted to bioactive D-resolvins / maresins	Resolution-incompetent — substrate-limited (low omega-3 index) or pathway-limited (15-LOX / aspirin-acetylated COX-2 not generating intermediates)	Low SPMs + post-flare hs-CRP not returning to baseline → CP5b deficit; reframe omega-3 toward DHA emphasis per supplements-stack.md and check omega-3 index (target >8%); consider RvE1/LTE4 ratio (CP6a↔CP5b axis, spm-resolution-pathway.md §12) rather than absolute SPM number
hs-CRP	Systemic inflammation endpoint — no specific chokepoint; the integrated downstream readout	Active systemic inflammation; non-specific to mechanism. Also an upstream stratifier for CP0 (CRP is the dominant classical-pathway initiator on MSU surfaces, complement-c5a-gout.md §11.3)	Systemic inflammation suppressed regardless of which chokepoint is doing the work	hs-CRP elevated + all three mechanism-specific markers normal → look outside the NLRP3 axis (occult infection, metabolic, training load); CRP doubling from baseline is already a §6 universal halt criterion

Red-flag decision rules. Each rule is a pattern across the four-biomarker panel that points to a specific next compound to discuss with the prescribing physician. None of these are clinical thresholds — they are interpretive heuristics for an n=1 PD readout.

C5a elevated + LTE4 normal + hs-CRP elevated → CP0 is the rate-limiting bottleneck; the stack's downstream (CP1/CP2/CP6a) coverage is fine but priming is unchecked → consider avacopan discussion with the prescribing physician.
LTE4 fails to drop despite quercetin / AKBA / zileuton at adequate dose → "non-absorber, not non-responder" — PK / bioavailability problem, not target-engagement problem; check formulation and absorption (Phytosome quercetin, fed-state dosing) before concluding the drug doesn't work in this patient.
Plasma SPMs low + hs-CRP elevated post-flare (>2 weeks) → CP5b resolution-incompetent; reframe omega-3 toward DHA emphasis per supplements-stack.md and verify omega-3 index >8% before adding pharma at CP5a (anakinra / canakinumab / rilonacept).
C5a normal + LTE4 normal + SPMs normal + hs-CRP elevated → the inflammation signal is not coming from the NLRP3 axis this panel covers; investigate non-NLRP3 sources (occult infection, training load, metabolic) before adjusting the stack.
If a flare occurs AND the standard panel returns ambiguous on the resolution axis (specifically: plasma SPMs borderline + urinary LTE4 borderline + hs-CRP elevated, with no clear bottleneck pattern from the four rules above) → add a citH3 + cfDNA + MPO-DNA complex panel at the next post-flare draw (~$200–400 specialty lab). Reads out the free-vs-aggregated NET ratio — distinguishes a resolution-competent flare (cytokines being sequestered into aggNETs) from a resolution-stuck flare (free NETs amplifying inflammation), per the aggNET vs. free-NET framing in nlrp3-exploit-map.md v1.2 (Schauer 2014, PMID 24784231; In Vitro + Animal Model for the framing, Mechanistic Extrapolation for the resolution-competence interpretation) and the upstream SPM driver of aggNET formation in spm-resolution-pathway.md §5. Don't run routinely; the existing SPM panel covers the resolution axis indirectly under most circumstances — citH3/cfDNA is the consequence readout where SPMs are the signal, and the marginal information rarely justifies the cost. (V4-Pro 2026-04-25 peer-review flagged routine NET panels as "impractical for n=1"; this contingent rule keeps the option available without burning $200–400/flare on routine cases.)

Cross-references: nlrp3-exploit-map.md (canonical chokepoint definitions, v1.2; aggNET vs. free-NET framing), complement-c5a-gout.md (CP0 biomarker handling and avacopan), spm-resolution-pathway.md (CP5b SPM panel methodology, RvE1/LTE4 ratio, §5 SPM-driven NET resolution), zileuton.md (urinary LTE4 as 5-LOX PD readout).

5. Symptom diary¶

Daily, low-friction entries. Pick fields relevant to what you're testing. Common scales:

Likert (0–10) for severity/intensity (pain, fatigue, energy, cognitive clarity, sleep quality)
Bristol Stool Scale (1–7) for GI consistency
Likert (0–3) for binary-with-gradient signals (bloating: none / mild / moderate / severe)
Free-text "novel signal" field for anything not covered (always include this)
Timestamp + one row per day — even a "nothing today" entry documents adherence

Storage: spreadsheet, plain text file, journaling app, daily log markdown — whatever you'll actually use consistently. The best diary is the one you'll fill out. Export to a structured format at endpoint for analysis.

6. Red-flag halt criteria¶

These are universal halt criteria — apply regardless of what intervention is being tested:

New GI bleeding (blood in stool, melena, hematemesis) — halt + seek care same-day
Acute liver injury (ALT or AST >2× upper limit of normal on a draw) — halt + re-draw at 2 weeks
Kidney function decline (eGFR drop >15% from baseline, or creatinine rise) — halt + evaluate
New allergic / hypersensitivity signal (rash, urticaria, angioedema, anaphylaxis) — halt immediately; seek care if airway involved
Unexplained weight loss >5 lb over 4 weeks — halt + evaluate
New fever without identified infection — halt + seek care
CRP doubling from baseline on a scheduled draw — halt + re-draw at 1 week to confirm
Persistent diarrhea >72 hours — halt + evaluate for dysbiosis or infection
Any new severe symptom not present at baseline — halt + evaluate

In addition to universal criteria, define experiment-specific halt criteria at design time. Examples: - NLRP3-modulating interventions → unmasked infections (canakinumab side-effect class) - T-modulating interventions → hematocrit >50%, mood / aggression, lipid degradation - Anticoagulant changes → bruising, bleeding gums, blood pressure - Microbiota interventions → dysbiotic shift on 16S - Thyroid interventions → palpitations, anxiety, sleep disruption (T3 over-replacement signature)

"Halt" means stop the investigational intervention. It does not mean stop medically-prescribed therapy unless the trigger specifically implicates that therapy. When in doubt, contact your prescriber.

7. Logging and version control¶

Principle: personal n=1 data does not live in the public Open Enzyme repository.

Lab results, daily logs, self-experiment plans, and stack tracking are PHI-bearing. Keep them in a separate private location of your choice. Common patterns:

Pattern	When it fits
Private GitHub repository	Multi-machine sync, collaborator access, version history, off-site backup
Local folder, not in git	Simplest; single-user; manual backup
Encrypted volume + cloud backup	Higher privacy posture

The public Open Enzyme repo's .gitignore should exclude whatever directory your private storage lives in if you nest it inside the working tree. The choice of folder name, internal layout, and tooling is yours.

Consent on partner / family data: if your storage includes someone else's data (e.g., a spouse's labs), that data is theirs. Document their agreement before adding anything — a dated note in the relevant location is the lowest-friction way.

Summary log (public, committed): a stripped, de-identified summary can be added to logs/self-experiment-log.md in the public repo (append-only). Fields: date, intervention, observation, any protocol deviation. Safe to commit because it contains no raw PHI. Never reference specific lab values; use qualitative framing ("CRP trending up" rather than "CRP = 4.2"). Numeric trending stays in your private storage.

When a stack compound is added or removed: note it with a dated rationale in your private location. The public-log version (if any) can be a stripped one-liner.

8. What this protocol does NOT do¶

Establish efficacy against a control. n=1 is uncontrolled, unblinded. Efficacy signals are suggestive, not generalizable. They can motivate a future controlled trial; they cannot replace one.
Replace medical supervision. Your primary care provider and any specialists should know what you're doing. Halt criteria are escalation-to-care triggers, not self-directed-recovery instructions.
Cover regulatory territory. This is personal self-experimentation, not a clinical trial. Do not distribute engineered strains, compounded products, or off-label prescriptions to others under this protocol.

9. Review and update¶

Review this document and your active experiment designs: (a) before starting a new intervention arm, (b) after any halt, © annually, (d) whenever your understanding of the relevant biology changes. Changes go through git, not inline edits.

10. Example: PERT-Timing Sub-Experiment (EPI Track, April 2026)¶

An example of this protocol applied to the EPI track. A structured self-experiment on BoulderBio (wild-type A. oryzae OTC, 40,000 FIP lipase per capsule) dose and timing was run across ~30 meals (2026-04-19 → present). Key design elements:

Hypothesis: Label-default 1-cap dosing is insufficient for meals >15 g fat; 2-cap or split-dose protocol will improve symptom outcomes.
Intervention variants: A (1 cap at first bite), B (2 caps at first bite), C (1+1 split), D (pre-emptive during cooking).
Metrics: Post-meal stool consistency (Bristol Scale), pain (0–10 Likert), floaters/steatorrhea (binary), fat content per meal (estimated g).
Single-variable rule: Enzyme dose/timing varied; diet, other supplements held constant within each variant window.
Confound flagged: Lying flat <90 min post-meal identified as a strong contributor to overnight episodes — must be controlled separately from enzyme-dose effects.

Interim findings (n=1, unblinded, uncontrolled; source: digestive-enzyme-optimization.md): - Variant B (2 caps at first bite) decoupled liquid-stool from pain on 2026-04-25 breakfast (~15–20 g fat) — a clear shift from a long-stable baseline. - Variant C (1+1 split) successful for >25 g fat meals. - No adverse reactions across 30+ meals; no allergic response.

Evidence level: Clinical n=1, single subject, unblinded, uncontrolled. Suggestive only. Generates hypotheses for formal testing; does not establish efficacy. Paired stool-fat (steatocrit) measurement before and after a controlled trial would be the next-rigor step.

Full daily log lives in the experimenter's private storage (e.g., <your-private-repo>/<subject>/experiments/<date>_<topic>.md). Only de-identified pattern findings are reproduced here, per the PHI policy in §7 above.

11. Optional ex vivo monitoring add-ons¶

Lower-cost subject-specific assays that supplement the standard four-biomarker panel for specific intervention contexts. Each is opt-in, runs as a quarterly add-on to the standard blood draw, and stays in the subject's private storage per §7.

11.0 Selenium + yanthine — PDB function screen (added 2026-05-15)¶

When relevant: Any draw while hyperuricemia is active or gout risk is being managed. One-time triage, not ongoing monitoring.

Rationale: The PDB (purine-degrading bacteria) gut pathway enzyme DOPDH is selenium-dependent and runs 27x faster with selenium than without. Selenium deficiency could functionally knock out PDB activity without any change in bacterial abundance — the bacteria are present, they just can't work. Yanthine (2,8-dioxopurine) is the first PDB pathway intermediate; elevated serum yanthine indicates PDB are depleted or dysfunctional (can't process it downstream). Together these two markers answer whether Brian's gut-compartment urate disposal is operating normally or is constrained by a potentially trivial dietary factor. See purine-degrading-bacteria.md §"Cofactor Requirements".

What to order: - Serum selenium — standard clinical test, available at any reference lab (Quest/LabCorp), ~$40–80. Normal range 70–150 ng/mL; optimal for DOPDH function is likely mid-to-upper-normal. If low-normal (<90 ng/mL), supplementation at 100–200 µg/day (selenomethionine) is safe and cheap. - Yanthine (2,8-dioxopurine) — not on standard panels. Triage step: check whether Metabolon Precision Metabolomics, Genova NutrEval, Great Plains Organic Acids, or a clinical pharmacology lab measures it. If a metabolomics panel (Metabolon, ~$300–600) already on the radar covers it, add at no incremental cost. If not available without a research lab arrangement, defer — serum selenium alone is the practical first step.

Cost: Selenium ~$40–80 (clinical). Yanthine: $0 if a planned metabolomics panel includes it; $300–600 for a standalone metabolomics run; defer if unavailable commercially.

Cross-references: purine-degrading-bacteria.md §"Open Questions" PDB-Q3 and PDB-Q4.

11.0a Cranberry juice n=1 — direct hippuric-acid → ABCG2 axis test (added 2026-05-15)¶

When relevant: Same draw context as §11.0 — any draw while hyperuricemia is active or gout risk is being managed. Particularly informative when run in the same window as a §11.0 selenium / yanthine draw (the two protocols test parallel mechanisms hitting the same downstream node, ABCG2).

Rationale: Alistipes indistinctus produces hippuric acid via aromatic amino acid catabolism; hippuric acid enhances PPARγ binding to the ABCG2 promoter and promotes ABCG2 localization to the apical brush border via PDZK1 (Xu et al. 2024 Cell Host & Microbe, PMID 38412863). The standard dietary route is polyphenol-rich foods → gut catabolism → hippuric acid — slow, indirect, microbiome-dependent. Cranberries bypass the bacterial step: cranberries contain unusually high natural benzoic acid; benzoic acid is conjugated with glycine in the liver to produce hippuric acid directly. This is the established mechanism behind the cranberry-UTI lore (cranberry consumption → elevated urinary hippuric acid). For an n=1 test of whether the hippuric-acid → ABCG2 mechanism moves serum urate at dietary doses, cranberry juice is the cleanest probe — independent of needing to colonize A. indistinctus. See abcg2-modulators.md Alistipes Tier 2 entry.

Protocol: - 4 weeks unsweetened cranberry juice, ~8 oz/day with breakfast (or split across day if GI tolerance is an issue). - Cost: ~$20 for the protocol. - No other intervention changes during the 4 weeks (hold supplements stack constant; hold allopurinol dose constant). - Standard 4-biomarker panel (serum urate + hs-CRP + fasting insulin + ApoB) at week 0 baseline + week 4. - Optional add: urinary hippuric acid (specialty test, ~$40 at some reference labs) at week 0 + week 4 — confirms the cranberry → hippuric-acid mechanism is engaging in this individual.

Expected effects: - Serum urate down 0.2–0.5 mg/dL if the hippuric-acid → ABCG2 axis is meaningful at dietary doses (Mechanistic Extrapolation; the Animal Model + Human Observational data in Xu et al. 2024 doesn't quantify the magnitude at dietary cranberry doses). - Urinary hippuric acid up substantially at week 4 vs. baseline — confirms the mechanism is engaging. - No change in hs-CRP / insulin / ApoB — cranberry-juice carb load is small (unsweetened); no expected metabolic-syndrome effects at this dose.

Why this is informative regardless of outcome: - If serum urate moves AND urinary hippuric is up → mechanism replicates in this physiology, supports the broader chassis-pending PDB / A. indistinctus axis. - If serum urate doesn't move AND urinary hippuric is up → mechanism engages but doesn't translate to ABCG2 / SUA effect at dietary dose; the A. indistinctus / hippuric axis may need higher concentrations than dietary route delivers, or may not be rate-limiting in this patient. - If urinary hippuric doesn't move → cranberry isn't getting absorbed / conjugated as expected; the experiment didn't actually test the mechanism. Interpretation problem, not a mechanism falsification.

Cross-references: abcg2-modulators.md §"Tier 2 — Alistipes / Hippuric acid"; chassis-pending-interventions.md §1 (PDB entry, "Cheapest first move" includes this).

11.1 Ex vivo MSU PBMC challenge (androgen-elevated subjects, Tier 4 of `validation-experiments.md` §1.23)¶

When relevant: Subject is on clomid, TRT, anabolic-androgenic steroids, or has high baseline endogenous testosterone, AND has gout / hyperuricemia history. The 2026-05-05 androgen × NLRP3 literature scan (androgen-urate-axis.md §"Beyond transporters") identified a gap: testosterone × MSU-crystal × NLRP3 in macrophages has zero indexed papers. The general-tissue literature suggests androgens are anti-inflammatory in macrophages (Norata 2006 in vitro), but cardiac macrophages flip the direction (testosterone → ↑NLRP3 → male-skewed myocarditis). Whether gout-relevant macrophages follow the general-tissue pattern or the cardiac-tissue pattern is an open question. This add-on provides a low-cost personal signal in the absence of formal Tier 1–3 wet-lab data.

What it measures: Whether subject's PBMCs (peripheral blood mononuclear cells) produce more or less IL-1β in response to MSU crystal challenge ex vivo, tracked across panels as serum testosterone fluctuates (e.g., mid-cycle vs. trough on clomid; pre/post a TRT dose adjustment).

Protocol: - Sample: Add 10 mL EDTA tube to the standard quarterly draw. Send to a clinical lab offering ex vivo cytokine release assays (these exist; vendors include Cellular Technology Limited and several specialty hospital labs). - Lab protocol (request from vendor): Isolate PBMCs by Ficoll gradient → seed at 1×10⁶/well → 6-hr challenge with MSU crystals (100 μg/mL) ± LPS pre-priming (100 ng/mL × 3 hr, optional second arm) → measure IL-1β in supernatant by ELISA. Negative control: vehicle only. Positive control: nigericin 5 μM (NLRP3 activator). - Cost: $500–1,000 per panel (vendor-dependent). Add-on to standard quarterly draw — no extra venipuncture. - Tracking: IL-1β (pg/mL) per panel, alongside serum total + free testosterone, hs-CRP, and the standard four-biomarker panel from §4. Plot quarterly trajectory.

What you can and can't conclude: - Can: Detect within-subject directional signal — does your MSU-induced IL-1β response track your serum T fluctuations? Useful for personal protocol decisions (e.g., does a heavier anti-inflammatory layer correlate with lower MSU-IL-1β release in your own cells?). - Cannot: Prove causality (n=1, uncontrolled). Cannot generalize to other androgen-elevated subjects. Cannot substitute for the formal validation-experiments.md §1.23 Tier 1–3 cascade — that's where mechanistic claims get established.

Evidence level: Clinical n=1, single subject, unblinded, uncontrolled. Suggestive only for personal protocol decisions. Generates hypotheses; does not establish efficacy.

Cross-references: validation-experiments.md §1.23 (the formal Tier 1–3 cascade this is the n=1 parallel of); androgen-urate-axis.md §"Beyond transporters" (the literature gap being probed); nlrp3-inflammasome.md (NLRP3 activation mechanism background).

12. Genotype-informed supplement quantification workflow¶

Promoted to standalone page 2026-05-16. The five-step workflow composing genotype-informed selection + home/community-biolab batch QC + biomarker tracking now lives at genotype-informed-supplement-workflow.md. That page is the user-facing canonical surface for the closed-loop n=1 pharmacogenomics pipeline.

This protocol page focuses on the per-subject self-experiment mechanics (§1–§11 above) — daily log, biomarker tracking, intervention scheduling. The genotype-informed-workflow page focuses on the cross-component pipeline that those mechanics participate in.

When walking a new intervention end-to-end, follow genotype-informed-supplement-workflow.md for the workflow shape; come back here for the biomarker-tracking detail.