A blood biomarker panel is a structured set of laboratory values used to evaluate metabolic, inflammatory, nutritional, and cardiovascular status. Common panels may include ApoB, hsCRP, fasting glucose, HbA1c, fasting insulin, ferritin, vitamin D 25-OH, lipid panel, CBC, and a CMP.
Why it matters
Wearables cannot see cholesterol, inflammation, iron storage, or nutrient status. Lab results can. For stacks aimed at cardiovascular risk, inflammation, metabolic health, or nutrient status, a blood panel may carry more direct signal than a wearable proxy.
What a panel may include
Blood panels vary by clinician, lab, health history, and goal. Common categories discussed in supplementation-aware reviews include:
| Goal | Tests |
|---|---|
| Cardiovascular | ApoB, Lp(a), lipid panel, hsCRP |
| Metabolic | fasting glucose, fasting insulin, HbA1c, uric acid |
| Nutrient status | vitamin D 25-OH, ferritin, B12, RBC magnesium, homocysteine |
| Thyroid | TSH, free T4; TPO antibodies for suspected autoimmune thyroid disease |
| Hormonal | total + free testosterone, estradiol, SHBG, DHEA-S, cortisol AM |
| Inflammation | hsCRP, ESR, ferritin (doubles as inflammation marker) |
Not every user needs every row, and glossary entries should not decide which tests are appropriate. The useful content layer is the vocabulary for reading a clinician-ordered panel alongside a stack log.
How to interpret a delta
Panel interpretation depends on test method, lab reference range, timing, and the user's broader clinical context. Comparing two values is cleaner when the surrounding conditions are similar, but those conditions are part of clinical interpretation rather than a glossary instruction.
Effect sizes are often smaller than users expect. A mechanistically plausible compound with observational-study support can still deliver only a 5–10% biomarker shift across several months; whether that matters for chronic-disease risk depends on the biomarker, baseline risk, and clinical context. This is where understanding effect size matters.
Pairing with stack logs
Unfair stores the draw date alongside stack-activation dates so panel deltas are interpreted against what was actually in the stack during that window, not against whatever the user thinks they were taking. This is the cleanest way to check whether a mechanism of action translated into a measurable outcome, and it feeds the recommendation ranking when the user chooses to weight panel outcomes.
How this appears in Unfair
Blood biomarker values are stored as long-horizon objective proxies attached to the relevant stack cycle. The panel view shows baseline → most recent with delta annotation and confidence note.
Clinical safety note
Abnormal lab results are clinician conversations, not stack adjustments. Especially for ApoB, HbA1c, TSH, and ferritin, treat out-of-range results as a reason to consult a clinician before self-titrating supplements.