Biology Scores turn your lab results into a system-level map of your body.
Instead of making you inspect one biomarker at a time, getbased groups related markers into biological domains: metabolism, thyroid, cardiovascular risk, inflammation, methylation, kidney and hydration, liver and bile flow, immune balance, iron and blood flow, hormones, recovery, stress resilience, cellular energy, gut-immune signaling, nerve-muscle context, and lipid membrane quality.
The point is not to give you another number to obsess over. The point is to answer better questions:
- Which systems look strong?
- Which systems look strained?
- Is the score reliable, or is the evidence thin?
- Which markers would improve the picture most?
- What should I look at first?
The scores are calculated from your stored marker values, ranges, draw dates, profile context, calculated ratios, and supported specialty tests. AI can explain the result in plain language, but AI does not invent the score or change the marker math.
Biology Scores are educational decision support. They can help you spot patterns, missing evidence, stale panels, and useful follow-up tests, but they are not medical diagnosis or treatment advice.
The short version
Open Biology Scores from the sidebar or mobile menu.
When enough data exists, the page is organized into four parts:
| Area | What it helps you answer |
|---|
| Biological Coherence | What does the whole system look like overall? |
| What matters now | What should I inspect first? |
| Coverage Planner | Which missing markers would improve confidence without overtesting? |
| Score cards | What is happening inside each biology domain? |
Biological Coherence
Biological Coherence is the whole-body overview.
It combines the active core domains and asks: do your major systems look internally coherent, or are several areas strained, stale, or under-tested?
A good Biological Coherence card should tell you:
- your overall score;
- the strongest domains;
- the most strained domains;
- whether key evidence is missing;
- where to start if you want to understand the result.
Core domains can include metabolic health, cardiovascular risk, inflammation, immune balance, iron and blood-flow context, methylation, kidney and fluid-balance context, liver and bile flow, bone and mineral balance, thyroid hormones, hormone axis, and recovery capacity when relevant.
Kidney and hydration is not a direct body-water measurement. In Biology Scores, this domain is better understood as kidney filtration, electrolyte, and fluid-balance context. It can use markers such as eGFR, creatinine, urea/BUN, BUN/creatinine ratio, sodium, potassium, chloride, cystatin C or cystatin-C eGFR, and albumin. Those markers can suggest dehydration, hemoconcentration, electrolyte strain, or filtration context, but direct hydration assessment is better supported by urine osmolality, urine specific gravity, urine sodium, and intake/output context.
Why the context check comes first
Biology is not context-free. The same marker can mean different things depending on the person, the timing, and the situation.
Before scores unlock, run the Context check. getbased reviews context that could change how markers should be handled, then unlocks all timeframe tabs for that profile.
The context check can suggest structured flags, but you stay in control. Suggestions are not applied silently.
Examples:
- Low muscle mass or neuromuscular disease can make creatinine-derived kidney markers less reliable. Cystatin C may be a better filtration signal.
- Female hormones need cycle, menopause, contraception, or hormone-therapy context before they should be scored against ordinary cycling ranges.
- Cortisol needs sample-time context. A morning cortisol and an afternoon cortisol are not the same evidence.
- Light exposure can change how vitamin D and inflammation patterns are interpreted.
- SNPs and genome data can add context to methylation, vitamin D, lipids, iron, and related domains without becoming standalone score inputs.
How to read a score card
Each score card separates the score from the quality of the evidence behind it.
| Field | Meaning |
|---|
| Pattern | How the available marker pattern looks on a 0–100 scale. |
| Coverage | How much useful evidence is available for that score. |
| Confidence | Whether the result is robust enough to trust or should be treated carefully. |
| Evidence | Whether the score is production-backed, profile-aware, contextual, or experimental. |
| Recency / retest state | Whether inputs are stale or drawn too far apart to combine cleanly. |
How scores are calculated
Biology Scores are deterministic. AI can explain a score, but the number comes from the scoring engine.
The basic flow is:
- getbased finds the latest usable value for each input marker in the selected timeframe.
- Each marker is scored from 0–100 against its reference, optimal, or context-specific range. A marker inside range usually scores 100. A marker outside range loses points as it moves farther away from the range.
- Each marker score is multiplied by its weight. Higher-weight markers affect the score more than lower-weight context markers.
- The score is the weighted average of the available marker scores.
- Coverage is calculated separately from the score: available marker weight divided by total planned marker weight.
- Confidence is based on coverage, missing core markers, context-only markers, and whether the data is fresh enough to combine.
A simplified version:
score = sum(marker score × marker weight) / sum(available marker weights)
coverage = sum(available marker weights) / sum(all planned marker weights)
A high score with thin coverage is not an all-clear. It means the markers you have look good, but the score may still have low or medium confidence because important markers are missing.
What weights mean
Weights are relative. A marker with weight 2.0 has about twice as much influence as a marker with weight 1.0 when both are available. A marker with weight 0.3 is usually context only: useful, but not meant to drive the score.
Markers marked core are important for confidence. If a core marker is missing, the score can still show available evidence, but getbased warns that the result is provisional.
Freshness and timing
Scores are not trusted when the inputs are too stale or drawn too far apart. Current rules:
- a marker older than about 180 days can block the score until retested;
- inputs spanning more than about 90 days can trigger a retest together warning;
- timing-sensitive markers, such as cortisol or cycle-dependent hormones, may need sample-time, cycle, menopause, therapy, or training context before they are scored normally.
Biological Coherence calculation
Biological Coherence is built from the minimum-panel Biology Score domains. It does not directly average every biomarker. It first computes each live domain, then combines the domain scores with domain-level weights and coverage adjustment.
Extended-only scores such as Lipid Membrane, Cellular Energy, Stress Resilience, Gut–Immune Signal, and Nerve–Muscle Signal add depth when available, but they do not make baseline Biological Coherence worse just because you did not order specialty tests.
Marker rows and missing markers
Open a score to see the marker evidence behind it:
- the plain-English question the score is checking;
- core markers used for baseline signal;
- markers that add better confidence;
- available markers and their fit;
- missing markers and whether they are core, optional, derived, or context-dependent.
getbased uses lab-orderable marker names in the UI. For example, it should say Homocysteine, Triglycerides, AST, ALP, CRP, or hs-CRP — not confusing aliases like “homocysteine load” or “liver muscle signal.”
CRP and hs-CRP are not the same marker. hs-CRP is used for low-grade inflammatory and cardiovascular-risk context. Standard CRP is broader inflammation context. getbased keeps them separate instead of treating them as interchangeable rows.
Score reference: what each score measures
This reference shows the user-facing meaning of each score and the main weighted inputs behind it. The exact score still depends on marker ranges, selected timeframe, range mode, profile context, recency, and missing data.
Measures glucose-insulin strain and fuel-flexibility context from fasting labs.
Core / high-weight inputs: HOMA-IR 1.7, fasting insulin 1.45, TG/HDL ratio 1.15, fasting glucose 0.95.
Context inputs: HbA1c 0.85, triglycerides 0.75, HDL 0.55, C-peptide 0.35, fructosamine 0.3. Fructosamine is optional and low-weight; it is mainly useful when HbA1c is unreliable, when recent glycation changed, or when high insulin is compensating for otherwise normal glucose markers.
Thyroid Coherence
Measures thyroid signal quality, Free T3 activity, and T4-to-T3 conversion coherence.
Core / high-weight inputs: FT3/FT4 conversion 1.35, Free T3 1.3, TSH 1.0.
Context inputs: Reverse T3 0.45, TPO antibodies 0.35, thyroglobulin antibodies 0.25.
Cardiovascular Risk
Measures atherogenic lipoprotein and vascular-risk context.
Core / high-weight inputs: ApoB 2.0, ApoB/ApoA-I ratio 1.5.
Context inputs: ApoA-I 1.0, Lp(a) 1.0, LDL cholesterol 0.8, total cholesterol/HDL ratio 0.6, homocysteine 0.5, hs-CRP 0.4, triglycerides 0.4.
Inflammatory Load
Measures low-grade inflammation and liver-metabolic burden context.
Core / high-weight inputs: hs-CRP 1.55, GGT 1.25.
Context inputs: uric acid 0.6, CRP 0.55, homocysteine 0.55, ferritin 0.45, vitamin D 0.3, bilirubin 0.25, selenium 0.25. CRP and hs-CRP are not interchangeable.
Lipid Membrane
Extended score for fatty-acid architecture and membrane-quality context.
High-weight inputs: omega-3 index 2.0, DHA 1.15, EPA 0.9.
Context inputs: AA/EPA ratio 0.55, omega-6/3 ratio 0.45, DPA 0.25, linoleic acid 0.25, arachidonic acid 0.25.
Blood Flow Context
Measures blood concentration, plasma-viscosity, and clotting-context signals. It is not a direct blood-viscosity measurement.
Core / high-weight inputs: hematocrit 1.15, hemoglobin 0.75, platelets 0.55.
Context inputs: fibrinogen 0.95, D-dimer 0.8, albumin 0.35, BUN/creatinine ratio 0.35, CRP 0.35, sodium 0.25. Elevated D-dimer is a clinical-context signal, not a wellness score.
Iron Handling
Measures iron storage, transport, overload/deficiency context, inflammation, and red-cell utilization.
Core / high-weight inputs: transferrin saturation 1.25, ferritin 1.15, hemoglobin 0.8.
Context inputs: MCH 0.65, CRP 0.65, serum iron 0.55, soluble transferrin receptor 0.55, MCV 0.5, transferrin 0.45, TIBC 0.35, copper 0.35, ceruloplasmin 0.3.
Methylation
Measures one-carbon / methylation coherence using homocysteine, B12, folate, and red-cell context.
Core / high-weight inputs: homocysteine 1.8, active B12 / holotranscobalamin 1.15, folate 1.1, total B12 0.85. Active B12 and total B12 can satisfy the same B12-status requirement.
Context inputs: methylmalonic acid 0.7, MCV 0.45, MCH 0.25, RDW 0.25, creatinine 0.25.
Kidney and fluid-balance context
Currently labeled Kidney & Hydration in some places. Measures filtration, electrolyte, and fluid-balance context. It is not a direct hydration measurement.
Core / high-weight inputs: eGFR 1.45, creatinine 1.0, sodium 0.85, potassium 0.85.
Context inputs: urea/BUN 0.75, cystatin C 0.75, BUN/creatinine ratio 0.65, cystatin-C eGFR 0.65, chloride 0.45, albumin 0.35. Direct hydration precision improves with urine osmolality, urine specific gravity, urine sodium, urine output, and intake context when available.
Liver & Bile Flow
Measures liver enzyme, bile-flow, protein synthesis, and metabolic-burden patterns.
Core / high-weight inputs: GGT 1.2, ALT 1.15, AST 0.9, ALP 0.9.
Context inputs: total bilirubin 0.75, albumin 0.55, AST/ALT ratio 0.4, platelets 0.35, ferritin 0.35, triglycerides 0.3.
Bone & Mineral Balance
Measures vitamin D, calcium-phosphate balance, kidney-mineral context, and bone-turnover signals.
Core / high-weight inputs: 25-OH vitamin D 1.45, total calcium 1.0, phosphorus 0.85.
Context inputs: ALP 0.75, serum magnesium 0.55, RBC magnesium 0.45, calcitriol 0.45, creatinine 0.35, eGFR 0.35.
Immune Cell Balance
Measures CBC and differential patterns for immune activation, suppression, allergy/eosinophil, and stress-skew context.
Core / high-weight inputs: white blood cells 1.25, neutrophils 1.1, lymphocytes 1.1.
Context inputs: neutrophil/lymphocyte ratio 0.75, monocytes 0.55, eosinophils 0.45, hs-CRP 0.45, platelets 0.35, vitamin D 0.25, basophils 0.25.
Recovery Capacity
Measures anabolic/catabolic and recovery context from hormones, proteins, inflammation, thyroid, and tissue-stress markers.
Core / high-weight inputs: free testosterone 1.25, total testosterone 1.05, albumin 0.85, total protein 0.65. Testosterone weights are reduced for female profiles.
Context inputs: IGF-1 0.8, DHEA-S 0.75, free androgen index 0.65, SHBG 0.55, hs-CRP 0.55, hemoglobin 0.45, estradiol 0.4, vitamin D 0.35, creatine kinase 0.35, Free T3 0.3, TSH 0.25, LH 0.25, FSH 0.25, urea 0.25, creatinine 0.25.
Cellular Energy
Extended score for mitochondrial-energy and organic-acid context.
High-weight inputs: lactate 1.25, pyruvate 1.1, succinate 0.85, fumarate 0.75, malate 0.75, ethylmalonic acid 0.75.
Context inputs: 2-oxoglutarate 0.65, 3-methylglutaconic acid 0.65, HMG 0.6, aconitate 0.55, methylsuccinic acid 0.55, carnitine 0.55, adipic/suberic/sebacic acids 0.45 each, creatine kinase 0.35, Free T3 0.3.
Stress Resilience
Extended score for HPA-axis and recovery-pressure context.
High-weight inputs: cortisol 1.35, DHEA-S 0.95, testosterone/cortisol ratio 0.8. Cortisol requires sample-time context.
Context inputs: hs-CRP 0.55, fasting glucose 0.45, insulin 0.45, WBC 0.35, neutrophils 0.35, lymphocytes 0.35, Free T3 0.35, TSH 0.25.
Hormone Axis
Measures sex-hormone and pituitary-axis coherence with sex, age, cycle, menopause, contraception, and hormone-therapy context.
Core / high-weight inputs: free testosterone 1.1, total testosterone 1.0, estradiol 0.9, progesterone 0.75, SHBG 0.65, LH 0.65, FSH 0.65, prolactin 0.6. Male profiles use total/free testosterone as androgen-status core; female profiles use estradiol and/or progesterone as sex-hormone-status core.
Context inputs: DHEA-S 0.55, DHT 0.45, cortisol 0.35, androstenedione 0.35.
Gut–Immune Signal
Extended score for gut-barrier, microbiome-adjacent, inflammation, eosinophil, and nutrient/protein context.
High-weight inputs: calprotectin 1.2, zonulin 0.75, secretory IgA 0.7, arabinose 0.7, HPHPA 0.65.
Context inputs: 4-cresol 0.55, D-arabinitol 0.55, hs-CRP 0.55, eosinophils 0.45, albumin 0.35, vitamin B12 0.3.
Nerve–Muscle Signal
Extended score for neuromuscular stress and support context.
High-weight inputs: neurofilament light 1.25, creatine kinase 0.95, active B12 0.75, homocysteine 0.75.
Context inputs: creatinine 0.55, total B12 0.55, HbA1c 0.55, vitamin D 0.45, magnesium 0.45, hs-CRP 0.4, glucose 0.35, lactate 0.35.
Calculated ratios
Some score inputs are calculated from ordinary labs instead of ordered directly.
Examples:
- HOMA-IR from glucose + insulin;
- TG/HDL ratio from triglycerides + HDL;
- Total cholesterol/HDL ratio from total cholesterol + HDL;
- ApoB/ApoA-I ratio from ApoB + ApoA-I;
- BUN/creatinine ratio from urea/BUN + creatinine;
- NLR from neutrophils + lymphocytes.
If you have the component markers, getbased can derive the ratio. If the lab provides the ratio directly, getbased can use that too.
Overlapping optional markers
Some optional markers overlap with core markers. They are included at low weight because they can improve confidence or reveal discordance, not because everyone needs to order them.
For example, fructosamine often tracks glucose and HbA1c, so it may add little when those markers already agree. Its main value is context: it reflects a shorter glycation window than HbA1c and can help when recent glucose control changed or when HbA1c may be misleading because of red-cell turnover, anemia, hemolysis, blood loss, kidney disease, or related factors.
In that kind of case, fructosamine can improve confidence in the metabolic picture. It should not drive the metabolic score when the core markers already agree.
Specialty tests
Biology Scores can use specialty tests when those markers are relevant.
Examples:
- fatty acid panels can feed Lipid Membrane through omega-3 index, EPA, DHA, AA/EPA, and omega-6/3 context;
- OAT / Metabolomix-style markers can feed Cellular Energy and Gut–Immune domains;
- BioStarks markers can feed standard and specialty domains where the marker is mapped safely;
- active B12 can satisfy B12 context in methylation when total B12 is absent.
Specialty tests should improve depth, confidence, or advanced domains. You should not see a worse baseline Biological Coherence score just because you have not ordered advanced testing.
Coverage Planner
The Coverage Planner answers a practical question: what should I order next if I want better Biology Scores coverage?
It separates missing evidence into:
| Group | Meaning |
|---|
| Baseline gaps | Common markers that improve the core whole-body picture. |
| Score-by-score gaps | Missing markers grouped by the domain they help. |
| Optional advanced depth | Specialty or deeper markers that can improve confidence, but are not required for baseline validity. |
If cost matters, start with baseline gaps before optional advanced depth. A good basic panel should get most people to useful coverage without needing fatty-acid testing, OAT, metabolomics, or other specialty panels.
Timeframes
The timeframe controls (3M, 6M, 1Y, All) filter the lab data used for the scores.
Use them when you want to ask: what does my biology look like recently, not across my whole history?
One context check unlocks all timeframe tabs. The score can still change by timeframe if the selected range changes which markers are available.
AI explanations
Each score can show Explain this score or Refresh explanation.
The explanation is generated from the score, marker values, exact ranges, missing markers, confidence state, and context flags. It should not invent thresholds. If marker evidence changes after an explanation was generated, getbased warns you and asks you to refresh it.
AI explanations are stored with the profile so they survive browser refresh and cross-device sync.
A good way to use the page
- Start with Biological Coherence.
- Check what is strongest and what is most strained.
- Open the most relevant score card.
- Look at coverage and confidence before reacting to the number.
- Use the Coverage Planner if the result is thin or important markers are missing.
- Ask for an AI explanation when you want the reasoning in plain language.
Good questions to ask:
- Which domain is most strained right now?
- Is this score low because markers are actually strained, or because coverage is thin?
- Are any inputs stale or drawn too far apart?
- Which missing marker would improve the most scores?
- Is a specialty test adding real information, or is a baseline marker missing first?
Avoid reading the page as:
- a diagnosis;
- a replacement for clinical review;
- proof that one supplement or habit caused a change;
- a reason to order every advanced marker at once.
Troubleshooting
| Symptom | What to check |
|---|
| Scores are locked | Run the context check first. |
| A score says “Need inputs” | Open the score or Coverage Planner to see missing core markers. |
| A calculated ratio is missing | Confirm both source markers exist in the selected timeframe and are not stale. |
| A hormone score looks unavailable | Add sex, age, cycle/menopause, therapy, and draw timing context where relevant. |
| A specialty marker is visible in Labs but not in a score | The marker may not be mapped to that score, may be optional depth, or may be too stale. |
| An AI explanation shows a stale warning | Click Refresh explanation so it uses the current marker evidence. |
| Scores differ by timeframe | That is expected when the selected timeframe changes the available marker set. |
