How Lumenai computes the numbers you see. Every score is grounded in peer-reviewed evidence, with the source cited.
Your biological age is one number that integrates multiple peer-reviewed dimensions of aging into a single, transparent estimate. PhenoAge (Levine et al., 2018) is the anchor — a validated lab-based estimator of biological age. We then apply bounded lifestyle adjustments grounded in independent mortality literature for cardiorespiratory fitness, resting heart rate, body composition, HRV trend, and sleep.
Each adjustment has a defined range, a source citation, and a visible contribution on your dashboard. The total adjustment is bounded to ±5 years so a single dimension can't override the others. PhenoAge itself is unconstrained — it speaks the truth of your blood work.
When wearable or body-composition data isn't available, your score degrades gracefully to the pure PhenoAge anchor with a "limited data" indicator. Adding more data sources increases the precision and unlocks more accurate adjustments.
| Adjustment | Range | Source |
|---|---|---|
| Cardiorespiratory fitness | −2.5 to +3.0 | Mandsager 2018, JAMA Netw Open |
| Resting heart rate | −0.5 to +1.0 | Fox 2008, NEJM (BEAUTIFUL) |
| Body composition | −0.5 to +1.0 | Tomiyama 2016, Int J Obes |
| HRV trend | −0.3 to +0.5 | Shaffer & Ginsberg 2017 |
| Sleep duration + quality | −0.2 to +0.6 | Cappuccio 2010, Sleep |
PhenoAge (Levine et al., 2018) is validated against all-cause mortality in the NHANES dataset and consistently outperforms chronological age as a predictor of healthspan.
The model uses nine standard biomarkers from any comprehensive blood panel, plus your chronological age:
Each marker contributes proportionally to a linear combination that maps to a 10-year mortality probability via a Gompertz proportional hazards model, which is then transformed back to an age estimate. Lower than your chronological age means your biomarkers look younger than your years; higher means older.
The Health Score is a transparent composite from 0 to 100, computed as a weighted average across marker categories. Each category produces a sub-score from 0 to 100 based on how many of your tracked markers in that category sit in the optimal, watch, or flag bands.
Each marker in a category contributes to that category's sub-score:
Categories are weighted by their contribution to long-term mortality and disability-adjusted life years, informed by the Global Burden of Disease Study 2019 (Institute for Health Metrics and Evaluation). Weights are renormalized to categories you have data for, so you're never penalized for not running a marker.
| Category | Weight | Why |
|---|---|---|
| Cardiovascular | 22% | Leading cause of mortality globally (GBD 2019). Includes lipid profile, ApoB, Lp(a), homocysteine, inflammation. |
| Metabolic | 18% | Drives downstream cardiovascular, cognitive, and cancer risk. Includes glucose, HbA1c, insulin, HOMA-IR. |
| fitness | 13% | Cardiorespiratory fitness is the strongest single predictor of all-cause mortality (Mandsager et al., JAMA Netw Open 2018). |
| Hormones | 10% | Cross-system effects on metabolism, mood, recovery, body composition. |
| Inflammation | 8% | hsCRP independently predicts cardiovascular events (Ridker et al., NEJM 2002). |
| Kidney | 6% | CKD prevalence ~10% globally; irreversible if missed early. KDIGO 2024. |
| Vitamins | 6% | Broadly modifiable, broad health effects. Vitamin D, B12, folate. |
| Liver | 5% | NAFLD prevalence ~25% globally; rising with metabolic syndrome. |
| body_composition | 3% | Body composition independent of BMI (Tomiyama et al., Int J Obes 2016). |
| Thyroid | 3% | Subclinical thyroid dysfunction is common and modifiable. |
| Bone | 3% | Osteoporosis screening, especially post-menopause. |
| Cancer screen | 2% | Screening signal only — informative but not diagnostic. |
| Minerals | 1% | Trace elements (zinc, selenium, magnesium) and iron status. |
For every marker in your record, Lumenai classifies your value as optimal, watch, or flag against ranges sourced from peer-reviewed literature — not the broad reference ranges most labs report. Reference ranges are designed to capture 95% of the population and miss most subclinical risk. Optimal ranges are designed to capture healthy.
Where age or sex meaningfully changes the expected range — testosterone, eGFR, ferritin, AMH, PSA — the optimal band is stratified accordingly. A 32-year-old man and a 58-year-old man have different optimal testosterone ranges, and Lumenai reflects that.
Where the evidence is mature, we cite a specific source on the marker detail page (Endocrine Society 2018, AHA/ACC 2018, ESC/EAS 2019, ATA 2014, KDIGO 2024). Where peer-reviewed consensus on "optimal" doesn't exist, we use the lab reference range and flag this in the UI rather than invent a number.
Lumenai doesn't diagnose. It doesn't prescribe. It synthesises your data, flags what's worth attention, and tells you what the evidence says about each marker. Final clinical decisions rest with you and your physician.
We also don't hide our methodology behind an opaque score. If you ever want to see exactly why a number is what it is, ask the chat — "how is my PhenoAge computed?" or "why is my cardiovascular sub-score 72?" — and you'll get a marker-by-marker explanation, not a black box.