How Aescle works

Threats are the cause-of-death buckets — heart disease, stroke, the major cancers, dementia, accidents, suicide, respiratory, kidney, liver, diabetes. Population-level shares come from CDC NVSR by sex × race/ethnicity, then are weighted by age band using CDC WONDER's underlying-cause queries. The model carries one baseProb per threat for the demographic and a peak age — personalized to your survival curve — for when each threat does its damage.

Levers are the measurable variables that move threats: biomarkers (ApoB, Lp(a), HbA1c, hsCRP, eGFR …), vitals (resting HR, blood pressure, VO₂max, sleep duration, SpO₂), and behaviors (smoking status, alcohol intake, diet pattern, physical activity). Each lever → threat edge carries a years weight and a sources array — the underlying RCT, Mendelian-randomisation study, or meta-analysis that establishes the causal magnitude.

Actions are the interventions that move levers — quit smoking, Mediterranean / DASH-style diet, resistance training, statin, GLP-1, colonoscopy at 45, hearing aids when needed, and so on. Per-target years are normalised to the empirical modifiable ceiling from Li 2018 so the magnitudes sum to a realistic budget for the user's age and sex, rather than an additive fantasy.

Click any node to see its 1-hop neighborhood in both directions. Then pivot — or hit "View sources" on a focal node to inspect the actual citations.