Structured Biological Data for Longevity AI

Longevity AI is only as good as its input data. The field's core challenge isn't modeling — it's data quality, continuity, and standardization. Most biological datasets are fragmented across institutions, formatted inconsistently, missing longitudinal depth, and impossible to verify.

BSP addresses the data layer directly.

The Data Problem in Longevity Research

Precision medicine models require longitudinal data: not a single snapshot, but a time series of biological measurements tracked consistently over years. The current infrastructure makes this nearly impossible at scale:

• Fragmentation — a single patient's data lives across multiple labs, clinics, wearables, and apps with no unified identifier
• Format inconsistency — the same biomarker (e.g., fasting glucose) arrives in different units, labeled differently, with different reference ranges depending on the source
• Verification gaps — there's no cryptographic proof that a lab result wasn't modified after the fact
• Access friction — building a longitudinal dataset requires negotiating API access with each institution separately, subject to terms that can change at any time
• Consent complexity — using data across studies requires re-consenting participants for each use case

The result: longevity AI teams spend the majority of their engineering time on data pipelines, not models.

How BSP Solves the Data Layer

Canonical schema. BSP's biomarker taxonomy assigns a standardized code, unit, and type to every biological measurement. Data from different labs, devices, and platforms maps to the same vocabulary. Your model sees glucose as glucose.fasting.mmol_l regardless of the source.

Longitudinal continuity. Because BEOs are owned by individuals and live permanently on Arweave, the longitudinal record accumulates over a lifetime. A user who starts with BSP at 30 has a 20-year biological history by 50 — fully intact, fully portable, with no institutional dependency required to maintain it.

Cryptographic verification. Every BEO is signed by the individual's private key at write time. Your AI pipeline can verify that data hasn't been tampered with without calling any external API.

Consent-native architecture. BSP's consent token system lets individuals grant time-bounded, scope-limited access to specific biomarker categories. A longevity study can request access to "Level 1 biomarkers, 2020–2025" without seeing the full record. When the grant expires, access ends cryptographically — not just by policy.

No API dependency. Data lives on Arweave, a permanent decentralized network. Your pipelines read from an open protocol, not from an institution's API that can be deprecated, rate-limited, or shut down.

What BSP Data Looks Like in Practice

A structured BSP BioRecord for a blood panel includes:

{
  "type": "biorecord",
  "category": "blood_panel",
  "timestamp": "2025-03-15T08:30:00Z",
  "biomarkers": [
    {
      "code": "glucose.fasting.mmol_l",
      "value": 4.8,
      "unit": "mmol/L",
      "level": 1,
      "verified": true
    },
    {
      "code": "hba1c.percent",
      "value": 5.1,
      "unit": "%",
      "level": 1,
      "verified": true
    }
  ],
  "source": {
    "type": "lab",
    "ieo": "arweave://IEO_TX_ID"
  }
}

Every field is typed, every code is canonical, every record links back to a verified institutional source. Ready for ingestion with no preprocessing.

Applications

Biological age models. Feed consistently structured longitudinal data into aging clock models (epigenetic, proteomics-based, or composite) without custom ETL for each data source.

Personalized intervention tracking. Measure the effect of diet, exercise, supplementation, and therapeutics against a stable biomarker baseline — tracked across years, across providers, in one place.

Research cohort assembly. Recruit participants who've opted into BSP and grant research access through the consent token system. No data warehouse required. No re-consent for each study.

Clinical trial data quality. Trial participants with BSP records bring verified pre-trial baselines. No recall bias. No missing records. Cryptographic provenance on every data point.

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Read the BioRecord Schema | Explore the Taxonomy | Developer Quickstart