Population transferability
Many biological age models were developed outside Asian populations, leaving calibration and population-specific performance unresolved.
For Researchers
From promising clocks to deployable science.
BioAge Connect combines study execution, reproducible biological-age measurement, and a harmonised public comparison base for intervention and validation research.
01 — The translation gap
Biological age science has advanced. Translation has not kept pace.
Methodological heterogeneity
Platforms and preprocessing choices can produce discordant estimates. The 16 Atlas studies were re-derived from source data through one pipeline so comparison is meaningful.
Slow translation
Methods advance faster than the operational, governance, and clinical pathways needed to use them reproducibly.
Few interventions are tested
Conventional endpoints take decades. Biological age brings an outcome inside a study-feasible window.
02 — Singapore context
A distinctive setting for ageing biology research
The opportunity is real, but access must be created study by study rather than implied in advance.
Multi-ethnic Asian population
Singapore offers a valuable setting for studying variation in biological ageing, subject to study-specific recruitment and access.
Linked health data foundations
Singapore’s cohort and health-system infrastructure can support endpoint validation. Building study-specific access is part of collaboration, not a resource BioAge Connect currently holds.
Policy pathway with real stakes
Preventive care, early detection, and ageing-in-place make rigorous validation consequential.
Applied study settings
Clinical study sites can create longitudinal intervention data when a partnership and governance framework are in place.
03 — What BioAge Connect provides
Operational capacity first, then measurement and data.
Study execution is often the scarcest input to translational work. The platform removes that operational drag without obscuring provenance or authorship.
Study execution
Protocol, consent and ethics documentation, recruitment support at partner sites, biospecimen workflows, follow-up, and analysis — the operational work required to make a study happen.
EpiClock
Preprocessing, QC, harmonisation, clock-coverage gating, and scoring from raw IDATs or a processed β-matrix. Twelve clocks across eight dimensions, with provenance per value.
Harmonised comparison base
Sixteen public intervention studies, 1,042 participants, 2,038 samples, and 22,366 results processed consistently across intervention categories.
Data management and governance
Storage, harmonisation, provenance, and access control, with PDPA and IRB alignment established before collection.
04 — Measurement panel
12 clocks across 8 dimensions
The panel is specific, reproducible, and available now. It replaces broad multi-omics claims with the measurements actually implemented in EpiClock.
Chronological
- What it measures
- Calendar-age estimate
- Clocks
- Horvath · Hannum
Phenotypic
- What it measures
- Morbidity and mortality risk
- Clocks
- PhenoAge · GrimAge · PCPhenoAge · PCGrimAge
Pace of ageing
- What it measures
- Rate of ageing per year
- Clocks
- DunedinPACE
Causal
- What it measures
- Causal chronological signal
- Clocks
- CausAge
Damage
- What it measures
- Accumulated molecular damage
- Clocks
- DamAge
Adaptation
- What it measures
- Protective adaptation
- Clocks
- AdaptAge
Mitotic
- What it measures
- Stem-cell division count
- Clocks
- epiTOC2
Telomere
- What it measures
- DNA-methylation telomere length
- Clocks
- DNAmTL
| Dimension | What it measures | Clocks |
|---|---|---|
| Chronological | Calendar-age estimate | Horvath · Hannum |
| Phenotypic | Morbidity and mortality risk | PhenoAge · GrimAge · PCPhenoAge · PCGrimAge |
| Pace of ageing | Rate of ageing per year | DunedinPACE |
| Causal | Causal chronological signal | CausAge |
| Damage | Accumulated molecular damage | DamAge |
| Adaptation | Protective adaptation | AdaptAge |
| Mitotic | Stem-cell division count | epiTOC2 |
| Telomere | DNA-methylation telomere length | DNAmTL |
On the roadmap
Proteomics
Metabolomics
Digital and wearable-derived measures
Multi-omic composites
05 — Collaboration
Multiple ways to contribute, analyse, and publish.
Study co-design
Develop an intervention study together and run it at partner sites. BioAge Connect handles operations and measurement; the research partner leads the science.
Population-specific calibration
Benchmark validated models in a defined study population with access and endpoints established for that collaboration.
Secondary dataset access
The harmonised public-study database is available to research partners. Access to study-generated data follows each site’s governance framework.
Governance and standards
Shape evaluation standards, interpretability thresholds, and responsible-use guidance before adoption expands.
Agreed before any sample moves
Data ownership and IP attribution are defined at the outset. Processing aligns with PDPA and IRB requirements. Explicit do-not-use policies cover insurance underwriting and employment decisions. Anti-discrimination safeguards are built into access conditions.
Research collaboration
Bring the scientific question. Build the study around it.
Co-authorship, data use, institutional responsibilities, and access conditions are agreed before collection or analysis begins.