A longitudinal, multi-omic infrastructure designed to support structured prevention and clinical triage.
BioTwin builds a continuously evolving virtual twin of each individual by integrating biological, behavioral, lifestyle, and clinical data into a structured longitudinal baseline. Below are the data layers that power your twin.
Multi-layer biological and physiological inputs
Metabolites
Direct readout of phenotype, capturing real-time physiological state and early biological change before clinical symptoms.
Glucose, lactate, amino acids, cortisolLipids
Direct readout of phenotype, capturing real-time physiological state and early biological change before clinical symptoms.
Cholesterol, triglycerides, phospholipidsProteomics
Functional layer translating genetic risk into active biology, reflecting organ function, immune activation, and disease-relevant processes.
Albumin, CRP, enzymes, inflammatory markersGenetics
Stable baseline defining inherited risk, treatment response, and long-term susceptibility that contextualizes all downstream signals.
SNPs, polygenic risk scores, pharmacogenomicsVisual biomarkers
Capture measurable changes in facial features, skin patterns, and morphology that correlate with aging-related risk and other health signals from photos.
Facial analysis, skin health, morphological patternsVocal biomarkers
Analyze speech patterns to identify shifts in stress load, cognitive performance, and neuromotor stability.
Stress levels, speech patterns, vocal tremorRetinal & eye biomarkers
Use eye imaging to assess vascular integrity and detect early indicators linked to various health risks.
Retinal imaging, ocular pressure patternsWearables & biometrics
Continuous biometric data from consumer and clinical devices for longitudinal physiological tracking.
Heart rate, sleep, activity, HRV, SpO2How we build the virtual twin
BioTwin integrates these multi-modal signals into a structured, continuously evolving virtual representation personalized to each individual's longitudinal baseline.
Data integration
Multi-modal data fusion across laboratory results, wearable biometrics, structured questionnaires, imaging inputs, and EHR systems, etc. All signals are normalized, time-stamped, and synchronized to maintain longitudinal reliability.
- Biomarkers, biometrics, and patient-reported outcomes
- Genomic, microbiome, and molecular data
- Visual, vocal, and retinal biomarker analysis
- EHR integration via HL7 FHIR
AI risk models
Domain-specific signature models trained on longitudinal datasets, generating interpretable probability scores across monitored clinical domains.
- Disease-specific risk models with interpretable outputs
- Trajectory prediction using temporal deep learning
- Causal inference for intervention modeling
- Uncertainty quantification for clinical decision support
Longitudinal tracking
Structured repeat measurements over time establish a personal baseline and detect meaningful biological drift.
- Continuous baseline refinement
- Drift detection against personal norms
- Adaptive follow-up intensity
- Structured anomaly flagging
How the virtual twin works
From sample collection to clinical decision support — a continuous, evolving process.
Collect
Multi-omic samples and multimodal biomarkers are collected via structured at-home kits or clinic visits.
Analyze
Signals are integrated into a longitudinal virtual twin anchored to your personal biological baseline.
Risk screening
Structured scoring across 5 clinical domains and 15 health conditions, with longitudinal drift detection and anomaly flagging.
Support
Clinicians receive structured risk summaries and triage support. Diagnosis and treatment decisions remain under physician authority.
Scientific Foundations
BioTwin's approach is grounded in peer-reviewed biomedical research supporting longitudinal profiling, multi-omic integration, and virtual twin modeling for improved clinical risk stratification.
Oncology
Metabolomic profiling research supports blood-based early detection and risk stratification across major cancers.
Neurology
Metabolomic and microbiome research supports earlier biological signal identification in neurodegenerative and mood disorders.
Cardiology
Metabolomic profiling enhances cardiovascular risk stratification and phenotype differentiation beyond single biomarker measurements.
Lifestyle medicine
Large-scale metabolomic research demonstrates predictive biological signatures preceding metabolic disease onset.
Life & Health Span
Metabolomic aging research demonstrates reproducible associations with healthspan and long-term disease risk.