[WIP] Biological Age
Overview
Biological age represents the true physiological state of the body, as opposed to chronological age. Multiple scientifically validated methods exist to assess biological age, each with different data requirements and accuracy levels.
Scientifically Validated Methods
1. Epigenetic Clocks (DNA Methylation)
Horvath Clock (2013)
Accuracy: Correlation with chronological age: 0.97, median error: 2.9 years
Mechanism: Measures DNA methylation at 353 CpG sites
Training Data: ~8,000 samples across 51 tissue and cell types
Algorithm: Elastic net regression on Illumina DNA methylation array data
DNAm PhenoAge
Accuracy: r = 0.94 with chronological age
Mechanism: Based on 513 CpG sites
Advantages: Outperforms earlier clocks in predicting:
All-cause mortality
Cancer risk
Alzheimer's disease
Overall healthspan
EpiAge Test (2025)
Method: Next-generation sequencing-based epigenetic clock
Sample Type: Saliva or blood
Accuracy: Comparable to more complex established epigenetic clocks
Data Requirements:
DNA samples (blood or saliva)
Specialized laboratory equipment (Illumina arrays or NGS)
High cost per test (~$300-$500)
2. Phenotypic Age (PhenoAge)
Clinical Biomarker-Based Method
Most accessible for digital health applications.
Core Formula: Linear combination of chronological age + 9 clinical chemistry biomarkers
Required Blood Biomarkers:
Albumin - Liver/kidney function, nutritional status
Glucose - Metabolic health
C-reactive protein (CRP) - Inflammation marker
Creatinine - Kidney function
Alkaline phosphatase - Liver/bone health
Blood urea nitrogen (BUN) - Kidney function
Mean cell volume (MCV) - Red blood cell size
Red cell distribution width (RDW) - Red blood cell variation
White blood cell count (WBC) - Immune function
Additional Important Markers:
Cystatin C - Strongest predictor of mortality risk
Alanine aminotransferase (ALT) - Liver function
Vitamin D - Overall health marker
Systolic blood pressure (SBP) - Most frequently used in models
Physical Measurements:
Body mass index (BMI)
Waist circumference
Blood pressure
Performance Metrics:
Walking speed
Chair stand test
Standing balance
Grip strength
Muscle mass
3. Klemera-Doubal Method (KDM)
Status: Most reliable method according to 2025 systematic review of 56 studies
Approach:
Statistical method combining multiple biomarkers
Superior to other methods in predicting mortality
Calculates biological age using weighted combination of biomarkers
Accounts for variability in aging rates across different systems
Advantages:
Most validated in peer-reviewed literature
Best predictor of mortality outcomes
Can use standard clinical biomarkers
4. Machine Learning Approaches
AI-Driven Models (2025)
Recent models incorporate 27 clinical factors using multiple algorithms:
Algorithms Used:
Linear regression
LASSO (Least Absolute Shrinkage and Selection Operator)
Ridge regression
Elastic Net
Random forest
Support vector machine (SVM)
Gradient boosting
K-nearest neighbors (KNN)
Performance:
Elastic-Net penalised Cox model: C-Index = 0.778
PhenoAge model: C-Index = 0.750
Data Sources:
UK Biobank study: 306,116 participants with 60 circulating biomarkers
Multiple long-term studies with 40,000+ people
5. Health Octo Tool (2025)
Publication: Nature Communications, May 5, 2025
Innovation: Organ system-specific aging assessment
Approach:
Defines disease states and severity for 13 major organ systems
Measures aging rate of each organ system separately
Recognizes that organs age at different rates
Key Finding: Comprehensive multi-system assessment provides more accurate biological age than single-metric approaches
Data Points for Implementation
Minimum Viable Product (Basic Assessment)
Essential Blood Tests:
Complete Blood Count (CBC)
White blood cell count
Red blood cell count
Mean cell volume (MCV)
Red cell distribution width (RDW)
Comprehensive Metabolic Panel (CMP)
Glucose
Creatinine
Blood urea nitrogen (BUN)
Albumin
Alkaline phosphatase
Inflammation Marker
C-reactive protein (CRP)
Physical Measurements:
Height and weight (BMI)
Blood pressure
Waist circumference (optional)
Demographic:
Chronological age
Sex
Enhanced Assessment (Recommended)
Additional Blood Biomarkers:
Cystatin C (strongest mortality predictor)
Alanine aminotransferase (ALT)
Vitamin D
Lipid panel (cholesterol, triglycerides, HDL, LDL)
Hemoglobin A1c (HbA1c)
Functional Tests:
Grip strength
Walking speed
Chair stand test (lower body strength)
Standing balance
Lifestyle Data:
Smoking status
Alcohol consumption
Physical activity level
Sleep quality
Advanced Assessment (Premium)
Specialized Biomarkers:
Telomere length
DNA methylation patterns (epigenetic clock)
Proteomics markers
Metabolomics markers
Glycomics markers
Wearable Data:
Heart rate variability (HRV)
Sleep patterns
Activity levels
Resting heart rate
Organ-Specific Assessments:
Lung function (FEV1 - forced expiratory volume)
Cardiac markers
Liver enzymes panel
Kidney function markers
Implementation Recommendations
For Aarogyadost Platform
Phase 1: Basic Biological Age Calculator
Input Requirements:
Standard blood test results (CBC + CMP + CRP)
Basic physical measurements (height, weight, blood pressure)
Chronological age
Algorithm Choice:
Implement PhenoAge or Klemera-Doubal method
Both are well-validated and use accessible biomarkers
Estimated Development Effort:
Low to medium (standard blood tests widely available)
Algorithm implementation: Well-documented in literature
Phase 2: AI-Enhanced Model
Input Requirements:
Expanded biomarker panel (20-30 markers)
Functional performance tests
Lifestyle questionnaire data
Algorithm Choice:
Train machine learning model (Elastic Net or Random Forest)
Use established models as baseline
Validate against Indian population data
Estimated Development Effort:
Medium to high
Requires model training and validation
Need to collect local population data for accuracy
Phase 3: Comprehensive Assessment
Input Requirements:
Epigenetic testing (saliva/blood for DNA methylation)
Wearable device integration
Advanced biomarkers
Organ-specific assessments
Algorithm Choice:
Multi-modal approach combining:
Epigenetic age
Phenotypic age
Functional age
Organ-specific ages
Estimated Development Effort:
High
Requires partnerships with specialized labs
Integration with wearable platforms
Advanced analytics infrastructure
Key Considerations
Accuracy vs. Accessibility Trade-offs
Most Accurate: Epigenetic clocks (DNA methylation)
Pros: Gold standard, highly accurate
Cons: Expensive, requires specialized labs, slow turnaround
Most Practical: PhenoAge / KDM with blood biomarkers
Pros: Uses standard blood tests, affordable, fast
Cons: Slightly less accurate than epigenetic methods
Most Scalable: AI models with basic biomarkers
Pros: Can improve over time, handles incomplete data
Cons: Requires validation, needs large datasets
Clinical Validation Requirements
Models should be validated on diverse populations
Indian population may have different aging patterns
Need age-specific and sex-specific models
Consider socioeconomic and environmental factors
Regulatory Considerations
Medical device classification (if providing clinical recommendations)
Data privacy (sensitive health information)
Laboratory certifications for biomarker testing
Medical supervision for interpretation
Research Gaps & Opportunities
Indian Population Data: Most studies based on Western populations (UK Biobank, US cohorts)
Cost Optimization: Identify minimum biomarker set for accurate prediction in Indian context
Integration: Combine traditional health markers (Ayurveda) with modern biomarkers
Continuous Monitoring: Track biological age changes over time to validate interventions
Sources
Last updated