Health Clarity

Our product decodes your internal health by analyzing key body systems, identifying at-risk organs, and revealing real physiological trends giving you a clear view of how your body is functioning.

Health Clarity

Our product decodes your internal health by analyzing key body systems, identifying at-risk organs, and revealing real physiological trends giving you a clear view of how your body is functioning.
Transform Your Health Data into Actionable Insights

Health Clarity brings together clinical data, scientific methodology, and advanced scoring logic to translate your lab biomarkers into a clear health picture.

Health Clarity brings together clinical data, scientific methodology, and advanced scoring logic to translate your lab biomarkers into a clear health picture.

Health Score

The Health Score is a composite health scoring system that converts biomarker values into a single overall health score using normalization, weighted scoring, age impact, and biological aging.

Health Score

The Health Score is a composite scoring system that converts biomarker values into a single overall health score using normalization, weighted scoring, age impact, and biological aging.
The Health Score is a composite scoring system that converts biomarker values into a single overall health score using normalization, weighted scoring, age impact, and biological aging.

Bio Age

The Bio Age is a biological aging model that estimates an individual’s physiological age using standard blood biomarkers, regression modeling, and mortality-risk calculations. The framework is based on clinically validated Phenotypic Age and Gompertz mortality methodologies to assess long-term health risk and accelerated aging.
The Bio Age is a biological aging model that estimates an individual’s physiological age using standard blood biomarkers, regression modeling, and mortality-risk calculations. The framework is based on clinically validated Phenotypic Age and Gompertz mortality methodologies to assess long-term health risk and accelerated aging.

How Health Score is Calculated

01

Biomarker Evaluation

Each biomarker is compared against:
Normal Range
Low/high thresholds
Extreme abnormal ranges
Based on deviation severity, a normalized score between 0-100 is assigned:
  • Mild abnormalities reduce the score proportionally
  • Extreme abnormalities score lowest
  • Normal values score highest

Age and gender Penalty

An age and gender sensitive penalty is applied using a sigmoid model:
  • Borderline and high-risk biomarkers are penalized more heavily with increasing age.
  • Abnormal biomarkers have a greater negative impact at older ages.

02

03

Weighted Biomarker & System Scoring

Each biomarker is mapped to a health system such as:
Heart Health
Kidney Health
Liver Health
Metabolic Health
Blood Health
Biomarkers and systems are assigned:
Age-specific weights
Gender-specific weights
Biomarker scores are aggregated into system-level scores and then combined into a composite health score.

Biological Age Adjustment

The final score is adjusted based on the difference between:
Biological age
Chronological age
If biological age is higher than actual age, the score is penalized more aggressively.

04

Final Output

Overall Health Score
Cohort position
System-wise Health Scores
Risk level

The framework is designed to reflect not just abnormal lab values, but also physiological aging, organ impact, and long-term health risk.

Condition Prediction

The Condition Prediction framework identifies early cardiometabolic risk using clinically validated medical prediction models and biomarker-based risk calculations. The framework currently supports ASCVD risk estimation using the Framingham Risk Score and fatty liver risk estimation using the Fatty Liver Index (FLI), both of which are globally established and research-backed methodologies.

Health Clarity

Our product decodes your internal health by analyzing key body systems, identifying at-risk organs, and revealing real physiological trends giving you a clear view of how your body is functioning.
Transform Your Health Data into Actionable Insights

Health Clarity brings together clinical data, scientific methodology, and advanced scoring logic to translate your lab biomarkers into a clear health picture.

Health Score

The Health Score is a composite scoring system that converts biomarker values into a single overall health score using normalization, weighted scoring, age impact, and biological aging.

How Health Score is Calculated

01

Biomarker Evaluation

Each biomarker is compared against:
Normal Range
Low/high thresholds
Extreme abnormal ranges
Based on deviation severity, a normalized score between 0-100 is assigned:
  • Mild abnormalities reduce the score proportionally
  • Extreme abnormalities score lowest
  • Normal values score highest

Age and gender Penalty

An age and gender sensitive penalty is applied using a sigmoid model:
  • Borderline and high-risk biomarkers are penalized more heavily with increasing age.
  • Abnormal biomarkers have a greater negative impact at older ages.

02

03

Weighted Biomarker & System Scoring

Each biomarker is mapped to a health system such as:
Heart Health
Kidney Health
Liver Health
Metabolic Health
Blood Health
Biomarkers and systems are assigned:
Age-specific weights
Gender-specific weights
Biomarker scores are aggregated into system-level scores and then combined into a composite health score.

Biological Age Adjustment

The final score is adjusted based on the difference between:
Biological age
Chronological age
If biological age is higher than actual age, the score is penalized more aggressively.

04

Final Output

Overall Health Score
Cohort position
System-wise Health Scores
Risk level

The framework is designed to reflect not just abnormal lab values, but also physiological aging, organ impact, and long-term health risk.

Bio Age

The Bio Age is a biological aging model that estimates an individual’s physiological age using standard blood biomarkers, regression modeling, and mortality-risk calculations. The framework is based on clinically validated Phenotypic Age and Gompertz mortality methodologies to assess long-term health risk and accelerated aging.

How the Bio Age is Calculated

01

Biomarker Collection & Standardization

Laboratory biomarkers are collected and standardized using LOINC mappings and unit conversions to ensure consistency across reports. Key biomarkers include:
Albumin
Glucose
Creatinine
Lymphocyte %
RDW
Mean Cell Volume (MCV)
C-Reactive Protein (CRP)
Alkaline Phosphatase (ALP)
White Blood Cell Count (WBC)

Biomarker Risk Transformation

Each biomarker is transformed into a mortality-risk contribution using regression coefficients derived from survival models such as:
  • Elastic Net Regression
  • Cox Proportional Hazard Models
Each biomarker contributes positively or negatively to physiological risk depending on its association with long-term mortality and aging.Age itself is also included as a weighted risk factor.

02

03

Biological Risk Score Calculation

All biomarker regression terms are aggregated into a single biological risk score representing the individual’s predicted physiological risk profile. Higher-risk biomarker patterns result in:
Higher mortality-risk scores
Accelerated biological aging
Healthier biomarker profiles produce:
  • Lower mortality-risk scores
  • Younger biological ages

Gompertz Mortality Modeling

The biological risk score is passed through a Gompertz hazard model to estimate long-term mortality probability.
This converts biomarker abnormalities into a physiologically interpretable mortality-risk estimate based on aging science literature.

04

05

Biological Age (PhenoAge) Conversion

The mortality-risk estimate is mathematically inverted into a Biological Age (PhenoAge) value. This represents:
  • The equivalent age at which a healthy individual would carry a similar mortality risk
If the calculated biological age is:
  • Higher than chronological age → indicates accelerated aging
  • Lower than chronological age → indicates healthier physiological aging

Final Output

Biological Age
Pace of aging
Impacting Biomarkers

The framework is designed to reflect physiological aging, systemic health deterioration, and long-term mortality risk rather than simply identifying abnormal lab values.

Condition Prediction

The Condition Prediction framework identifies early cardiometabolic risk using clinically validated medical prediction models and biomarker-based risk calculations. The framework currently supports ASCVD risk estimation using the Framingham Risk Score and fatty liver risk estimation using the Fatty Liver Index (FLI), both of which are globally established and research-backed methodologies.

How the Condition Prediction is Calculated

01

Clinical & Biomarker Data Collection

The framework collects and standardizes relevant clinical parameters and biomarkers such as:
Age
Gender
Cholesterol Levels
Blood Pressure
BMI
Waist Circumference
Triglycerides
GGT
Smoking Status
Diabetes Indicators
Invalid, missing, and inconsistent values are cleaned before risk computation.

Risk Score Computation

The biomarkers and clinical inputs are processed through established disease-risk prediction models including:
  • Framingham Risk Score for ASCVD risk
  • Fatty Liver Index (FLI) for fatty liver probability
Each model evaluates the combined impact of metabolic, cardiovascular, and lifestyle-related risk factors to estimate disease likelihood and long-term health risk.

02

03

Risk Stratification

The computed scores are translated into clinically interpretable risk categories such as:
  • Low Risk
  • Moderate Risk
  • High Risk
Higher scores indicate greater probability of cardiometabolic disease progression and associated long-term complications.

Final Output

Condition Probability
Impacted Organs
Impacting Biomarkers
Prevention Techniques

The framework is designed to support early detection, preventive intervention, and longitudinal health monitoring using clinically validated and evidence-backed risk prediction methodologies.

Condition Detection

The Condition Detection framework identifies the current presence of clinically significant health conditions using biomarker-based diagnostic logic and evidence-backed clinical thresholds. The framework evaluates laboratory biomarkers against predefined medical criteria derived from established clinical guidelines and research-supported diagnostic models to detect active metabolic, cardiovascular, liver, kidney, endocrine, and nutritional conditions.

How the Condition Detection is Calculated

01

Biomarker Collection & Standardization

The framework collects and standardizes relevant laboratory biomarkers such as:
HbA1c
Fasting Glucose
Lipid Profile
TSH, Free T3, Free T4
Creatinine, eGFR, Urine ACR
ALT, AST, GGT, ALP, Bilirubin
Vitamin B12
Vitamin D
Biomarker values are validated, cleaned, and normalized before condition evaluation.

Clinical Threshold Evaluation

Each condition is detected using predefined clinical rules and diagnostic thresholds based on combinations of biomarkers. The framework currently supports detection of:
  • Diabetes
  • Dyslipidemia
  • Hypothyroidism
  • Hyperthyroidism
  • Kidney Dysfunction
  • Liver Dysfunction
  • Vitamin B12 Deficiency
  • Vitamin D Deficiency
Conditions are flagged only when the biomarker combination satisfies the required clinical criteria.

02

03

Risk & Condition Classification

The biomarker patterns are evaluated against disease-specific thresholds to determine whether the condition is currently detectable or clinically significant. The framework helps identify:
  • Active metabolic abnormalities
  • Organ dysfunction indicators
  • Nutritional deficiencies
  • Cardiometabolic risk patterns

Final Output

Detected Conditions
Undetected Conditions

The framework is designed to support early detection, preventive screening, and health-risk identification using clinically established diagnostic criteria and biomarker-driven condition logic.

Early Health Signals

The Early Health Signals framework identifies emerging physiological and metabolic risk patterns using abnormal biomarker correlations, multi-biomarker relationships, and system-level health deviations. Unlike direct condition detection, these signals represent early-stage dysfunctions, hidden physiological stress, or worsening health trends that may progress into clinically significant disease if left unmanaged.

How the Early Health Signals are Calculated

01

Biomarker Deviation Detection

The framework continuously evaluates biomarkers against their healthy reference ranges and identifies biomarkers that are:
Borderline abnormal
Persistently elevated or reduced
Clinically correlated with systemic dysfunction
The severity of deviation, number of abnormal biomarkers, and direction of abnormality are considered during signal generation.

Intra-System & Inter-System Correlation Analysis

Abnormal biomarkers are grouped and correlated both within the same physiological system (intra-system) and across multiple physiological systems (inter-system). The framework evaluates relationships across systems such as:
Cardiovascular Health
Metabolic Health
Liver Health
Kidney Health
Inflammatory Response
Nutritional Health
This enables detection of:
  • Single-system dysfunction patterns
  • Cross-system metabolic and inflammatory interactions
  • Early multi-organ stress signatures
The framework identifies biomarker combinations commonly associated with future disease progression, chronic inflammation, metabolic dysfunction, and organ stress based on established medical correlations and clinical literature.

02

03

Risk Stratification

Based on biomarker relationships and deviation severity, the framework generates interpretable health signals such as:
Cardiovascular Inflammation
Metabolic Dysregulation
Early Insulin Resistance
Liver Stress Patterns
Chronic Inflammatory Activity
Each signal is assigned:
  • A signal title
  • Correlation type (Intra-system / Inter-system)
  • Risk severity level
  • Critical biomarker contributors
  • Physiological interpretation
  • Possible causes
  • Preventive action recommendations
Higher-risk signals indicate stronger evidence of ongoing physiological imbalance and increased probability of future disease development.

Final Output

Inter-System Correlations
Intra-System Correlations
Impacting Biomarker Clusters
Risk Severity Levels
Possible Causes

The framework is designed to identify early warning patterns before overt disease manifestation, enabling proactive intervention, preventive care, and longitudinal health monitoring.

How the Bio Age is Calculated

01

Biomarker Collection & Standardization

Laboratory biomarkers are collected and standardized using LOINC mappings and unit conversions to ensure consistency across reports. Key biomarkers include:
Albumin
Glucose
Creatinine
Lymphocyte %
RDW
Mean Cell Volume (MCV)
C-Reactive Protein (CRP)
Alkaline Phosphatase (ALP)
White Blood Cell Count (WBC)

Biomarker Risk Transformation

Each biomarker is transformed into a mortality-risk contribution using regression coefficients derived from survival models such as:
  • Elastic Net Regression
  • Cox Proportional Hazard Models
Each biomarker contributes positively or negatively to physiological risk depending on its association with long-term mortality and aging.Age itself is also included as a weighted risk factor.

02

03

Biological Risk Score Calculation

All biomarker regression terms are aggregated into a single biological risk score representing the individual’s predicted physiological risk profile. Higher-risk biomarker patterns result in:
Higher mortality-risk scores
Accelerated biological aging
Healthier biomarker profiles produce:
  • Lower mortality-risk scores
  • Younger biological ages

Gompertz Mortality Modeling

The biological risk score is passed through a Gompertz hazard model to estimate long-term mortality probability.
This converts biomarker abnormalities into a physiologically interpretable mortality-risk estimate based on aging science literature.

04

05

Biological Age (PhenoAge) Conversion

The mortality-risk estimate is mathematically inverted into a Biological Age (PhenoAge) value. This represents:
  • The equivalent age at which a healthy individual would carry a similar mortality risk
If the calculated biological age is:
  • Higher than chronological age → indicates accelerated aging
  • Lower than chronological age → indicates healthier physiological aging

Final Output

Biological Age
Pace of aging
Impacting Biomarkers

The framework is designed to reflect physiological aging, systemic health deterioration, and long-term mortality risk rather than simply identifying abnormal lab values.

Condition Detection

The Condition Detection framework identifies the current presence of clinically significant health conditions using biomarker-based diagnostic logic and evidence-backed clinical thresholds. The framework evaluates laboratory biomarkers against predefined medical criteria derived from established clinical guidelines and research-supported diagnostic models to detect active metabolic, cardiovascular, liver, kidney, endocrine, and nutritional conditions.

How the Condition Prediction is Calculated

01

Clinical & Biomarker Data Collection

The framework collects and standardizes relevant clinical parameters and biomarkers such as:
Age
Gender
Cholesterol Levels
Blood Pressure
BMI
Waist Circumference
Triglycerides
GGT
Smoking Status
Diabetes Indicators
Invalid, missing, and inconsistent values are cleaned before risk computation.

Risk Score Computation

The biomarkers and clinical inputs are processed through established disease-risk prediction models including:
  • Framingham Risk Score for ASCVD risk
  • Fatty Liver Index (FLI) for fatty liver probability
Each model evaluates the combined impact of metabolic, cardiovascular, and lifestyle-related risk factors to estimate disease likelihood and long-term health risk.

02

03

Risk Stratification

The computed scores are translated into clinically interpretable risk categories such as:
  • Low Risk
  • Moderate Risk
  • High Risk
Higher scores indicate greater probability of cardiometabolic disease progression and associated long-term complications.

Final Output

Condition Probability
Impacted Organs
Impacting Biomarkers
Prevention Techniques

The framework is designed to support early detection, preventive intervention, and longitudinal health monitoring using clinically validated and evidence-backed risk prediction methodologies.

Early Health Signals

The Early Health Signals framework identifies emerging physiological and metabolic risk patterns using abnormal biomarker correlations, multi-biomarker relationships, and system-level health deviations. Unlike direct condition detection, these signals represent early-stage dysfunctions, hidden physiological stress, or worsening health trends that may progress into clinically significant disease if left unmanaged.

How the Condition Detection is Calculated

01

Biomarker Collection & Standardization

The framework collects and standardizes relevant laboratory biomarkers such as:
HbA1c
Fasting Glucose
Lipid Profile
TSH, Free T3, Free T4
Creatinine, eGFR, Urine ACR
ALT, AST, GGT, ALP, Bilirubin
Vitamin B12
Vitamin D
Biomarker values are validated, cleaned, and normalized before condition evaluation.

Clinical Threshold Evaluation

Each condition is detected using predefined clinical rules and diagnostic thresholds based on combinations of biomarkers. The framework currently supports detection of:
  • Diabetes
  • Dyslipidemia
  • Hypothyroidism
  • Hyperthyroidism
  • Kidney Dysfunction
  • Liver Dysfunction
  • Vitamin B12 Deficiency
  • Vitamin D Deficiency
Conditions are flagged only when the biomarker combination satisfies the required clinical criteria.

02

03

Risk & Condition Classification

The biomarker patterns are evaluated against disease-specific thresholds to determine whether the condition is currently detectable or clinically significant. The framework helps identify:
  • Active metabolic abnormalities
  • Organ dysfunction indicators
  • Nutritional deficiencies
  • Cardiometabolic risk patterns

Final Output

Detected Conditions
Undetected Conditions

The framework is designed to support early detection, preventive screening, and health-risk identification using clinically established diagnostic criteria and biomarker-driven condition logic.

How the Early Health Signals are Calculated

01

Biomarker Deviation Detection

The framework continuously evaluates biomarkers against their healthy reference ranges and identifies biomarkers that are:
Borderline abnormal
Persistently elevated or reduced
Clinically correlated with systemic dysfunction
The severity of deviation, number of abnormal biomarkers, and direction of abnormality are considered during signal generation.

Intra-System & Inter-System Correlation Analysis

Abnormal biomarkers are grouped and correlated both within the same physiological system (intra-system) and across multiple physiological systems (inter-system). The framework evaluates relationships across systems such as:
Cardiovascular Health
Metabolic Health
Liver Health
Kidney Health
Inflammatory Response
Nutritional Health
This enables detection of:
  • Single-system dysfunction patterns
  • Cross-system metabolic and inflammatory interactions
  • Early multi-organ stress signatures
The framework identifies biomarker combinations commonly associated with future disease progression, chronic inflammation, metabolic dysfunction, and organ stress based on established medical correlations and clinical literature.

02

03

Risk Stratification

Based on biomarker relationships and deviation severity, the framework generates interpretable health signals such as:
Cardiovascular Inflammation
Metabolic Dysregulation
Early Insulin Resistance
Liver Stress Patterns
Chronic Inflammatory Activity
Each signal is assigned:
  • A signal title
  • Correlation type (Intra-system / Inter-system)
  • Risk severity level
  • Critical biomarker contributors
  • Physiological interpretation
  • Possible causes
  • Preventive action recommendations
Higher-risk signals indicate stronger evidence of ongoing physiological imbalance and increased probability of future disease development.

Final Output

Inter-System Correlations
Intra-System Correlations
Impacting Biomarker Clusters
Risk Severity Levels
Possible Causes

The framework is designed to identify early warning patterns before overt disease manifestation, enabling proactive intervention, preventive care, and longitudinal health monitoring.

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