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Personalization SolutionsPrecision LongevityLongevity Assessment

Precision Longevity and Healthy Aging

LifeNome’s personalized longevity solution is designed to help individuals understand their unique longevity potential and make informed lifestyle choices to enhance their health and well-being. By leveraging advanced data analytics, including genetic, lifestyle, personal attributes, and health data, LifeNome provides personalized insights and recommendations to optimize health and longevity. This solution empowers individuals to take control of their health journey, enabling them to make proactive decisions that can positively impact their longevity and overall quality of life.

This article describes how to use LifeNome’s CORE API to get personalized longevity assessments.

Personalized Longevity Assessments

Lifenome CORE API provides a powerful engine for generating personalized longevity assessments. The engine uses a combination of user profile, lifestyle assessments and DNA traits to compute personalized longevity assessments. The engine is exposed via a single API endpoint:

  • API path: /core-api/ax-services/longevity-assessment/
  • OpenAPI schema: /core-api/ax-services/longevity-assessment/openapi

Longevity Assessment is computed based on:

  • User Profile (required): collection of personal attributes, socioeconomic attributes, health history
  • Lifestyle Assessments (optional): collection of lifestyle habits, such as smoking, alcohol consumption, diet, physical activity, sleeping habits and mental wellbeing
  • DNA Traits Assessments (optional): collection of DNA traits, such as genetic predisposition to certain diseases, physical characteristics, and other health-related traits
  • BioAge assessments (optional): biological age assessments based on blood biomarkers and epigenetic (methylation) data.

The engine uses a combination of these inputs to compute two types of longevity assessments:

  • Standard Life Expectancy - a standard life expectancy assessment based on partial User Profile (current age and gender).
  • Personalized Life Expectancy - a personalized life expectancy assessment based on user profile, lifestyle assessments, DNA traits and bioage assessments

User Profile Input

The User Profile object represents set of user properties that are directly used for computing Longevity Assessments.

Attributes

  • gender
  • age
  • height
  • weight
  • body_frame_size
  • socioeconomic:
    • education
    • income_level
    • location_type
  • health_history:
    • personal_history
    • family_history

Example

{
    "gender": "male",
    "age": 42,
    "height": 178,
    "weight": 79,
    "body_frame_size": 2,
    "socioeconomic": {
        "education": 2,
        "income_level": 3,
        "location_type": 1
    },
    "health_history": {
        "personal_history": {
            "diabetes_type_1": false,
            "diabetes_type_2": false,
            "hypertension": true,
            "selected_diseases_under_control": true
        },
        "family_history": {
            "diabetes": false,
            "cancer": false,
            "cardiovascular": false
        }
    }
}

Lifestyle assessments input

The Lifestyle Assessments object represents user’s lifestyle described by a set of estimated lifestyle habits: smoking and alcohol consumption, diet, physical activity, sleeping habits and mental wellbeing. Most of the assessments are expressed as a numeric value on a continuous scale from 1 to 5.

For example, smoking habit is expressed by value of smoking attribute where smoking : 1 represents non smoker and smoking: 5 represents chain smoker.

Lifestyle Assessment Attributes

  • smoking
  • alcohol
  • diet
    • quality
    • habits
  • physical_activity
    • duration
    • intensity
    • heart_health_points
  • sleep
    • duration
    • quality
  • mental_wellbeing

Lifestyle Assessment Example

{
  "lifestyle_assessments": {
    "smoking": 1,
    "alcohol": 1,
    "diet": {
      "quality": 2,
      "habits": 2
    },
    "physical_activity": {
      "duration": 3,
      "intensity": 3,
      "heart_health_points": 3
    },
    "sleep": {
      "duration": 3,
      "quality": 3
    },
    "mental_wellbeing": 3
  }
}

How to create Lifestyle Assessments object

LifeNome customers can ask their users to directly provide estimated values of Lifestyle Assessments attributes. While this approach seems straightforward it is potentially error prone as user’s can use wrong mental models for estimating the attributes.

The better way is to create questionnaire with multiple sets of questions related to particular attribute. Then given answers can be used to calculate estimates based on well defined assessment model.

Longevity solution provides one such Self Assessment Questionnaire that is decribed …

DNA Trait Assessments Input

In order to include user’s DNA traits into longevity assessment, API client needs to prepare DNA Trait Assessments object. DNA Trait Assessments object is a dictionary of trait codes and trait assessment levels:

  • trait_code: a valid trait code
  • assessment level: integer value from 0 to 2

DNA Trait Assessments Example

{
  "dna_traits": {
    "low-resting-metabolic-rate": 0,
    "high-resting-metabolic-rate": 1,
    "vitaminB12-deficiency": 1,
    "vitaminD-deficiency": 1,
    "calcium-deficiency": 1,
    "iron-deficiency": 1,
    "joint-injury-risk": 2,
    "muscle-damage-risk": 1,
    "muscle-strength-loss": 1,
  }
}

If you previously created a sample and initialized a genotype for the sample, the list of DNA Trait Assessments can be fetched using the List Sample’s Trait Assessments endpoint.

BioAge Assessments Input

If you have BioAge assessments available for your users, you can include them in the Longevity Assessment request.

Currently, Longevity Assessments engine supports two types of BioAge assessments:

  • BioAge based on blood biomarkers
  • BioAge based on methylation (epigenetic) data

Both bioage assessments are represented as a single numeric value (representing biological age) and are included in the Longevity Assessment request as a single attribute bioage_assessments with two properties: blood_biomarkers and epigenetic.

{
  "bioage_assessments": {
    "blood_biomarkers": 45,
    "epigenetic": 50
  }
}
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