Health IndicesOverview

Health Indices

Shen.AI provides functionalities to compute key indices based on your user’s data to assess their health, body composition, and cardiovascular risk, offering valuable insights to help them better understand their body and make informed health decisions.

Health Risk Indices:

  • Vascular Age
  • Cardiovascular Disease Risk
  • Cardiovascular Event Risk
  • Cardiovascular Risk Score

Body Composition and Metabolism Indices:

  • Waist-to-Height Ratio (WHtR)
  • Body Fat Percentage (BFP)
  • Basal Metabolic Rate (BMR)

How can I use Health Indices?

Shen.AI SDK offers two independent ways to leverage Health Indices:

  1. Embedded UI Flow: As part of the video measurement experience, the end-user can enter the health risk factors in a guided UI flow after the measurement. The SDK then displays the computed indices (e.g., vascular age, body fat %, etc.) directly in your app.
  2. Direct API Calls: You can call the Health Indices API (computeHealthRisks(), etc.) completely standalone, without needing to perform a video measurement or show the SDK’s UI. In this scenario, you simply pass the relevant data (e.g. age, systolic BP, cholesterol, etc.) into the API, and the SDK will return the indices programmatically. You can then show or handle them any way you like.

Important: These two approaches are independent. Using the embedded UI flow does not require you to manually call the API, and calling the API methods does not require you to show the embedded UI or run a video measurement first.

Health Risk Indices:

Vascular Age

Vascular age is calculated as the age of a person with the same predicted overall risk for developing atherosclerotic cardiovascular disease (CVD), but with all risk factors at normal levels (total cholesterol 180 mg/dL, HDL 45 mg/dL, untreated systolic blood pressure 125 mm Hg, no diabetes, no smoking).

Note: The vascular age model is based on studies with subjects aged 30 to 74. Therefore, for individuals younger or older than that range, the estimated vascular age might be artificially higher or lower than their true value.

Data needed to compute vascular age:

  • age
  • gender
  • current smoking status
  • diabetes (fasting glucose > 125 mg/dL or use of insulin or hypoglycemic medications)
  • treated hypertension
  • systolic blood pressure
  • total cholesterol level and HDL level or body height and weight

Depending on which data is provided, the calculations are done either based on cholesterol levels (more accurate) or on body mass index (less accurate).

Cardiovascular Disease Risk

The estimated 10-year risk of atherosclerotic cardiovascular disease is based on multivariable, gender-specific risk prediction algorithms derived from the well-known Framingham Heart Study (Framingham risk functions). These functions consider age, high systolic blood pressure (treated or untreated), dyslipidemia (total and high-density lipoprotein cholesterol) or high body mass index (BMI), smoking, and diabetes (D’Agostino et al., Circulation, 2008;117:743-753). The Framingham risk functions are primarily based on data from Caucasian/white populations; therefore, for other races, the actual risk may differ. In particular, compared to Caucasian/white populations, cardiovascular risk is generally higher for African American and American Indian populations, and lower for Hispanic/Latino and Asian populations.

Note: The CVD risk model is based on studies with subjects aged 30 to 74. Therefore, for individuals younger or older than that range, the estimated risk might be artificially higher or lower than their true risk.

The following risks are computed:

  • Atherosclerotic cardiovascular disease (CVD) — overall risk
  • Specific CVD risks:
    • Coronary heart disease (myocardial infarction, coronary death, coronary insufficiency, angina)
    • Stroke (ischemic stroke, hemorrhagic stroke, transient ischemic attack)
    • Heart failure
    • Peripheral vascular disease (intermittent claudication)

Data needed to compute CVD risk:

  • age
  • gender
  • current smoking status
  • diabetes (fasting glucose > 125 mg/dL or use of insulin or hypoglycemic medications)
  • treated hypertension
  • systolic blood pressure
  • total cholesterol level and HDL level or body height and weight

Depending on which data is provided from the last point, risks are computed either based on cholesterol level (more accurate) or on body mass index (less accurate).

Cardiovascular Event Risk

The estimated risk of a first hard atherosclerotic cardiovascular event in the next 10 years (coronary death, myocardial infarction, or stroke) for someone without CVD. The estimation is based on the race- and gender-specific Pooled Cohort Equations developed in 2013 by the Risk Assessment Work Group of the American College of Cardiology/American Heart Association, using a combination of risk factors (age, high systolic blood pressure (treated or untreated), dyslipidemia (total and high-density lipoprotein cholesterol), smoking, and diabetes) examined in several cohorts of patients in the USA (Goff Jr et al. Circulation, 2014;129(25 Suppl 2):S49-73). For races other than Caucasian/white or African American, the risk estimation is based on the model validated on Caucasian/white populations; hence the actual risk may differ. In particular, compared to Caucasian/white populations, the cardiovascular risk is generally higher for American Indian populations and lower for Hispanic/Latino and Asian populations.

The following risks are computed:

  • Hard CV events (coronary death, myocardial infarction, or stroke) — based on Pooled Cohort Equations (USA)
  • Fatal CV events (European SCORE):
    • Coronary death
    • Fatal stroke
    • Total cardiovascular mortality (coronary + stroke)

Note: The hard CV events risk model is based on studies with subjects aged 40 to 79, and the fatal CV events risk model on subjects aged 40 to 65. Therefore, for individuals outside those age ranges, the estimated risk might be artificially higher or lower than their true risk.

Data needed to compute CV event risks:

  • age
  • gender
  • current smoking status
  • diabetes status (fasting glucose > 125 mg/dL or use of insulin or hypoglycemic medications) — only for hard CV events
  • treated hypertension (only for hard CV events)
  • systolic blood pressure
  • total cholesterol level
  • HDL level (only for hard CV events)
  • race (only for hard CV events)
  • country (only for fatal CV events)

Cardiovascular Risk Score

In addition to computed risks, Shen.AI can provide information on how much each factor contributes to the overall risk of developing CVD, presented in a scoring format.

Scores computed for factors:

  • age
  • current smoking status
  • diabetes
  • systolic blood pressure
  • total cholesterol level and HDL level (or BMI)
  • total risk score

Body Composition and Metabolism Indices:

Waist-to-Height Ratio (WHtR)

Waist-to-Height Ratio (WHtR) is a metric that estimates the risk of obesity-related conditions, such as cardiovascular diseases, diabetes, and metabolic syndrome, by assessing abdominal fat distribution through the ratio of waist circumference to height. Higher WHtR values indicate greater abdominal fat, which is associated with increased health risks.

Note: Depending on the available data, WHtR may either be directly calculated from waist circumference and height or estimated using regression models based on demographic data

Data needed to compute WHtR:

  • age
  • gender
  • height
  • weight
  • ethnicity

or

  • height
  • waist circumference

Body Fat Percentage (BFP)

Body Fat Percentage (BFP) estimates the proportion of fat in the body relative to total body weight. It is an important indicator of overall health and fitness, helping to assess the risk of obesity-related conditions such as heart disease, diabetes, and metabolic syndrome. A higher body fat percentage is associated with an increased risk of cardiovascular disease. Understanding BFP helps users monitor their health status and encourages lifestyle changes to reduce fat levels.

Data needed to compute Body Fat Percentage (BFP):

  • age
  • gender
  • height
  • weight

Basal Metabolic Rate (BMR)

Basal Metabolic Rate (BMR) is the number of calories your body requires at rest to maintain basic physiological functions, such as breathing, circulation, and cell production. BMR accounts for a significant portion of total daily energy expenditure (TDEE) and is influenced by factors such as age, gender, weight, height, and body composition.

BMR doesn’t have strict “normal ranges” like some other health metrics (e.g., blood pressure or cholesterol levels) because it is highly individual. However, understanding your BMR helps assess whether your energy needs are typical for your age, gender, and body composition.

Data needed to compute Basal Metabolic Rate (BMR):

  • age
  • gender
  • weight
  • height
ResultsIntegrating indices functionality
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