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A point-of-care way to predict subclinical atherosclerosis in patients…

- - - - Genetic tests
      - BMI
      - Body Fat Ratio
      - Past Cholesterol Measurements
      - Family History
      - Age
      - High Blood Pressure
      - Cardiac Output
      - Quality of Sleep
      - Test for Pressure of veins/bood/etc
    - - Lifetime LDL Exposure
      - Likelihood of early cardiac event
      - Likelihood of FH
      - Likelihood of cardiac event
      - Rate of LDL Uptake
    - - Equation with TC+non-LDL
    - - Machine learning to predict FH using health record data
      - Using symptoms tracking to find potion with high LDL
      - Look at past cholesterol and estimate
      - Using blood lab data to find those with high LDL
      - Use statin prescription data to predict people with FH
      - Ratio of HDL toLDL
- - - - Ear wax
      - Fingernails
      - Hair
      - Tears
      - Feces
      - Urine
      - Sweat
      - Skin oil
      - Retinal exam
      - Saliva
    - - Plaque buildup on Achilles tendon (tendon xanthomas)
      - Blood Sample (not hard to acquire, but invasive)
      - Harvest calcification/plaque on arteries