In short
Overview
Precision medicine is usually sold as genomics. The precision that actually changes what happens to you today comes from somewhere less exciting.
Direct Answer
Three things genuinely personalise a prevention plan: using a risk model validated for your population, adding the risk factors that model leaves out, and measuring disease directly where a prediction is not good enough.
None requires a genome. All of them change what gets prescribed.
The comparison that settles it: a coronary calcium score outperformed polygenic risk scores for predicting coronary events in two large population studies.
Who This Applies To
- Anyone whose risk was calculated with a model not built for them
- People with diabetes, kidney disease or an autoimmune condition, which standard models handle badly
- Anyone holding a genetic or polygenic risk report and unsure what it means
- Anyone who has been given two different risk figures by two clinics
Option Comparison / Decision Criteria
LDL of 190 mg/dL or above, where risk is very high regardless
Familial hypercholesterolaemia, where calculators do not apply at all. It affects about 1 in 300 people
Type 1 diabetes, underestimated by most models even under 40
Type 2 diabetes, where models omitting kidney function, albuminuria and glucose control understate it
Anyone with plaque already visible on a scan done for another reason
Why an In-Person Physician Review Matters
What direct measurement adds A calcium score reclassified 52 per cent of intermediate risk older adults in one study, upward above 615 or downward below 50, and it was better than age at getting the risk right.
In 6739 middle aged women rated low or borderline risk, 36 per cent had detectable calcium, its presence doubled event risk, and it moved 20 per cent into a more accurate category. Where genetic testing does earn its place Mainly in suspected familial hypercholesterolaemia, and even there it is not routine, because the diagnosis is usually made on clinical criteria and the result rarely changes management. It helps when the clinical picture is unclear, or when identifying the family variant lets relatives be screened for exactly that. A negative result does not exclude the condition, since up to half of people meeting clinical criteria have no identifiable variant. The honest limits Models estimate the average risk of people like you, not your risk External validation is imperfect. One newer model underestimated observed incidence in three of four health systems in one study Genetic databases underrepresent people of African ancestry, so variants there are more often classified as uncertain
Frequently Asked Questions
There is no evidence it improves outcomes for a healthy adult. The useful question is what you would do differently with the result. If the answer is nothing, it is not worth doing.
Almost certainly a different calculator. The same person can get 4.3 per cent from one model and 8.0 per cent from another, and the gap is largest in older adults. Thresholds belong to the calculator and cannot be swapped between them.
Not for routine decisions. For coronary events, a calcium score improved risk prediction and reclassification in two large studies where the polygenic score did not.
Mainly in suspected familial hypercholesterolaemia where the clinical picture is unclear, or to identify a family variant so relatives can be screened for it. Even then it is not routine.
Evidence
Where this information comes from
Every figure on this page is taken from the sources below. All of them are free to read, so you can check them yourself.
This page is general medical information and does not replace a personal consultation. Trial results are averages across large groups, not a prediction for any one person.