Metabolic syndrome is diagnosed clinically by the NCEP ATP III rule: a patient meets the diagnosis when 3 or more of these 5 independent criteria are true at once (no single test decides it β it's a threshold count over correlated cardiometabolic risk markers):
1. Waist circumference β₯102 cm (M) / β₯88 cm (F)
2. Triglycerides β₯150 mg/dL
3. HDL cholesterol <40 mg/dL (M) / <50 mg/dL (F)
4. Systolic BP β₯130 mmHg (or on treatment)
5. Fasting glucose β₯100 mg/dL (or on treatment)
Diagnosis: count(criteria true) β₯ 3
The 3D cloud is a synthetic cohort of 400 patients generated from one shared latent metabolic-risk variable r β [0,1] plus independent noise on each axis β this is why the points aren't scattered uniformly but stretch along a diagonal "risk axis": in real populations these five markers are genuinely correlated (insulin resistance drives abdominal fat, dyslipidaemia and glucose intolerance together), which is exactly why the syndrome is scored as a cluster rather than any one number.
- Position β each point (including your patient, the large highlighted marker) sits at (waist, fasting glucose, triglycerides), the three axes drawn.
- Color β a smooth ramp from cool (0 criteria met) to red (5 met), independent of position, since HDL and blood pressure also count toward the total but aren't spatial axes.
- Cohort percentile β the share of the 400 synthetic patients whose criteria count is β€ yours, i.e. how your metabolic risk ranks against the simulated population.
- Risk score Ξ£ β Ξ£ of clamped(0,1) fractional distance past each threshold, summed across the 5 markers; unlike the integer criteria count, it keeps rising as values drift further past a cutoff, so two "3/5" patients can still be told apart.
Real-world relevance: this is the same rule cardiologists and primary-care physicians use at a checkup β it flags roughly a quarter of adults in industrialised countries and roughly doubles cardiovascular-disease risk and increases type-2-diabetes risk five-fold when 3+ criteria are present.