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Concept

Lipids & Cardiometabolic Risk

4 studiesEvidence last moved Sep 20, 2026

Blood lipids are the most-measured modifiable cardiovascular risk markers, and the literature on them spans short randomised trials of what people eat, large observational cohorts, genetic causal inference and animal mechanism work. Each answers a different question, and their answers are not interchangeable.

Dietary fat advice is contested in public in a way it is not in the trial literature, largely because a four-week change in a lipid marker and a lifetime change in event risk get quoted as if they were the same evidence. Seeing the designs side by side is what makes the difference legible.

Studies

4

Findings

4

4 supporting · 0 challenging · 0 qualifying citations

Open tensions

1

Latest change

Concept page published

Lipids & Cardiometabolic Risk

Currently

What we know

  1. A randomised marker effect over four weeks — with no clinical events measured.
  2. Mendelian randomisation raises this above the observational association it accompanies.
  3. No single index dominated; the better predictor changed with the endpoint.
  4. The knockdown reproducing the drug effect is what places SGK1 on the path.

Largest unresolved question

The randomised trial and the genetic study support different claims about the same molecules. The trial randomises a real dietary exposure but measures markers over four weeks in healthy volunteers; the Mendelian randomisation estimates lifelong effects on hard endpoints but from a single baseline lipid measurement in a healthier-than-average cohort, with instrument pleiotropy unresolved.

Common misconceptions

  • A trial showing coconut oil does not raise LDL more than olive oil shows it is heart-healthy.

    The trial ran four weeks in healthy volunteers and measured lipids, not cardiovascular events. It establishes a marker comparison over a month; it cannot establish event risk, and the authors say it does not displace guidance on saturated fat.

  • A risk index that predicts better is measuring the underlying biology better.

    TyG-WHtR beat TyG for mortality and heart failure while TyG beat TyG-WHtR for coronary disease and angina, in the same dataset. An index that wins on one endpoint and loses on another is capturing endpoint-specific prediction, not a single better measure of risk.

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