2022 · International Journal of Environmental Research and Public Health
Serum Cholesterol Levels and Risk of Cardiovascular Death: A Systematic Review and a Dose-Response Meta-Analysis of Prospective Cohort Studies
Verdict
UNSCIENTIFIC
Neither the paradigm nor the study inside it passes. The verdict is unscientific.
1·Study methodology · inside the paradigm
UNSCIENTIFIC
Does not follow the scientific method within Allopathic Medicine.
2·Paradigm · Allopathic Medicine
UNSCIENTIFIC
Fails the tests. The worse layer decides.
How the verdict is decided
The verdict grades both layers: the paradigm this study assumes, and how the study was carried out inside it. Both must be scientific for the whole thing to be scientific. A clean method inside an unscientific paradigm is unscientific. An unscientific method inside a better paradigm is unscientific. A split on either layer is mixed. The worse layer decides.
1·Study methodology
Did this study test its claim with methods that are independent, falsifiable, and non-circular?
UNSCIENTIFIC
This study claims that higher total cholesterol and LDL-C levels increase cardiovascular death risk while higher HDL-C levels decrease it, based on a pooled analysis of 14 prospective cohort studies. All three tests fail.
Independently Verifiable
Fail
The blood tests produced chemical measurements, but the model decided which fraction was LDL-C and which deaths counted as cardiovascular, so without the model you have cholesterol numbers and dead people, not LDL-driven cardiac mortality.
Falsifiable
Fail
The meta-analysis pooled 14 observational studies inside the cholesterol hypothesis, and a pooled null would have been called confounding or poor study quality rather than evidence that cholesterol does not cause cardiovascular death.
Non-Circular
Fail
LDL-C was calculated using a formula that assumes the lipoprotein model, and deaths were sorted into the cardiovascular category using diagnostic criteria from the same disease model the study claims to support.
Why
They took blood from over a million people and measured chemical fractions in it. For total cholesterol, the measurement is direct. For LDL-C, the standard method calculates it by subtracting HDL-C and a triglyceride estimate from total cholesterol using a formula that assumes the lipoprotein model. They followed these people for years and counted who died. But they did not count all deaths. They sorted deaths into "cardiovascular" and everything else using diagnostic criteria from the cardiovascular disease model. Then they compared cholesterol fractions to cardiovascular deaths and reported hazard ratios. The higher the LDL-C, the higher the cardiovascular death rate. The model defined the fraction, calculated the value, classified the death, and then reported the association as evidence the model works. A stranger without the model sees blood tests and death certificates. They do not see LDL causing heart attacks.
2·Paradigm · Allopathic Medicine
Does the framework this study assumes pass the three tests?
UNSCIENTIFIC
Independently Verifiable
Fail
The allopathic model names cholesterol fractions as disease agents, builds formulas to calculate them, and classifies deaths using its own diagnostic codes, so the observation only exists inside the model that produced it.
Falsifiable
Fail
When cholesterol levels fail to predict cardiovascular death, the model calls it confounding, dietary factors, or genetic predisposition, so no result can disprove the claim.
Non-Circular
Fail
The disease model named cholesterol as a culprit, built tests to measure cholesterol fractions, classified deaths by its own diagnostic criteria, and reported the association as proof the model is correct.
Why
The allopathic model treats cardiovascular disease as a plumbing problem where cholesterol clogs arteries. The model names cholesterol fractions as disease agents, builds formulas to calculate them, classifies deaths by its own diagnostic criteria, and reports associations as confirmation. When cholesterol levels fail to predict death, the model calls it confounding, dietary factors, or genetic predisposition. No result can disprove the model because every negative has a ready explanation. Cholesterol is a substance the liver produces in response to tissue damage. The model treats the substance as the disease rather than as part of the body's response to underlying damage. Suppressing cholesterol with medication is not the same as healing the patient. A study that proves high cholesterol correlates with cardiovascular death does not prove the model understands why the patient was sick. It proves the model can find its own categories in its own data.
From the study
the exposure was total serum cholesterol, HDL-cholesterol, and LDL-cholesterol levels measured before the cardiovascular event, and the outcome was cardiovascular disease (CVD) mortality, including coronary heart disease (CHD) mortality.
We used the final results of the studies after adjustment for potential confounders from a multivariable model.
The funnel plot asymmetry test for measuring publication bias was not used because there were less than 10 studies included in each analysis.