Medical Mistakes with Probability, 2

Optimal LDL Levels

Abstract: risk factors for LDL/ApoB underestimate the risk factor for Lp(a) positive subjects and overestimate for Lp(a) negative ones, a case of base-rate fallacy.

Base rate fallacy and correction of risk factors. I simplify by calling Lp(a) positive and negative those with numbers above and below the average population.

I am just using basic probabilistic logic here.

Risks factors for ASCVD from LDL (or ApoB) levels are computed for a general population which includes people with low and high Lp(a) levels. Now if having a high Lp(a) increases the cardiac risk over the baseline (up to 2-3 times!) and the proportion of subjects with high Lp(a) is between 15 and 28% of the population, then, necessarily, those with low Lp(a) will have, for a given level of LDL, a considerably lower risk and many might be treated unnecessarily.

The risk factor for nonLp(a) can be ~ 30% lower! Statins don’t come for free. There are hidden and less hidden side effects.

Note: it appears from papers (the latest, Bhatia et al, “Independence of Lipoprotein(a) and Low-Density Lipoprotein Cholesterol–Mediated Cardiovascular Risk: A Participant-Level Meta-Analysis”, Circ., 2025) that Lp(a) is additive (hence independent) which simplifies the computation.

I also note the possible presence of a “J curve” of LDL for primary prevention that has the optimum too far above what can be expected from guidelines, but this inference is largely from Chinese billing data, though with an impressive sample ~4 10^5, Chang et al, “Low-Density Lipoprotein Cholesterol, Cardiovascular Disease Risk and Mortality in China”, JAMA Network Open, 2024. JAMA Network Open. doi:10.1001/jamanetworkopen.2024.22558