Individual evaluation

My understanding is that a statin would reduce the risk of a CV event in the treated group so that it is, for example, 40% lower than in the control group. If you look at all the subgroups (e.g. those with high LDL-C > 200mg/dl , diabetes, smoking, SBP > 150, age > 70, etc.) then you get 40% reduction in CV events in all these groups and their complements. However, if you look at these predictor variables after treatment it will only be the LDL-C that reduces in line with the drop in frequency of a CV event, suggesting a causal connection. The other predictor variables will not change and will therefore be non-causal associations. Does this answer your question?

I’m not sure that certifies causality.

Assuming the existence of unknown pleiotropies and for example unmeasured vascular effects correlated with LDL-c, I think we could be in trouble.

But the main thing is that as an analogy if I have a pipe with holes in it and I remove the oil (LDL-c) from the pipe, it will improve as long as I remove the LDL-c and linearity would be expected. But if I remove the holes from the people in both group and placebo the effect will disappear. Match or gasoline?

This I think is embodied in studies of PRS ( polygenic risk ), where the value of LDL-c disappears, or the lack of associations if CAC=0 is seen recently, or the adjustment in some studies by APOC-III and ApoB blurring.

Hence the doubt as to whether Mendelian randomization overcomes these limitations that I see in RCTs and more evident in observational studies. Or maybe my reading is wrong at some point that I fail to see, and maybe I am making some basic statistical knowledge error.

Thank you Mr. Llewelyn

Good point. However, by ‘causal connection’, I did not intend to imply a direct causal effect but that LDL-c was connected in some way to the causal effect. The latter may or may not be a direct causal effect. There may be some other mechanism (e.g. that a statin acts directly on an atheromatous plaque and at the same time reduces the LDL-c that does not directly cause the plaque). However, from a predictive point of view we can use the lowering of LDL-c to predict a lowering of vascular event risk but as age is not lowered by a statin, we cannot use age in the same way. When we press the ‘Intervention’ button in the Mayo calculator and apply a 40% reduction from a high dose statin to the overall risk due to age, lipid profile , SBP etc we do assume that all the predictor variables, including age, have a ‘causal connection’.

We are getting closer to my point, that the existing evidence does not guarantee us that LDL is the end of the road or a physiological cause, and that we are still on the limitation side of mechanistic knowledge of the state of the art, i.e., would you agree with these statements?

  • The existing evidence strongly suggests to us a causal relationship, from a probabilistic group perspective, but not an individual one, since we are not always limited on mechanistic grounds?.

  • None of the three existing lines of evidence ( MR, RCT, Epy ) can free us from the bias of mechanistic knowledge to establish physiological and therefore purely individual causation.

I am finding the discussion very enriching.

I think that it is generally agreed that we can only ever have a partial understanding of disease mechanisms or any other natural process (e.g., climate change). The best we can do is to apply this imperfect knowledge to choose items of information to allow us to estimate as accurately as possible the probabilities of important outcomes with and without interventions in order that we can make decisions that are as well informed as possible.

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