Yeah, this would make sense if you ignored all dietary and solar variations in Vitamin D productions. So yeah, this person has a bias towards high or low levels but you're not actually measuring it
I don't think it's an honest assessment
No, actually, MR specifically accounts for these variations.
However, one idea (one that would need a lot of evidence in my opinion, but to give you a clearer picture) that is not captured by MR is horizontal pleiotropy. So, if the genetic variants used in this study e.g. actually cause someone to seek out dietary modifications/solar augmentation of vitamin D, that would not be accounted for here.
I don't think the biology in this case allows for that to any significant degree, but I'm not an expert in vitamin D metabolism/action or COVID.
> I don't think it's an honest assessment
The title here is maybe a bit simplified, but the paper is quite honest about its weaknesses if you read it in its entirety.
How exactly? The only mention I see is using the British Biobank data which is 10-15 years old
The basic idea is to identify a variable Z (genotype, in the case of MR) that causally impacts your outcome Y (COVID, in this case) only through your exposure X (vitamin D levels). If you can find and measure such a variable Z (which meets a few other conditions [2]), then you can robustly identify whether X has a causal impact on Y only from observational data.
[1] https://www.youtube.com/watch?v=NLgB2WGGKUw
[2] https://en.wikipedia.org/wiki/Instrumental_variables_estimat...
> I don't think it's an honest assessment
Of what though? This is just a study trying to prove a correlation with VitD deficiency and COVID prevalence, of which they couldn't find any. Any stronger conclusion is out of the scope of the paper.