Many have pointed out the limitations of BMI, so let me pretend it is a perfect marker for the sake of discussion for the rest of this reply (even though we know it is not). Just substitute “an accurate measure” for “BMI” in your mind’s eye.
I am on a GLP-1. I would not recommend for people with a BMI of 22. There is a financial cost and an unknown long-term side effect profile to be concerned with here.
The data we have so far suggest that GLP-1’s improve metabolic health systemically. Things typically improved: weight, blood sugar, kidney function, liver (NASH), heart health. Studies are ongoing on all of these.
But if your risk for poor health outcomes based on poor metabolic health is low (in other words, if your metabolic health is generally good), I am not sure we can infer with a high degree of confidence that taking a GLP-1 for an extended period of time will provide a benefit.
There may be some downsides for health that we haven’t detected yet, and if the upside is low (since the person wasn’t particularly ill to begin with) this may not pencil out. Of course it may pencil out; we just don’t know right now with a high degree of confidence.
As well there are other side effects to the drugs, such as gastro-intestinal distress, lower quality sleep, and etc. So quality of life is a bit diminished in my experience. These are not the kind of drug that most don’t even know they are taking.
Regarding ApoB, we’ve had this conversation. I personally take a high dose statin (20 mg Rosuvastatin), Ezetimibe, and low dose aspirin. And I am on a GLP-1, which is helpful as well. My recent LDL was 42 ng/dL, HDL 52, Trigs 66, Total 109. I share this only to establish that I am not anti-cholesterol medicine in a general sense. I am personally taking these drugs.
Why do I take them? My risk factors for ASCVD are so high (based on an atrocious CAC scan result and a family history) that it makes sense to treat to reduce my very high risk. My cardiologists says I should almost consider myself to be in the secondary prevention group when looking at data, instead of the primary prevention group.
Whereas my wife’s risk is, seemingly, very low for ASCVD. Despite her LDL being around 130 ng/dL (I don’t know or track ApoB but I presume these correlate) I don’t think she should manage her cholesterol through medicine at this time. Reducing her cholesterol will reduce her risk of ASCVD; we all know this. But it may slightly increase her risk of something else. Depending on how that math works out, it may or may not be life extending.
What we care about in her case is all cause mortality (ACM). The ACC/AHA risk calculator says her risk is very low - 1.0% on a 10 year basis. We don’t have a lot of data on ACM impact from statin therapy for people whose risk was so low that the guidelines do not recommend the use of statins (to my limited knowledge).
On HbA1c, I am not sure what the target should be, but 5.7 is too high in my opinion. I think somewhat lower is more appropriate, say 5.3. Above that you might want to treat, perhaps with GLP-1’s? I am not sure on this, but I will say that A1c is an extremely important marker.
In any event, as an over all comment, I would say
-
This is a useful list & thought experiment and I think it is helpful to go down this path.
-
You can consider adding in some lifestyle elements for those not optimizing those yet - sleep optimization, sun exposure, exercise, and so on. Diet is clearly important but the quality of evidence is pretty poor on exactly what we should eat.
-
Some more thought could go in to determining risk versus reward for different patients. Medicines tend to reduce risk of the thing they are treating, but tend to have some small risks of making other things worse.
If the risk of the thing being treated is small enough, the small increase in risk from the other things can be greater than the benefit. An example is aspirin in primary prevention, where the benefit in heart disease reduction turned out to be more than offset by the increased risk of bleeds.
In many cases we lack the data needed to make a well-informed risk:reward calculation when we are considering treating people who are at much lower risk than the group that was studied in RCTs was. That is a tricky issue.