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I think there's a a bit of a paradox here: cardiovascular disease is solved biomedically, yet still remains the #1 cause of death worldwide.

From a biomedical standpoint, we have highly accurate biomarkers (e.g., ApoB, Lp(a), hs-CRP), long-term risk prediction models, knowledge of nutritional biochemstry, and next generation drugs like PCSK9 inhibitors and lepodisiran that can lower ApoB and Lp(a) by 90%. So there's no fundamental reason why cardiovascular disease has to be in even the top 10 causes of death.

Practically speaking, providing guideline-recommended preventive care would require ~27 hours per doctor per day. And the incentives are misaligned: health systems profit when hospital beds are full, so they lack the business model to actually invest in prevention.

So it's a clear illustration of a systematic gap between research and care delivery.

5 comments

> I think there's a a bit of a paradox here: cardiovascular disease is solved biomedically, yet still remains the #1 cause of death worldwide.

Because most people don't give a shit about their health, no amount of pills will save you if you eat like the average american.

I also think that many people don’t know - I would wager for men that a significant percentage of them do not go to see a doctor preventively unless injured or sick and not that may know their blood pressure or cholesterol trends

Thanks for sharing this and empowering others to improve their heart health outcomes.

I’m not in love with the idea of sharing my biomarkers with multiple health-tech companies and really want a self-hosted solution to import biomarkers from multiple sources such as Apple Health, arbitrary csv and jsons while avoiding duplication.

Claude Code is something that will make this dream a reality for me pretty soon.

Do you have any tips on biomarker data design or import gotchas?

The thing that took the most time was normalizing biomarker names and units across labs. Even for the same lab chain (say, Quest), you'll get the same biomarker with slightly different names (e.g., Lp(a) vs Lipoprotein(a) vs Lipoprotein a) or units (e.g., cells/uL vs 10^9/L).

that was my experience just comparing Apple Health export using Stanford Labs data vs say FunctionHealth.

Im hoping to use LLMs to help with this process.

Will try to use LOINC dataset to standardize against

Well, and everyone knows they should exercise, and many know they should avoid dietary saturated fats, but most people neither exercise nor avoid highly fatty foods.

Couple of reasons why I think this is nonsense .

the mainline guideline is more exercise and better diet which is the treatment to much more than just heart disease. that's not something 27 hours of doctors a day can provide unless you give them guns

the treatments reduce risk, but they don't change the fact the human body is very reliant on the heart and increasingly vulnerable to cardiac death with age, even with perfect biomarkers

given the entrenched attitudes and the time it takes to actually get people to do the thing as evidenced by all the contrarians in the thread...

it would take a lot more than that. Ain't no doc got all that time to go through all this with every person who should take cholesterol lowering medicine but wants to argue their internet sourced bs