"Fibermaxxing" is admittedly a silly term, but not only does a high fiber diet reduce cardiovascular mortality by 26%, it also reduces risk of cancer by 22%. Your grandparents were right!
Long Covid patients often face post-exertional malaise (PEM).
One of the main treatments for Long Covid is graded exercise therapy. This works for some subset of patients. But for other patients, it actually makes things worse. Right now we have no objective test that tells who is in what category.
I like this study since it identifies a specific mechanism of why PEM happens. And maybe that will lead to a more objective test for different sub-types of long covid, so that we can actually prescribe the right treatments to people.
Congrats on the launch. I always love to see smart ML founders applying their talents to health and bio.
What were the biggest challenges in getting major pharma companies onboard? How do you think it was the same or different compared to previous generations of YC companies (like Benchling)?
Thanks! I think advantages we had over previous generations of companies is that demand and value for software has become much clearer for biopharma. The models are beginning to actually work for practical problems, most companies have AI, data science or bioinformatics teams that apply these workflows, and AI has management buy-in.
Some of the same problems exist, large enterprises don't want to process their un-patented, future billion-dollar drug via a startup, because leaking data could destroy 10,000 times the value of the product being bought.
Pharma companies are especially not used to buying products vs research services, there's also historical issues with the industry not being served with high quality software, so it is kind of a habit to build custom things internally.
But I think the biggest unlock was just that the tools are actually working as of a few years ago.
What tools are "actually working" as of a few years ago? Foundation models, LLMs, computer vision models? Lab automation software and hardware?
If you look at the recent research on ML/AI applications in biology, the majority of work has, for the most part, not provided any tangible benefit for improving the drug discovery pipeline (e.g. clinical trial efficiency, drugs with low ADR/high efficacy).
The only areas showing real benefit have been off-the-shelf LLMs for streamlining informatic work, and protein folding/binding research. But protein structure work is arguably a tiny fraction of the overall cost of bringing a drug to market, and the space is massively oversaturated right now with dozens of startups chasing the same solved problem post-AlphaFold.
Meanwhile, the actual bottlenecks—predicting in vivo efficacy, understanding complex disease mechanisms, navigating clinical trials—remain basically untouched by current ML approaches. The capital seems to be flowing to technically tractable problems rather than commercially important ones.
Maybe you can elaborate on what you're seeing? But from where I'm sitting, most VCs funding bio startups seem to be extrapolating from AI success in other domains without understanding where the real value creation opportunities are in drug discovery and development.
These days it's almost trivial to design a binder against a target of interest with computation alone (tools like boltzgen, many others). While that's not the main bottleneck to drug development (imo you are correct about the main bottlenecks), it's still a huge change from the state of technology even 1 or 2 years ago, where finding that same binder could take months or years, and generally with a lot more resources thrown at the problem. These kinds of computational tools only started working really well quite recently (e.g., high enough hit rates for small scale screening where you just order a few designs, good Kd, target specificity out of the box).
So both things can be true: the more important bottlenecks remain, but progress on discovery work has been very exciting.
As noted, I agree on the great strides made in the protein space. However, the over saturation and redundancy in tools and products in this space should make it pretty obvious that selling API calls and compute time for protein binding, annd related tasks, isn’t a viable business beyond the short term.
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.
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).
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.
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
There are multiple independent risk factors for heart disease. The major ones are:
- LDL / ApoB
- Blood pressure
- inflammation (hs-CRP)
- Insulin resistance (HbA1c)
- Lp(a): strongest hereditary risk factor.
- eGFR: a measure of kidney function
Non-statin drugs like PCSK9 inhibitors have been shown to reduce heart attacks, strokes, and other cardiovascular events on top of statin therapy. One randomized control trial was FOURIER in 2017: https://www.nejm.org/doi/full/10.1056/NEJMoa1615664
I think the title is deliberately provocative, but they're not wrong.
Heart disease is largely solvable from a biomedical standpoint: we have accurate biomarkers (e.g., ApoB, Lp(a), hs-CRP), long-term risk prediction models, precision nutrition, and highly effective next-generation drugs (PCSK9 inhibitors, lepodisiran, etc).
But practically speaking, heart disease remains the #1 cause of death due to bottlenecks in care delivery: e.g., 46% U.S. counties have no cardiologists, providing guideline-recommended preventive care would require ~27 hours per doctor per day, and incentives are misaligned (health systems profit when hospital beds are full, not from prevention).
But practically speaking, heart disease remains the #1 cause of death due to bottlenecks in care delivery: e.g., 46% U.S. counties have no cardiologists, providing guideline-recommended preventive care would require ~27 hours per doctor per day, and incentives are misaligned (health systems profit when hospital beds are full, not from prevention).
Supposed that we have an incentive aligned health care system. What would that look like?
I think one outcome is that the healthcare system eventually expands due to population growth and less death. Accidents happen, rare cases become more common, even as we get good at fixing or preventing them.
I started a company that does exactly that (except we also have doctors who can prescribe the medications, not just LLMs). So I don't think the approach you describe is naive, but others might. :)
Layering PCSK9 inhibitors, ezetimibe, and statins can lower ApoB/LDL cholesterol by 85–90%, which would have been unheard of until recently.
On the horizon, drugs in clinical trials lower Lp(a) (the strongest hereditary risk factor for heart disease) by 94%. Currently, there are four RNA-based drugs in trials that effectively silence the gene that makes Lp(a) in liver cells: lepodisiran, olpasiran, pelacarsen, and zerlasiran.
Thanks! I've been looking into this a little recently, so thanks for the very timely article and advice.
Do you have any info on good ways to nail down personal sources of inflammation relevant to cardio health, or do you think that just general anti-inflammatory diet/habits is the best we can do right now? We were hunting down a source of mold in our house recently due to some blood markers recently, which got me thinking about inflammation sources and cardio health (we found a big patch of mold between our floors and removed it, blood markers immediately improved).
I had the brain fog. I switched from atorvastatin (dizziness) to pravachol (no dizziness), and then again to higher effective dosage of rosuvastatin (still no dizziness). I went from LDL 152 to LDL 83.
But I have high Lp(a) and so I'm prescribed a baby aspirin every other day. This counteracts the Lp(a) clotting effect but doesn't fix its genetic cause.
I was recently prescribed rosuvastatin (my first time taking any statin), and I had some very intense brain fog. How would you describe the feeling of what you experienced? The best way I could describe it to my friends and family was that I felt like I lost a quarter of my IQ and slept only 5 hours every night(I was getting great sleep, it just felt that way).
It was such a strange feeling. It's a weird feeling to know you're brain just isn't working as it has always worked.
I had dizziness from atorvastatin and nothing from Pravachol and Rosuvastatin. The conclusion I draw is that you have to find something that works. But this is uncharted territory. Your docs won't know and you have to kind of force the issue.
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