brandonb

Pretty extensive discussion here: https://news.ycombinator.com/item?id=46707572

"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)?

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).

Lepodisiran has reduced Lp(a) by 94% in clinical trails. After it gets approved, IMO the author should re-write this section.

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. :)

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.

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).

The main side effects of statins are muscle pain and brain fog (from some statins -- others cross the blood-brain barrier much less).

The benefit of statins is to not only lower LDL cholesterol, but also inflammation, which is now actually a stronger risk factor for cardiovascular disease than cholesterol: https://www.empirical.health/blog/inflammation-and-heart-hea...

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.

This is cool! It'd be interesting to correlate this with Vitamin D too.

This is a little different since the Apps SDK lets developers create specialized tool calls to their servers, and create specialized in-chat UI components. It's an evolution of the same concept as the GPT store, but a very different take on the idea.

HbA1c, or just diabetes as a binary variable, has been one of the main inputs into predicting heart attack risk for a long time.

The main marker of kidney function, eGFR, was added with the AHA/ACC's PREVENT equations in 2023.

I wrote a bit about the science behind heart risk calculators, and their various inputs like cholesterol, blood pressure, A1c, eGFR, and so on here: https://www.empirical.health/blog/heart-attack-risk-calculat...

The acoustic model of a speech recognizer used to be a GMM, which mapped a pre-processed acoustic signal vector (generally MFCCs-Mel-Frequency Cepstral Coefficients) to an HMM state.

Now those layers are neural nets, so acoustic pre-processing, GMM, and HMM are all subsumed by the neural network and trained end-to-end.

One early piece of work here was DeepSpeech2 (2015): https://arxiv.org/pdf/1512.02595

I learned speech recognition from the 2nd edition of Jurafsky's book (2008). The field has changed so much it sometimes feels unrecognizable. Instead of hidden markov models, gaussian mixture models, tri-phone state trees, finite state transducers, and so on, nearly the whole stack has been eaten from the inside out by neural networks.

But, there's benefit to the fact that deep learning is now the "lingua franca" across machine learning fields. In 2008, I would have struggled to usefully share ideas with, say, a researcher working on computer vision.

Now neural networks act as a shared language across ML, and ideas can much more easily flow across speech recognition, computer vision, AI in medicine, robotics, and so on. People can flow too, e.g., Dario Amodei got his start working on Baidu's DeepSpeech model and now runs Anthropic.

Makes it a very interesting time to work in applied AI.

It's a cool idea! This is going to become easier in the next year since TEFCA will let patients request their own medical records through the health information exchanges that have already been set up for treatment.

(OP here) Happy to answer any questions on this work!

If the fire alarm didn't go off, you didn't sear hard enough. :)

D’oh. You’re right. HN doesn’t let me edit the URL after posting, so will re-submit.

> When I see that it is widely accepted that ApoB is better to measure than LDL-C, but the industry continues to measure LDL-C, but not ApoB, I wonder why. It makes me skeptical.

Part of this is just that insurance coverage lags science. We've known that ApoB is more accurate than LDL since the 1990's or 2000's, but to be covered by insurance, several more steps have to happen.

First, the major professional societies (like the American College of Cardiology or National Lipid Associations) have to issue formal guidelines.

Then, the USPSTF (US Preventive Services Task Force) needs to review all of the evidence. They tend to do reviews only every 5 or 10 years. (Countries aside from the US have different organizations that perform a similar role.)

If the USPSTF issues an "A" or "B" rating, then insurance companies are legally obligated to cover ApoB testing. But that also introduces a year or two lag since medical policies are revised and apply to the next plan year.

The net effect is that the entire system is 17 years, on average, behind research.

Very interesting!

Unfortunately, this is a limitation of nearly all nutritional studies.

Partly we use mechanistic evidence to separate cause from effect--that's part of why the article goes into detail about, e.g., how soluble fiber binds to bile in the liver. If there's an association between A and B, and a known physiological mechanism where A causes C which causes B, it makes it more likely that ultimately A is the cause of B.

I see -- yeah, the Android metadata says "in-app messages". That refers to features where you can message support or a doctor within the app. We don't attempt to read your text messages or anything like that.

Your data isn't shared with insurance companies.

FYI: $190 is the price for the comprehensive health panel which includes a blood test and video visit with a doctor.

You can track nutrition for free within the iPhone/Android apps.

(OP) Yep--this is my philosophy. Obviously I'd love it if everybody used our product to improve their heart health, but I think it's better to write stuff that's informative and if people genuinely have the problem, then they can try us out.

Photos are used to track nutrition -- you choose each photo to upload within the app.

Location is only used, in context, to help find healthy meals near you. (You can use the app with or without enabling this location-based feature; if you don't use it, then we don't ask for location.)

Where are you seeing messages? We don't track messages, so this is probably a mistake in our metadata.

This is honestly a limitation of nearly all nutrition research -- it's based on observational data.

Part of the reason we expect fiber to reduce mortality, rather than simply being a marker of other factors correlated with mortality, is that we can identify physiological mechanisms. For example, for cardiovascular mortality, fiber reduces LDL cholesterol / ApoB which lowers heart attack and stroke risk.

(OP here) - no affiliation with Olipop. But we've tried all the fiber supplements around the office, and we found Olipop is a pretty palatable option compared to, say, psyllium husk (which forms a gel when combined with water).

Soluble for heart disease, insoluble for cancer!