danielgomari

It just means that you are the owner of the data.

If you want to have your data, you can download them at any time and share them with anyone you want.

If you want us to delete the data, we will do that.

If you don't say anything, we will never share your data with anyone.

The difference is that we will disclose and publish as much of the science behind the product as possible, while Theranos was all promises, no delivery.

I'd be happy to cancel it for you. The reason we have a subscription model is that most value can be derived from the metabolite trends rather than a single snapshot.

We understand it's not for everyone! https://news.ycombinator.com/item?id=32285242

Yes, we agree that being confronted with a health issue without any way to go forward would be emotionally stressful. For aging specifically, the good news is that an overall healthy lifestyle (diet, exercise, sleep) appears to slow the biological aging process. So, if we report an accelerated aging rate to you, this could also serve as a simple wake-up call to do something.

The idea of masking certain aspects with different levels of granularity during onboarding and according to each person's comfort level is excellent. We will take this into consideration.

This is an excellent idea. Community engagement is something we have definitely thought about and is now part of our roadmap. Matchmaking between people could add an interesting level of communication and exchange. We will need to make sure to address any possible privacy concerns, and we'll be working on it.

Thanks and great question. The extractor already exists. It has already been developed and is actively being used in studies at Stanford, Cornell, pharma and other institutions. We are using this collection technology to obtain blood in a pain-free and stable way. Our main R&D is in the data extraction, processing, and analysis.

Every piece of information reported to the users will be presented in a digestible way. This will include both, hard scientific evidence but also a lay version of what it means. If you want to, you will be able to look up and verify everything yourself. We will also make sure to draw the line between definitive statements ("this measurement means you are sick") and suggestive statements ("this might mean something, go see a doctor") in order to not oversell anything.

We're working on it captain!

Thanks! Right now, the recommendation we'd be providing will be based on published studies that are known to positively impact the metabolome and health. We match your metabolomic profile, based on the deviations we see, with recommendations that could benefit your metabolome the most. For example some well-studied interventions that we could match you with include the DASH diet [1] (which reduces the risk for heart disease), fasting [2, 3], targeted physical activity [4], and soon, statin intake [5], metformin [6] medication (which has been shown to extend healthspan), and many more. As you build your metabolomic trends when you test over time, we'll also be able to train personalized ML models for you and give you better and better recommendations that you'll actually respond to.

References:

[1] https://academic.oup.com/ajcn/article/108/2/243/5038205

[2] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412259/

[3] https://pubmed.ncbi.nlm.nih.gov/32931723/

[4] https://www.cell.com/cell/fulltext/S0092-8674(20)30508-0

[5] https://www.ahajournals.org/doi/10.1161/CIRCGENETICS.117.001...

[6] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8508882/

By at-home we mean that the person will gather samples at home and mail them to us.

References:

[1] Wishart DS. Metabolomics for Investigating Physiological and Pathophysiological Processes. Physiol Rev. 2019 Oct 1;99(4):1819–75. https://journals.physiology.org/doi/full/10.1152/physrev.000...

[2] Wishart DS, Tzur D, Knox C, Eisner R, Guo AC, Young N, et al. HMDB: the Human Metabolome Database. Nucleic Acids Res. 2007 Jan;35(Database issue):D521-526. https://academic.oup.com/nar/article/35/suppl_1/D521/1109186

[3] Wishart DS. Current progress in computational metabolomics. Brief Bioinform. 2007 Sep;8(5):279–93. https://academic.oup.com/bib/article/8/5/279/217981

[4] Nordström A, O’Maille G, Qin C, Siuzdak G. Nonlinear data alignment for UPLC-MS and HPLC-MS based metabolomics: quantitative analysis of endogenous and exogenous metabolites in human serum. Anal Chem. 2006 May 15;78(10):3289–95. https://pubs.acs.org/doi/10.1021/ac060245f

[5] Wishart DS, Guo A, Oler E, Wang F, Anjum A, Peters H, et al. HMDB 5.0: the Human Metabolome Database for 2022. Nucleic Acids Research. 2022 Jan 7;50(D1):D622–31. https://academic.oup.com/nar/article/50/D1/D622/6431815

[6] Ahadi, Sara, et al. "Personal aging markers and ageotypes revealed by deep longitudinal profiling." Nature Medicine 26.1 (2020): 83-90. https://www.nature.com/articles/s41591-019-0719-5

[7] Pietzner, Maik, et al. "Plasma metabolites to profile pathways in noncommunicable disease multimorbidity." Nature medicine 27.3 (2021): 471-479. https://www.nature.com/articles/s41591-021-01266-0

[8] Merino, Jordi, et al. "Metabolomics insights into early type 2 diabetes pathogenesis and detection in individuals with normal fasting glucose." Diabetologia 61.6 (2018): 1315-1324. https://pubmed.ncbi.nlm.nih.gov/29626220/

[9] Wang, Thomas J., et al. "Metabolite profiles and the risk of developing diabetes." Nature medicine 17.4 (2011): 448-453. https://www.nature.com/articles/nm.2307