[craigdalton]

Medical epidemiologist here. This has such a Theranos ring to it. No response to the questions about the evidence base for claims of new predictive insights - only references to studies that provide prediction and guidance based on currently available routine blood workups and common dietary interventions. Everything "new" appears to be speculative and to come (perhaps) from insights based on longitudinal data collection.

As I read this it is hoped that the technology will provide new predictive disease insights but these are not established yet. So many metabolic/biochemical screens have poor prediction at the individual level.

If I have misunderstood can you cite one NEW predictive insight your technology provides with some kind of performance parameter - predictive value positive, 10 year incidence, or receiver operating curve performance?

Couple of years ago I was part of a pre-accelerator group assessing the product market fit for independent assessment of new medical technologies. The consultation revealed that most investors in new medical technologies had no idea how to evaluate the technology, didn't know they they didn't know how to assess the technology, and didn't want to pay someone else to do it.

[danielgomari]

In the first generation of our tests, we’ll be basing our reports on already published studies. For example, we look at spermidine (a polyamine), which is not available as a routine blood test and is a metabolite that could be acted upon. Epidemiological studies have shown that spermidine-rich diets are protective against cardiovascular disease and reduce the risk of cardiac death in humans. See references here:

- https://www.nature.com/articles/nm.4222, Figure 5

- https://www.mdpi.com/2076-3271/9/2/22/htm

- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8750749/

Even though these studies have looked into metabolites in relation to chronic conditions, in the first generation of our reports, we’ll not be providing individuals with any diagnostic information and our tests right now are only intended for wellness purposes.

Regarding diagnostic predictive markers, I want to reiterate that we are not a diagnostics company at this stage and to quote from our post, “as our [longitudinal] metabolomics database grows [we will] look for new signatures of age-related diseases at earlier and earlier stages. (Such analysis will only be done on de-identified data, only with consent, and only for our work towards extending healthspan.) “.

With that being said, there are other groups that have done a great job of validating metabolite biomarkers that do provide relatively new predictive insights into chronic disease prediction and risk. One example is this paper where they looked at type 2 diabetes risk in individuals with *normal fasting glucose* (https://link.springer.com/article/10.1007/s00125-018-4599-x):

Nineteen metabolites were selected repeatedly in the training dataset for type 2 diabetes incidence classification and were found to improve type 2 diabetes risk prediction beyond conventional type 2 diabetes risk factors (AUC was 0.81 for risk factors vs 0.90 for risk factors + metabolites, p = 1.1 × 10-4).

In adjusted Cox proportional hazard models, the type 2 diabetes risk per 1 SD increase in glycine, taurine and phenylalanine was 0.65 (95% CI 0.54, 0.78), 0.73 (95% CI 0.59, 0.9) and 1.35 (95% CI 1.11, 1.65), respectively. Mendelian randomisation demonstrated a similar relationship for type 2 diabetes risk per 1 SD genetically increased glycine (OR 0.89 [95% CI 0.8, 0.99]) and phenylalanine (OR 1.6 [95% CI 1.08, 2.4]).

The same group also published on this topic before: https://www.nature.com/articles/nm.2307

Although we already measure these metabolites and others in our current panel and we are able to calculate these score, we will not be providing these score since we’re not a diagnostics company at our current stage.