What to expect as a Gen P user? Will it determine my molecular age?

AgeCurve Limited’s mission is to provide deep molecular, comparative and non-diagnostic age profiles and later trajectories for every adult.
We will generate high-dimensional biomolecular data directly to our Users and track the many things
that change quantitatively. When taken multiple times across a time period these profiles will help users understand their molecular age trajectory compared to other users.

So a potential user might ask: “How do you determine the molecular age”?

Take our first, flagship product, Gen P that measures thousands of human proteins and bacterial proteins quantitatively from the saliva. To phrase the ‘how do you determine the molecular age’ question applied to Gen P means asking: ‘how do you determine proteomic age?’

The answer is that we won’t provide one objective number ‘proteomic age’ to our Users.

Instead of giving you one aggregated magical number back, that might be misleading concerning the complexity and many parts of the body (tissues age differently so what number do you have in mind exactly?), Gen P users will be provided many different stories backed by tens, hundreds, thousands of numbers. Saliva consists of cells from different tissues and as a bodily fluid is capable to provide general molecular patterns.

To provide one numeric value ‘proteomic age’ upfront to our users would be scientifically ungrounded, the least, fundamentally wrong, the worst.

First of all, assuming that such a meaningful number can be created, in the lack of enough users (we are just getting started here) we cannot come up with an algorithm that calculates such a number in a statistically sound way.

Secondly, let’s assume eventually we might come up with such a number assuming enough data and reliable error bars so we can offer something like:
Hey, you are 44 year old chronologically but based on the quantitative patterns of many thousands of your human proteins compared to many thousands of other Gen P users we think that your proteomic age is more like 38 +- 3 years, or your proteomics age is more like 49 +- 3 years,
or your proteomic age is what your chronological age is, +- 3 years. But do these numbers represent the ‘proteomic age’ of the immunological or epithelial cells of your saliva? Or is it the fibroblasts? In order to combine these together into one model outputting an aggregated metric, well that’s not here in the corner just yet, takes some resources to do and even then not sure it can deliver what is expected. And don’t forget that even in that case the numbers are relative to the sample composed of Gen P users’ data, and not of the whole population.

But the good news is that you don’t need a way too simplified and underperforming ‘proteomic age’ indicator in order for Gen P delivering something useful for you.

Right now Gen P can equip our Users with meaningful comparisons to other age adjusted Gen P peers (Gen Peers) or to other age groups in general and provide many stories based on
different proteins and soon group of proteins (like immunological proteins, inflammatory proteins, mitochondrial proteins) that will lead to potential actionable information. Say, in our social view, 50 year old user A is consistently
more similar and closer to some 45 years old users than to her age adjusted peers, and another 50 year old, person B is
more in line with her chronological age see some anonymised metadata related to user A (or a group of 50
year olds more similar to user A), some patterns emerging, suggesting to have this or that type of
environmental change, or take this or that supplement or follow this or that diet, or this or that oral
hygiene protocol, or this or that fitness regimen. And then user B will have a chance to make those
changes based on her call but using information provided by us to come back and purchase another Gen P
kit 3 months or 6 months later and get another baseline point to see how her proteomic information has
changed and how does it compare.

What we aim to provide here in the long run is to have control over the differences between the members
of the same age adjusted group and help Users to get into a feedback cycle enabling them to grow a healthy disconnect between their chronological and proteomic age, the latter being a potentially powerful way of expressing molecular age.

Eventually all these different stories will help our Users to come up with a rational life (period) plan and
plan their next decade or so, if we can achieve that, if we can provide means to live our life with more
control amongst the many accidental happenings that we have already reached our aim.

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