Becoming X-Man: My Data-Driven Health Transformation

Tracking 40+ biomarkers to develop intense clarity, strength, and slower aging

mgoesdistance.eth
In Fitness And In Health

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Source: Wikimedia Commons

When it comes to my health and fitness, broadly speaking I have three goals — named after my three favorite X-Men.

That’s right, my goal is to turn myself into an X-Man — ie the next evolution of humans, with special powers that I can put to use. The three X-Men are:

  • Professor X: I want to achieve superhuman mental clarity and self-control.
  • The Beast: I want to achieve superhuman strength, endurance, and agility.
  • Wolverine: I want to slow down, then stop, then reverse (!) my aging process.

1 — How It All Started

I started paying attention to my health about 10 years ago, after I faced a burnout accompanied by a panic attack, after I first began work. Fast forward into today, I went down the splippery slope all the way to having ran 100km+ ultra marathons and collecting 40 different biomarkers using 5 smart devices, 2 self-trackers, and various labs.

For more on the backstory and my early initiatives, see my previous article.

Click the link above to read this piece from 2 years ago

2. Gimme Some Data!

I love data. Obviously, having worked in startups for the past 7 years, I’m massively biased. But still. Data is beautiful. Data-driven insights are the most actionable insights. And most importantly, what gets measured gets managed.

I started off my fitness journey by addressing the big 4: sleep, exercise, diet, and stress management. I moved on from simple lifestyle initiatives aimed at addressing these to more complex ones — including microroutines and supplements. I treat every new initiatve as an experiment — designed at establishing whether it has an impact on one or more of the desired areas.

Needless to say, it can be extremely hard to establish causation, or even correlation, without good data. This simple realisation has lead me down the path of collecting an increasing amount of data — starting with the sets collected by my Garmin smartwatch (Forerunner 945) and my Oura ring (v3), to my continuous glucose monitor (Freestyle Libre), detailed diet self-tracking (MyFitnessPal), blood testing for ketones (Accugence), and self-reporting via Daylio.

3. I’m Tracking 40+ Biomarkers Daily

At the moment, I am collecting around 40 daily biomarkers using the four smart devices and 2 self-tracking apps listed above. On top of that, I am doing weekly measurements of my weight, body composition, and body measurements using a combination of my Xiaomi smart scale, skinfold calipers, and a simple tape measure. I have recently started adding in bi-monthly lab blood tests for testosterone and some other hormones.

That’s a lot of data! I am compiling it all inside a single google sheet, which looks like this -

My master data sheet — ie The Bible. Contains daily data on the 40 biomarkers I’m tracking, using 4 smart devices, 2 self-trackers, and some lab tests.

The architecture of the main tab is fairly simple — one row represents one day, and each column is a different data point. Here you can see the data coming in from Oura, Garmin, Freestyle Libre, and part of the data from MyFitnessPal. To make it easier to read, I’ve set up conditional formatting which color-codes everything from green to red, based on how close it is to my ideal reading. That’s a good start!

4. Cracking the Da Vinci Code

That’s not enough though. In order to glean deeper insights, I’ve set up a couple of additional tabs which draw data from the main tab and extract higher level insights, based on formulas I’ve set up. Let’s dive into this now —

First of all, the burnout dashboard takes each measured marker and calculates its average value — over the course of 7 days, 30 days, 1 year, and lifetime. Displayed side by side and, once again, color-coded, this allows me to see trends in each marker over various periods, without having to scroll too much.

Reading from left to right — if the cell is becoming greener that means the marker is trending in the direction of improvement. And vice versa. The below is looking pretty good, right now! But it’s not easy to keep it that way. The most important thing is that, should I be slipping on some metrics, I get to see it in the 7 and 30 day columns, before it seeps into a long-term issue. Amongst other things, this is a very effective burnout prevention.

My personalised burnout alerting dashboard. Everything should be getting greener from left to right if I’m making progress. If it’s getting redder, that’s danger zone.

Second, I have started to look at correlations between various pairs of the markers I am measuring. Anything above 0.3 is an interesting level of correlation worth paying attention to. Negative correlations are as interesting as positive ones.

In some sense, this dashboard is the most insigthful one — and at the core of helping me determine what’s having an impact on what. That said, I’m still in the early days of having set this up, and there’s work to be done. I’m also planning to add data on substance and supplement use from Daylio into this — which might yield some of the most interesting insights.

See a tweet on the key insights from this dashboard thus far.

Correlations between different pairs of the biomarkers I am tracking.

Thirdly, charts can be a great tool at visualising both longer-term trends and correlations. The art of chartmaking is picking the right data points and the right scales. With some experimentation, I have managed to put together some fairly useful charts so far. The best example is this one —

My weight vs my waist-to-chest ratio over the course of 2+ years. The further apart the two lines, the more succesfully I have bulked with actual muscle, instead of fat. I’ve also plotted some key events separated by grey vertical lines (partially redacted here), to help me link turning points in the charts to my life events.

To understand what the above chart means in detail, check out this twitter thread.

5. Connect and Conquer

That’s it for now. Some of my initiatives are a couple of years old; I started my google sheet only 6 months ago. I am updating the data weekly (mostly via manual exports for now) and I am still experimenting with the best ways of both visualising and understanding it, each time. It is an ongoing journey.

In order to stay sane, share my insights, and connet with other likeminded people, I have started sharing these via twitter, reddit, and inside some discords I am a member of. Thanks to this, I have gathered a fair amount of comments and feedback. I have also been reached out to by one of the largest US-based integrated healthcare companies for a research interview, as well as invited to beta test a bunch of new products by different startups.

Let’s see what other opportunities this opens up. What’s most important is that, each day, I’m moving closer to one of my three X-Men, one step at a time.

Source: X-Men Movie Screenshots

I have already achieved a lot, but there is so much more ahead of me. Follow me on here ➡️ and especially on twitter 🐦, to keep up to date with my progress! Feel free to also clap, if you enjoy this -

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