Biological Coherence: the missing layer in lab interpretation
What follows is not poetry, but a reality for many people trying to debug and solve their health issues.
Some markers are green.
Some are red.
The system is still failing.
And you have no idea why.
This quote from The Expanse nails it hard.
Artificial ecosystem, you ask? That's right.
We do not realize it, but our environment resembles almost nothing of what our biology evolved and adapted to.
So the less we comply with Nature, the more we need to use technology to fix what was not meant to be broken in the first place.
Which is ironic, because our technologies are what makes us sick.
And if you look at a map of biological pathways, you quickly realize that whatever you are about to do, there is a pretty good chance your plan has holes and leaks all over the place.
Biology scores, the human-readable puzzle
Markers from blood, urine, and other tests can be useful, but only if:
a) you can interpret them right
b) the interpretation is context-aware
Neither is easy.
Hell, for most people it is almost impossible.
This is where state-of-the-art AI can help. If it is good at one thing, it is making sense of a big chunk of incoherent data thrown at it.
getbased just got Biology Scores, a first iteration of a new scoring system. A user-facing, readable format that computes and interprets your health data into something that can tell you what is working well and what is broken.
Biological Coherence is an overview score composed of up to 13 baseline scores and 5 extended scores.
Each score has two levels of inputs: core markers, as the bare minimum, and extended markers for better confidence.
Take Metabolic Flexibility, for instance.
The viable minimum is:
fasting glucose
fasting insulin
triglycerides
HDL
For better confidence, add:
HbA1c
C-peptide
fructosamine
Every subscore will not just show you a meaningless number. It will explain what it means, what drives it, what drags it down, and most importantly, how it is tied to your other data.
That includes DNA/SNPs and mtDNA, lifestyle data like diet, sleep, and stress, environmental context like light and nnEMF, medical and family history, and biometrics like HRV, heart rate, blood pressure, weight, BMI, and more.
Scores are context-aware.
Meaning if, for whatever reason, creatinine levels are not relevant for you, getbased will not count it and penalize you for low eGFR while screaming that your kidneys suck.
To make the whole thing easier, there is a Coverage Planner that helps you pick what to test next so the scores become more complete and actionable.
We all know that compiling the test list without leaving anything important out, and without wasting money on nonsense, is the real pain in the ass.
getbased just got a bit more powerful, and with user-facing scores, hopefully more useful for everyone, including non-geeks and non-nerds.
The best part of getbased is that you can just talk to it, and it will explain everything.
Go try it with female or male demo data: https://app.getbased.health
As always, feedback, issues and PRs are welcome: https://github.com/elkimek/get-based
And in case you want private no-KYC inference, both @routstr and @PayPerQ are supproted
What follows is not poetry, but a reality for many people trying to debug and solve their health issues.
Some markers are green.
Some are red.
The system is still failing.
And you have no idea why.
This quote from The Expanse nails it hard.
Artificial ecosystem, you ask? That's right.
We do not realize it, but our environment resembles almost nothing of what our biology evolved and adapted to.
So the less we comply with Nature, the more we need to use technology to fix what was not meant to be broken in the first place.
Which is ironic, because our technologies are what makes us sick.
And if you look at a map of biological pathways, you quickly realize that whatever you are about to do, there is a pretty good chance your plan has holes and leaks all over the place.
Biology scores, the human-readable puzzle
Markers from blood, urine, and other tests can be useful, but only if:
a) you can interpret them right
b) the interpretation is context-aware
Neither is easy.
Hell, for most people it is almost impossible.
This is where state-of-the-art AI can help. If it is good at one thing, it is making sense of a big chunk of incoherent data thrown at it.
getbased just got Biology Scores, a first iteration of a new scoring system. A user-facing, readable format that computes and interprets your health data into something that can tell you what is working well and what is broken.
Biological Coherence is an overview score composed of up to 13 baseline scores and 5 extended scores.
Each score has two levels of inputs: core markers, as the bare minimum, and extended markers for better confidence.
Take Metabolic Flexibility, for instance.
The viable minimum is:
fasting glucose
fasting insulin
triglycerides
HDL
For better confidence, add:
HbA1c
C-peptide
fructosamine
Every subscore will not just show you a meaningless number. It will explain what it means, what drives it, what drags it down, and most importantly, how it is tied to your other data.
That includes DNA/SNPs and mtDNA, lifestyle data like diet, sleep, and stress, environmental context like light and nnEMF, medical and family history, and biometrics like HRV, heart rate, blood pressure, weight, BMI, and more.
Scores are context-aware.
Meaning if, for whatever reason, creatinine levels are not relevant for you, getbased will not count it and penalize you for low eGFR while screaming that your kidneys suck.
To make the whole thing easier, there is a Coverage Planner that helps you pick what to test next so the scores become more complete and actionable.
We all know that compiling the test list without leaving anything important out, and without wasting money on nonsense, is the real pain in the ass.
getbased just got a bit more powerful, and with user-facing scores, hopefully more useful for everyone, including non-geeks and non-nerds.
The best part of getbased is that you can just talk to it, and it will explain everything.
Go try it with female or male demo data: https://app.getbased.health
As always, feedback, issues and PRs are welcome: https://github.com/elkimek/get-based
And in case you want private no-KYC inference, both @routstr and @PayPerQ are supproted
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