9 posts
A direct look at blood-chemistry clocks, methylation clocks, and plasma proteins: which markers carry the signal, how to compute biological age from your own lab data, and how much of the number is noise.
Morning, fasted, rested, and identical from draw to draw. Here is which analytes move across the day, what the 48 hours beforehand do to your numbers, and how to write a draw protocol you can repeat for years.
A working setup for longitudinal biomarker tracking: a schema that survives lab changes, your own analytical and biological variation estimates, reference change values instead of reference ranges, and trend fitting that does not fire on noise.
A technical assessment of the consumer blood panel companies: who runs the assays, what the $349-$499 membership buys, how to get structured data out, and why a 100-biomarker panel flags something for almost everyone.
A working guide to pulling your lab data out of patient portals, normalizing it into one machine-readable table, computing derived values yourself, and telling a real change from assay noise.
A working list of blood biomarkers that change decisions, why panel size is a poor proxy for value, and how to handle biological variation so you don't chase noise.
A direct look at what a maximal blood panel contains, which assays add real information, where the marketing exceeds the biology, and how to get the raw data in a form you can analyze yourself.
What annual bloodwork is worth drawing, why vendor 'optimal ranges' are weaker than they look, and how to build a personal baseline using biological variation and reference change values.
The best blood test tracker is a long-format table you control plus fifty lines of Python, not an app. Here is the schema, the LOINC and unit normalization, the reference change value math, and the failure modes that make most trackers wrong.