25 posts
RDW measures how much your red blood cells vary in size. This page explains what the analyzer is computing, why 15.9% is flagged high, how to read RDW alongside MCV and hemoglobin, and how to track it as a longitudinal signal in your own data.
A practical pipeline for normalizing personal proteomics data, from raw intensity matrices through log transformation, method selection, batch correction, and longitudinal re-normalization when new draws arrive.
A working account of what belongs in a comprehensive blood panel, which preanalytical variables move the numbers, and how to tell a real change from assay noise using biological variation and reference change values.
A step-by-step guide to processing personal plasma proteomics data, from raw mass spectrometry files or affinity panel exports through quantification, quality control, normalization, and longitudinal modeling in R.
A decade-by-decade view of which blood measurements carry information, how often to repeat them, and why interpretation gets harder after 70. Written for people who want to keep their own longitudinal record.
How to choose a blood test cadence from analyte kinetics and biological variation, how to tell a real change from assay noise using reference change values, and how to structure the data so you can analyze it yourself.
An extended panel measures a few dozen analytes. How to choose them, how to space the draws, and how to store the results so you can compute on them.
A specific panel, drawn quarterly, with the analytic variation and pre-analytic rules that decide whether a change in a number is real. What to order, what to skip, and how to handle the data.
A working list of blood, molecular, and continuous biomarkers worth tracking on yourself, with the biological-variation math that tells you when a change is real and when it is noise.
A working guide to blood biomarker analysis for technically fluent readers: what a single result can and cannot tell you, how to separate biological variation from signal, and how to structure your own longitudinal data.
A working method for reading your own labs: get the data as structured records, compare against your own history rather than the population reference interval, and use the reference change value to tell a real shift from noise.
A specific panel to start from, the pre-analytical variables that corrupt a longitudinal series, and how to decide whether a change between two draws is real.
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.
A fitness or gym blood panel is only interpretable if you control what you did in the 72 hours before the draw. Here is the panel we would order, the confounders that corrupt it, and how to analyze the results over time.
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. Covers a schema that survives lab changes, reference change values and trend fitting.
What a blood biomarker test measures, what it costs and how to get the numbers out. Covers the math that shows whether a change between draws is real.
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.
The 56-biomarker performance panels sold as a full-body MOT test the wrong thing in the wrong way. Here is the panel we would order, how to control preanalytics, and how many repeats you need before a number means anything.
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. No app is needed. Covers the schema and LOINC codes.
A practical protocol for estimating functional age from physical performance tests, blood chemistry algorithms, and DNA methylation clocks, with the reliability problems that make most single-timepoint results uninterpretable.
A working pipeline for taking Olink, SomaScan, or DIA-NN output into R: QC, missingness, normalization, and longitudinal within-person modeling of a single individual's plasma proteome.
A practical comparison of DNA methylation tests: which array, which clocks, how to get the raw IDATs, and how to process them yourself with sesame and dnaMethyAge. Plus the noise sources that make a single epigenetic age number close to meaningless.
There is no single 'Huberman blood test.' Here is the panel that gets discussed on the podcast, which markers carry real signal, and how to get the data in a form you can analyze yourself.