15 posts
The Yale calculator is Levine's PhenoAge: a nine-biomarker mortality model fit in NHANES III and rescaled into years. Here are the coefficients, the unit traps, the noise floor, and how we would track it over time.
Upload-a-PDF analyzers mostly do OCR and template text. A better setup is structured lab data with units and LOINC codes, your own longitudinal baseline, and a general reasoning model you drive yourself.
A baseline blood test is only useful if it establishes your personal set point under controlled conditions and lands in a file you can compute on. Here is the panel we would order, the preanalytical controls that matter, and the math for deciding when a later change is real.
A technical guide to building a baseline blood panel you can track over years: which analytes to order, how to control preanalytical variation, and the math that tells you whether a change is real.
A homocysteine test measures total plasma homocysteine, a sensitive readout of B12/folate status, renal function, and methylation flux. Here is how to order it so the number is real, what to run alongside it, and how genotype fits in.
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 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. Covers a schema that survives lab changes, reference change values and trend fitting.
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 and which assays add real information. Covers marketing claims and getting the raw data.
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.