46 posts
A working guide to hs-CRP, ESR, fibrinogen, ferritin, and IL-6: what each marker tracks, how much of a single value is noise, how to handle samples so cytokine numbers mean something, and where affinity proteomics panels go beyond the standard five.
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 technical account of what blood-based cancer detection measures, where the numbers come from, and how to read cfDNA methylation tests, tumor markers, and routine CBC results without fooling yourself.
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.
A glycan age score is a regression of IgG N-glycan peak ratios onto chronological age. Here is what the assay measures, how the number is built, where it is informative, and how to work with the raw peak data yourself.
MTHFR variants are common, their effect on depression risk is small, and the clinically useful signal sits in downstream biochemistry rather than the genotype. How to find your own genotype in a VCF and what to measure instead.
Yes, you can measure CRP at home, but only some kits report the low range that matters for cardiovascular risk stratification. How the assay types differ, what the numbers mean, and how to build a usable time series.
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 technical read on epigenetic clocks, blood-chemistry clocks, and survey-based 'bio age' calculators: what each one measures, how precise it is, and how to compute one yourself from your own data.
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.
Two different tests share the name methylation testing: genotyping of one-carbon metabolism genes like MTHFR, and direct measurement of DNA methylation across the genome. This explains what each one measures, how to extract the data yourself, and where the interpretation stops.
A1c and glucose disagree when either your glucose sampling is unrepresentative or your red cell biology and hemoglobin assay distort the glycation measurement. Here is how to separate the two with CGM data, a CBC, an assay method check, and a look at your genome.
A technical walkthrough of DNA methylation testing: array versus bisulfite sequencing versus nanopore, the IDAT-to-beta-value pipeline we would run, which epigenetic clocks are worth computing, and where the measurement stops being informative.
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 results out of a patient portal as structured data, normalizing them, and analyzing them with the right statistics: reference intervals, analytical and biological variation, and reference change values.
A technical account of what a comprehensive blood panel measures, what the CMP's 14 analytes do and don't tell you, and how to get from a panel of numbers to data you can analyze yourself.
Why a post-meal glucose reading in the 80s is usually a healthy result, why you can feel shaky at that number anyway, and how to tell a real reading from sensor noise using your own CGM and lab data.
What a blood biomarker test measures, what it costs, how to get the numbers out in a form you can compute on, and the math that tells you whether a change between two draws is real.
What a post-meal glucose of 83, 87, or 94 mg/dL means, why the timing of the reading matters more than the number, and how to measure your own postprandial response properly with CGM and blood work.
A fasting glucose of 106 mg/dL places you just inside the prediabetes range by ADA criteria, but a single number carries more measurement noise than most people assume. Here is how to interpret it, what to measure next, and how to read your own CGM data.
What continuous glucose monitoring shows about post-meal glucose in people without diabetes: typical peak height, time to peak, return to baseline, and how to measure your own response properly rather than reacting to one alarming number.
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.
Heavy lifting and long endurance work can push ALT and AST into the hundreds with a healthy liver. Here is how to separate muscle-derived enzyme release from hepatic injury using GGT, CK, ALT/AST ratio, and a controlled retest.
Why total testosterone and SHBG rise together, how to work out whether your free testosterone is genuinely high, low, or normal, and which measurements are worth paying for.
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.
Most online biological age calculators are quizzes. Here is what the published estimators take as input, how to compute them from your own blood panel or methylation array, and how much weight the output deserves.
You can draw blood on any cycle day. The question is which analytes move with cycle phase, how much, and how to record the phase so your own longitudinal data stays interpretable.
Levine's PhenoAge is a nine-marker blood algorithm you can compute yourself in about twenty lines of Python. Here is the formula, the unit conversions that break it, and how it compares to the methylation clocks from the same lab.
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.
A practical guide to direct-access lab testing: which channels will draw you without a referral, how to design a panel that answers a question, how to control preanalytical variables, and how to get the results out as structured data you can analyze.
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 the post-meal glucose numbers are for people without diabetes, where the thresholds come from, and how to compute peak, time-to-peak, and incremental AUC from your own CGM export.
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.
A technical guide to choosing and using a biomarker testing service: what analytes are worth measuring, what the analytical error looks like, how to store and version the results, and where a clinician has to be involved.
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.
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.
There is no single lifespan blood test. There is a small set of analytes that, combined, predict all-cause mortality better than any one of them, and a way to order, store, and recompute them so the numbers mean something over time.
What direct-access lab testing is, how self-ordered blood work is priced, and how to store and interpret your own results as structured data rather than PDFs.
What biological age tests measure, which clocks are worth running, how to compute them yourself from IDATs and a blood panel, and how much of the number is noise.
In the US, FreeStyle Libre 2 and 3 still require a prescription; Libre Rio, Dexcom Stelo, and Abbott Lingo do not. What each one costs, how they differ, and how to get the raw glucose data out.
A postprandial reading of 97 mg/dL is a normal, unremarkable value in a person without diabetes. Here is what the number tells you, what it does not, and how to read postprandial glucose properly from CGM or fingerstick data.
Dehydration concentrates plasma by a few percent, which is far too small to explain most ALT and AST elevations. Here is what usually causes them, how to draw a clean measurement, and how to read enzymes against your own genomic and proteomic baseline.
A CGM measures the output of your glucose system, not its input. Here is what a sensor can and cannot tell you about insulin sensitivity, and the paired measurements that make the trace interpretable.
The Sinclair-associated biological age test is a buccal DNA methylation clock. Here is what methylation clocks measure, how noisy they are, and how to get data you can analyze yourself.
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.
A technical read of the MPMD/Marek Health blood panel: which assays are chosen well, which markers mislead in trained people, how to control pre-analytic noise, and where a large chemistry panel stops telling you anything new.