99 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.
Epigenetic age kits report a number derived from DNA methylation at a few hundred CpG sites. Here is how that number is computed, how much of it is noise, and what to measure instead if you want something you can track.
Biological age tests run from about $75 for a blood-chemistry clock to $300-$500 for a methylation array, with outliers above $1,000. Here is what each tier measures, how reliable it is, and how to compute the same clocks yourself from raw data.
A practical guide to choosing, timing, and interpreting blood and molecular markers for endurance and strength training, including how to tell a real change from analytical noise.
A practical guide to blood biomarker panels: which analytes carry information, how analytical and biological variation limit interpretation, and how to store and analyze your own results over time.
A single fasting glucose of 115 mg/dL is one noisy measurement of a tightly regulated system. Here is how to read it, what measurements resolve the ambiguity, and how to analyze the data yourself.
A technical guide to biological age testing: what epigenetic clocks measure, which ones to compute, how to get raw methylation data, and the failure modes that make a score move when your biology hasn't.
Most blood analytes do not change enough in a month to beat their own noise. Here is the short panel worth drawing monthly, what belongs on a slower cadence, and how to standardize the draw so the numbers mean something.
A quantitative high-sensitivity CRP assay, repeated on a schedule and paired with a CBC differential, is the only at-home inflammation measurement worth acting on. Here is how to collect it, when to collect it, and how to analyze the series.
There is no single best vendor. What matters is assay methodology, longitudinal consistency, and whether you can get structured data out. Here is how we compare the options and what a 100-biomarker panel really contains.
A specific panel for athletes, the pre-analytical protocol that makes the numbers comparable over time, and how to compute your own reference intervals instead of relying on population ranges.
A technical comparison of over-the-counter continuous glucose monitors in the US, including accuracy figures, data export paths, and how to analyze the resulting time series yourself.
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.
How to choose an at-home C-reactive protein test, why the high-sensitivity assay matters, and how to analyze repeated CRP measurements without fooling yourself with biological noise.
A curated list of blood, genomic, transcriptomic, proteomic, and glucose markers that carry real information about one person, with the variability numbers and file formats you need to work with them.
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 practical survey of proteomics applications, the main measurement methods, and what you can do with your own plasma protein data once you have the files.
Population reference intervals are built from non-athletes, and the few published athlete cohorts are wide and sport-specific. The usable answer is a personal baseline with a computed reference change value.
A normal HbA1c with high glucose readings is common and usually explained by red cell turnover, postprandial spikes averaging out, or assay interference. Here is how to tell which, and what to measure next.
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 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.
One fasting glucose of 100 sits at the edge of a threshold that was drawn by committee, and a single draw carries more measurement noise than most people expect. Here is how to read the number, what to measure next, and how to analyze the data yourself.
Why fasting glucose is often the first value to drift upward, how to tell a real dawn rise from a sensor artifact, and how to analyze your own overnight CGM traces.
A technical answer to what biomarker testing at home can and cannot deliver: which analytes survive a mail-in kit, what dried blood spots and point-of-care sensors are good for, and how to build a longitudinal series you can analyze yourself.
How to read a non-fasting (random) plasma glucose number, which pre-analytical and physiological factors move it, and what to measure next if you want a real answer about glucose handling.
What biomarker labs measure, why panel size is a poor proxy for quality, and how to build a blood panel you can reason about over time.
A high fasting glucose usually reflects overnight hepatic glucose output and the pre-waking hormone surge. How to check it in your own CGM export.
The thresholds that define a high glucose value, why a CGM reading and a lab draw disagree, and how to analyze your own glucose export instead of reacting to single numbers.
A fasting glucose of 86 mg/dL sits in the middle of the normal range and is not low. The more useful question is how much your fasting glucose moves from day to day, which one draw cannot answer.
Fasting glucose above post-meal glucose is a common and mostly explainable pattern: overnight hepatic glucose output, dawn hormones, sensor and meter measurement error, and meal composition. Here is how to tell them apart with CGM data and lab numbers.
A low red cell distribution width means your red blood cells are unusually uniform in size. How to read it alongside MCV, and when it is an artifact.
A technical read of the comprehensive metabolic panel: the fourteen analytes, the derived quantities worth computing, and the common errors.
85 mg/dL fasting sits comfortably inside the normal range. The more useful question is how much your own fasting glucose varies day to day, which a single venous draw cannot answer and CGM can.
A fasting glucose of 93 mg/dL sits comfortably in the normal range. The more useful question is what the rest of your glucose curve looks like, and how to measure it yourself.
A practical guide to continuous glucose monitoring for prediabetes: which device to use, how long to wear it, and which metrics to compute.
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 measurement-first look at the common MTHFR variants: what the genotype does to enzyme activity, how to pull it from your own VCF, and which blood markers carry real signal instead of a symptom list.
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.
Hemoglobin is a concentration, not a mass, and that fact explains most of the confusion around a single result. Here is what the assay does, how much of a change is real, and what to pair it with.
MCHC is a computed ratio of hemoglobin to hematocrit, and most high values are analyzer artifacts rather than biology. How to tell the difference using your own CBC history, and the few red cell conditions that genuinely raise it.
89 mg/dL is a normal glucose value, but a single number carries less information than most people assume. Here is how to read it given measurement error, and what to compute from a CGM trace instead.
A lipoprotein panel goes past total cholesterol to count particles: apoB, Lp(a), LDL-P by NMR, and oxidized LDL. Here is what each number means, which assay to order, and how to handle the units problem.
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.
Which vitamin and mineral analytes are worth measuring, which ones are nearly uninterpretable in serum, and how preanalytical handling and inflammation decide whether your panel means anything.
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.
6 mmol/L is 108 mg/dL, and an A1C of 6.0% is a different number entirely. Here is how to tell which one you have, why a single reading carries little information, and how to get a real answer from CGM and lab data.
A fasting glucose of 99 mg/dL is one number from one morning. Here is how to compute your own morning baseline from CGM data, what the dispersion means, and where the measurement itself is the limiting factor.
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 functional biomarker measures how well a system is working — a ratio, a rate, or a response to a challenge — rather than a single resting concentration. Here is how to build them from CGM, blood panels, proteomics, and RNA-seq, and where they break.
What fasting, post-meal, and 24-hour continuous glucose numbers look like in people without diabetes, what the reference data say, and how to analyze your own CGM export.
Mean platelet volume is a derived number from a fitted histogram, and it drifts with tube age and analyzer model. Here is how to tell a real low MPV from a preanalytical artifact, and what to measure alongside it.
A working guide to the five standard inflammation markers, from hs-CRP to IL-6. Covers what each tracks, noise in a single value and sample handling.
A Nature Medicine study staged 80 midlife women by hormone criteria and found 16 blood proteins that track the menopause transition better than age does. What the menopause proteomic score measures, how well it replicated, and what the dementia numbers do and do not support.
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. Covers what the assay measures and how the number is built.
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. Covers a schema that survives lab changes, reference change values and trend fitting.
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. Compares arrays, bisulfite sequencing and nanopore, then covers the IDAT pipeline and clocks.
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 and how to get the numbers out. Covers the math that shows whether a change between 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 and which assays add real information. Covers marketing claims and getting the raw data.
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. 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.
No single lifespan blood test exists. A small set of analytes together predicts all-cause mortality. How to order, store and recompute them.
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 and which clocks are worth running. Covers computing them from IDATs and a blood panel, and how much 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. It does not measure the input. What a sensor can and cannot tell you about insulin sensitivity.
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.