What I am working with: TSH · orforglipron.
This is a planning question. I know what my options are; I do not know how to weigh them.
What I want is the minimum viable version, which I suspect is smaller than what I would design.
What is the minimum version of this that is still defensible?
Answer first: decide what you would do differently for each possible result before you order the panel. Anything that fails that test is a number you will worry about and not act on.
A sensible core for this population is a full blood count, renal function with electrolytes, liver enzymes with bilirubin, a fasting lipid panel with apolipoprotein B, HbA1c and thyroid-stimulating hormone.
Keep the reports rather than the numbers. Units, reference intervals and methods all vary, and a bare number two years later is not comparable to anything.
Reference intervals are conventionally the central ninety-five per cent of a reference population, which is the direct cause of the one-in-twenty out-of-range rate on a healthy panel.
Baseline first, then a repeat under identical conditions. Everything else is secondary.
Aggregated, published test results and vendor ratings built from submitted batches. Methodology stated, dataset browsable, no listing fees.
Browse resultsThe short version: a small, well-chosen panel with a baseline beats a large one without.
Repeat before you react. A single abnormal value has a substantial probability of being within the combined biological and analytical variation of a normal one.
Concretely, delta checks — comparing against your own previous value — are far more sensitive than comparing against a population interval, which is the argument for keeping a series rather than a snapshot.
Biological variation data are published per analyte and are the basis for the reference change value — the difference between two results that is larger than noise.
Keep the full report, not the number. You will need the units and the interval later.
Start with a baseline. A result taken before anything started converts most later ambiguity into a simple comparison, and it cannot be obtained retrospectively.
Same laboratory, same method, same time of day, same fasting state. Between-laboratory differences on several common analytes are larger than the changes people are trying to detect.
Timing matters per analyte: cortisol and testosterone are diurnal, triglycerides are postprandial, and creatinine responds to hydration and to recent training. Fixing the conditions removes most of the noise.
External quality assurance schemes document between-laboratory differences on common analytes that routinely exceed the size of clinically interesting changes.
The caveat is that a panel is not a diagnosis and interpreting one is a clinician's job, particularly when several values move together.
Decide the action for each result before you order the test.
Before reacting to any single value, check whether it is outside the interval by an amount larger than the assay's own variation.
A twenty-analyte panel run on a healthy person will produce, on average, one out-of-range result purely from how reference intervals are constructed. That is arithmetic rather than pathology.
Pre-analytical factors — posture, tourniquet time, fasting, sample handling — are the largest source of error in routine biochemistry, well ahead of the analysis itself.
One out-of-range value on a twenty-analyte panel is expected. Two on a repeat is a finding.
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