What interval makes sense for repeating HbA1c on liraglutide?

The case in front of me: HbA1c · liraglutide.

The failure mode I am trying to avoid is making this decision emotionally.

I have twelve months in view and I would like the plan to survive that long.

What should I decide now, and what should I defer?

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.

Headline results, principal programmes

Haemolysis in the sample raises potassium and several enzymes spuriously. If a result is bizarre, ask whether the sample was flagged before building a theory on it.

Pre-analytical factors — posture, tourniquet time, fasting, sample handling — are the largest source of error in routine biochemistry, well ahead of the analysis itself.

Nothing here is medical advice. If something is out of range and you do not know why, that is a consultation rather than a research project.

One out-of-range value on a twenty-analyte panel is expected. Two on a repeat is a finding.

Before reacting to any single value, check whether it is outside the interval by an amount larger than the assay's own variation.

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.

To be exact about it, 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.

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.

Research-use compounds are not approved for human use, and no panel makes that safer.

Baseline first, then a repeat under identical conditions. Everything else is secondary.

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.

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.

Decide the action for each result before you order the test.

edited 14 Jul 2025 by rosa_mendieta — expanded the table to cover the lower concentration

Standardise the conditions — same time of day, same fasting state, same laboratory — or you are measuring the conditions rather than yourself.

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.

External quality assurance schemes document between-laboratory differences on common analytes that routinely exceed the size of clinically interesting changes.

Keep the full report, not the number. You will need the units and the interval later.

The short version: a small, well-chosen panel with a baseline beats a large one without.

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.

SELECT reported a hazard ratio of 0.80 (95% CI 0.72–0.90) for the primary composite major adverse cardiovascular event endpoint with semaglutide 2.4 mg in overweight or obese adults with established cardiovascular disease and without diabetes[1].

Same laboratory, same time, same fasting state, or the comparison is not a comparison.