Is lipase worth drawing at baseline before starting a GLP-1 receptor agonist?

Setup, so nobody has to ask: lipase · a GLP-1 receptor agonist.

I am trying to build something sustainable rather than something thorough that I will abandon.

I have already decided the broad direction; this is about the specifics.

How would you structure this, and what thresholds would you set in advance?

Answering this needs to distinguish screening from monitoring. A screening panel looks for the unexpected; a monitoring panel tracks something you already have a reason to watch.

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.

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.

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

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

Start with a baseline. A result taken before anything started converts most later ambiguity into a simple comparison, and it cannot be obtained retrospectively.

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.

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.

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

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

The honest position is that most people order too many analytes and too few time points, when the reverse would be more informative.

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.

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

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

edited 14 May 2025 by Dr_Ravi_Selvarajah — corrected a unit error in the worked example

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

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.

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.

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