I have three data points across nine months, which I hope is enough to see a trend.
I would rather understand the derivation than memorise the outcome.
Two people I asked gave two answers that differ by a factor of ten, which is suggestive.
Can someone walk through the arithmetic step by step?
The underlying point is that read the estimand before the effect size. Almost every apparent contradiction between two published figures from the same trial resolves once you notice that one is a trial-product estimand and the other is a treatment-policy estimand.
The rodent thyroid C-cell findings that generated the labelled warning appear to be species-specific: rodent C-cells express GLP-1 receptors at high density, human C-cells at very low density, and human calcitonin data across large trial populations has not reproduced the signal. A family history of medullary thyroid carcinoma or MEN2 is nonetheless a genuine contraindication rather than a theoretical one.
Creatinine is a muscle-derived metabolite, so a substantial loss of lean mass lowers serum creatinine and mathematically raises estimated GFR without anything happening to the kidney. If you have lost twenty kilograms, your creatinine-based eGFR is flattering you. Cystatin C is not muscle-dependent and is the measure to use when the two disagree.
STEP 1 reported a mean weight change of approximately −14.9 per cent with semaglutide 2.4 mg versus −2.4 per cent with placebo at 68 weeks[1]; the difference between the figures quoted from this trial in different places is an estimand difference.
The caveat is the population. Trial participants were screened, monitored and supported; the effect size in an unmonitored setting is not the trial effect size, and it is not obvious in which direction the difference runs.
Read the confidence interval, read the estimand, and compute the absolute effect yourself. It takes two minutes and it changes how the result feels.
Aggregated, published test results and vendor ratings built from submitted batches. Methodology stated, dataset browsable, no listing fees.
Browse resultsA single laboratory value is a point on a noisy curve. What you want is a trend across at least three draws under comparable conditions, and "comparable" is doing a lot of work in that sentence.
A network meta-analysis can rank agents that were never compared directly, but only under a transitivity assumption — that the trials being linked are similar enough in population, duration and endpoint definition for the indirect comparison to hold. In this field that assumption is often visibly violated, which is why indirect rankings should be read as hypotheses.
Mechanically, liver enzymes are a poor surrogate for hepatic histology in both directions: substantial steatohepatitis with normal transaminases is common, and modest enzyme elevation with minimal fibrosis is common. If the question is fibrosis, the answer comes from a non-invasive score such as FIB-4 or a stiffness measurement, not from ALT.
Worth being explicit that this is interpretation of published data and not medical advice. Laboratory results belong in a conversation with whoever ordered them.
The papers are readable. Read the paper rather than the summary of the paper, especially where the summary is enthusiastic.
edited 26 May 2026 by amara_nwachukwu — corrected a unit error in the worked example
The confidence interval is the informative part. A point estimate with an interval spanning no effect is a different object from the same point estimate with a tight interval, and the abstract presents them identically.
Absolute risk reduction, worked: if the control-arm event rate is 8.0 per cent over the follow-up period and the hazard ratio is 0.80, the treated rate is approximately 6.4 per cent, the absolute risk reduction is 1.6 percentage points, and the number needed to treat is 1 ÷ 0.016 ≈ 63 over that period. A 20 per cent relative reduction and a number needed to treat of 63 are the same finding stated two ways, and only one of them sounds impressive.
To be exact about it, apoB and LDL-C disagree because they measure different things: LDL-C is the cholesterol mass carried in the LDL fraction, ApoB is a count of atherogenic particles. Small dense particles carry less cholesterol each, so a person with many small particles has a concordantly higher ApoB than their LDL-C suggests. When they disagree, ApoB is the better risk marker.
SURMOUNT-4 randomised participants after an open-label lead-in to continued tirzepatide or placebo, and the withdrawal arm regained a substantial proportion of the lost weight over the following year[1].
One qualification: a trial that demonstrates an endpoint at a given dose has demonstrated it at that dose. Extrapolating the endpoint down the dose ladder is an assumption, not a finding.
None of this replaces a clinician who can see the whole picture, and the whole picture is usually where the answer is.
Start with the population. The inclusion criteria of the trial determine what its result can be extrapolated to, and the extrapolation people want is usually to a population the trial excluded.
HbA1c is a weighted average, not a flat one: roughly half the signal comes from the most recent month. That is why a value drawn six weeks after a change already reflects most of the effect, and why a value drawn during rapid haematological turnover reflects something other than glycaemia.
I would resist reading a subgroup finding as a result. Subgroups in these trials were not powered, and a striking subgroup in a large trial is the expected consequence of multiplicity.
If the trend across three draws is flat, the difference between draws one and two was noise. Most of what people react to is noise.
The hazard ratio is the relative effect. What changes decisions is the absolute effect, and converting between them requires the event rate in the control arm, which is usually in the same table and rarely in the abstract.
A fasting lipid panel drawn during rapid weight loss reads oddly for a mechanical reason: mobilised adipose tissue delivers free fatty acids to the liver, and hepatic triglyceride export rises. Triglycerides can transiently increase while the person is doing exactly the right thing. Draw the panel when weight has been stable for a few weeks if you want an interpretable number.
SUSTAIN 6 was the original cardiovascular outcomes trial for semaglutide in type 2 diabetes and is the reference point for the class effect that SELECT later extended to a non-diabetic population[1].
The limitation is that surrogate endpoints and hard endpoints have come apart before in metabolic medicine, so a favourable biomarker is a reason for optimism rather than a conclusion.
Convert everything to an absolute effect before you compare two interventions. Relative effects are not comparable across different baseline risks.
Ask PeptideStack is a static archive. Posting is closed, but the norms are worth stating: answer the question that was asked, show your working, cite the trial or the certificate, and say plainly where the evidence runs out.