Conditions: HbA1c · liraglutide.
I want to decide this in advance so that I am not deciding it under pressure later.
Assume I will follow the plan I write down, so I would like it to be a good one.
What should I decide now, and what should I defer?
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.
The early fall in estimated glomerular filtration rate on treatment is haemodynamic rather than structural. Reduced intraglomerular pressure lowers the filtration rate acutely and preserves the glomerulus chronically — the same pattern seen with renin-angiotensin blockade and with SGLT2 inhibition. A dip of a few millilitres per minute in the first weeks, followed by a shallower long-term slope, is the desired trajectory, not a warning sign.
| Trial | Agent | n | Duration | Primary result |
|---|---|---|---|---|
| STEP 1 | Semaglutide 2.4 mg | 1,961 | 68 wk | −14.9 % vs −2.4 % weight |
| STEP 2 | Semaglutide 2.4 mg, T2DM | 1,210 | 68 wk | −9.6 % vs −3.4 % weight |
| SURMOUNT-1 | Tirzepatide 5/10/15 mg | 2,539 | 72 wk | −15 / −19 / −21 % weight |
| SURMOUNT-4 | Tirzepatide, withdrawal | 670 | 88 wk | Continued loss vs substantial regain |
| SELECT | Semaglutide 2.4 mg | 17,604 | ~40 mo | MACE HR 0.80 (0.72–0.90) |
| FLOW | Semaglutide 1.0 mg, CKD | 3,533 | ~3.4 yr | Renal composite reduced; stopped early |
| SURMOUNT-OSA | Tirzepatide, OSA | 469 | 52 wk | AHI reduced with and without PAP |
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.
The PIONEER programme established oral semaglutide’s efficacy and the constraints on its administration, and the bioavailability figure of roughly one per cent is what drives the fasting and water-volume requirements[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.
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.
Analytical standards and reagents with traceable certificates. Every quantitative result you read inherits the accuracy of the standard behind it.
Shop standardsThe underlying point is that this is a question about what the trial was designed to answer, and the honest response is that it was not designed to answer this.
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.
On the detail: 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.
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.
Convert everything to an absolute effect before you compare two interventions. Relative effects are not comparable across different baseline risks.
edited 21 Sept 2025 by label_claim — added a caveat about sampling
To be exact about it, 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.
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.
Specifically, 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.
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.
Read the confidence interval, read the estimand, and compute the absolute effect yourself. It takes two minutes and it changes how the result feels.
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.
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.
SURMOUNT-OSA reported reductions in the apnoea-hypopnoea index with tirzepatide in adults with obesity and moderate-to-severe obstructive sleep apnoea, both with and without concurrent positive airway pressure therapy[1].
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.
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.
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.
The papers are readable. Read the paper rather than the summary of the paper, especially where the summary is enthusiastic.
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.