The case in front of me: survodutide · four weeks.
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.
How would you structure this, and what thresholds would you set in advance?
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 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.
Mechanically, 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.
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].
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.
edited 21 Dec 2024 by fib4_reader — added a caveat about sampling
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Browse resultsRead 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.
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.
None of this replaces a clinician who can see the whole picture, and the whole picture is usually where the answer is.
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.
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.
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.
Read the confidence interval, read the estimand, and compute the absolute effect yourself. It takes two minutes and it changes how the result feels.
It helps to be literal here: 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.
The papers are readable. Read the paper rather than the summary of the paper, especially where the summary is enthusiastic.
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.
Estimated average glucose from HbA1c: eAG in mg/dL = 28.7 × A1c − 46.7, or in mmol/L, 1.59 × A1c − 2.59. An A1c of 6.5 per cent is therefore about 140 mg/dL or 7.8 mmol/L. The relationship is a population regression, so an individual can sit well off the line.
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].
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.