Why do two papers on ATTAIN-1 report different headline figures?

This is a question about interpretation, not about whether to act — I will take action questions elsewhere.

I keep seeing this stated as a fact with no explanation attached, and unexplained facts make me suspicious.

My background is quantitative but not chemical, so I can follow an equation more easily than a hand-wave.

Why does this happen, and what would falsify the usual explanation?

Worth being precise here: 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.

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.

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.

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.

edited 27 Nov 2025 by t_oyelaran — updated for the 2026 guidance change

The part that matters: a 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.

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.

Worth being precise here: 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.

SURMOUNT-1 reported mean weight reductions of approximately 15, 19 and 21 per cent at tirzepatide 5, 10 and 15 mg respectively at 72 weeks[1].

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.

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 27 Nov 2025 by Dr_Colm_Fitzhenry — tightened the wording; no substantive change

Put another way, 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.

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-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].

Worth being explicit that this is interpretation of published data and not medical advice. Laboratory results belong in a conversation with whoever ordered them.

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 part that matters: 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.

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 papers are readable. Read the paper rather than the summary of the paper, especially where the summary is enthusiastic.