What interval makes sense for repeating lipase on mazdutide?

What I am working with: lipase · mazdutide.

I would like to set this up properly once, rather than adjust it repeatedly.

My budget is real but not tight, and my tolerance for uncertainty is low.

What should I decide now, and what should I defer?

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.

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.

On the detail: 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.

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 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.

Convert everything to an absolute effect before you compare two interventions. Relative effects are not comparable across different baseline risks.

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.

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.

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.

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.

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

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-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 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.