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Does AST tell me anything about hepatic fat on ecnoglutide?

Asked 22 Jan 2026Modified 2 months agoViewed 11k times
16

The particulars: AST · ecnoglutide.

I would like the mechanism, because I want to be able to reason about the cases nobody has written about.

I have tried to reason it out from first principles and got to two contradictory conclusions.

So what is the mechanism, and how well established is it?

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askedsamir_bennani13k1822 Jan 2026

5 Answers

Accepted answer first, then by votes
46

Accepted answer

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.

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.

Relative to absolute, worked

QuantityValueDerivation
Control-arm event rate8.0 %From the trial table, not the abstract
Hazard ratio0.80Reported
Treated event rate6.4 %8.0 × 0.80
Absolute risk reduction1.6 pp8.0 − 6.4
Number needed to treat631 ÷ 0.016
Relative risk reduction20 %1 − 0.80

The last two rows describe the same finding. Only one of them is used in headlines.

To be exact about it, 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.

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.

Read the confidence interval, read the estimand, and compute the absolute effect yourself. It takes two minutes and it changes how the result feels.

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answered · acceptedforty_two_c43k387 May 2026
3This is the first explanation of that which has actually made sense to me. – bufferline42 4 months ago
4Note that the label instructions differ between agents on precisely this point. – Dr_Yusuf_Adeyemi 6 months ago
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38

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.

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

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answeredDr_Tomas_Kral37k3818 May 2026
5Do you have a reference for the last claim? Not disputing it, just want to read it. – claudia_ferrante 4 months ago
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18

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.

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 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.

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].

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.

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

edited 26 May 2026 by Dr_Idris_Coulibaly — clarified the distinction between purity and content

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answeredDr_Idris_Coulibaly40k13826 Apr 2026
15

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.

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.

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

None of this replaces a clinician who can see the whole picture, and the whole picture is usually where the answer is.

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answeredDr_Ilse_Vandenberg78k24815 Apr 2026
12

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.

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 10 Mar 2026 by rukhsana_iqbal — corrected a unit error in the worked example

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answeredrukhsana_iqbal14k284 Mar 2026
2Good answer, but the confidence interval in the cited trial is wider than implied. – Dr_Fatima_Belkacem 30 days ago
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