What is confounding by indication?

When the condition that prompted treatment, rather than the treatment itself, causes the outcome observed — making a drug appear responsible when it is not.

Can it be adjusted away?

Not fully. Severity and duration of the underlying condition are often incompletely captured, and that residual is what matters most.

How did the 2026 study demonstrate it?

The same drugs showed raised preterm birth risk in the diabetes indication but not the weight-management one — so the association tracks the indication.

GLP-1 & Incretin Science

When the Reason for Treatment Causes the Outcome

JMWritten & reviewed by Jack Muncaster · Founder, UK PeptidesLast reviewed 2026-08-233 cited sources

Confounding by indication occurs when the condition prompting treatment, rather than the treatment, causes the outcome observed. A 2026 Danish nationwide cohort demonstrates it directly: the same drugs showed an association in one indication and not in another.

Key facts

Problem: The reason for treatment causes the outcome
Affects: All observational drug studies
Not fixed by: Adjusting for measured confounders alone
Worked example: Hviid 2026 (PMID 41852577)
The tell: Association in one indication, not another
Randomisation: The only complete solution

What the problem actually is

People who receive a medicine differ from people who do not, and the most important way they differ is that they had a condition warranting it. If that condition also affects the outcome being studied, an association between drug and outcome appears even when the drug does nothing. The confounder is the indication itself.

Why it is harder to handle than ordinary confounding

Age or sex can be measured and adjusted for. The severity and duration of the condition that prompted prescribing often cannot be captured fully in registry data, and the residual is exactly the part most strongly related to both treatment and outcome. Statistical adjustment reduces the problem without eliminating it.

Research material referenced

Retatrutide 10mg — third-party HPLC tested

View — £59.99

The 2026 study that shows it plainly

Hviid and colleagues examined periconceptional GLP-1 receptor agonist exposure and obstetric outcomes in Danish health registries covering October 2009 to December 2023. Preterm birth risk was raised where the drugs were used for diabetes — liraglutide adjusted odds ratio 1.70, 95% confidence interval 1.17 to 2.48; semaglutide 1.84, 1.24 to 2.7 — but not where they were used for weight management.

Why that pattern is the informative part

Same drugs, same registry, same outcome, same period. The only thing differing between the two groups is why the drug was prescribed. If the medicine were causing preterm birth it should do so regardless of indication. That it did not is strong evidence the association tracks the underlying diabetes, and the authors state that conclusion themselves.

Why this is a model for reading any observational drug study

Comparing across indications is a natural experiment built into the data. Where a study can show that an association holds in one indication and not another, it has tested its own confounding rather than merely adjusting for it. Most observational studies cannot do this, and it is worth noticing when one can.

How coverage typically gets this wrong

By reporting the headline association and omitting the split. A summary saying these drugs are linked to preterm birth would be technically defensible and would invert the study's actual conclusion, which is that the medication is probably not the causal factor. The finding is in the comparison, not the association.

Extended research context

The GLP-1 & Incretin Science deep dive

Deep dive: the two routes to a bigger effect

Every compound trying to beat GLP-1 alone has taken one of two routes. The first adds more receptors from the same hormone family — GIP in tirzepatide, GIP and glucagon in retatrutide. The second adds a non-incretin satiety hormone, which in practice means amylin: CagriSema combines cagrilintide with semaglutide, and amycretin engages both receptors from one molecule. Both routes work, because they recruit signalling pathways that do not fully overlap. Neither has escaped the constraint that binds all of them, which is that gastrointestinal tolerability worsens as effect size grows.

Deep dive: why a percentage is not a result

The most-quoted numbers in this field are the least comparable. REDEFINE 1 reported 22.7% and 20.4% for the same compound in the same trial — the first among participants who adhered to treatment, the second across everyone randomised. TRIUMPH-1 reported 28.3% in an uncomplicated obesity population while TRIUMPH-3 reported up to 22.6% in adults with established cardiovascular disease, using the same compound. Before any two figures can be compared they have to match on estimand, population, duration, comparator and whether the number is placebo-adjusted. Most published comparisons match on none of them.

Deep dive: what happens after the trial stops

Every headline figure describes weight while treatment continues. The STEP-1 extension found that a year after semaglutide was stopped, participants had given back roughly two-thirds of what they lost, moving from 17.3% mean reduction to a net 5.6% — though average weight remained below baseline and nearly half stayed at least 5% down. Meta-analysis puts regain at around 0.8 kg per month. This is why maintenance studies such as TRIUMPH-6 matter more to the field's future than another two points of peak reduction.

Research applications

▸Comparing incretin and amylin compounds on a like-for-like basis
▸Interpreting estimands, thresholds and placebo-adjusted figures in trial reports
▸Tracking the obesity pipeline across sponsors and jurisdictions
▸Understanding receptor pharmacology behind GLP-1, GIP, glucagon and amylin
▸Distinguishing licensed medicines from investigational compounds

Handling checklist

✓Identify which estimand a quoted percentage comes from before citing it
✓Check the trial population and baseline BMI against the comparison you are making
✓Confirm the duration and whether the reduction curve had plateaued
✓Read discontinuation rates alongside efficacy figures
✓Verify every NCT identifier against ClinicalTrials.gov rather than secondary reporting

Common research-handling mistakes

Learnt from thousands of researcher orders across our UK labs.

✗ Comparing headline percentages across different trials

Fix: Population, duration, estimand and comparator all differ; the numbers are not interchangeable.

✗ Quoting the larger of two figures from the same trial

Fix: Name the estimand. Efficacy and treatment-policy answer different questions.

✗ Treating peak reduction as a durable outcome

Fix: Substantial regain follows cessation across the class; peak figures describe a maintained state.

✗ Assuming an oral route means a weaker mechanism

Fix: Route and receptor count are independent. Orforglipron is weaker because it hits one receptor, not because it is a tablet.

✗ Reading investigational compounds as available treatments

Fix: Most of this pipeline holds no authorisation anywhere; mazdutide is approved only in China.

Continue researching

Peer-reviewed guides, comparators and matched reference materials.

Frequently asked questions

What is confounding by indication?: When the condition that prompted treatment, rather than the treatment itself, causes the outcome observed — making a drug appear responsible when it is not.
Can it be adjusted away?: Not fully. Severity and duration of the underlying condition are often incompletely captured, and that residual is what matters most.
How did the 2026 study demonstrate it?: The same drugs showed raised preterm birth risk in the diabetes indication but not the weight-management one — so the association tracks the indication.

Primary sources & clinical trials

Peer-reviewed research and registered trials from PubMed, ClinicalTrials.gov, PubChem, FDA and NIH. All links open in a new tab and point to the primary source, so every claim can be verified at origin.

Written and reviewed by

Jack Muncaster · Founder, UK Peptides

Jack founded UK Peptides in Manchester after repeatedly receiving research compounds with missing or recycled paperwork. He is responsible for supplier selection, batch release decisions and the content published in this research library. Every article here is sourced to primary literature and every product page to a signed third-party certificate.

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