What does FLOW tell me about headache at the 15 mg dose?

What I have: FLOW · headache · 15 mg.

This is presented as though it settles something, and I am not convinced it does.

I have two documents that appear to disagree, which is what prompted this.

What does this actually establish, and what does it not?

Specifically, 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.

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 relevant detail is that 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.

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

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

To be exact about it, 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.

The underlying point is that 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.

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.

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

It helps to be literal here: 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.

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.

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].

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

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.

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.

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 11 Sept 2025 by tobias_reint — added a caveat about sampling

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.

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.

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].

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.