Posting this because the summary going around does not say what the paper says, and the difference matters for how people here are using it.
HbA1c reflects roughly three months of average glycaemia weighted toward the most recent weeks, which is why repeating it at six weeks tells you very little. The improvement on this class comes from two directions — direct glucose-dependent insulin secretion and glucagon suppression, plus the indirect effect of weight loss on insulin sensitivity — and the second continues after the first has plateaued.
Where I think it is weakest: the population was selected and supported in ways a real cohort is not, so I would read the effect size as a ceiling rather than an expectation.
So the question, as narrowly as I can put it: why A1C lags the way it does, and what to look at in the meantime if you want to know sooner. I would rather have one careful answer than five confident ones.
Figures above are from the primary publication rather than the press summary. If a number here disagrees with one you have, post yours and we will work out which of us is reading a secondary source.
Dr.Martinez said:HbA1c reflects roughly three months of average glycaemia weighted toward the most recent weeks, which is why repeating it at six weeks tells you very…
Glycemic variability as the key metric for glycaemic control success: my coefficient of variation (CV) on CGM dropped from 39% to 22%. Target is <36%, with <30% being ideal.
Why this matters more than average glucose: large glucose swings cause oxidative stress, endothelial damage, and promote advanced glycation end-products (AGEs). A flat glucose line at 95 mg/dL is metabolically healthier than oscillating between 60 and 160, even if the average is the same.
Dr.Martinez said:HbA1c reflects roughly three months of average glycaemia weighted toward the most recent weeks, which is why repeating it at six weeks tells you very…
Patient selection optimization for glycaemic control: emerging predictive biomarkers for GLP-1 agonist response include:
| Biomarker | Association | Evidence Level |
|---|---|---|
| Baseline BMI | Higher BMI → greater absolute weight loss | Strong |
| Fasting insulin | Higher insulin → better response | Moderate |
| GLP1R gene variants | rs6923761 → variable response | Preliminary |
| Baseline hsCRP | Higher CRP → greater CV benefit | Moderate |
| Early weight loss (4 wk) | ≥3% at 4 wks → strong predictor of ≥10% at 68 wks | Strong |
The 4-week early responder criterion is the most clinically actionable: if you haven't lost ≥3% by week 4 at a therapeutic dose, discuss optimization strategies with your provider.
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Shop Reference Standardssarah.morrison said:Patient selection optimization for glycaemic control: emerging predictive biomarkers for GLP-1 agonist response include: Biomarker Association…
PCOS success story with glycaemic control: as someone with polycystic ovary syndrome, this medication has been transformative beyond weight loss.
After 11 months: periods became regular for the first time in a decade, testosterone levels normalized, acne cleared significantly, and — unexpectedly — my fertility specialist is optimistic about future conception.
GLP-1 agonists address the insulin resistance at the root of PCOS. For PCOS patients, this isn't "just" a weight loss drug — it's treating our underlying metabolic dysfunction.
FDA_TrackerJim said:Glycemic variability as the key metric for glycaemic control success: my coefficient of variation (CV) on CGM dropped from 39% to 22%.
This is my experience too, for whatever a second data point is worth. Posting only so the count is not one.