The political fight over 503A compounders is real and I hope people are making noise abt it. But I keep thinking abt the health piece specifically - what happens when an access gap isn’t chosen. I had a three-week break earlier this year. Voluntary, vacation. Even that felt like data. Hunger came back with information attached. I remembered what actual hunger signals felt like, what my appetite timing was, what emotional eating looked like when the suppression wasn’t there. Disorienting and also clarifying. A forced gap w/ no preparation is different. You don’t have a baseline. You don’t know what your glucose did in the trough window, what your hunger looked like before the drug, or what “stable” meant for ur body before suppression started. The prep work I’d want done before any access disruption: - Meal timing logs from a trough period, when suppression was at its lowest. Not calorie counts. Timing, hunger rating, what was going on emotionally.
- At least two weeks of CGM data showing glucose response when the drug isn’t actively covering it.
- Whatever body composition baseline you have, because the scale is going to move in ways that won’t make sense without a reference window. If you’re tracking any of this, the monthly trend view is worth exporting now. I use the weekly summary in CareClinic for this - it surfaces patterns across mos that single-week memory can’t hold. The rebound tells you what happened. The baseline tells you what it means.
the trough window data point is where I’d push back a little. the case for capturing CGM during trough is solid, it’s the closest proxy to drug-free you can get without stopping. but “glucose response when the drug isn’t actively covering it” isn’t quite what trough data shows for tirz. half-life is ~5 days. day 6-7 of a weekly injection, you’re still at roughly 50% plasma concentration. trough data is “lowest-active-drug” data, not no-drug data. if you actually want a true pre-drug glucose baseline, you need 3+ weeks off, which is 3-4 half-lives out. trough logs are useful context, just not the thing you’re naming them as.
The “50% plasma concentration” framing is right, and it matters for how you label what you’re collecting. Trough data isn’t baseline data, it’s lowest-active-drug data, and conflating those two things gives you a reference window that looks more drug-free than it actually is. That’s a real distinction worth naming before someone uses it to predict what a true off-drug glucose picture looks like. imo Where I’d add a caveat: for most people in an access disruption scenario, three-plus weeks off to reach genuine washout isn’t available. The trough window, labeled correctly, still has value. What ur glucose does at 50% concentration versus 100% is meaningful context, even if it’s not the no-drug baseline. The slope of that trough curve, tracked across multiple cycles, starts to suggest what the trajectory off drug would look like, even if it doesn’t give you the endpoint directly. The issue is less that trough data is the wrong tool and more that it’s being used without a clear label. “Lowest-active-drug” context, collected with that framing, tells you something. “Pre-drug baseline proxy” does not accurately describe it, and that framing is where the confusion comes from
“labeled correctly” is where I’d push on the framing a little. the labeling fix is genuinely useful for interpretation hygiene, but the slope extrapolation claim carries a hidden assumption underneath it: that the concentration-effect relationship is roughly linear, so you can read the trough-to-zero trajectory off the 100%-to-trough slope. for glucose response specifically, I’m not sure that holds. my trough CGM data looks qualitatively different from the data I have from a week I accidentally delayed by four days, not just weaker suppression but different timing and pattern. if there’s a threshold concentration below which receptor-mediated effects change character and not just magnitude, then the slope approach gives you a directional guess at best, and maybe a misleading one at worst. the rebound physiology at true washout may not be a smooth extension of the trough curve at all. still agree trough data has value, still agree labeling is the minimum fix. but “starts to suggest what the trajectory off drug would look like” is doing a lot of extrapolation work that the slope framing kinda obscures rather than resolves.