glp1medication.guide

Estimation tool · shows its working

Build your stack.
See the honest rate.

Most weight-loss maths adds things up that do not add up. OMAD plus 5:2 plus keto is not three deficits, it is one deficit reached three ways. This tool picks the strongest lever in each overlapping group, penalises complexity, models metabolic adaptation, and tells you plainly which of your choices are doing nothing.

About you

Used only for the BMR equation.
This does not change the deficit. It changes how much of the loss is muscle. The untrained and 4+ figures are measured, from the STEP 1 DXA substudy, SURMOUNT-1 and a trained case series. The two middle figures are interpolated between those anchors, not measured. The 4+ figure assumes 1.6–2.3 g protein per kg fat-free mass, which is what the case series actually did.

Pick your levers

Tap everything you genuinely intend to run. Overlapping picks get struck through with an explanation, not silently dropped.

Estimated outcome

0 kg lost
0% of bodyweight
0% per week, averaged

Ending weight — · of which roughly — is lean tissue

Pick some levers

The weekly figure is an average across the whole period, not a steady rate. Real loss is front-loaded: faster in the first months, slower later. The curve below shows the shape.

Where the deficit comes from

Maintenance now—
Intake cut, raw—
Complexity discount—
Intake cut, applied—
Extra weekly burn—
What this is and is not.
It is a transparent arithmetic model built on average effect sizes from published trials. It is not a prediction about you, and it cannot be: individual response to the same intervention varies several-fold. Treat the output as a sanity check on whether a plan is roughly in the right shape, never as a target to punish yourself against. If you have a history of disordered eating, tools like this one are worth skipping entirely.

Why the numbers do not simply add up

Overlap

Interventions are grouped. Within a group, only the strongest one counts. Fasting protocols share a group because they are all mechanisms for eating less in a day. Diet compositions share a group. Stimulant supplements share a group, because caffeine and green tea extract are pulling the same lever, and yohimbine on top of both mostly adds side effects. A GLP-1 sits in its own group, so a microdose and a full dose never stack.

Complexity

Every additional thing you commit to is another thing that can lapse on a bad week. The model applies a discount that grows with the number of active levers: no penalty at two, roughly a quarter off by the time you are running nine. This is the part most calculators omit, and it is the reason ambitious stacks underperform simple ones in real life.

Adaptation

A smaller body burns less, and beyond that it burns a little less than its new size predicts. The model applies both: the predicted drop, plus a settling term of up to about 80 kcal a day that phases in over roughly three months. That is what turns a straight line into a curve that flattens.

A ceiling

Weekly loss is capped at 1.0% of bodyweight regardless of what you stack. That is the CDC-anchored upper bound the underlying research requires this model to respect. Beyond that rate the composition of the loss changes for the worse, and the model refuses to pretend otherwise.

Questions people ask about this

Why did it strike out my second fasting protocol?
Because OMAD and 16:8 are not additive. Both work by shrinking the window in which you eat. Running both means running the stricter one. The model keeps the stronger effect and tells you the other is doing nothing rather than double-counting it.
Creatine shows a zero deficit. Is that a bug?
No. Creatine has no fat-loss effect and the model says so. It is in the list because it helps hold strength and lean mass through a deficit, which changes the composition of what you lose, not the amount.
Can I stack a GLP-1 with fasting?
The model allows it, because they are different groups. In practice the combination frequently drives intake below what is nutritionally sensible, and protein and micronutrients are the first casualties. If you are doing both, the number to watch is not the scale, it is grams of protein.
Why is my estimate lower than what I see people posting?
Because published averages include the people it did not work for, and social posts do not. Both numbers are real. Only one of them is representative.

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