“It takes five or six doses to reach equilibrium.” — Dr. Ian Ellis

In this episode of Compound Wisdom, Steve Suen sits down with Dr. Ian Ellis — former ER physician, fitness specialist, and founder of a multi-state telehealth clinic — to break down what most people misunderstand about GLP-1 medications and why standard dosing protocols often lead to unnecessary side effects, muscle loss, and early drop-off. Instead of fixed weekly dose ladders, Dr. Ellis argues for a pharmacokinetic, level-based model that targets the exact drug concentration where a patient feels and functions best.

Dr. Ellis shares his personal journey from obsessive fitness and disordered eating patterns through emergency medicine burnout and significant weight gain, to discovering GLP-1 therapy firsthand. His early experience with semaglutide produced dramatic appetite control — but also severe side effects and unexpected muscle loss under standard dosing. That failure pushed him to study the drug’s half-life and accumulation curves, leading to a key insight: each weekly dose stacks on top of what’s already in the body, meaning patients are often escalating into overdose territory without realizing it.

From there, the conversation turns practical and technical. Dr. Ellis explains his “My Level” dosing approach — a calculator-driven system that models drug levels in the body and adjusts each dose to return patients to their personal sweet spot instead of blindly increasing amounts. He describes how this method helps patients use significantly less medication, experience fewer side effects, retain more muscle mass, and stay on therapy longer — while still matching or exceeding expected weight-loss outcomes.

They also cover real-world scenarios most protocols don’t handle well: travel timing, missed doses, running out of medication, and plateau phases. Dr. Ellis explains why standard instructions fail in these cases and how level-targeted dosing provides precise catch-up and adjustment strategies. The broader theme is that GLP-1 drugs are powerful but narrow-window tools — and without precision, the industry risks creating a thinner but weaker, less functional population instead of a healthier one.

The throughline of the episode is straightforward: GLP-1s are potentially transformative, but only if dosing becomes individualized, data-driven, and physiology-aware rather than schedule-based.

Takeaways

Dr. Ian Ellis is a former ER physician who left emergency medicine to focus on metabolic and longevity care.
He founded a telehealth clinic focused on GLP-1s, peptides, and regenerative health.
His interest in weight and fitness began in his teens and evolved into extreme dieting patterns.
He describes a long period of obsessive training, restriction, and rebound weight gain.
Medical school, residency, and family pressures led to major weight gain and burnout.
He first used semaglutide under a standard dosing ladder without tight supervision.
Early GLP-1 use reduced appetite dramatically but triggered escalating side effects.
Weekly GLP-1 dosing stacks because half the drug is still present at the next dose.
Patients reach higher drug levels each week even if the dose number is unchanged.
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Standard dose escalation can push patients into intolerance and GI distress.
He recorded severe nausea, GI symptoms, and functional impairment at higher levels.
A body composition scan showed large muscle loss during rapid GLP-1 weight loss.
He argues muscle loss + frailty risk is under-discussed in GLP-1 protocols.
He studied GLP-1 pharmacokinetics and built spreadsheet models of blood levels.
This led to his “My Level” concept — target the best-feeling drug level, not a fixed dose.
The method asks: how much do I take today to get back to my target level?
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He built a dosing engine and app to automate these calculations at scale.
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Patients identify their “sweet spot” based on hunger, energy, and side-effect profile.
Doses are then adjusted dynamically to maintain that level.
Micro-adjustments are preferred over large dose jumps.
Small level increases often restart weight loss after plateaus.
Many patients never need to reach manufacturer max doses.
Clinic patients often use roughly ¼–½ of labeled max dosing.
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Reported outcomes match or exceed expected weight-loss averages.
Lower dosing reduces cost burden and dropout risk.
Industry attrition rates approach ~50% in the first year.
He attributes most dropouts to side effects and expense.
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Level-based dosing aims to reduce both drivers.
The system also handles travel timing and missed doses precisely.
Catch-up dosing is calculated instead of guessed.
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He believes GLP-1s can be population-level game changers if dosed correctly.
Poor dosing could instead produce a thinner but weaker population.
His stated mission is expanding access to precision GLP-1 dosing.

Chapters

00:00 – Opening hook: the idea of a “best level” of medicine

00:58 – Guest intro: ER physician, fitness background, telehealth founder

02:00 – Early life, sports, and fear of science

04:10 – Injury, rehab, and path into medicine

07:30 – Emergency medicine reality vs prevention

10:30 – Frustration with chronic disease management model

12:00 – Extreme fitness and dieting behaviors

15:00 – Binge–restrict cycles and metabolic fallout

18:00 – Weight gain during medical training and burnout

21:40 – Discovering semaglutide

23:00 – First GLP-1 appetite suppression experience

27:00 – Side effects begin under fixed dosing

29:30 – Severe reaction after dose escalation

30:30 – Body comp scan shows major muscle loss

32:30 – Pharmacokinetics deep dive

34:00 – Drug accumulation and steady state explained

36:00 – The “sweet spot” level insight

37:30 – From fixed dose to target level model

38:15 – Building the My Level calculator

40:45 – Travel, missed doses, and catch-up logic

42:30 – Plateaus and micro-level increases

44:00 – Why lower long-term doses win

45:20 – Clinic outcomes vs manufacturer dosing

48:00 – App development and scaling the model

49:00 – Dropout rates and adherence problem

50:00 – Vision for the future of GLP-1 dosing

Closing – Precision over protocol