Episode 27 : Inside Your Veins: Are Glucose Monitors (CGM) the Ultimate Endurance Cheat Code? 🩸

Episode 27 : Inside Your Veins: Are Glucose Monitors (CGM) the Ultimate Endurance Cheat Code? 🩸💬 Got a question or feedback? Write us at: [email protected]☕ Buy a Gel Caf for Lactate to support the work: ko-fi.com/lactateSummary: The transition of Continuous Glucose Monitoring (CGM) from a clinical diabetes tool to an endurance "fuel gauge" promises to demystify metabolic flux in real-time; however, the science reveals a complex gap between interstitial fluid readings and actual blood glucose. You must navigate the "compartmental lag time" of 5 to 15 minutes and the "interstitial inversion" during high-intensity efforts where sensor data may flatline while blood glucose actually spikes due to catecholamine-driven glycogenolysis. Training optimization relies on identifying your unique "glucotype" rather than following a universal Glycemic Index; elite athletes use these sensors to find their individualized fueling threshold—often targeting 90–120g of carbohydrates per hour—while monitoring for signs of mitochondrial impairment and overtraining. Beware of "orthorexia" and false readings caused by heat, Vitamin C, or "compression lows" during sleep; the data on your watch is often a "post-mortem" of your state 10 minutes ago rather than a live stream. The disqualification of Kristen Faulkner at the 2023 Strade Bianche highlights the growing tension between data-driven performance and the "human element" of racing.Keywords: cgm, metabolic flux, glycemic variability, glucotypes, interstitial fluid, fueling strategy, endurance performance, glycogenolysis, overtraining, biohacking🎙️ Lactate, the podcast that deciphers science to improve your performance.Key references :Flockhart, M., et al. (2021). Excessive exercise training causes mitochondrial functional impairment and decreases glucose tolerance in healthy volunteers. Cell Metabolism. https://doi.org/10.1016/j.cmet.2021.02.005Thomas, F., et al. (2016). Glycemic Profiles of Sub-Elite Athletes with Normal Glucose Tolerance. Journal of Diabetes Science and Technology. https://doi.org/10.1177/1932296816648344Zeevi, D., et al. (2015). Personalized Nutrition by Prediction of Glycemic Responses. Cell. https://doi.org/10.1016/j.cell.2015.11.001Oliver, N. S., et al. (2024). Accuracy of continuous glucose monitoring during exercise. Journal of Applied Physiology.Rodriguez, J. A., et al. (2024). Continuous glucose monitoring for people without diabetes. Diabetes Technology & Therapeutics. https://doi.org/10.1089/dia.2024.0152Voices generated by artificial intelligence from the scientific report produced by the Lactate team.