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Ep. 631 The Metabolic Health Masterclass | Menopause, Perimenopause, Metabolic Health

Ep. 631 The Metabolic Health Masterclass | Menopause, Perimenopause, Metabolic Health

Published 1 month, 2 weeks ago
Description

Today, we have a metabolic health masterclass, featuring Dr. Robert Lustig, Ben Azadi, Dr. Ben Bikman, Mark Sisson, and Dr. Nick Norwitz. 

In today’s masterclass, we explore why metabolic dysfunction is not simply a calories-in, calories-out issue or a failure of willpower, how insulin and leptin influence appetite and behavior, how early signs of insulin resistance can appear years before a diabetes diagnosis, and the important relationship between muscle, glucose regulation, and insulin sensitivity. We also discuss how movement supports fat burning without sacrificing muscle, why chronic cardio may not produce the results many people expect, and how emerging technology could provide a far more personalized approach to metabolic care.

Stay tuned for today’s informative discussion, where our panel of experts shares their valuable perspectives on metabolic health.

IN THIS EPISODE, YOU WILL LEARN:

  • Leptin resistance and how it can drive the overeating and inactivity commonly associated with obesity

  • How elevated insulin blocks the effects of leptin

  • Why it’s essential to pay attention to fasting insulin rather than waiting for blood glucose to rise 

  • Ben Azadi highlights metabolic health markers that can provide information beyond fasting glucose.

  • How our muscles help to regulate glucose and support insulin sensitivity

  • The importance of building and preserving muscle to support insulin sensitivity as we age, and how even a brief period of inactivity can reduce insulin sensitivity

  • Mark Sisson explains why running may not be the most effective fat loss strategy 

  • How walking can increase your fat-burning capacity while also helping to preserve your muscles

  • Dr. Nick Norwitz explains how type 2 diabetes can involve different underlying pathologies, and how CGM data and machine learning can eventually help to identify specific subphenotypes

Connect with Cynthia Thurlow  

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Connect with Ben Azadi

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