In this episode, Dave sits down with Austin, a metabolic data enthusiast and early adopter of continuous glucose monitoring (CGM) who brings a fascinating blend of self-experimentation, performance optimization, and deep curiosity about human physiology. From endurance training to dietary tracking, Austin shares his journey through the data-driven side of health — how he uses CGM, heart rate, and nutrient timing to reveal the body’s hidden patterns. Together, Dave and Austin explore how metrics can empower individuals to take ownership of their health, the tension between conventional guidelines and personal experimentation, and what the future of open-source health data could look like.

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⏱ Chapters

0:00 – Introduction & Setting the Stage

5:45 – Opening Reflections on Austin’s Energy and Setting

10:30 – Early Experiences That Sparked Curiosity

15:15 – First Encounters with Data, Health, and Experimentation

20:00 – The Origins of a Systems Approach to Nutrition

25:00 – Breaking Down the Lipid Energy Model Concept

30:15 – What Early Self-Experiments Revealed

35:20 – Exploring LDL and APOB from a New Perspective

40:10 – Why Traditional Cholesterol Framing Falls Short

45:00 – Digging Into Lipoprotein Transport Mechanisms

50:05 – Triglycerides, Remnants, and Particle Flow

55:15 – When Energy Demand Shapes Lipid Behavior

1:00:10 – The Lean Mass Hyper-Responder Pattern

1:05:00 – Genetics, Metabolism, and Individual Variation

1:10:30 – LPL and LDL Receptor Pathways in Context

1:15:20 – Familial Hypercholesterolemia and Diverse Risk Profiles

1:20:15 – How Population Data Can Mislead Individual Cases

1:25:10 – Mendelian Randomization and Its Hidden Assumptions

1:30:00 – Study Design: What We Miss When We Aggregate

1:35:00 – The Duration vs. Magnitude of LDL Exposure

1:40:10 – Interpreting Meta-Analyses with Caution

1:45:15 – Revisiting the PESA Trial and Imaging Insights

1:50:05 – Understanding the “Three-Line Graph” Debate

1:55:00 – Statistical Power, Noise, and Over-Interpretation

2:00:10 – Regression Models and Data-Slicing Pitfalls

2:05:20 – Plaque Progression and Clinical Translation

2:10:00 – PCSK9 Insights and Unexpected Outcomes

2:15:00 – Beyond LDL: Inflammation and Contextual Risk

2:20:05 – Revisiting the Bradford Hill Criteria for Causality

2:25:10 – Consistency, Dose Response, and Biological Plausibility

2:30:00 – The Changing Landscape of Trial Reporting

2:35:05 – How 2004 Altered Medical Transparency Rules

2:40:00 – Scientific Discourse, Debate, and Misinterpretation

2:45:15 – The Role of Skepticism in Evidence Review

2:50:10 – The Value of Epistemic Humility in Science

2:55:00 – Open Data, Collaboration, and Collective Learning

3:00:10 – Case Studies and Self-Experimentation Insights

3:05:00 – Reflections on N=1 Studies and Public Data Sharing

3:15:00 – Designing Smarter Studies for the Future

3:20:05 – Lessons Learned from Real-World Observation

3:25:00 – Future of Lipid Research and Citizen Science

3:30:00 – Revisiting Key Misconceptions About Cholesterol

3:35:10 – Bridging Gaps Between Clinicians and Researchers

3:40:00 – Empowering Individuals Through Accessible Data

3:50:00 – Community, Collaboration, and Scientific Openness

3:55:10 – Final Thoughts on Evidence, Curiosity, and Persistence

4:00:00 – Closing Reflections & Gratitude

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