Deep Dive on LDL, ApoB, and Cardiovascular Disease – TFP #011 | Austin Dudzinski
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.🙏We only have one sponsor -- and it's us: https://OwnYourLabs.com🩸So keep getting your private blood testing through our platform and it will support the podcast. 🔗 CONNECT WITH DAVE FELDMAN PROTOCOLMain Channel: https://youtube.com/@realDaveFeldmanX/Twitter: https://x.com/realDaveFeldmanInstagram: https://instagram.com/realDaveFeldmanWebsite: https://thefeldmanprotocol.com⏱ Chapters 0:00 – Introduction & Setting the Stage5:45 – Opening Reflections on Austin’s Energy and Setting10:30 – Early Experiences That Sparked Curiosity15:15 – First Encounters with Data, Health, and Experimentation20:00 – The Origins of a Systems Approach to Nutrition25:00 – Breaking Down the Lipid Energy Model Concept30:15 – What Early Self-Experiments Revealed35:20 – Exploring LDL and APOB from a New Perspective40:10 – Why Traditional Cholesterol Framing Falls Short45:00 – Digging Into Lipoprotein Transport Mechanisms50:05 – Triglycerides, Remnants, and Particle Flow55:15 – When Energy Demand Shapes Lipid Behavior1:00:10 – The Lean Mass Hyper-Responder Pattern1:05:00 – Genetics, Metabolism, and Individual Variation1:10:30 – LPL and LDL Receptor Pathways in Context1:15:20 – Familial Hypercholesterolemia and Diverse Risk Profiles1:20:15 – How Population Data Can Mislead Individual Cases1:25:10 – Mendelian Randomization and Its Hidden Assumptions1:30:00 – Study Design: What We Miss When We Aggregate1:35:00 – The Duration vs. Magnitude of LDL Exposure1:40:10 – Interpreting Meta-Analyses with Caution1:45:15 – Revisiting the PESA Trial and Imaging Insights1:50:05 – Understanding the “Three-Line Graph” Debate1:55:00 – Statistical Power, Noise, and Over-Interpretation2:00:10 – Regression Models and Data-Slicing Pitfalls2:05:20 – Plaque Progression and Clinical Translation2:10:00 – PCSK9 Insights and Unexpected Outcomes2:15:00 – Beyond LDL: Inflammation and Contextual Risk2:20:05 – Revisiting the Bradford Hill Criteria for Causality2:25:10 – Consistency, Dose Response, and Biological Plausibility2:30:00 – The Changing Landscape of Trial Reporting2:35:05 – How 2004 Altered Medical Transparency Rules2:40:00 – Scientific Discourse, Debate, and Misinterpretation2:45:15 – The Role of Skepticism in Evidence Review2:50:10 – The Value of Epistemic Humility in Science2:55:00 – Open Data, Collaboration, and Collective Learning3:00:10 – Case Studies and Self-Experimentation Insights3:05:00 – Reflections on N=1 Studies and Public Data Sharing3:15:00 – Designing Smarter Studies for the Future3:20:05 – Lessons Learned from Real-World Observation3:25:00 – Future of Lipid Research and Citizen Science3:30:00 – Revisiting Key Misconceptions About Cholesterol3:35:10 – Bridging Gaps Between Clinicians and Researchers3:40:00 – Empowering Individuals Through Accessible Data3:50:00 – Community, Collaboration, and Scientific Openness3:55:10 – Final Thoughts on Evidence, Curiosity, and Persistence4:00:00 – Closing Reflections & Gratitude#FeldmanProtocol #LDL #HDL #Cholesterol #ASCVD #ContinuousGlucoseMonitoring #CGM #MetabolicHealth #DataDrivenHealth #CitizenScience #OwnYourLabs #QuantifiedSelf #HealthData #PerformanceOptimization #DaveFeldman #HumanPerformance #MetabolicFlexibility #OpenSourceScience
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