#270 ‒ Journal club with Andrew Huberman: metformin as a geroprotective drug, the power of belief, and how to read scientific papers

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter Andrew Huberman, Professor of Neurobiology at Stanford University and host of the Huberman Lab podcast joins us in a special journal club episode. Peter and Andrew each present a recent paper that sparked their interests, delving into the findings, dissecting their significance, discussing potential confounders and limitations, and exploring remaining questions. Importantly, they share their methodologies for comprehending research studies, providing valuable insights for listeners to navigate this process independently. Peter presents an epidemiological study reevaluating a noteworthy metformin result that intrigued the anti-aging community, leading to discussions on metformin's geroprotective potential (or lack thereof) and the current lack of aging biomarkers. Andrew introduces a paper examining how our beliefs about the medications we take influence their biological effects, distinguishing the "belief effect" from a placebo effect and highlighting its exciting implications for the future. We discuss: The motivation behind this journal club conversation [2:45]; Why Peter chose a paper on metformin, how metformin works, and why it generated excitement as a longevity-enhancing agent [9:00]; Defining insulin resistance and its underlying causes [16:15]; Metformin as a first-line treatment for type 2 diabetes, and Peter's evolving interest in metformin as a geroprotective drug [22:00]; Defining the term "geroprotection" [24:45]; The 2014 study that got the anti-aging community interested in metformin [26:00]; Peter presents the 2022 paper that repeats the analytical approach from the 2014 Bannister study [33:15]; Greater mortality in the metformin group: how results differed between the 2022 paper and the 2014 paper [40:00]; Understanding statistical significance, statistical power, sample size, and why epidemiology uses enormous coho