LEVELS – A Whole New Level

Latest Episodes:

#318 - How Sleep Changes What and How Much You Eat | Dr. Marie-Pierre St-Onge & Mike Haney Nov 05, 2026

Show notes

Sleep and diet don’t operate independently. Controlled studies suggest that when people sleep less, they tend to eat more, move less, and gradually gain weight. The relationship also appears to run in the other direction: what and when you eat can influence how well you sleep.

Mike Haney talks with Columbia sleep and nutrition researcher Dr. Marie-Pierre St-Onge about what the evidence actually shows at that intersection. Rather than chasing a perfect sleep score or a single “sleep food,” her work points toward a simpler idea: adequate, consistent sleep makes healthy eating easier, and a high-quality diet may help support better sleep in return.

Free course: Improve your metabolic health

Get our free email course on how glucose, nutrition, exercise, sleep, and measurement can help you build habits that support better energy and long-term health: ⁠⁠⁠https://levels.link/wnl

What We Cover:

Why even modest chronic sleep loss can accumulate into meaningful sleep debt
What controlled experiments show about short sleep, calorie intake, and weight gain
Why sleep deprivation tends to shift food choices toward less healthy options
How sleep loss affects appetite hormones differently in men and women
What the research says about eating close to bedtime
Whether tryptophan, melatonin-rich foods, and specific “sleep foods” actually help
Why fiber, fruits and vegetables, saturated fat, and refined carbohydrates may influence sleep quality
How much attention to pay to wearable sleep scores versus how you actually feel

🎙️ About the Guest:

Dr. Marie-Pierre St-Onge is Professor of Nutritional Medicine at Columbia University Irving Medical Center and Director of its Center of Excellence for Sleep & Circadian Research. Her research focuses on the relationships among sleep, diet, energy balance, body weight, and cardiometabolic health, with extensive work in controlled inpatient and outpatient sleep studies. Vagelos College

📍What Dr. Marie-Pierre St-Onge & Mike Haney discussed:

05:01 - What popular sleep talk still gets wrong
09:30 - A wearable score versus how you actually feel
10:37 - Sleep pressure and the body clock
13:00 - How a little lost sleep becomes real sleep debt
19:43 - Short sleep, and about 300 extra calories
27:11 - Six weeks short, and about a pound gained
29:05 - Why short sleep shifts you toward worse food
30:30 - Appetite hormones don’t respond the same in men and women
37:03 - What eating close to bedtime actually does
40:20 - Tryptophan, melatonin, and “sleep foods”
42:42 - Fruits and vegetables, and less broken sleep
46:10 - Saturated fat, refined carbs, and a better cycle

🔗 Helpful Links:

Marie-Pierre St-Onge — Columbia University: Columbia profile
Short sleep duration increases energy intake — American Journal of Clinical Nutrition (2011): PubMed
Prolonged short sleep and body weight — Annals of Internal Medicine (2026): PubMed
Sleep extension reduces calorie intake — JAMA Internal Medicine (2022): National Institutes of Health
Fruit and vegetable intake and sleep fragmentation — Sleep Health (2025): PubMed
Fiber, saturated fat, and sleep architecture — Journal of Clinical Sleep Medicine (2016): Vagelos College
Eat Better, Sleep Better: ihn.cuimc.columbia.edu

Watch the conversation: https://youtu.be/WxvbJu_GnSo

Find us on YouTube: ⁠⁠⁠https://youtube.com/levelshealth?sub_confirmation=1⁠

👋 Who we are:

Levels helps you understand your metabolic health with personalized data, expert guidance, and tools that connect your daily choices to measurable changes in your body. Our goal is to help you make better decisions about food, exercise, sleep, and long-term health.

Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#317 - What It Takes to Build Medical AI Doctors Will Trust | Dr. Suchi Saria & Mike Haney Oct 29, 2026

Show notes

Medical AI is full of impressive models that work on retrospective datasets. The much harder problem is turning one into a tool that can operate continuously inside a hospital, earn clinicians’ trust, fit into an overloaded workflow, stay reliable across different health systems, and ultimately improve patient outcomes.

Mike Haney talks with Johns Hopkins researcher and Bayesian Health founder Dr. Suchi Saria about the decade-long journey from an academic sepsis-prediction algorithm to AI that clinicians are using in hospitals today. Her experience is a reality check on what it actually takes to make medical AI useful, and an argument for aiming higher than documentation and billing: toward systems that continuously watch patients, catch deterioration earlier, and help prevent avoidable harm.

Free course: Improve your metabolic health

Get our free email course on how glucose, nutrition, exercise, sleep, and measurement can help you build habits that support better energy and long-term health: ⁠⁠https://levels.link/wnl

What We Cover:

Why sepsis fits continuous AI monitoring
How causal reasoning separates signal from a noisy alert
Why real-time access to the full record matters
When a working algorithm still failed to get attention
How adoption and human-machine teaming became engineering problems
What happened when the system went live
How the same infrastructure extends beyond sepsis
Why healthcare is too conservative about medical AI

🎙️ About the Guest:

Dr. Suchi Saria is the John C. Malone Associate Professor at Johns Hopkins University, where she directs the Machine Learning and Healthcare Lab, and is the founder and CEO of Bayesian Health. Her work focuses on building and deploying machine-learning systems that use real-world clinical data to improve diagnosis, treatment, and patient safety.

📍What Dr. Suchi Saria & Mike Haney discussed:

04:30 - Why the real AI risk is what it is allowed to do
08:32 - Why sepsis is a race against organ failure
11:57 - From checklists to reading the full medical record
16:30 - How causal reasoning finds a patient’s baseline
18:50 - Using uncertainty to avoid another noisy alert
22:11 - Why an hour-late warning is already too late
25:06 - Reading Epic in real time without slowing it down
32:35 - A working algorithm still couldn’t get clinicians’ attention
35:53 - What happened when the system went live
45:32 - Human-machine teaming, and who still makes the call
54:17 - The same infrastructure, beyond sepsis
58:35 - Why billing and coding is too small a target

🔗 Helpful Links:

Suchi Saria — Johns HopkinsResearch, publications, and academic background.
Bayesian Health:Saria’s company commercializing real-time clinical AI.
TREWScore — Science Translational Medicine (2015): The original real-time early warning model for septic shock.
TREWS patient outcomes — Nature Medicine (2022): Prospective multisite evaluation after deploying the sepsis warning system in clinical care.
TREWS clinician adoption — Nature Medicine (2022): Study of provider adoption and effects on sepsis treatment timing.
Human-machine teaming — npj Digital Medicine (2022): Clinician interviews on what made the deployed AI system trustworthy and usable.
Bayesian Health Sepsis Flagging Device — FDA (2026): FDA 510(k) clearance for the sepsis prediction software discussed in the episode.

Watch the conversation: https://youtu.be/uVEKgSvei4s

Find us on YouTube: ⁠⁠https://youtube.com/levelshealth?sub_confirmation=1⁠

👋 Who we are:

Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#316 - What Should You Actually Do About High Cholesterol? | Dr. Seth Martin & Mike Haney Oct 22, 2026

Show notes

Heart disease is the leading cause of death, but it’s also one of the most preventable. Decades of research have established a strong causal role for LDL and other ApoB-containing particles in atherosclerosis, and we now have multiple proven ways to lower them. The harder question is how to turn that evidence into a decision for an individual person.

Mike Haney talks with Johns Hopkins cardiologist Dr. Seth Martin, a co-author of the new 2026 dyslipidemia guidelines, about what the latest evidence says we should actually measure and act on. The throughline is pragmatic: lower exposure to atherogenic particles earlier and for longer, use additional tests when they can change a decision, and distinguish interesting new measurements from ones that have actually been shown to improve outcomes.

Free course: Improve your metabolic health

Get our free email course on how glucose, nutrition, exercise, sleep, and measurement can help you build habits that support better energy and long-term health: ⁠https://levels.link/wnl

What We Cover:

Why the evidence that LDL and ApoB-containing particles cause atherosclerosis is so strong
What “lower for longer” means and how the new LDL and ApoB goals change with cardiovascular risk
When ApoB adds useful information beyond a standard lipid panel
Why the guidelines recommend against routine LDL particle-size testing
How PREVENT risk scores and coronary calcium scans can help decide when treatment makes sense
What decades of statin research tell us about benefits, muscle symptoms, and diabetes risk
How much lifestyle changes typically move cholesterol, and when non-statin drugs enter the picture
Why common heart-health supplements don’t have the evidence behind proven lipid-lowering therapies

🎙️ About the Guest:

Dr. Seth Martin is a professor of medicine and preventive cardiologist at Johns Hopkins, where he directs the Advanced Lipid Disorders Program and Digital Health and AI Lab at the Ciccarone Center for the Prevention of Cardiovascular Disease. He is a co-developer of the widely used Martin-Hopkins LDL cholesterol equation and served on the writing committee for the 2026 ACC/AHA dyslipidemia guideline.

📍What Dr. Seth Martin & Mike Haney discussed:

00:04 - LDL and ApoB are causal. Lower for longer
07:10 - Guidelines set the standard. Not your doctor
11:40 - 2026 shift: earlier treatment, PREVENT, lower goals
17:01 - LDL near zero does not eliminate cholesterol
19:59 - Hard numbers are back, and lower
23:05 - ApoB if a high result changes treatment
27:42 - No trial says treat particle size instead of LDL
34:51 - PREVENT is for people not already high risk
38:52 - Calcium 300 can push the goals. Zero can mean lifestyle first
44:29 - The 3% statin line, and the choice just under it
51:22 - Muscle symptoms uncommon. Statin can move prediabetes diagnosis earlier
56:21 - Lifestyle often lowers LDL about 10%, not the whole benefit
59:05 - Other proven drugs when a statin is not enough
1:05:31 - Statin beat the supplements. Fish oil can raise AFib risk

🔗 Helpful Links:

2026 Guideline on the Management of Dyslipidemia: ⁠Read the guideline and supporting materials⁠
The New Cholesterol Guideline: What to Know — Johns Hopkins: Read the Johns Hopkins summary
PREVENT-ASCVD Calculator.: ⁠Use the PREVENT calculator⁠
Martin-Hopkins LDL Equation: Learn about the Martin-Hopkins method
SPORT Trial — JACC (2022): Read the SPORT trial

Watch the conversation: https://youtu.be/vRfhof5dOZw

Find us on YouTube: ⁠https://youtube.com/levelshealth?sub_confirmation=1⁠

👋 Who we are:

Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#315 - Can We Treat Disease Through the Nervous System? | Dr. Kevin Tracey & Mike Haney Oct 15, 2026

Show notes

Most of NextLevel starts with a measurement problem: we can see more biology than ever, but we still have to figure out what to do with it. Bioelectronic medicine flips that sequence. We already know how to send electrical signals into the nervous system to change biology, including an FDA-approved vagus nerve treatment for rheumatoid arthritis, even though we still can’t map or read most of the individual nerve fibers carrying those signals.

Mike Haney talks with Dr. Kevin Tracey, a pioneer of neuroimmunology and bioelectronic medicine, about what becomes possible when nerves become a therapeutic target. The opportunity is not simply vagus nerve stimulation, but learning which signals control specific organs, then building systems that can read and write them. That is still some distance away, and Tracey separates it from looser claims about vagal tone and vagus nerve hacks.

Free course: Improve your metabolic health

Get our free email course on how glucose, nutrition, exercise, sleep, and measurement can help you build habits that support better energy and long-term health: https://levels.link/wnl

What We Cover:

Why the vagus nerve is not one wire
How one minute a day became an FDA-approved rheumatoid arthritis treatment
Why a nerve target can be more precise than a drug
Why mapping the vagus fiber by fiber is only beginning
Why CRP and cytokines are not a true inflammation readout
What HRV, breathing, humming, and cold plunges can and cannot show

🎙️ About the Guest:

Dr. Kevin Tracey is a neurosurgeon, scientist, and President and CEO of the Feinstein Institutes for Medical Research. His work helped establish how the vagus nerve regulates inflammation and helped launch bioelectronic medicine. He is also co-founder of SetPoint Medical and author of The Great Nerve.

📍What Dr. Kevin Tracey & Mike Haney discussed:

00:00 - A nerve can treat one organ. A pill cannot
04:43 - The vagus nerve is 160,000 fibers, not one wire
07:53 - Why the first fiber-by-fiber map of the human vagus is only months old
10:29 - FDA-approved vagus nerve stimulation for rheumatoid arthritis
14:33 - Why one minute a day can quiet inflammation for 12 to 24 hours
17:39 - Why it took 30 years to get from the mouse finding to patients
31:53 - How one minute of current reaches millions of immune cells
41:19 - Why arthritis has a known mechanism and epilepsy and depression do not
49:16 - What single fibers could change in diabetes, the liver, the gut, and stroke recovery
52:59 - Why CRP and cytokine levels still fall short of a true readout
1:04:18 - Resting heart rate, not HRV, is the cleaner vagus signal
1:09:55 - Why deep breathing and humming are not vagus nerve stimulation
1:16:35 - A cold plunge is fight-or-flight first, vagus second
1:20:23 - The spleen signal he would measure if he could

🔗 Helpful Links:

Watch the conversation: https://youtu.be/x_h6Dd_Ap_o

Find us on YouTube: https://youtube.com/levelshealth?sub_confirmation=1

📲 Connect:

Connect with Dr. Kevin Tracey at Feinstein Institutes for Medical Research

👋 Who we are:

Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#314 - How to Make Health Decisions When the Science Is Unclear | Dr. Anupam Jena & Mike Haney Oct 08, 2026

Show notes

Most health decisions happen before the science is settled. A study points one way, another points somewhere else, experts disagree about what matters, and even strong population-level evidence may not tell you what will happen to you.

In this episode, Mike Haney talks with physician and economist Dr. Anupam Jena about how to make decisions under that kind of uncertainty. The goal isn’t to find a single hierarchy of evidence or a perfect source to trust. It’s to understand how much weight to put on any one piece of information, when personal experimentation can actually teach you something, and how the potential downside of an intervention should change the amount of evidence you demand before trying it.

Free course: Improve your metabolic health

Get our free email course on how glucose, nutrition, exercise, sleep, and measurement can help you build habits that support better energy and long-term health: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://levels.link/wnl⁠

What We Cover:

Why even a high-quality study should rarely change your mind by itself
Why a meta-analysis is only as useful as the studies that go into it
What randomized trials can establish, and where they can still fail to apply to real life
How to judge whether a study’s outcome is actually meaningful, not merely statistically significant
When N-of-1 experiments can help, and how they can fool you
A practical framework for deciding whether an intervention is worth trying: potential harm, quality of evidence, biological plausibility, and cost

🎙️ About the Guest:

Dr. Anupam Jena is the Joseph P. Newhouse Professor of Health Care Policy at Harvard Medical School, a physician at Massachusetts General Hospital, and an economist whose research uses natural experiments to uncover unexpected influences on health and medical care. He is also the host of Freakonomics, M.D. and co-author of Random Acts of Medicine.

📍What Dr. Anupam Jena & Mike Haney discussed:

04:04 - Why taxi and ambulance drivers die least often of Alzheimer's
09:20 - Why half of people still skip a free heart medication
13:02 - Why the headlines on coffee keep reversing
14:55 - Judge the study on its merits, not on who funded it
17:43 - Why even a high-quality study should rarely change your mind
23:04 - Why a cold plunge trial cannot separate the bath from the belief
26:19 - Why a meta-analysis is only as useful as the studies inside it
27:15 - What a randomized trial can establish, and where it still fails in real life
33:53 - The coin-flip test for whether a study shows cause and effect
36:25 - Absolute risk, relative risk, and what "significant" leaves out
43:06 - When an N-of-1 experiment can teach you what a trial cannot
46:02 - Getting better does not mean the experiment worked
53:55 - Harm, evidence, plausibility, cost: is it worth trying?

🔗 Helpful Links:

Dr. Anupam Jena — Harvard Medical School profile
Random Acts of Medicine — Anupam B. Jena & Christopher Worsham
Freakonomics, M.D. https://freakonomics.com/series/bapu
Alzheimer’s mortality among taxi and ambulance drivers (BMJ, 2024). Hypothesis-generating, not evidence that driving prevents Alzheimer’s: Read the BMJ study
Marathon road closures and cardiac mortality (NEJM, 2017): New England Journal of Medicine

Watch the conversation: https://youtu.be/k9XI7ym7iP8

Find us on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://youtube.com/levelshealth?sub_confirmation=1⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

📲 Connect:

Connect with Dr. Anupam Jena on https://x.com/AnupamBJena

👋 Who we are:

Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#313 - What Happens When We Start Imaging Healthy People? | Dr. Daniel Durand & Mike Haney Oct 01, 2026

Show notes

Most medical imaging happens after a symptom, injury, or abnormal test gives a clinician a reason to look. But what changes if imaging becomes a more routine measurement, creating a record of the body before something goes wrong?

In this episode of Next Level, Mike Haney talks with Dr. Daniel Durand, chief medical officer at Prenuvo, about whole-body MRI as a test case for a broader shift from episodic medicine toward longitudinal health data. Whole-body MRI is controversial, and Durand is clearly an advocate. The conversation takes the skepticism seriously, then asks a larger question: as measurement gets cheaper, more frequent, and increasingly visible to patients themselves, how should medicine decide what is worth knowing?

Free course: Improve your metabolic health

What We Cover:

How X-rays, CT scans, ultrasound, and MRI differ, and why radiation matters when you start thinking about repeated imaging
What whole-body MRI sees well, and where CT, colonoscopy, or blood-based approaches may still be better
The concerns around incidental findings, overdiagnosis, anxiety, and unnecessary follow-up, and Durand’s response to them
What the current cancer-detection evidence for whole-body MRI does, and does not, establish
Why having prior scans could help distinguish a stable abnormality from something genuinely new
How faster scans and AI interpretation could eventually connect imaging with genomic, proteomic, and other health data

🎙️ About the Guest:

Dr. Daniel Durand is chief medical officer at Prenuvo and president of the Prenuvo Medical Group. He trained at Johns Hopkins, is board-certified in adult and pediatric radiology, and previously served as chief clinical officer and chief innovation officer at LifeBridge Health.

📍What Dr. Daniel Durand & Mike Haney discussed:

03:32 - Why imaging belongs in a series about continuous measurement
15:32 - Imaging 101: X-rays, CT, ultrasound, and MRI
28:57 - How AI is shortening MRI scan times
38:52 - Screening versus surveillance
40:26 - Which cancers whole-body MRI can miss
46:39 - The case against scanning healthy people
52:40 - What the cancer-detection data actually shows
54:39 - "We're all going to get a body scan"
1:03:31 - What would make routine imaging more useful?
1:11:49 - Building a baseline of the healthy body
1:17:43 - When AI can see more than a radiologist
1:21:28 - Is it better to know, even when you can't treat it?

🔗 Helpful Links:

Dr. Daniel Durand, Prenuvo: Prenuvo medical leadership
Meta-analysis behind the ~9,000 participants and ~1.6% detection figures. It notes limits, including incidental findings (European Radiology, 2026): PubMed
Prenuvo-associated study of 1,011 people, 2.2% confirmed cancer detection (Cancer Research/AACR, 2025): AACR abstract
Incidental findings in whole-body MRI and their psychosocial effects (European Journal of Radiology, 2021): PubMed
Prenuvo's research overview, including Polaris data (company-reported): Prenuvo clinical research

Watch the conversation: https://youtu.be/rFZVbzUPSaA

Find us on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://youtube.com/levelshealth?sub_confirmation=1⁠⁠⁠⁠⁠⁠⁠⁠⁠

📲 Connect:

Connect with Dr. Daniel Durand at LinkedIn

👋 Who we are:

Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#312 - Is Moderate Drinking Actually Healthy? | Dr. Tim Naimi & Mike Haney Sep 24, 2026

Show notes

Alcohol has spent the last 40 years bouncing between two health narratives: a little is good for your heart, or any amount is dangerous. The science doesn’t really support either slogan.

Dr. Tim Naimi has spent decades studying the health effects of alcohol and, just as importantly, why the research is so difficult to interpret. His read of the evidence is less dramatic but more useful: there’s no good reason to start drinking for your health, very low intake may carry little measurable added risk, and risk rises as consumption does. Understanding why earlier studies made moderate drinking look protective helps explain a lot of the whiplashing headlines.

Free course: Improve your metabolic health

What We Cover:

Why alcohol is a Group 1 carcinogen causally linked to at least seven cancers
Whether small amounts of alcohol really protect your heart
Why researchers still don’t have a definitive randomized trial of drinking and long-term health
How the “sick quitter” problem, selection bias, and confounding made moderate drinkers look healthier
Why two major recent reviews of alcohol and health produced such different headlines
How the risk changes with dose, including Naimi’s estimate of a 1 in 25 lifetime risk of an alcohol-caused death at 14 drinks per week
Why alcohol consumption is falling, and whether GLP-1 drugs could be contributing

🎙️ About the Guest:

Dr. Tim Naimi is a physician and alcohol epidemiologist who directs the Canadian Institute for Substance Use Research at the University of Victoria, where he is a professor in the Division of Medical Sciences.

📍What Dr. Tim Naimi & Mike Haney discussed:

00:45 — Why the science on alcohol is so confusing
05:48 — How alcohol damages DNA and raises cancer risk
09:43 — Is alcohol actually good for your heart?
14:22 — Why we still don't have a definitive randomized trial
20:30 — The "sick quitter" problem
24:56 — Selection bias and the problem with "healthy" moderate drinkers
29:35 — Why two major reports produced different headlines
31:37 — How alcohol risk changes as drinks per week increase
38:21 — Industry funding and conflicts of interest
46:15 — How much alcohol is too much?
49:48 — Why younger generations are drinking less
51:53 — Health consciousness, GLP-1s, and the decline in drinking

🔗 Helpful Links:

Alcohol Intake and Health Study: No Protective Effect at Low Levels, With Mortality Increasing to 1 in 25 at 14 Drinks Per Week (Journal of Studies on Alcohol and Drugs, 2026) Paper
Review of Evidence on Alcohol and Health (National Academies of Sciences, Engineering, and Medicine, 2025) Report
Association Between Daily Alcohol Intake and Risk of All-Cause Mortality: A Systematic Review and Meta-analyses (JAMA Network Open, 2023) Paper
Do “Moderate” Drinkers Have Reduced Mortality Risk? (Journal of Studies on Alcohol and Drugs, 2016) Paper
Alcohol Abstinence in Drinkers with Atrial Fibrillation (New England Journal of Medicine, 2020) Paper
Canada’s Guidance on Alcohol and Health (2023) Guidance and supporting research
Alcohol and Cancer (World Health Organization / IARC) WHO overview

Watch the conversation: https://youtu.be/8GNmcjPfg7U

Find us on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://youtube.com/levelshealth?sub_confirmation=1⁠⁠⁠⁠⁠⁠⁠⁠

📲 Connect:

Connect with Dr. Tim Naimi at University of Victoria

👋 Who we are:

Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#311 - Can Your Blood Predict Your Future Health? | Dr. Tony Wyss-Coray & Mike Haney Sep 17, 2026

Show notes

From a single blood sample, new proteomic tools can now measure thousands of proteins at once. Dr. Tony Wyss-Coray’s lab is using that flood of data, together with machine learning, to build a much higher-resolution picture of what’s happening throughout the body and where someone’s health may be headed.

The early signals are striking. In one analysis of people who appeared healthy by conventional measures, every additional 4.1 years of estimated heart age was associated with nearly 2.5 times the risk of heart failure over the following 15 years. Wyss-Coray sees this kind of measurement eventually becoming a “health compass”: something that could show which parts of the body are changing fastest, then help track whether an intervention is actually moving them in the right direction.

Free course: Improve your metabolic health

What We Cover:

How a drop of blood can now reveal information from thousands of proteins
Why aging appears to happen in waves rather than at a steady rate
Why one part of the body may age faster than the rest
What young-blood experiments in mice taught Wyss-Coray about proteins and brain aging
How the work is moving from whole organs down to individual cell types
Why the field’s biggest constraint may now be data collection rather than better AI

🎙️ About the Guest:

Dr. Tony Wyss-Coray is the D.H. Chen Distinguished Professor of Neurology and Neurological Sciences at Stanford University and director of the Phil and Penny Knight Initiative for Brain Resilience.

📍What Dr. Tony Wyss-Coray & Mike Haney discussed:

04:03 Proteins 101: the building blocks hiding information about your health
05:09 How one blood sample can measure thousands of proteins
09:07 Using machine learning to find patterns in the proteome
12:17 Can blood reveal what’s happening inside the brain?
13:11 What happened when young blood was given to old mice
19:45 Why aging may happen in waves rather than at a steady rate
29:24 How stable are protein measurements over time?
32:37 Can proteins predict disease years before it appears?
40:35 Why one part of your body may age faster than the rest
46:31 The missing test: does an intervention actually change the measurement?
51:04 Going from whole organs to individual cell types
54:10 Why more data, not better AI, is the current bottleneck
57:18 A future “health compass” for the body

🔗 Helpful Links:

Organ aging signatures in the plasma proteome track health and disease (Nature, 2023)Paper
The foundational organ-aging work discussed in the episode, using plasma proteins to estimate aging across 11 organs.
Plasma proteomic signatures of cellular aging predict human disease (Nature Medicine, 2026)Paper
The newer study extending the approach to more than 40 cell types using over 7,000 plasma proteins.
Undulating changes in human plasma proteome profiles across the lifespan (Nature Medicine, 2019)Paper
Young blood reverses age-related impairments in cognitive function and synaptic plasticity in mice (Nature Medicine, 2014)Paper

UK Biobank Research resource
The large longitudinal cohort used for much of the disease-prediction work discussed in the episode.

Watch the conversation: https://youtu.be/1dfMBAZiP8o

Find us on YouTube: ⁠⁠⁠⁠⁠⁠⁠⁠⁠https://youtube.com/levelshealth?sub_confirmation=1⁠⁠⁠⁠⁠⁠⁠⁠

📲 Connect:

Connect with Dr. Tony Wyss-Coray at Stanford

👋 Who we are:

Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#310 - Can We Measure Aging Well Enough to Treat It? | Dr. Nir Barzilai & Mike Haney Sep 10, 2026

Show notes

Aging science has gotten much better at describing who appears to be aging faster or slower. What medicine still lacks is a reliable feedback loop: a way to tell whether something we do is actually changing the biology that drives age-related disease.

Dr. Nir Barzilai argues that solving that measurement problem could turn geroscience from an interesting field of research into something doctors can actually use: measure where you are, intervene, measure again, and learn what works.

Free course: Improve your metabolic health

Get our free email course on how glucose, nutrition, exercise, sleep, and measurement can help you build habits that support better energy and long-term health: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://levels.link/wnl⁠

What We Cover:

What centenarians reveal about living longer and spending less time sick
Why changing one hallmark of aging can affect several others
How TAME was designed to test metformin as an anti-aging drug and to convince the FDA to treat aging itself as an endpoint—and why it failed to get off the ground
Why metformin, SGLT2 inhibitors, GLP-1 agonists, and some osteoporosis drugs are being studied as potential gerotherapeutics, or anti-aging drugs
How FAST is searching existing clinical trials for biomarkers that change when an intervention affects aging biology
Whether aging needs continuous monitoring or can be meaningfully tracked a few times a year

🎙️ About the Guest:

Dr. Nir Barzilai is the Ingeborg and Ira Leon Rennert Chair in Aging Research and a professor of medicine and genetics at Albert Einstein College of Medicine, where he directs the Institute for Geroscience.

📍What Dr. Nil Barzilai & Mike Haney discussed:

05:10 - Aging is a condition, not just a label
05:59 - Why aging is what drives disease
08:19 - Should we call aging a disease?
10:20 - Why the species may have a ceiling around 115
14:34 - Why changing one hallmark of aging changes the others
23:08 - Metformin was anti-aging before it was a diabetes drug
31:55 - Centenarians live longer and spend less time sick
40:12 - How TAME tried to make aging an FDA endpoint
48:17 - Metformin, SGLT2s, GLP-1s, and osteoporosis drugs as gerotherapeutics
52:26 - FAST: finding biomarkers that actually change with treatment
56:49 - Whether aging needs continuous monitoring, or a few checks a year
1:01:16 - Where behavior still fits once we have anti-aging drugs

🔗 Helpful Links:

Hallmarks of Aging: An Expanding Universe (Cell, 2023) Paper
Compression of Morbidity Is Observed Across Cohorts with Exceptional Longevity (JAGS, 2016) Paper
Metformin as a Tool to Target Aging (Cell Metabolism, 2016) Paper
Geroscience-Guided Repurposing of FDA-Approved Drugs to Target Aging (Aging Cell, 2022) Paper
Biomarkers of Aging for the Identification and Evaluation of Longevity Interventions (Cell, 2023) Paper
FAST: Finding Aging Biomarkers by Searching Existing Trials FAST Initiative
ARPA-H PROSPR / FAST award ARPA-H FAST project

Watch the conversation: https://youtu.be/HbeGjwCw6vk

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Look for new shows every month on A Whole New Level, where we have in-depth conversations with thought leaders about metabolic health.

#309 - What AI Sees That Doctors Can’t | Dr. Ziad Obermeyer & Mike Haney Sep 03, 2026

Show notes

Medical AI is already getting good at doing things doctors do. Dr. Ziad Obermeyer thinks the bigger opportunity is using AI to discover things medicine doesn’t yet know.

His work shows both sides of that future: algorithms can amplify bad assumptions when trained on the wrong targets, but they can also uncover signals in medical data that humans miss. Obermeyer argues that as more health data is collected outside the hospital, AI could turn it into a continuous picture of health rather than a series of isolated snapshots.

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What We Cover:

Why AI should learn from patients and outcomes, not just doctors
How an ECG model found hidden risk of sudden cardiac death
Why medical data access is still a major bottleneck
How more measurement could actually mean fewer unnecessary tests
Why healthcare may be entering a “mainframe to PC” transition

🎙️ About the Guest:

Dr. Ziad Obermeyer is an emergency medicine physician and an Associate Professor at the UC Berkeley School of Public Health. He also co-founded Dandelion Health and the non-profit Nightingale Open Science.

📍What Dr. Ziad Obermeyer & Mike Haney discussed:

02:43 Why better data is fundamental to medical AI
05:54 The problem: There’s no variable called “get sick”
09:27 Algorithms optimize exactly what you tell them to
23:03 Why teaching AI to copy doctors limits what it can discover
26:28 How AI could create a new science of medicine
28:40 Could AI predict sudden cardiac death before it happens?
34:14 Can an algorithm teach us what it sees?
36:50 Medicine’s biggest AI bottleneck: access to data
49:00 Healthcare’s “mainframe to PC” transition
55:08 Why more measurement could actually mean fewer tests
58:46 Why medical AI is aiming too low
1:02:13 What the next generation of wearables needs to measure

🔗 Helpful Links:

Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations (Science, 2019)https://www.science.org/doi/10.1126/science.aax2342
An Algorithmic Approach to Reducing Unexplained Pain Disparities in Underserved Populations (Nature Medicine, 2021)https://www.nature.com/articles/s41591-020-01192-7
An ECG Biomarker for Sudden Cardiac Death Discovered with Deep Learning (Nature, 2026)https://www.nature.com/articles/s41586-026-10674-6
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine (NEJM, 2016)https://www.nejm.org/doi/full/10.1056/NEJMp1606181
Nightingale Open Sciencehttps://www.nightingalescience.org/
Dandelion Healthhttps://dandelionhealth.ai/