LEVELS – A Whole New Level

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
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:
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:
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.

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

📲 Connect:
Connect with Dr. Nil Barzilai on X: https://x.com/NirBarzilaiMD

👋 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.

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.

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 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/

Watch the conversation: https://youtu.be/WDo-iL6O5nU
Find us on YouTube: ⁠⁠⁠⁠⁠⁠⁠https://youtube.com/levelshealth?sub_confirmation=1⁠⁠⁠⁠⁠⁠

📲 Connect:
Connect with Dr. Ziad Obermeyer on https://ziadobermeyer.com/

👋 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.

Why do we crave some foods the more often we eat them? Are we addicted?
Dr. Dana Small’s research suggests that food craving reaches far beyond taste or willpower, and that “addiction” isn’t a helpful descriptor. Every time we eat, unconscious signals from the body teach the brain which foods deliver useful nutrients, and train us to want more of those. In other words, we don’t eat crave foods because they taste good; foods taste good because our body craves them.
Understanding that means we can rethink overeating. It’s not a matter of addiction or self-control; it’s our body behaving the way nature intended it to, but in the setting of foods not found in the wild, like those high in both fat and carbohydrates. But we may also be able to train our brain away from these cravings.

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
Taste vs. flavor, pleasure, motivation, and food reward
How gut nutrient signals teach the brain which foods to seek
Why fat + carbs produce an unusually strong reward response
What eight weeks of high-fat, high-sugar snacks did to the brain
How GLP-1s may cut food wanting while preserving pleasure

🎙️ About the Guest:
Dr. Dana Small is the Canada Excellence Research Chair in Metabolism and the Brain and a professor at McGill University, with appointments in Neurology and Neurosurgery and Medicine. She directs the Modern Diet and Physiology Research Center, and her research examines how sensory, metabolic, and neural signals interact to shape food reward, eating behavior, and metabolic health.

📍What Dr. Dana Small & Mike Haney discussed:
02:44 - Is food actually addictive?
05:02 - Taste and flavor are different senses
08:21 - Why “hedonic” vs. “homeostatic” eating is misleading
10:42 - The reward from sugar begins after you swallow it
22:32 - The “high road” and “low road” of food reward
27:41 - How scientists teach the brain to associate a flavor with calories
38:30 - Why combining fat and carbohydrates produces more reward
47:34 - How beliefs about food can change physiological responses
50:02 - Eight weeks of snacks changes how the brain responds to food
57:51 - What this science means for people trying to eat differently
1:03:50 - How GLP-1 drugs may change food wanting
1:07:30 - Can GLP-1s help retrain eating behavior for the long term?

🔗 Helpful Links:
Habitual Daily Intake of a Sweet and Fatty Snack Modulates Reward Processing in Humans (Cell Metabolism, 2023)https://pubmed.ncbi.nlm.nih.gov/36958330/
Supra-Additive Effects of Combining Fat and Carbohydrate on Food Reward (Cell Metabolism, 2018)https://www.cell.com/cell-metabolism/fulltext/S1550-4131(18)30325-5
Integration of Sweet Taste and Metabolism Determines Carbohydrate Reward (Current Biology, 2017)https://pubmed.ncbi.nlm.nih.gov/28803868/
Rethinking Food Reward (Annual Review of Psychology, 2020)https://pubmed.ncbi.nlm.nih.gov/31561741/
In Defense of Pleasure: We Need to Rethink Food Reward and Obesity (PLOS Biology, 2025)https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3003497
Mind Over Milkshakes: Mindsets, Not Just Nutrients, Determine Ghrelin Response (Health Psychology, 2011)https://pubmed.ncbi.nlm.nih.gov/21574706/

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

Twenty-five years ago, sequencing the human genome seemed poised to usher in an era of personalized medicine. Dr. Euan Ashley helped turn that promise into an actual clinical experiment, leading one of the first efforts to interpret an entire human genome in the context of a real patient’s health. The challenge quickly became apparent: medicine could suddenly generate billions of data points about one person, but making sense of them was painfully slow.
That history feels newly relevant. Ashley recently gave an AI system his own 15-year-old genome data and watched it reproduce most of an analysis that had originally required months of work by a team, in about 30 minutes. Meanwhile, genomics is being joined by wearables, multi-omics, and other technologies capable of tracking far more of our biology over time.
In this episode of NextLevel, we use the genome revolution as a case study for what happens when our ability to measure the body advances faster than our ability to interpret and use the information. The next leap in personalized medicine may depend less on collecting another mountain of data than on building the tools, reference datasets, and healthcare systems capable of turning that information into decisions.

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

🎙️ About the Guest:
Dr. Euan Ashley is Chair of the Department of Medicine at Stanford University and the Roger and Joelle Burnell Professor of Genomics and Precision Health, with appointments in medicine, genetics, and biomedical data science. He’s also the author of The Genome Odyssey: Medical Mysteries and the Incredible Quest to Solve Them.
📍What Dr. Euan Ashley & Mike Haney discussed:
03:33 What happened to the promise of personalized medicine?
06:33 The first clinical interpretation of a human genome
11:26 AI interprets in 30 minutes what once took months
21:25 Why genomics still needs better, more diverse reference data
27:41 Building a molecular map of exercise with MoTrPAC
32:01 Exercise changed nearly every organ researchers measured
39:05 Why your personal baseline can matter more than a "normal" range
42:46 How wearables opened a new era of longitudinal health data
47:48 The biggest barrier to continuous, personalized healthcare
51:39 What medical AI needs to become genuinely useful
57:05 How AI can ace medical benchmarks for the wrong reasons
58:20 The heart measurement Ashley considers a medical "holy grail"

🔗 Helpful Links:
Clinical Assessment Incorporating a Personal Genome, The Lancet, 2010. Landmark integrated clinical analysis of a complete human genome.
Molecular Transducers of Physical Activity Consortium (MoTrPAC), NIH Common Fund. Large-scale project mapping how exercise changes tissues and organs. 
MoTrPAC: Human Studies Design and Protocol, Journal of Applied Physiology, 2024. Human study of molecular responses to endurance and resistance training.
The Mitochondrial Multi-Omic Response to Exercise Training Across Rat Tissues, Cell Metabolism, 2024. MoTrPAC study on molecular changes across organs after exercise.
Harmonizing Standards and Resources for the Medical Genome, Nature, 2026. Ashley et al. on genomic reference standards and clinical-grade sequencing.

Watch the conversation: https://youtu.be/bD2YVGdwviE
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.