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In this episode of Longevity by Design, host Dr. Gil Blander sits down with Dr. Nathan Price, Professor and Co-Director at the Buck Institute for Research on Aging. Together, they explore how systems biology, artificial intelligence, and deep health data are changing the way we approach aging and prevention. Nathan explains why looking at single biomarkers falls short and why a network view of biology gives a clearer path to understanding disease and resilience.
Nathan shares how new tools, like genetics, proteomics, and the emerging field of digital twins, can help predict disease risk years in advance and guide more effective, personalized interventions. He also discusses how integrating data from wearables, blood tests, and the microbiome can help people move from reactive medicine to proactive health decisions, allowing for interventions that fit the individual.
The conversation highlights the promise and practical limits of current technologies, the trade-offs involved in optimizing health, and the power of AI to accelerate both research and personal health journeys. Nathan makes a strong case for the unique biology each person brings to the table and shows how the tools available today can help anyone take charge of their own healthspan in ways not possible before.
💡 Name: Nathan Price
💡 What he does: Professor and Co-Director of the Center for Human Healthspan
💡 Company: Buck Institute for Research on Aging
💡 Noteworthy: Known for advancing systems biology and using AI and multi-omics to personalize health and healthspan interventions.
💡 Where to find him: https://www.linkedin.com/in/nathandprice
Episode highlights:
[00:00:00]: Introduction
[00:00:49]: Overview of Systems Biology and Its Role in Health
[00:03:23]: Systems Biology vs. Reductionist Approaches
[00:04:28]: Systems Biology and the Shift from Treatment to Prevention
[00:06:29]: Defining Scientific Wellness
[00:08:52]: Early Detection of Disease with Omics and AI
[00:12:03]: Genetics and Early Risk Prediction
[00:15:18]: Polygenic Risk Scores and Population Diversity
[00:17:05]: Explaining Polygenic Risk Scores and Their Applications
[00:19:01]: Genetics, Lifestyle Interventions, and LDL Cholesterol
[00:20:51]: Integrating Multi-Omics Data for Personalized Health
[00:23:15]: AI Revolution in Health Data Analysis
[00:24:06]: Personalized Health Guidance and AI Agents
[00:27:15]: Scaling Scientific Discovery with AI
[00:32:23]: AI’s Impact on Healthspan and Personalized Recommendations
[00:34:47]: Human-AI Collaboration and Current Limitations
[00:36:23]: Accelerating Health Innovation and the Adjacent Possible
[00:38:36]: N-of-1 Experiments and Individualized Health Insights
[00:40:25]: Aggregating N-of-1 Data and Community Science
[00:42:54]: Digital Twins: Building Personalized Biological Models
[00:45:05]: Aging as a Network-Level Phenomenon
[00:49:43]: Trade-Offs in Aging and Optimizing Health Behaviors
[00:52:01]: Accessibility and Adoption of Omics Technologies
[00:55:39]: Pyramid of Health Data: From Dense Omics to Passive Measures
[00:59:28]: Rapid-Fire Questions and Key Takeaways
Key Insights
Systems Biology Reveals the Limits of Single-Marker Thinking
Chronic diseases like diabetes and heart disease rarely have just one cause or easy fix. Looking at a single biomarker or isolated factor doesn’t capture the complexity of human biology. Systems biology takes a wider view, connecting the dots between genes, proteins, and the environment. This approach can reveal how different parts of the body influence each other and why simple solutions often fall short. By recognizing that health and aging happen across networks, not in silos, we gain new tools for prevention and care. This shift matters for anyone seeking better long-term health, because it pushes both science and medicine to move beyond the old “find the one cause, fix the one thing” mindset.
Genetics and Lifestyle Interact to Shape Health Outcomes
Genetic data now makes it possible to predict risk for many conditions, from heart disease to diabetes, long before symptoms appear. But genetics is not destiny. The difference between a person’s actual health markers and what their genes predict can reveal who will benefit most from lifestyle changes. For example, someone with a genetic risk for high LDL cholesterol may find it harder to lower their levels through diet alone, while others might see big improvements. Combining genetic insights with blood tests and even microbiome data leads to smarter, more personalized recommendations. This means people can focus efforts where they matter most, using the right mix of lifestyle, nutrition, or medical support based on their unique biology.
Artificial Intelligence Unlocks Deeper, Personalized Health Insights
Artificial intelligence (AI) is changing how we study and manage health. By analyzing vast amounts of data, genetics, lab results, wearables, and more, AI can spot patterns and generate new knowledge faster than any human team. This power helps both scientists and everyday people make sense of complex health information. AI-driven tools can now give personalized guidance, automate research, and even help build “digital twins” for testing interventions before trying them in real life. While not every technology will reach everyone right away, the ability to learn from many sources at once is bringing a new level of personalization to health. The key is to use these tools thoughtfully, bridging human insight with machine speed to drive lasting improvements in how we prevent and treat disease.
Why Complex Diseases Need a Systems Approach
Chronic conditions like diabetes and heart disease don’t arise from a single gene or pathway. They’re the result of many biological systems working together and sometimes failing together. A reductionist approach, focusing on one biomarker or one target, misses the big picture. Systems biology offers a way to understand these complex interactions and design better prevention and treatment strategies. This episode explains why the “one problem, one solution” model doesn’t fit modern health challenges, and how a broader, network-based perspective opens new doors for care.
“I think systems biology can best be understood by thinking about molecular biology sort of as a starting point, right? Because in molecular biology, what we did is we really took all the complexity that we see in our bodies and in the natural world, and we started to break it down into single components. Systems biology is really trying to take those puzzle pieces and put them back together. How do all those pieces interact in order to cause the phenomenon that we see, our ability to be alive? How do our systems function as a whole and as a unit?”
Early Disease Detection Is Now Possible
Modern health tools can spot early biological signals that hint at disease years before symptoms show up. By tracking things like blood proteins, genetics, and imaging, researchers can identify subtle changes that warn of risk for conditions like Alzheimer’s, diabetes, or cancer. This early detection means there’s a real chance to intervene and steer health outcomes long before a crisis hits. The discussion covers how new data and analytics help people act sooner, with more confidence, and make better choices to protect their long-term health.
“You can make many, many measurements, coupled with forecasting, computation, and AI, to try to predict things out. So in some cases, we know you can do prediction far in advance. For example, Alzheimer’s, you get these areas of hypometabolism that can be observed. That’s like eight years in advance. We saw that elevated a couple of years in advance in each of the people who developed metastatic cancer. So we know that signals like that come up at least years in advance.”
The Push for Practical, Personalized Health Tools
While advanced analytics like proteomics and digital twins are powerful, not everyone will access them soon. The conversation highlights the need for simple, scalable tools, like voice analysis using smartphones, that remove barriers and help more people benefit from personalized health insights. The speakers stress that for health technology to truly help, it must become easy and appealing for everyday use. Simplicity, good design, and low friction are must-haves if we want to see widespread adoption and real impact on healthspan.
“We have to have this notion of a pyramid, the deepest measures, right? But they’re harder to get. Not so many people will do them. Up the pyramid, at a really low cost, it’s much more ubiquitous. One of the areas that I’m quite fascinated with right now is voice analysis, right? Just doing a deep analysis of harmonics and kind of hidden factors within the voice. So, as we learn from that base that is doing the dense omics, we want to always be pushing it toward what’s a cheaper, more passive way to get the same signals. You lose a little fidelity, but you learn a lot across the population. It triggers, ‘Hey, something might be happening.’”
Sauna and Simple Habits for Long-Term Health
Not all health interventions need to be complicated or high-tech. The discussion closes with practical habits that show big benefits, like regular sauna use. Drawing on strong research from Finland, the episode points out that frequent sauna sessions can slash the risk of cardiovascular disease and dementia. Simple, accessible routines can pay off in a big way when supported by evidence. This reminder grounds the episode, showing that while science and technology push the field forward, the basics still matter.
“Sauna, actually, I’ll go with that one because if you look at the data out of Finland, where they compared people who use the sauna four to seven times a week versus once a week, the data on that is incredibly striking: a 50% reduction in cardiovascular disease and a 60% reduction in dementia. It seems to be a really big, substantial effect.”
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