Michael Snyder | ML for Personalized Medicine @ Longevity Frontiers Workshop 2023

Summary

The speaker discusses the transformation of healthcare through big data and personalized approaches. They highlight a project collecting extensive longitudinal data on individuals to identify health issues pre-symptomatically. Wearables and microsampling are emphasized for real-time health monitoring, with examples showing varied individual responses to food and exercise. The goal is to bring personalized healthcare into homes, with the potential for early detection and mitigation of health risks.

Takeaways

• 🔬 The speaker is involved in extending health span using big data and novel approaches, aiming to move from 'sick care' to 'health care'.
• 🏥 Traditional healthcare is described as archaic, with little change in the way patients are examined and treated over the past 40 years.
• 📊 The project involves collecting deep longitudinal data on individuals, including genomics, molecular measurements, microbiome analysis, and clinical testing.
• 👨‍👩‍👧‍👦 The speaker has been personally involved in the project for 13 years, emphasizing the long-term nature of the study.
• 💡 The project has led to significant health discoveries, with many participants learning important pre-symptomatic health information.
• 📈 The data collected allows for the observation of individual aging patterns, with the concept of 'ageotypes' introduced to classify different aging trajectories.
• 🏠 There is a push to bring healthcare into the home, with a focus on wearables and micro sampling as methods for health monitoring.
• 📱 Wearables are highlighted as powerful tools for continuous health monitoring, with the potential to detect diseases pre-symptomatically.
• 🩸 Micro sampling involves taking small blood samples to perform deep omics profiles, allowing for tracking of over 2200 analytes.
• 🍹 The response to food intake, such as a nutritional shake, can vary greatly between individuals, as demonstrated by the project's findings.
• 🔍 The project uses continuous glucose monitors and other wearables to track detailed biochemical and physiological responses to activities like exercise.

Q & A

What is the main goal of the project discussed in the transcript?

The main goal of the project is to extend health span using big data and novel approaches to transform traditional healthcare into a more personalized and preventive system.

How does the current healthcare system differ from the ideal model presented in the transcript?

The current healthcare system is described as 'sick care' and is considered archaic, with limited and infrequent measurements based on population averages rather than individualized care.

What is the significance of longitudinal data collection in the project?

Longitudinal data collection allows for a more complete picture of an individual's health and health trajectory over time, which can lead to early detection of health issues and a better understanding of how individuals age differently.

How does the project utilize wearables in healthcare?

Wearables are used to track individuals 24/7, providing individualized baselines and detecting shifts in health, such as pre-symptomatic signs of infectious diseases like COVID-19.

What is the concept of 'micro sampling' mentioned in the transcript?

Micro sampling involves taking small, fixed aliquots of blood and analyzing them for deep omics profiles and molecular measurements, which can track about 2200 analytes and provide insights into an individual's biochemical and physiological responses.

How does the project aim to bring healthcare into the home?

The project aims to bring healthcare into the home through the use of wearables for continuous monitoring and micro sampling for periodic, detailed health assessments that can be done at home and mailed to labs for analysis.

What is the potential impact of early detection of health issues as described in the project?

Early detection of health issues can be life-saving, allowing for intervention before symptoms appear, and can also lead to a better understanding of individual health trajectories and aging patterns.

How does the project approach the idea of personalized health care?

Personalized health care is approached through deep data dives on individuals, longitudinal sampling, and the use of wearables and micro sampling to understand individual responses to various stimuli, such as food and exercise.

What are some of the health discoveries made from the project's data?

Some of the health discoveries include early detection of conditions like lymphoma, pre-cancers, and serious heart issues, as well as insights into how different individuals respond to the same food or drink.

How does the project's approach to data collection and analysis differ from traditional healthcare methods?

The project's approach differs by focusing on deep, longitudinal data collection from individuals, using a combination of genomic sequencing, molecular measurements, imaging, and other data sources to create a comprehensive view of health that is personalized and predictive.

What is the potential role of continuous glucose monitors (CGM) in the project?

CGMs play a significant role in the project by providing real-time data on glucose levels, which can help individuals understand and change their eating behaviors to prevent conditions like diabetes and gestational diabetes.