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In this episode of Longevity by Design, our host Dr. Gil Blander welcomes Dr. Renee Deehan and Nimisha Schneider from InsideTracker to discuss the role of personalized health and the advancements made through data science and artificial intelligence. Dr. Deehan elaborates on her background in molecular biology and how she transitioned to computational biology, emphasizing the importance of integrating large-scale biological data to develop high-resolution molecular models of diseases.
Nimisha Schneider shares her journey from basic immunology research to computational biology, highlighting the significance of building mathematical models to understand biological scenarios better. She explains how InsideTracker uses AI and machine learning to analyze users' blood biomarkers, genetic data, and fitness tracker information to provide personalized health recommendations. The discussion includes the integration of over 7,000 clinical studies into InsideTracker's AI engine, Segterra X, to offer tailored advice based on individual health data.
The conversation dives into the findings from a recently submitted study involving 20,000 users, showing significant improvements in key health markers like LDL cholesterol, A1c, and ApoB over several years. Dr. Deehan and Schneider stress the importance of lifestyle changes and sustained efforts to achieve long-term health benefits. They also discuss the challenges posed by genetic predispositions and how personalized recommendations can help mitigate these risks. The episode concludes with insights into future research directions and the continuous development of personalized health solutions at InsideTracker.
Key Insights
Personalized Health Interventions Show Sustained Improvements
A study involving 20,000 users of InsideTracker demonstrated that personalized health interventions correlate with significant and sustained improvements in key health markers. Users who followed personalized recommendations for nutrition, exercise, and lifestyle changes saw notable reductions in LDL cholesterol, A1c, ApoB, and many other biomarkers related to healthspan. These improvements were observed over several years, indicating the long-term efficacy of personalized health plans. The data suggests that consistent adherence to tailored health recommendations can help manage and even reverse risk factors associated with chronic diseases. This underscores the potential of digital health platforms to drive lasting positive health outcomes.
Genetic Risk Influences Health Outcomes
The study explored the relationship between genetic risk scores and health outcomes, particularly focusing on cholesterol levels and metabolic health. Users with higher genetic risk for high LDL cholesterol, total cholesterol, or Ferritin levels found it more challenging to improve these markers compared to those with lower genetic risk. Despite the genetic predisposition, significant improvements were still achievable with persistent lifestyle changes. This highlights the importance of understanding one's genetic risk as a factor in personal health management and the potential benefits of personalized interventions in overcoming genetic disadvantages. It also emphasizes that genetics is not a definitive determinant, and lifestyle changes can substantially mitigate genetic risks.
Activity Levels Correlate with Health Improvements
Analysis of fitness tracker data revealed that increased physical activity, measured via step count, was a key differentiator between users who successfully improved their health markers and those who did not. On average, users who increased their daily step count to around 11,000 steps showed significant improvements in cholesterol levels. In contrast, those who maintained lower activity levels saw less progress. Additionally, higher quality sleep, particularly increased REM sleep, was associated with better health outcomes. These findings illustrate the critical role of regular physical activity and good sleep hygiene in achieving and sustaining health improvements, providing actionable insights for users looking to optimize their health through lifestyle modifications.
Episode highlights:
- Introduction: 00:00:00
- What is AI: 00:04:00
- What is ML: 00:05:00
- What is a foundation model? What is a large language model (LLM): 00:00:08
- What are the limitation of LMM in text development: 00:14:00
- How LLM is used for health and wellness questions? What InsideTracker developed in this domain: 00:17:00
- What is Ask InsideTracker: 00:25:00
- What is the difference between Ask InsideTracker and Chat GPT: 00:30:00
- What is next for LLM in InsideTracker: 00:36:00
- What is the future of LLM generally: 00:42:00
- What would you recommend to a person who like to live better and longer: 00:47:00
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