David Sinclair: Extending the Human Lifespan Beyond 100 Years | Lex Fridman Podcast

David Sinclair is a geneticist at Harvard and author of Lifespan.

This Podcast is a repost which originally appeared on lexfridman.com
Podcast notes are a repost which originally appeared on PodcastNotes
Lex Fridman Podcast #189 with David Sinclair - June 6, 2021
Edited for content and readability - Images sourced from Pexels

Key Takeaways

  • Wearables have the potential to revolutionize medicine
  • The goal is doctors being able to look at a dashboard of our body based on swabs, blood tests, and biosensors and make real-time, tailored recommendations
  • Top causes of aging: broken chromosomes, cell stress, smoking
  • Lifestyle methods to slow aging: fasting (skip 1-2 meals per day), eat more vegetables and less red meat, exercise, get good quality sleep (quality more important than quantity)

Introduction

Dr. David Sinclair (@davidsinclair) is a biologist, professor of genetics at Harvard, author, and expert on aging and longevity. His research and biotech companies focus on understanding why we age and how to slow its effects.

In this episode of the Lex Fridman Podcast, Lex and guest David Sinclair discuss the determinants of why we age, solving aging, the trend of wearables and tracking health data, artificial intelligence, social perspectives of lifespan , and death, and lifestyle factors to improve lifespan.

Host: Lex Fridman (@lexfridman)

Book: Lifespan: Why We Age and Why We Don’t Have To by David Sinclair

Artificial Intelligence & Immortality

  • We live in a time we can leverage data to have the pieces of the life of people we can gather using technology, beyond just written books
  • AI makes it possible to bring back people that we love in some way and in essence achieve immortality
  • AI can be used to build experience, thoughts, speech
  • AI uses in aging: generate biological clocks, predict protein folding, assemble genomes, predict longevity in mouse in response to stimuli, diagnosing a virus

David Sinclair Interest And Predictions On Wearables

  • Wearables represent the merging of machines and humans  
  • Wearables help us collect biological data about our bodies
  • We can use data to keep ourselves in optimal shape
  • “Picture a future where you’re monitored constantly so you wouldn’t have a heart attack, you’d know that was coming.” – David Sinclair
  • It’s feasible that wearables and similar technology will indicate what antibiotic or medication to take, what to eat, etc. – and augment physicians who would just need to sign off on the protocol
  • COVID-19 accelerated biological technologies & medical advances
  • There will be day doctor’s use wearable technology to send patients home for monitoring instead of keeping them in the hospital
  • Wearables will revolutionize medicine – it can collect data which can be used to predict sickness, diagnose disease

InsideTracker

  • InsideTracker: David Sinclair co-founded a company that creates personalized and actionable plans to help people optimize their bodies through nutrition, supplements, and lifestyle
  • Connects scientific papers to individual data and make recommendations for lifestyle
  • InsideTracker leverages hundreds of thousands of human data points and thousands of scientific articles to create a formula of what works and what doesn’t for your body
  • Recommendation of food and nutrition was better than leading drug at treating type 2 diabetes
  • Soon, the current model of medicine is going to outdated as machines and data will know us better than our doctor
  • “We wouldn’t drive a car without a dashboard so why would doctors do the same?” – David Sinclair

How And Why We Age

  • Aging is both a feature and bug of evolution
  • We only need to live as long as we need to in order to replace ourselves – some breed slowly and build a body that lasts, some breed quickly and die quickly
  • We can do better at aging
  • Hallmarks of aging include: loss of telomeres, senescent cells, loss of energetics
  • Defining factor of aging: preservation of information and loss of entropy
  • “Loss of information in our bodies is a root cause of aging.” – David Sinclair
  • We have information regulator genes in our bodies – upregulation could preserve health
  • Information in cells = DNA and epigenome
    • DNA is usually intact in animals and humans over time
    • Epigenome: regulators of genetic information
  • Question of importance: is there a repository of information in the body to restore from?
  • Antagonistic pleiotropy: a system built to keep us alive when we’re young but has damaging effects later in life
  • Causes of aging: (1) broken chromosomes and (2) cell stress – smoking also dramatically accelerates biological age
    • It’s hard to repair something that’s constantly breaking: we have 1000 chromosome breaks per day – the break is recognized by proteins and is usually fixed but not always
  • You can slow down aging using three embryonic genes to reset the age of tissues to a certain point – but if you don’t do it right it can cause tumors  

Data Sharing In Biology

  • “We’re living through what’s going to be seen as one of the biggest revolutions in human health through the gathering of data about our bodies.” – David Sinclair
  • Ultimately, we’re all going to be monitored
  • There will be a reversal where blamed will be assigned for not collecting data
  • Decisions are made based on very few tests when we have the opportunity to collect more
  • Consumer health is going in the direction of the patient having access to better data than the doctor (through private lab tests, biotech companies, etc.)
  • Doctors are becoming excited and interested about seeing and using privately collected patient data to make more informed decisions
  • The U.S. currently spends 17% of GDP on healthcare – we can save money by monitoring using wearables and prevention
  • Ideally, we can create a system where we can share data as we’d like and keep what we wouldn’t

Lifestyle Methods To Slow Aging

  • Fasting is one of the oldest ways to improve health – we need to optimize how long and the frequency
  • “If there’s one thing I can recommend to anybody to slow down aging it’s to skip a meal or two a day.” – David Sinclair
  • Note: David Sinclair is a big fan of one meal a day; the carnivore diet has made Lex feel really good
  • When you eat seems to be more important than what you eat
  • Data says plant-based foods are better than meat-based foods
    • People who live longer tend to eat Mediterranean diets with little red meat
    • High meat consumption stimulates mTor
    • Could take rapamycin to counteract effects of meat
    • Meat produces immediate health benefits (muscle, energy) but potentially at the expense of long term effects
  • Eat a diet full of leafy greens, avoid spikes in sugar, possibly explore supplementing with resveratrol
  • Exercise clearly extends longevity
  • You don’t need much exercise to get great benefit – exercise aerobically a few times per week (even 10 minutes) and lift weights a few times per week
  • Sleep is critical for longevity to avoid premature aging and adverse health outcomes
  • Sleep quality seems to matter more than quantity
  • The brain is the center for longevity so we have to take care of stress levels, mental health

Data Collection Methods

  • We’ll likely work to moving away from blood draws for data
  • Currently: swab and ship to the lab to test hormones, stress levels, blood glucose, etc.
  • In the next 10 years: spit on paper and stick in a machine for analysis
  • Home tests are really easy and scalable if they can become democratized (price reduced)

Realistic Goals Of Lifespan

  • If you start eating cleaner in your 20s, that has been shown to improve lifespan in animal models
  • If you are in your 20s, aim to reach 100
  • There’s no maximum limit to human lifespan

Death & Denial

  • We seem to draw meaning from life being rooted in our existence – most of us find it distressing to face our own mortality
  • All living beings have evolved to want to live and survive
  • It’s possible we evolve to naturally deny aging because we need to use our energy and focus for innovation and life instead of death
  • It might be easier to be lazy if you are immortal

Note: Wearable Oura ring was referred to multiple times throughout the show

Scientists show how AI may spot unseen signs of heart failure

A special artificial intelligence (AI)-based computer algorithm created by Mount Sinai researchers was able to learn how to identify subtle changes in electrocardiograms (also known as ECGs or EKGs) to predict whether a patient was experiencing heart failure.

This article is a repost which originally appeared on Medical Xpress
The Mount Sinai Hospital - October 19, 2021 
Edited for content and readability Images sourced from Pexels 
DOI: https://www.jacc.org/doi/10.1016/j.jcmg.2021.08.004

“We showed that deep-learning algorithms can recognize blood pumping problems on both sides of the heart from ECG waveform data,” said Benjamin S. Glicksberg, Ph.D., Assistant Professor of Genetics and Genomic Sciences, a member of the Hasso Plattner Institute for Digital Health at Mount Sinai, and a senior author of the study published in the Journal of the American College of Cardiology: Cardiovascular Imaging. “Ordinarily, diagnosing these type of heart conditions requires expensive and time-consuming procedures. We hope that this algorithm will enable quicker diagnosis of heart failure.”

The study was led by Akhil Vaid, MD, a postdoctoral scholar who works in both the Glicksberg lab and one led by Girish N. Nadkarni, MD, MPH, CPH, Associate Professor of Medicine at the Icahn School of Medicine at Mount Sinai, Chief of the Division of Data-Driven and Digital Medicine (D3M), and a senior author of the study.

Affecting about 6.2 million Americans, heart failure, or congestive heart failure, occurs when the heart pumps less blood than the body normally needs. For years doctors have relied heavily on an imaging technique called an echocardiogram to assess whether a patient may be experiencing heart failure. While helpful, echocardiograms can be labor-intensive procedures that are only offered at select hospitals.

However, recent breakthroughs in artificial intelligence suggest that electrocardiograms—a widely used electrical recording device—could be a fast and readily available alternative in these cases. For instance, many studies have shown how a “deep-learning” algorithm can detect weakness in the heart’s left ventricle, which pushes freshly oxygenated blood out to the rest of the body. In this study, the researchers described the development of an algorithm that not only assessed the strength of the left ventricle but also the right ventricle, which takes deoxygenated blood streaming in from the body and pumps it to the lungs.

“Although appealing, traditionally it has been challenging for physicians to use ECGs to diagnose heart failure. This is partly because there is no established diagnostic criteria for these assessments and because some changes in ECG readouts are simply too subtle for the human eye to detect,” said Dr. Nadkarni. “This study represents an exciting step forward in finding information hidden within the ECG data which can lead to better screening and treatment paradigms using a relatively simple and widely available test.”

Typically, an electrocardiogram involves a two-step process. Wire leads are taped to different parts of a patient’s chest and within minutes a specially designed, portable machine prints out a series of squiggly lines, or waveforms, representing the heart’s electrical activity. These machines can be found in most hospitals and ambulances throughout the United States and require minimal training to operate.

For this study, the researchers programmed a computer to read patient electrocardiograms along with data extracted from written reports summarizing the results of corresponding echocardiograms taken from the same patients. In this situation, the written reports acted as a standard set of data for the computer to compare with the electrocardiogram data and learn how to spot weaker hearts.

Natural language processing programs helped the computer extract data from the written reports. Meanwhile, special neural networks capable of discovering patterns in images were incorporated to help the algorithm learn to recognize pumping strengths.

“We wanted to push the state of the art by developing AI capable of understanding the entire heart easily and inexpensively,” said Dr. Vaid.

The computer then read more than 700,000 electrocardiograms and echocardiogram reports obtained from 150,000 Mount Sinai Health System patients from 2003 to 2020. Data from four hospitals was used to train the computer, whereas data from a fifth one was used to test how the algorithm would perform in a different experimental setting.

“A potential advantage of this study is that it involved one of the largest collections of ECGs from one of the most diverse patient populations in the world,” said Dr. Nadkarni.

Initial results suggested that the algorithm was effective at predicting which patients would have either healthy or very weak left ventricles. Here strength was defined by left ventricle ejection fraction, an estimate of how much fluid the ventricle pumps out with each beat as observed on echocardiograms. Healthy hearts have an ejection fraction of 50 percent or greater while weak hearts have ones that are equal to or below 40 percent.

The algorithm was 94 percent accurate at predicting which patients had a healthy ejection fraction and 87 percent accurate at predicting those who had an ejection fraction that was below 40 percent.

However the algorithm was not as effective at predicting which patients would have slightly weakened hearts. In this case, the program was 73 percent accurate at predicting the patients who had an ejection fraction that was between 40 and 50 percent.

Further results suggested that the algorithm also learned to detect right valve weaknesses from the electrocardiograms. In this case, weakness was defined by more descriptive terms extracted from the echocardiogram reports. Here the algorithm was 84 percent accurate at predicting which patients had weak right valves.

“Our results suggested that this algorithm may eventually help doctors correctly diagnose failure on either side of the heart,” Dr. Vaid said.

Finally, additional analysis suggested that the algorithm may be effective at detecting heart weakness in all patients, regardless of race and gender.

“Our results suggest that this algorithm could be a useful tool for helping clinical practitioners combat heart failure suffered by a variety of patients,” added Dr. Glicksberg. “We are in the process of carefully designing prospective trials to test out its effectiveness in a more real-world setting.”