One of the biggest challenges with slowing aging and improving health is that we don't closely measure how we age. Most people do annual blood tests, look for changes in how they look in the mirror, and wait until they get a specific disease. This is like taking basic measurements once a year or even less. It means our understanding of health and aging is very limited. We can't quickly try new things and see if they work. New therapies are beholden to the pace of slow, expensive clinical trials that take decades to bring new therapies to market. At this pace, we will have to get lucky to cure aging. In short, today's healthcare system leads to slow, incremental, analog improvements.
Instead, we should track our complete health real-time. We should have real-time, continuous, zero-marginal-cost data on the health of every tissue and system, our rate of molecular damage throughout our bodies, and frequent predictions on our mounting health risks. It would provide tremendous understanding of aging and health. Then, we would apply modern ML tools to this data across lots of people with lots of health outcomes over time. These ML tools would be able to model our health, predict our health risks, and suggest interventions to improve our health. It's hard to overstate what this will bring.
Imagine a Chat-GPT for our own personal health. Let's call it Doc-GPT. Doc-GPT would be trained on data from hundreds of millions of people over many years. It will comb your data looking for risks and making suggestions. You will get reminders and suggestions frequently based on your risks and behavior. It will become your primary preventative physician.
I believe we are trending to this world. We will have automatic, continuous measurement of aging and health. Two big trends are making this possible: 1) the explosion of health data tracked on our phones and wearables, and 2) the exponential decrease in the cost of 'omics measurements. My iPhone and Apple Watch already have the data to make much better sense of my health than any doctor. They know my heart rate, gait, voice, daily activities, and much more. Our heart rate variability, voice, and gait each provide windows into our health and molecular damage. They have my photo library from the last 10 years, and faces provide a good window into our overall health [1]. They know how social and active we are, and how much we exercise. They know when people have heart attacks. They know when we go to the hospital. They can sense dementia. They probably know when we die. Apple has over 1 billion iPhone users and 100 million Watch users. They can see what increases or slows our aging. They can see what helps and hurts our health.
Our wearables and phones don't yet directly know the molecular damage in our cells and DNA. This is fundamental data to aging and health. Fortunately, 'omics measurement tools are riding an exponential curve trending towards essentially zero cost. ~20 years ago it cost billions of dollars to map the human genome. Today, we can do it for around $100. This is trending towards zero. As it does, we will know the status our DNA, epigenetics, RNA, and proteins much more frequently. It's hard to imagine having this data in real-time, but we are trending in that direction.
This health data from phones, wearables, and omics is absolutely massive. It's on the scale to build the Doc-GPT of the future. Apple, for example, may already be able to build a Doc-GPT with the data they have that is better than any doctor today. These data may replace much of what clinical trials do. These tools are coming, and they can't come soon enough. The race is on.
References:
[1] Xia X, Chen X, Wu G, Li F, Wang Y, Chen Y, Chen M, Wang X, Chen W, Xian B, Chen W, Cao Y, Xu C, Gong W, Chen G, Cai D, Wei W, Yan Y, Liu K, Qiao N, Zhao X, Jia J, Wang W, Kennedy BK, Zhang K, Cannistraci CV, Zhou Y, Han JJ. Three-dimensional facial-image analysis to predict heterogeneity of the human ageing rate and the impact of lifestyle. Nat Metab. 2020 Sep;2(9):946-957. doi: 10.1038/s42255-020-00270-x. Epub 2020 Sep 7. PMID: 32895578.