Lifestyle changes this year + future aging measurement regimen

To try to slow my own aging, I made three lifestyle changes in the last year:

  • Diet: I switched to primarily vegan, in addition to not eating breakfast until 10-11am most days. Along with many other studies, a 2022 meta study suggested a vegan diet is associated with ~8-13 years of longer life than an average western diet [1]. I figure a mostly vegan gets me 80-90% of that benefit. After making this change, my total cholesterol went from consistently high (~220 mg/dL) to consistently in the "healthy" range (~150 mg/dL).
  • Daily exercise: I try to get 60+ minutes of exercise as measured by Apple Watch every day. This includes brisk walking. I get there probably ~80% of the days. Previously, I was probably reaching that mark only ~50% of the days. Study after study shows exercise to have broad health benefits, though it's still unclear exactly what types of exercise, when and in what quantities is optimal.
  • Regular weight lifting: This year, I tried to lift weights at least once per week. I may increase this to twice per week. Many studies show weight lifting can preserve muscle strength, bone density and balance, which are all important for long-term health and safety. I know I'm not as strong as I was when I was in college and lifting weights and playing a lot of ultimate frisbee, but beyond that it's hard to know exactly how well I'm doing on muscle and bone health. 

I would guess these changes could extend my healthy lifespan by ~3-5 years, given I already had a healthier lifestyle than the average American.

Still, these are blunt instruments. I am in the dark about how my body is aging. What lifestyle changes or other interventions can extend my healthy lifespan by 10+ years? How are my different organs aging? Where am I losing resilience? What has caused my aging to speed up? To slow down? I would like a more robust, affordable aging measurement regimen. I don't have insight into real-time molecular and tissue changes going on inside my body. Most physiological measurements are lag indicators. For example, the commonly-used frailty index is the result of many systems degrading over time.

Here are three possibilities to greatly improve aging measurement:

  1. Near real-time omics measurements. Ideally, I'd have omics measurements taken multiple times a day to measure cellular and molecular dynamics. Today, it costs ~$500 to get a single microbiome omics assessment from Viome or a single DNA methylation age from Elysium. If you did these each 3 times a day for a whole year, it would run you over $1 million. How could we get this to something affordable for every American, e.g. $50/month, like the average cost of a cell phone bill?
  2. Frequent imaging. In the future, I'd like a regular full-body, high-resolution imaging of my body to detect aging and disease. Maybe daily or weekly, if we can make it easy? MRI technology can safely image your entire body at high-resolution. It can detect spatiotemporal dynamics and the onset of disease (e.g., multiple sclerosis via brain legions). Companies like Prenuvo and Q Bio are going down this path. Unfortunately, the cost is still high (~$2K/scan) and the process of driving to a facility is inconvenient.
  3. Longitudinal comparisons to millions of other people. Once you have all the data from #2 and #3, we need to know what the measurements imply for aging and health. To do this, we need longitudinal comparisons across millions of other people. Will  biobanks will move fast enough to collect this data? Will Apple Health with the Apple Watch will find ways to correlate its troves of real-time physiological to cellular and molecular dynamics?
I've made lifestyle changes this year that I hope pay off. But they are only a start, and I need better data to know what to do.


[1] Fadnes LT, Økland J-M, Haaland ØA, Johansson KA (2022) Estimating impact of food choices on life expectancy: A modeling study. PLoS Med 19(2): e1003889. https://doi.org/10.1371/journal.pmed.1003889Estimating impact of food choices on life expectancy: A modeling study.