There is some good news and some not-so-good news in today’s blog post on biological aging.
The not-so-good news is that our health-reserve appears to decrease exponentially as we age. Because we have little appreciation for the power of exponential processes we tend to think that our age will progress linearly in chronological time. However, biological aging appears to be linked to an exponential process such that:
(1) BIOLOGICAL AGING = AeGt
The graphic above illustrates the exponential characteristic of health and aging, the significance of which we have failed to grasp fully. We generated the graph using data from a 2016 report from the US Centers for Disease Control and Prevention. It converts the average mortality rates per 100,000 people in seven 10-year-wide age brackets from age 15 to age 84 into a percentage of the sum of the mortality rates. Within each age bracket the number of deaths for a given year is tabulated per 100,000 members of the given age bracket to obtain a mortality rate. Thus the total number of deaths occurring in a population of 700,000 people (100,000 each for 7 brackets) constitutes 100% in the graph above. This approach isolates age as a mortality risk factor.
Note that the 75-84 year age bracket accounts for about 55% of the mortality rate. Note also that the age groups between 15 and 54 account for only 10% of the death rate. The 10 year age group between 55 and 64 has a mortality rate exceeding that of the 40 year group between 15 and 54. Given that I am currently 55 years old I find this rather eye-opening.
This relationship is true for every succeeding 10 year age group. Thus the mortality rate for the 65-74 group exceeds the total mortality of the for the previous 50 years, and the mortality rate for the 75-84 group exceeds the total mortality for the previous 60 years. The exponential characteristic of the mortality rate was proposed in 1825 by Benjamin Gompertz. The number of years required for the mortality rate to double (MDRT – Mortality Doubling Rate Time) is estimated to be 8 years.
Although we attribute mortality to many different causes the underlying exponential process of aging is the main factor contributing to the increasing mortality rates after about age 25. In fact the exponential model fits the mortality data relatively well even down to age 5! We tend to think that aging is only a major factor once we pass middle age but this appears to be false given the data.
The figure below shows the ‘actual’ mortality rates across age groups between 1950 and 2015. It uses the exact same data as the image above, however, it shows that the mortality rates have decreased substantially accounting for an increase in life expectancy from 68.2 in 1950 (CDC) to 78.74 in 2015 (World Bank data).
We can fit an exponential to the data for any given year with a very good fit occurring between 25 and 84 years of age as shown below for the year 1950. (Note: clicking on any of the graphs should allow you to zoom into the images.)
The fit is better when we look at mortality across ages for a single year than it is if we follow a particular cohort across time. For example, if we follow the cohort aged 25-34 in 1950 through the year 2000 and fit an exponential to the cohort data we obtain a coefficient of determination of R2 = 0.997 vs R2 = 0.9995 obtained in the graph above. This indicates that although aging decreases the individual’s health reserve exponentially, actual mortality rates are affected by environmental and social factors that vary from year to year.
But if aging is truly exponential what could be the good news?
The Good News about Exponential Aging
There are several factors that may provide light at the end of the aging tunnel. The first is that mortality rates have dropped relatively linearly since the 1950’s as shown in the graphic below.
The downward trend in mortality from 1950 to 2015 is very impressive ! The relative mortality between 1950 and 2015 was halved in most 10-year age groups!
However, although the mortality dropped to about half, the overall life expectancy only increased by about 15.5% (from 68.2 to 78.74 years) due to the exponential nature of the mortality rate.
We took a closer look at the exponential curve fit parameters for the 1950 data vs the 2015 data for age groups between 25 and 84 years of age, and found that decrease in mortality rates can be attributed mostly to a decreases in the baseline mortality rate ‘A’ in equation (1) above. However, there may also be a small downward trend in the senescent factor ‘G’ of about 0.0071 every 10 years.
Do we think it is possible to slow down the senescent factor “G’ that causes the exponential process of aging? The answer is yes! In the Ted Talk shown below, João Pedro de Magalhães, Ph.D., at the University of Liverpool, discusses how genetic modifications have resulted in a 10-fold increase in the lifespan of C. elegans roundworms, and 50% increase in the lifespan of mice. In this talk João focuses on genetics rather than lifestyle factors to account for the large variations in lifespan between animals such as mice (2-4 years) and naked mole rats (up to 31 years).
However, towards the end of his talk he mentions that one of his goals is to develop drugs that can simulate the effects of certain longevity genes in order to extend the healthy lifespan. Given this and what we already know about epigenetic factors it is clear that lifestyle factors will also be critical to activating longevity genes that may otherwise remain dormant.
Take Home Message for Chronic Disease Prevention
The take home message from this blog post is that efforts to prevent chronic disease and improve lifespan must focus on slowing down the exponential aging process. This exponential process is more powerful and relentless than most people realize and efforts to restrain it should begin early in life. Many people are caught off guard, looking forward to their retirements only to find that they are plagued with a low quality of life that deteriorates exponentially.
A recent study by Daniel W. Belsky et al. at Duke University School of Medicine found that in a group of participants with a chronological age of 38 years the biological ages had a normal distribution between 28 and 48 years (Mean = 38, Standard Deviation = 3.23 years). From this it is clear that we are not all aging at the same rate biologically. There is mounting evidence that lifestyle factors play a large role along with genetics in accounting for this variability. You must, however, be careful when selecting lifestyle modifications that target only one specific organ or organ system. Work on developing a complete, individualized, holistic anti-aging lifestyle.