Aging Biomarkers – Chronological v Biological Age

Chronological age can be defined as the time measured from an individual’s birth to a particular date. Biological age is more complex, since it positions an individual within its own lifespan and probability of survival, meaning that a 67 year old man with a biological age of 60 is more likely to live longer than a 67 year old man with a biological age of 70. These concepts are related and in some cases the values can be equal but they are not the same thing.

Chronological age is simply a number representing the length of someone’s life to a particular point; therefore it is difficult to associate biomarkers to it since any biomarker with any influence in the capacity for survival would immediately be more related to biological age. A strict chronological age biomarker should be a biological feature that changes over an individual lifespan but doesn’t directly affect the probability for survival.

There are several biomarkers currently being used that don’t influence survival greatly and are related to older individuals, these could be easily called “chronological age biomarkers”. Reduction of the coronal pulp cavity (using radiography) is a very common method used in forensic science, however, in adults most signs of aging like wrinkles and silver hair are features that can manifest at different points in someone’s life and won’t be useful to accurately determine someone’s biological age.

When looking for aging biomarkers that will reveal the biological age of an individual, these can be split between functional (macro) and physiological (micro) biomarkers.

Aging Biomarkers Infographic

Biological Biomarkers

After many decades of research, the scientific community now agrees on 9 hallmarks of aging that relate to physiological processes acting at the cellular level. These are: accumulation of genetic errors due to genomic instability, telomere attrition or degradation, epigenetic alterations, damage of the internal mechanism in charge of quality control for protein synthesis, deregulated nutrient sensing, mitochondrial dysfunction, loss of the capacity to grow and change stem cell exhaustion and altered intercellular communication.

All of the physiological biomarkers considered above provide data about the capacity of the organism to sustain operation of its own processes over time and also about its capacity to withstand different forms of stress.

These nine hallmarks of aging are robust candidates to be considered for any system dedicated to the determination of biological age; however, obtaining accurate measurements of any of them requires a lot of specialized equipment and capable staff, since they cannot be evaluated easily. Some of them like stem cell exhaustion or mitochondrial dysfunction can only be measured by taking a biopsy and performing a longitudinal study in vitro under laboratory conditions, something that most laboratories don’t provide as part of their usual services.

Functional Biomarkers

Fortunately, functional biomarkers are easier to measure and are considered equally valid to measure biological age. These cover both cognitive and physical performance and include visual acuity (Snellen chart), auditory acuity (pure tone audiometry), decision reaction time, grip strength (dynamometers), body mass index (height and weight measurement), blood pressure (systolic and diastolic pressure), lung capacity (spirometer) and memory.

Functional age is a specialized form of biological age, is task-oriented, and can provide valuable information in regards to an individual’s capacity to perform a particular task or its vulnerability within a certain set of conditions.

All biomarkers mentioned in this article have shown correlation with the process of aging in the past, however, a system designed to provide an accurate value for someone´s biological age will have to integrate a large number of these variables at the same time and incorporate an elegant method to accumulate, process and interpret data from a considerable amount of sources.

They don’t want you to hear about the end of ageing

Recently I attempted to crowd fund the Live Forever Manual – 101 practical tips on how to live forever. Didn’t hear about it? It was harder to publicise than I had expected.

The first hurdle was Kickstarter refusing to host the campaign. It decided it was in breach of its “cure, treat, or prevent an illness or condition” restriction. I appealed explaining that this was to be no miracle cure scientifically proven advice on increasing your lifespan. Maybe I should take heart that they at least recognised ageing as an illness to be cured rather than inevitability.

Oh well, I moved the campaign to Indiegogo which are a bit more lax with their rules.

So now to promote it. Although initially (automatically?) happy to take my money, Twitter later deemed the adverts ineligible based on their Unacceptable Business Practices policy – though didn’t explain which part of it – the book wasn’t going to be illegal or drugs and drug paraphernalia so I’m really not sure what their problem was. You don’t get much information even when you appeal.

I even tried offline, in old fashioned print, in Private Eye – a UK satirical magazine. All I got from them was “advert has not been approved” – and this is a publication which accepts classified ads for Thinking Twats’ T-shirts and anonymous bank transfer requests.

Maybe with enough clout, and a paid lawyer, these organisations could be persuaded to change their mind. But an interesting warning to others wanting to promote radical life extension – on top of overcoming the common reluctance in your audience to believe what you’re saying, the people passing on the message may stop them even hearing it in the first place.

Virtual Bodies are Accelerating Research and Diagnostics

Technology accelerates rapidly because it’s growing exponentially, but does this apply to medical technology? One place it certainly does is in virtual organisms – 3D digital models of cells, individual organs and even whole bodies.

Virtual Bodies are Accelerating Research and Diagnostics

Lots of medical research is initially based on animal models – that is, studying non-human organisms such as worms, mice or monkeys, to see what affect drugs, gene therapy and physical procedures have on them. One advantage of these models is ethics  – people are less concerned if 1000 worms die in the course of science than a single human, though of course there are many that would prefer to ban animal testing especially on our closest relatives such as other primates. Another advantage is timescales, and this is particularly important when looking at longevity treatments. Even if we had a drug that we were confident did no harm, and signed up 1000 willing human volunteers to test it, it would take decades before we would know if it was an effective anti-aging treatment. However, using worms with lifespans of weeks, or mice who only live for 1-2 years, the effectiveness can be seen quickly, enabling different types and dosages of drugs to be tested before considering human trials. But as well as the animal rights considerations, this still takes years of research and a lot of manual (and expensive) handling of the creatures under test.

So what if we could create a virtual model of an entire worm, mouse or even human. If this was accurate enough to respond to physical and chemical factors, taking into account the complex interactions within the body, we could test as many different drug candidates as we could feed into the computer. And as computing power continues to grow exponentially we can then turn up the speed and see the results overnight that even with worms might take several weeks.

We’re already getting close to this scenario. Research teams around the world are working on individual organs and also linking these up together to demonstrate how the entire body would react. Here are just a few examples:

  • University of California-Davis School of Medicine’s ion channel based heart model predicted adverse effects of 2 drugs used to treat abnormal heart rhythm.
  • The mechanics of the complex geometry of the skeletal system has been modelled by the University of Jyväskylä  to determine how different exercises induce bone strain and strain rates and to research the causes of degenerative arthritis.
  • Living Heart Project has developed a physics-based digital 3D model of the heart that can be used to virtually test new physical devices and aid heart disease research, and allows surgeons to walk inside a massive heart projection to really understand how the organ works.
  • Virtual worm brain (OpenWorm project) simulates all the connections between the c. elegans worm’s 302 neurons and is able to control a Lego robot without a single line of code.
  • The EU’s Human Brain Project has developed a simplified virtual mouse brain mapped to different parts of a virtual mouse body, including spinal cord, whiskers, eyes and skin.
  • Virtual Physiological Human programme aims to create a computer simulated replica of the human body (“in silico”).

This last project also is also applicable at the treatment end of healthcare. Once a detailed and accurate virtual model of a “standard” human has been developed, this could then be configured with the physiological parameters to match an individual. Their personal data could be input into their virtual avatar to predict how their specific body would react to drugs and other treatments. Already, for example, the University of Pittsburgh has modelled the complex interaction of multiple inflammation markers in blood enabling trauma patients’ risk of multi-organ dysfunction to be calculated and appropriate intensive care allocated.

Personalised medicine is mainly trying to predict how individuals will respond to pharmaceuticals based on their genes – which given the efficacy of most drugs is better than blindly working down a list of potential treatments; but what if you could then try that drug in a personalised virtual body and see how it really reacted given a multitude of individual factors? That could save time finding the best available option, save money in the wasted time of healthcare professionals and drug costs, and most importantly save lives.

Links to research mentioned in this blog post are available on the Digital Modeling page.