Since none of us walks around with a ticking clock showing how long we have left to walk the planet (thanks for the illustration, Nickelback), death prediction has always been an inexact science. Scientists, clinicians and insurance agents remain curious about how to measure mortality (apart from accidental death). It’s been a fascination since early history, but now, research is showing some concrete answers. In a 2021 study published in Nature, a team of researchers in Iceland published a large-scale study looking at plasma proteins in 22,913 Icelanders. Over an average follow-up of around 13.7 years, the team found that their proposed predictor could identify those at most risk of pending death. They suggest that predicting the amount of time we have till death from plasma protein levels might be a useful tool for assessing general health as well as measuring mortality.

Your Proteome Tells a Tale

The researchers in the Nature study used an analysis technology called SOMAmer-Based Multiplex Proteomics. According to a 2011 Public Library of Science paper, the method uses highly specific DNA-based capture reagents to capture multiple plasma proteins for measurement. These synthetic protein capture reagents are so sensitive that it is possible to combine them to capture multiple proteins within a single assay. This makes them ideal for populations studies involving large numbers of both people and analytes, as seen in the Icelandic study.

The research team used this kind of large-scale proteomic data set approach to measure levels of 4,684 plasma proteins collected from 22,913 Icelanders. According to a PR Newswire press release, over the course of the study, the team monitored health in participants and saw that more than 7,000 died.

When the scientists started crunching numbers in their protein database to see if the results showed how to measure mortality, they found that their prediction algorithm not only worked for death prediction but also gave a good assessment of general health. The press release for the study describes this as a “predictor of how much is left of the life of a person.”

Death Prediction Tools

As well as using proteins circulating in your bloodstream, researchers have also discovered other tools to measure mortality. These are usually based on how cells age and the processes that they go through.

One tool uses MRI to directly image structures within the brain. This neuroimaging approach, described in a 2017 Nature paper, involves combining machine learning with imaging data to spot how structures within a brain age and in turn, predict mortality.

Cell age can also be measured by looking at telomere length. These structures are the genetic end caps to chromosomes that protect DNA during replication, and they are lost with aging. Once too short, DNA replication stops, and so does the cell.

Another technique, known as the epigenetic clock, uses a measure of methylation and demethylation across regions of a person’s DNA to measure aging. Methyl groups on DNA are important as they either switch on or turn off expression of genes as an epigenetic regulator of genetic expression. The rate of accumulation or disappearance of these chemical groups from regions of the genetic code seem to show an organism’s age with certain accuracy. This approach has also been used to compare dog aging with their human owners.

Why Is Death Prediction Useful, and Why Is It Dangerous?

Aside from general morbid curiosity, why would knowing how to measure mortality be of benefit?

In clinical settings, especially palliative care and preparation for end of life, knowing how long someone will live for can be extremely useful in setting out treatment and other care plans. The National Institute for Health and Care Research notes that being able to plan for the remaining days, weeks or months can help to maximize quality of life in the final stages.

Quality of life is an important parameter for assessments beyond clinical care; QALYs or quality-adjusted life years give a numerical basis to determining the value of health outcomes. They can be used to determine most effective treatments by showing how changes drive impact. However, they can also be discriminatory.

The flip side to QALYs are DALYs: disability-adjusted life years. These show the shortening of life due to disability or chronic illness. The World Health Organization describes these as “a time-based measure that combines years of life lost due to premature mortality (YLLs) and years of life lost due to time lived in states of less than full health, or years of healthy life lost due to disability.” As death prediction tools, DALYs show how much shorter life is with a disability. Unfortunately, this also perpetuates an ableist view of life in that anything other than full health is a life less valuable.

During the recent SARS-CoV-2 pandemic, where rapid acceleration in case numbers frequently overwhelmed hospital capacity, many clinical centers formulated triage policies to try to optimize treatment. These were usually based around ventilator access and tried to guide decisions on who would best benefit from this treatment and survive.

A 2020 Forbes article described how assessing disabled people as living less of a life leads to clinical discrimination. Treatment triage policies based on how to measure mortality or DALYs could be used to deny ventilator access, for example, to people with Down Syndrome, developmental conditions such as autism, muscular dystrophy or mental impairments.

An article in The Atlantic also noted the potential for clinical discrimination, that with spread of the disease overwhelming clinical facilities, rationing treatment in the emergency could place certain groups at the back of the queue.

All this leads to questioning how being able to measure mortality might be used in the future; in less critical times, could death prediction also be used to assess viability and therefore limit access to treatment? Both the benefits and consequences will need to be weighed for the best use of such measurements.

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