New technology is predicting the probability of death.

"People have always tried to find out how long they will live. In the past, people tried to find out through fortune-telling, reading from bones, the entrails of animals, or the arrangement of the stars. Now, more scientific methods are being used. An Icelandic genetic research company claims it can accurately predict the life expectancy of a particular person. Scientists working for deCODE Genetics in Reykjavik, Iceland, developed a predictor of the time of death, specifically a method for checking 'how much is left of a person's life'. The study was based on over 5,000 blood samples collected from over 35,000 Icelanders - writes the portal wired.com.

Accurate death prediction technology promises to change the way we think about our mortality. For most people, death most of the time remains an obscure issue haunting the dark recesses of our minds. However, knowing when life ends can make us enjoy each day more, look differently at the risks associated with our existence and health, and perhaps even make most people try to prevent the inevitable.

However, for people with disabilities, death prediction technology can serve as a reminder that they are treated as dead or partially dead while they are still alive. Learning to predict life expectancy is also tempting to judge its value: more life equals a better or more worthwhile life. It is hard not to notice the power of the technocratic government, which reflects on the weakest - writes the portal wired.com.

Icelandic scientists Kari Stefansson and Thjodbjorg Eiriksdottir have found that individual proteins in our blood serum are linked to mortality - and that different causes of death have similar 'protein profiles'. Eiriksdottir says they can examine and measure these profiles in a single blood draw, identifying specific protein markers in the plasma. Scientists call these mortality tracking indicators biomarkers, and there are 106 of them. They help predict mortality by considering all possible physical causes of an individual. Their process is called SOMAmer-Based Multiplex Proteomic Assay and is capable of measuring thousands of proteins simultaneously.

The laboratories in the Silicon Valley are working on seemingly impossible - eternal life. Money flows, among others from Google and Jeff Bezos.

All these measurements result not in the exact date and time of death of the subjects whose samples are being tested, but in being able to accurately predict the highest percentage of patients with the highest likelihood of death and the highest percentage of patients with the lowest likelihood of death. Scientists from deCODE plan to improve the process to make it more "useful", such as supporting, for example, an Artificial Intelligence algorithm that analyzes when a patient should be referred for palliative care when there is nothing doctors can do for him. In many cases, doctors themselves have doubts as to whether this is the moment."

More useful numbers:

"In a group of 60–80 years old, the protein model could identify a group of 5% with an 88% probability of dying within 10 years and a 67% probability of dying within 5 years.

 

Furthermore, the protein model could identify a 5% group with a 1% probability of death within 10 years.

 

In contrast, a similar high-risk group identified by the baseline model had a 65% probability of dying within 10 years and a 40% probability of dying within 5 years, while a 5% low-risk group had a 5% probability of dying within 10 years.

 

This shows that with the protein model, a group at extremely high risk of death and another at very small risk can be identified.

 

The protein model also relied much less on age in separating high- and low-risk groups than the baseline model. The difference in age between the groups was less for the protein model than the baseline model and the variance in age in each group higher.

The model using age, sex, and protein levels outperformed the baseline model without having direct information about traditional risk factors. Thus, the protein approach only needs single blood draw to get prediction accuracy better than a model that includes multiple risk factor measurements and disease diagnosis. Recent technical advantages in simultaneously measuring a large number of proteins open up the possibility of accurate evaluation of an individual’s state of health from only one blood draw. If the number of proteins is a limitation, only measuring 1-20 proteins still yields a powerful predictor."

These data explain why "exercise and a healthy diet do not help you live longer." The biological clock is not tied to exercise and a healthy diet what biological clock is - we do not know yet, but in the plasma proteins we can see how much time we have left in this clock.