This research was based on historical data, and it will be important to demonstrate in clinical studies that such an algorithm improves patient outcomes, says collaborator Christopher Haggerty.
Read more: Mind meld — Artificial intelligence is improving the way humans think. Read more: How AI is ushering in a new era of superhuman doctors.
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Several studies using this technology have shown the plasma proteome to be heavily associated with age and life span 16 , 17 , 18 , 19 , A study of participants associated out of proteins with age, found that a protein proteomic age signature associated with all-cause mortality independent of chronological age, and created a seven-protein mortality predictor In a study of older adults, of proteins were associated with age. A proteomic age model using the age-associated proteins predicted mortality better than chronological age Another study of participants measured proteins to evaluate how circulating protein profile changes over the life span Some studies have used large proteomics datasets to predict other health-related factors.
A protein-based risk score for cardiovascular outcomes in a high-risk group was developed using candidate plasma proteins These studies underscore the value of using plasma levels of a large number of proteins to search for biomarkers in health and diseases.
Here we apply plasma levels of proteins determined with SOMAmers to predict both long- and short-term all-cause mortality. We developed and tested predictors using a dataset of 22, individuals, of whom died during the study period. Predictors using proteins were compared to predictors using only conventional risk factors, and we examined prediction performance for various causes of death. We also explored how individual proteins associate with all-cause mortality and various causes of death.
Using an independent dataset of individuals, we correlated the predictor with several frailty measures and known risk factors of mortality. The average follow-up time for this group was At the time of sample collection, 7. Since most of this dataset was collected for cancer research, it has about three times higher cancer prevalence than the more recently collected dHS sample set.
During the study period, participants Of those who died, Table 1 lists the baseline characteristics for all datasets. For every participant, protein measurements aptamers measuring levels of different proteins in plasma were available after a quality check. In Fig. The AUC for all participants using only age and sex age and sex model increases with the time from sample collection Fig. Adding disease and lifestyle variables to the model baseline model increases the AUC over the age and sex model.
Adding more protein measurements to the age and sex model gave an even better prediction model protein model. The difference between the four prediction models was greater for short-term predictions than long-term predictions.
The proteins in the protein model were chosen separately for predictions of all-cause mortality within 1,2,…,15 years. In general, the short-term predictions needed fewer proteins than the long-term predictions. For instance, the Boruta 25 feature selection chose protein measurements for prediction of death within 1 year, but protein measurements for death within 15 years.
The L1 26 penalty reduced the model to 81 protein measurements for prediction of death within 1 year and for death within 15 years, but the biggest model, which was for prediction of death within 13 years, used protein measurements. Ten protein measurements were chosen in every model. The 5-year predictor included protein measurements, the year predictor included , and the 2-year predictor included 98 protein measurements Supplementary Table 1.
The features and coefficients of the 5-year predictor are in Supplementary Data 1. As an example of short-, intermediate-, and long-term predictions, we looked at the prediction of death within 2, 5, and 10 years Supplementary Table 2.
Figure 1b depicts the ROC curves for all-cause mortality within 5 years for all participants. The AUC for the age and sex model was 0. The baseline model had an AUC of 0. Restricting the analysis to participants 60 years or older lowers the AUCs compared to models including all the participants.
However, the differences from the baseline model were greater Fig. The lower AUC and bigger AUC differences probably result from the exclusion of many easily classified participants younger than For example, the youngest age group is at very low mortality risk and easily distinguished using the age variable. That is, the smaller age range reduces the importance of age.
For the 5-year prediction, the AUCs were 0. For 5-year prediction for all participants, the integrated discrimination improvement IDI for the protein model vs. For older than 60 participants the IDI was 0. Supplementary Fig. When the predictors were applied to a subset of participants not diagnosed with any of the major diseases used in the baseline at the time of plasma collection, the protein model was still the best prediction model Supplementary Fig.
The baseline model and the GDF15 model still did better than the age and sex model, but the difference is much smaller than when the whole dataset is used. This is not surprising since information about the excluded diseases is essential to the baseline model.
We also examined the discrimination power in participants 80 years or older separately, using the models trained for participants older than The protein predictors discriminated better than the baseline model at every time point Supplementary Fig. Adding the baseline model variables to the GDF15 model and the protein model improved predictions for all time points, both for all and older than 60 participants Supplementary Fig.
However, we were more interested in what the protein measurements could do without information on lifestyle and diseases; therefore, we did not include the baseline variables in the GDF15 and protein models. Excluding age and sex from the protein model reduced prediction performance slightly, especially for long-term predictions, but age and sex are an essential part of the GDF15 model. Since age and sex are easily obtainable features, we saw no advantage in excluding them from the models.
GDF15 is a powerful predictor on its own and as a part of the protein model. These were the proteins in addition to GDF15 with the strongest association with 5-year mortality and were all useful in predicting mortality. However, the GDF15 model remains the only single protein model to surpass the baseline model in prediction performance Supplementary Fig.
Therefore, GDF15 cannot easily be swapped for any single candidate protein, but a combination of proteins can make up for performance loss from excluding GDF Other protein-based mortality predictors have been developed.
We tried to replicate them in our data and compared them to our predictors as shown in Supplementary Fig. The difference between protein-derived age and chronological age, sometimes called predicted age difference PAD , has been shown to be predictive of mortality 18 , We calculated a PAD and used it as a feature in a mortality prediction model. We also tried a mortality predictor using the seven proteins shown to be useful mortality predictors by Tanaka et al.
The seven-protein model performed better than our GDF15 model but did not reach our protein model performance. This is expected since GDF15 was one of the seven proteins and because our protein model makes use of more proteins. Finally, we tried using ten of twelve proteins used in a multivariable model by Ho et al. This model also included GDF15 and had prediction performance between our protein model and the seven protein model which might be expected since it used more proteins than seven and fewer than our protein model.
These proteins were selected out of a set of proteins targeted because of their high value for cardiovascular disease. That included participants with mean age The curves were plotted separately for the four prediction models. The Kaplan—Meier curves for participants are split by quantiles of predicted year risk by each model, demonstrating the different survival rates in the different risk groups.
The red dots show survival after 5 and 10 years. In a group of over year-old participants, we examined survival curves split by predicted ten-year risk. A visual examination showed that all models were reasonably well-calibrated, allowing predicted risk values to be interpreted directly as probabilities Supplementary Fig. We also examined the difference in the performance of the prediction models for different causes of death.
Figure 3 shows the predicted 5-year risk of all-cause mortality split by survival status after 5 years from plasma collection. Participants who died within 5 years are also shown separately for five cause-of-death categories; neoplasms, nervous system, circulatory system, respiratory system, and other. All five cause-of-death categories showed a higher predicted risk for the protein model than the baseline model, although the difference varied between categories.
Deaths from neoplasms were not as reliably predicted as deaths from other causes. Deaths from respiratory system causes and other causes showed the greatest improvement of the protein model over the baseline model.
The predicted risk for those who did not die within 5 years was lowest with the protein model. We also looked directly at the AUC for each cause of death within 5 years, excluding other causes Supplementary Fig. There the protein model had the highest AUC for every category, best for the respiratory system and other causes, and worst for neoplasms.
Participants who died within 5 years are shown as one group and categorized by cause of death and those who were alive after 5 years are shown as one group. The yellow center line represents the median, the box limits are the upper and lower quartiles, the whiskers represent the 1. In a univariate association corrected for age and sex, protein measurements out of were significantly associated with death within 5 years after a Bonferroni correction, positively and 72 negatively.
For participants older than 60 years, the number of associated protein measurements was , associated positively, and 66 negatively. Of the top ten single protein associations with death within 2, 5, 10, or 15 years, six proteins were common for all time points, with GDF15 always having the largest effect Supplementary Table 4.
As an example of protein measurement in our dataset we show the distribution and association of GDF15 with age in Supplementary Fig. Most of the proteins with the highest associations were positively correlated. For the top ten proteins associating with all-cause mortality within 5 years, most intercorrelations after correcting for age and sex were 0.
A notable exception is ANTXR2, which was not correlated with any other top protein and the only top protein negatively associated with death. The average correlation between any protein measurement pair of the measurements after correcting for age and sex was 0.
When univariate associations were examined separately for different causes of death Supplementary Table 5 , the top two proteins for all-cause mortality, GDF15 and WFDC2, were among the top five proteins for all the death categories except for the nervous system category. The nervous system category had a different protein profile from the other categories, with the top four proteins having negative associations with death and GDF15 far from being significantly associated with death.
This difference is probably partly responsible for the slight improvement of the protein model over the age and sex model in predicting nervous system deaths, as depicted in Fig. All top five proteins for neoplasms were also in the top ten for all-cause mortality.
Because neoplasms are the most common cause of death in our data, it is not surprising that all-cause mortality and neoplasm mortality have similar protein profiles. Single protein predictor performance was in accordance with the univariate associations.
WFDC2 was best at predicting respiratory system-related deaths, while GDF15 was best at predicting neoplasm and other deaths. All 5-year all-cause mortality protein associations are in Supplementary Data 2 and Supplementary Data 3. We also examined associations using the Cox proportional hazards model with similar results Supplementary Data 4.
We performed pathway analysis, finding the most over-or underrepresented protein pathways from the Reactome database 27 , in the subset of most relevant proteins, selected with the Boruta method, for 5-year mortality prediction. The subset includes protein measurements different proteins are included in the analysis relevant for 5-year mortality prediction without correcting for age and sex. Using a false discovery rate of 0.
We repeated the pathway analysis in a set of most relevant protein measurements corrected for age and sex before applying Boruta. In the corrected subset, we have protein measurements different proteins are included in the analysis , of which intersect with the subset from the uncorrected analysis.
Using the same significance criteria, no pathway was significantly over-or underrepresented. Other pathways with low p-values included many components of the Extracellular matrix organization pathway. When we used the full set of protein measurements that were significantly associated with 5-year mortality different proteins are included in the analysis , no pathway was significantly over-or underrepresented, but Regulation of IGF transfer and uptake by IGFBPs and Extracellular matrix organization were among the three with the lowest p -values.
The ten pathways with the lowest p -values for all sets are in Supplementary Data 5. Few protein measurements in addition to age and sex can achieve most of the discrimination performance. Five, ten, or twenty proteins, selected with a forward selection, yielded AUCs of 0. Sibling pairs older than 60 years were , and parent-offspring pairs in this age group were The sibling estimated heritability for predicted all-cause mortality risk was 0.
This method fails to account for similarities in the environment of relatives, making this an upper bound of heritability. The participants from the dHS underwent deep phenotyping at the time of sample collection. Although only 0. The predicted risk correlated negatively with the maximum oxygen uptake VO2 max in a graded cycle ergometer exercise tolerance test, max grip strength, forced expiration volume in one second FEV1 , number of correct codes in a digit coding test 28 , and lean appendicular body mass scaled to height squared measured with Dual-energy X-ray absorptiometry DXA.
The predicted risk correlated positively with time spent completing trail making test B 29 , resting heart rate, and average length from neck to waist over the back. Information on various diseases and other traits collected through the Icelandic health system was available for most participants. We looked at how six traits, known to be risk factors for mortality, were associated with the predicted 5-year risk corrected for age and sex in the combined dHS and VSP2 datasets.
The protein model predicted higher mortality risk for participants with type 2 diabetes T2D , MI, CAD, or cancer, and those who smoked, but predicted risk did not associate with clonal haematopoiesis Supplementary Table 7. The baseline had a C-index of 0. I cannot give you an accurate percentage, but I think we can easily say that more than 80 per cent of the people do not die a natural death or a timely death any more. Their death is unnatural or untimely because they die while their intent is still on.
This is unfortunate, and it has a bearing upon how the death happens and what happens after that. In a natural or timely death, Prarabdha Karma, or the information that runs the life, runs out and life becomes feeble. When the information runs out, life peters out slowly and this is not torturous. This is very beautiful. When your Prarabdha Karma runs out, even if you lived a bad life, the last few moments will become very peaceful, wonderful and perceptive.
Suddenly, you will see people become so wise. They are not attached to anything around them; they show an extraordinary sense of maturity— something they failed to show throughout their life. This is because it is a natural death. Natural death is not a bad death. It is a good thing for you and a good thing for those you are leaving behind because you are not being forced out of your body….
Sadhguru Jaggi Vasudev is a spiritual guru with a huge retinue of followers who look up to him for life lessons and guidance on their everyday way of living. In his latest book, Death: An Inside Story, Sadhguru goes one step further and tackles the ultimate, dark fate that awaits every human being: death. This can happen when a person is into spiritual sadhana and has achieved such a mastery over their energies that they are able to untangle their life energies from the physical body without damaging it….
Such a person becomes truly no more. This is considered the highest kind of death. This is also referred to as Mahasamadhi in the Hindu tradition and Mahaparinirvana in the Buddhist tradition.
In English, we simply call it Liberation, meaning one has become free from the very process of life, birth and death…. More precisely, we saw how it is karma, the software of life, that determines the span and the nature of life.
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