Tag: monitoring

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Critics by Nature.com

Predicting all-cause mortality risk is challenging and requires extensive medical data. Recently, large-scale proteomics datasets have proven useful for predicting health-related outcomes. Here, we use measurements of levels of 4,684 plasma proteins in 22,913 Icelanders to develop all-cause mortality predictors both for short- and long-term risk. The participants were 18-101 years old with a mean follow up of 13.7 (sd. 4.7) years. During the study period, 7,061 participants died.

Our proposed predictor outperformed, in survival prediction, a predictor based on conventional mortality risk factors. We could identify the 5% at highest risk in a group of 60-80 years old, where 88% died within ten years and 5% at the lowest risk where only 1% died. Furthermore, the predicted risk of death correlates with measures of frailty in an independent dataset. Our results show that the plasma proteome can be used to assess general health and estimate the risk of death.

The ability to predict when someone will die is not something you would wish upon yourself or your friends. It could, however, prove useful in the delivery of healthcare and biomedical research. It is often possible to give a meaningful prediction of how long individuals with specific diagnoses will live¹, but predicting when an individual will die from any cause is altogether a different matter.

Several diseases, lifestyle^2,3,4, social and psychological factors⁵ associate with all-cause mortality. Commonly used risk factors for all-cause mortality are age, sex, traditional cardiovascular risk factors such as systolic blood pressure, cholesterol levels, smoking, and diabetes, cardiovascular disease, cancer, alcohol consumption, body mass index (BMI), and creatinine levels^6,7,8. Among other biomarkers of all-cause mortality are brain age estimated from structural magnetic resonance images⁹, DNA methylation¹⁰, and telomere length¹¹.

Recently, circulating metabolic biomarkers have been found to associate with the risk of all-cause mortality. In a study of 44,168 individuals, where 5512 died during follow-up, 14 metabolic biomarkers were found to improve 5 and 10-year all-cause mortality predictions over conventional risk factors⁸. Another study of 17,345 participants identified 106 metabolic biomarkers that improved short-term all-cause mortality risk prediction over established risk factors⁷.

In a study of 3523 participants from the Framingham Heart Study, 38 of 85 preselected circulating protein biomarkers associated with all-cause mortality and improved all-cause mortality prediction over cardiovascular risk factors¹². Similarly, 56 peptides (31 proteins) correlated with 5-year mortality in a study of 2473 older men. A panel of those peptides improved the predictive value of a commonly used clinical predictor of mortality.

With the advent of new technology such as SOMAmers¹⁴ or proximity extension assays¹⁵, it is possible to simultaneously measure levels of thousands of proteins efficiently. Several studies using this technology have shown the plasma proteome to be heavily associated with age and life span^{16,17,18,19,20}. A study of 997 participants associated 651 out of 1301 proteins with age, found that a 76-protein proteomic age signature associated with all-cause mortality independent of chronological age, and created a seven-protein mortality predictor¹⁸.

In a study of 1025 older adults, 754 of 4265 proteins were associated with age. A proteomic age model using the age-associated proteins predicted mortality better than chronological age¹⁹. Another study of 4263 participants measured 2925 proteins to evaluate how circulating protein profile changes over the life span²⁰. 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 1130 candidate plasma proteins²¹.

In addition, ~5000 plasma proteins were used to predict health states, behavior, and incident diseases, with performance comparable to traditional risk factors, in 16,894 participants²². These studies underscore the value of using plasma levels of a large number of proteins to search for biomarkers in health and diseases..…To be continued….

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