The research, led by Duke Health with collaborators at the University of Minnesota, analyzed more than 1,200 blood samples from a North Carolina-based cohort established in a previous Duke-led study. Scientists measured 828 different small RNAs and examined 187 clinical factors, and they determined survival by linking participants to national mortality records.
Using causal artificial intelligence and machine learning, the team identified a set of six piRNAs that predicted two-year survival with accuracy as high as 86%. The finding was confirmed in a second, independent group of older adults. Lower levels of certain piRNAs were strongly linked with longer survival, a pattern that matches results from simple organisms where reducing these molecules can extend lifespan.
The study found that piRNAs outperformed age, cholesterol, physical activity and more than 180 other clinical measures for predicting short-term survival. The findings appear in the journal Aging Cell, and the senior author noted the strong signal came from a simple blood test.
Difficult words
- cohort — a group of people in a research study
- blood sample — a small amount of blood taken for testsblood samples
- survival — continued living after an illness or event
- mortality — the number of deaths in a group
- clinical — relating to medical care or doctors' tests
- machine learning — computer methods that learn from data
Tip: hover, focus or tap highlighted words in the article to see quick definitions while you read or listen.
Discussion questions
- Do you think a blood test that predicts short-term survival would be helpful? Why or why not?
- Would you agree to give a blood sample for a study like this? Explain your reasons.
- How could doctors or families use information about a person’s likely short-term survival?
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