Why do some people live longer than others? New research from the University of Minnesota and Duke University, recently published in Aging Cell, investigates how tiny molecules in the bloodstream - called small RNAs - may explain and determine differences in human longevity.
RNA is a molecule that helps to regulate the function of cells. Small RNA, including microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs), help regulate how genes function and may influence aging and survival. By analyzing blood samples from more than 1,200 adults aged 71 and older, researchers examined whether specific small RNAs are linked to longer life, whether they can help predict survival in a clinically meaningful way and whether they point to potential drug targets that could one day support healthier aging.
The study:
- Supports a causal link - a demonstrable cause-and-effect relationship - between the circulation of small RNAs and life expectancy.
- Developed a prediction model that combined small RNAs with clinical and demographic factors, which showed strong accuracy in predicting the sample group's two-year survival rates.
- Identified nine piRNAs - a specific type of RNAs - that were consistently lower in longer-lived individuals, highlighting them as potential therapeutic targets.
"There is compelling evidence that small RNAs are powerful predictors and highly promising determinants of survival in older adults and potential biomarkers of longevity," said Sisi Ma, the co-first author of the study and an associate professor in the University of Minnesota's Institute for Health Informatics. "By quantifying these molecules through a simple blood test, we can move toward personalized monitoring and the development of new therapeutics that intervene in the aging process to help people live healthier for longer."
Central to the success of the work was the causal predictive AI capacity at the University of Minnesota Institute for Health informatics. Traditional AI typically relies on correlation - identifying if two things happen together - whereas causal AI gets at the "why" behind the data. Through their partnership with aging experts from Duke University, the team was able to bridge computational discovery and medical application to ensure that the findings were both clinically relevant and grounded in the complex biology of human aging.
The research establishes a new framework for using causal predictive AI to streamline the journey from the lab to the clinic. The AI tools integrated into the biomedical discovery workflow establishes a faster, more scalable model for medical breakthroughs.
