New RNA-Based Measure Predicts Mortality Risk and Tracks Aging

Aging is a complex biological process that affects multiple systems in the body and increases the risk of disease and death. Scientists have long sought accurate ways to measure biological aging, which can help assess individual health risks and guide interventions. Traditional measures like DNA methylation and blood biomarkers have limitations. RNA, which reflects real-time gene activity, offers another path, but existing RNA-based aging tools often rely on small or non-representative datasets. Accurate, flexible, and scalable tools that can track biological aging across diverse populations remain in high demand. Now, researchers have developed a new solution that uses RNA to predict a person’s risk of dying and detect changes in their biological age as their health evolves.

The RNA-based aging tool, called Transcriptomic Mortality-risk Age (TraMA), was developed by researchers from the University of Southern California (Los Angeles, CA, USA) by analyzing RNA sequencing data from nearly 4,000 U.S. adults aged 50 and older. TraMA estimates biological aging based on gene expression profiles and was developed using modern sequencing technology and a nationally representative dataset. Unlike previous RNA-based aging measures, TraMA offers greater reliability and captures key biological changes associated with inflammation, immune function, and kidney and brain health. The tool is also responsive to behavioral and socioeconomic factors, with older TraMA scores linked to smoking, obesity, and low physical activity. It can track biological aging over time, offering insight into how lifestyle and health influence gene expression patterns.

TraMA was validated using data from the Long-Life Family Study and multiple publicly available datasets involving patients with diabetes, sepsis, and cancer. The findings, published in Aging (Aging-US), showed that TraMA is a strong and independent predictor of early death, multiple chronic diseases, poor cognitive function, and difficulties with daily activities. It outperformed well-known biological aging measures like GrimAge and PhenoAge by capturing distinct and complementary aspects of aging. TraMA also proved effective across different populations and sample sizes, including both large-scale surveys and small clinical cohorts. Going forward, researchers hope to use TraMA to enable more precise investigations into the interplay between genes, environment, and health outcomes, ultimately helping extend human lifespan.

“TraMA is likely to be of particular value to researchers interested in understanding the biological processes underlying health and aging, and for social, psychological, epidemiological, and demographic studies of health and aging,” said Eric T. Klopack, lead author of the TraMA study.