“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.”

In this study, led by Eric T. Klopack from the University of Southern California, researchers created a new RNA-based aging measure that predicts health risks and mortality. This measure, called Transcriptomic Mortality-risk Age (TraMA), uses gene expression data to estimate a person’s biological aging. This finding offers a new and potentially more accurate way to track aging and understand health risks, especially for older adults.

Aging is a complex biological process that affects multiple systems in the body and increases the risk of disease and death. Scientists have long looked for reliable ways to measure biological aging. While DNA methylation and blood biomarkers are commonly used, this study focused on RNA—a molecule that reflects gene activity. By analyzing RNA sequencing data from nearly 4,000 U.S. adults aged 50 and older, the team developed TraMA to predict the probability of dying within four years.

(A) Plan of analysis for the current study. (B) Nested regression results from the HRS testing data including associations between TraMA and sociodemographic factors and health behaviors; points represent regression coefficients and bars represent 95% confidence intervals; all models include cell type and batch as covariates. Model 1 includes demographic factors; Model 2 includes variables in Model 1, as well as socioeconomic factors; Model 3 includes variables in Model 2, as well as health behaviors. (C) Regression results from the HRS testing data of health/aging outcomes on TraMA; points represent regression coefficients and bars represent 95% confidence intervals; all models include age, race/ethnicity, sex/gender, cell type, and batch as covariates. (D). Validation results from nested regression of time to death on TraMA in HRS and LLFS. Model 1 includes batch as a covariate; Model 2 includes batch, age, race/ethnicity, and sex/gender as covariates; Model 3 includes variables from Model 2, as well as RNA-based cell type as covariates.

TraMA proved to be a strong and independent predictor of early death, multiple chronic diseases, poor cognitive function, and difficulties with daily activities. It was also tested in another large group of long-lived families and in several smaller datasets from patients with conditions like diabetes, sepsis, and cancer. The results confirmed the tool’s usefulness across different populations and health conditions.

“TraMA was also externally validated in the Long Life Family Study and several publicly available datasets.”

Unlike earlier RNA-based aging measures, which were often built using small or non-representative samples, TraMA was developed using modern RNA sequencing technology results and a nationally representative dataset. This increases its reliability and potential for broad public health applications. The tool also demonstrated unique advantages over popular biological aging measures like GrimAge and PhenoAge, capturing distinct aspects of aging and health decline.

Importantly, TraMA tracks biological processes related to inflammation, immune function, and kidney and brain health, systems that play key roles in aging. It was also sensitive to behavioral and socioeconomic factors. For instance, smoking, obesity, and low physical activity were associated with older TraMA scores.

TraMA was also sensitive to changes in biological aging. In one study, researchers measured TraMA at two different time points and found that the more recent scores were better at predicting who would die. This suggests that TraMA can track changes in a person’s aging as their health evolves. It also performed well in both large-scale surveys and small clinical samples, making it a useful tool in many types of research.

By offering a new, accurate, and flexible method for measuring biological aging, TraMA may help researchers better understand how genes, lifestyle, and environment influence aging. This tool opens the door to more precise research on improving health and extending lifespan.

Source – Impact Journals