New aging clock merges proteins and metabolites
A biological clock that simultaneously measures proteins and small molecules in blood predicts chronic diseases more accurately than earlier methods. It was validated in more than 30,000 participants.
Aging clocks are tools that measure biological age rather than calendar age. They look at signals in the body that indicate how quickly someone is aging. Existing clocks often rely on DNA methylation or a limited set of biomarkers. The new clock, called StackAge, combines two data types: the proteome (all proteins in blood plasma) and the metabolome (small molecules such as sugars and fatty acids that reflect metabolic processes).
The researchers analyzed data from 30,376 participants in the UK Biobank. StackAge predicted chronological age with a correlation of approximately 0.93, which is high for models of this kind. Across twelve chronic diseases, the clock measurably improved risk prediction, with the strongest effects for type 2 diabetes, Alzheimer’s disease, and chronic kidney disease.
What the clock measures
The biomarkers the clock relies on most fell into three biological categories: inflammation, metabolic stress, and changes in the tissue surrounding cells (extracellular matrix). These are three processes long linked to aging and age-related disease.
The study also shows that lifestyle factors such as exercise, smoking, and diet correlate with higher or lower biological age as measured by StackAge. That makes the clock potentially useful for prevention research.
More clocks, but also more questions
At the same time, commentary on this field raises an important caveat. Building new clocks is advancing quickly, but validating them for specific applications, such as measuring the effect of a therapy, is considerably harder. StackAge correlates with disease outcomes, but whether it accurately detects change due to treatment has not yet been demonstrated. That is a fundamental challenge for the entire aging clock field, and it applies to StackAge as well.
The results were published in Briefings in Bioinformatics.