Theoretical study by Limor Raviv, Damian Blasi and Vera Kempe, argues that children are not likely to be the main force behind linguistic innovation. For more than a century, scholars have repeated a powerful idea: that the mistakes children make when learning to speak are the seeds of language change. From 19th-century linguist Henry Sweet’s famous claim that “if languages were learnt perfectly by the children of each generation, then languages would not change,” to contemporary studies, the notion that children drive language evolution has been pervasive in linguistics, psychology, and even popular understanding. The new theoretical paper "Children are not the main agents of language change" argues that this popular belief is unfounded: language change is not the result of children’s acquisition errors. Instead, the real engine of change is everyday language use among older speakers, with adolescents and young adults being the more likely agents of change.  

Researchers at CIC bioGUNE, led by Prof. José M. Mato and Dr. Óscar Millet, have developed a “metabolic aging clock” that uses a simple blood test to predict biological age and detect early signs of disease. Published in npj Metabolic Health and Disease, the tool leverages NMR metabolomics and machine learning to analyze small molecules in the blood, providing a more accurate measure of health than chronological age.

Developed with data from over 13,500 participants in the AKRIBEA cohort (Basque Country), the clock can reveal discrepancies between metabolic and chronological age, potentially signaling early disease. For example, prostate cancer patients showed a metabolic age nearly 5 years older than their actual age, while those with fatty liver disease had a difference of over 14 years. The system also detects subtype-specific disease patterns that traditional tests may miss.

Beyond aging, the platform can estimate 25+ clinical parameters, such as inflammation or kidney function, from the same blood sample, offering a non-invasive, personalized health assessment. The team aims to further validate the tool for broader use in healthcare, supporting early detection, risk stratification, and healthier aging.