An Algorithm for Predicting Biological Age has been Developed

This predictive algorithm is the first to combine lifestyle and genetic and non-genetic biomarkers in the same model.
The predictive algorithm is the first to combine lifestyle and genetic and non-genetic biomarkers in the same model.. (Image: Unsplash)
The predictive algorithm is the first to combine lifestyle and genetic and non-genetic biomarkers in the same model.. (Image: Unsplash)
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According to information provided by the Spanish National Institute of Statistics (INE, in its Spanish acronym), the aging rate of the Spanish population rose from 0.908 in 1997 to 1.183 in 2017. In turn, life expectancy increased exponentially over the same period of time.

To obtain this data, the indicator which is commonly used is chronological age. However, the aging of each person is also related to their lifestyle, among other factors.

The Aristóteles project, carried out by a UC3M research team, has developed a new methodology for the use of a more precise indicator when calculating real population aging. The development consists of an algorithm that predicts people’s biological age by identifying factors that contribute to population aging and calculates the magnitude of the influence of each one.

The predictive algorithm is the first to combine lifestyle and genetic and non-genetic biomarkers in the same model.. (Image: Unsplash)
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“Ageing is progressive and very complex because each person ages differently. Some of the influential elements are modifiable, so using another type of indicator, such as biological age, gives us a tool that allows us to identify unhealthy factors in order to correct them and thus increase life expectancy”, says María Durbán, a researcher in the Department of Statistics and project coordinator.

This predictive algorithm is the first to combine lifestyle and genetic and non-genetic biomarkers in the same model. Its main applications are in the medical and public health fields, as well as in business fields. (SM/Newswise)

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