AcidAGE: a biological age determination neural network based on urine organic acids.
Abstract
Organic acids reflect the course of all important metabolic processes and the effects of diet, nutrient deficiency, lifestyle, and microbiota composition. In present work, we focused on identifying age-related changes in organic acids in urine, and creating a neural network model based on them to determine biological age. The investigation involves data on concentrations of 60 organic acids in urine of 863 samples. Due to data analysis we found these acids could be used to determine human biological age. Two models were created for calculating biological age: a comprehensive AcidAGE model and a concise AcidAGE model based on 10 indicators. Both models demonstrate high accuracy. The presented models are useful for dynamically assessing the impact of medical interventions, lifestyle and diet amendments, and taking nutraceuticals on overall health and the risk of disease occurrence or progression. Their advantage lies in their ability to quickly update estimates as the corresponding biological processes change.
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Research summary
The paper is relevant to longevity research as it focuses on biological age determination through the analysis of organic acids in urine, which can reflect metabolic processes associated with aging. The development of neural network models to assess biological age dynamically could have implications for understanding and potentially mitigating age-related decline. However, while the findings are solid, they appear to be an incremental advance rather than a groundbreaking discovery, limiting their overall impact on the field.
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