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<p>We hypothesised that clinical blood biochemistry and haematological data could predict the age of healthy dogs. Using a large veterinary electronic medical record (EMR) database, we investigated age-related relationships in Labrador Retrievers, a common large breed. Analysing over 3 million blood test values across 24 analytes and ~ 145,000 dogs, we modelled age and sex using a generalised additive model. Changes in multiple analytes were seen around 3–6 and 12&#xa0;years of age. As these findings demonstrated age-related changes in blood analytes, we considered the data suitable for modelling chronological age. Using eXtreme Gradient Boosting, we predicted age based on sex, 24 blood analytes, and 276 derived ratios. Training, test and validation sets for the model consisted of ~ 38.5&#xa0;k, ~ 4.7&#xa0;k and ~ 4.7&#xa0;k data points, respectively. The model achieved a mean absolute error of approximately 14.6&#xa0;months. We hypothesised that some aspect of differences between predicted and chronological age might represent features of biological age, linked to the onset of age-related diseases. We demonstrated that &gt; 6-month difference in predicted and chronological age was significantly associated with the first diagnosis of common age-related conditions such as arthritis, lumps/growths, nuclear sclerosis and hepatopathy. These findings suggest that standard clinical measures from a large EMR database can be used to predict age in healthy pets and also provide insights into biological age, aiding in the assessment of individual health risks.</p>

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A chronological age prediction model using clinical biochemistry and hematological data identifies features of biological age in healthy Labrador retrievers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月28日

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  • Carina Salt,
  • Phillip Watson,
  • David Allaway

摘要

We hypothesised that clinical blood biochemistry and haematological data could predict the age of healthy dogs. Using a large veterinary electronic medical record (EMR) database, we investigated age-related relationships in Labrador Retrievers, a common large breed. Analysing over 3 million blood test values across 24 analytes and ~ 145,000 dogs, we modelled age and sex using a generalised additive model. Changes in multiple analytes were seen around 3–6 and 12 years of age. As these findings demonstrated age-related changes in blood analytes, we considered the data suitable for modelling chronological age. Using eXtreme Gradient Boosting, we predicted age based on sex, 24 blood analytes, and 276 derived ratios. Training, test and validation sets for the model consisted of ~ 38.5 k, ~ 4.7 k and ~ 4.7 k data points, respectively. The model achieved a mean absolute error of approximately 14.6 months. We hypothesised that some aspect of differences between predicted and chronological age might represent features of biological age, linked to the onset of age-related diseases. We demonstrated that > 6-month difference in predicted and chronological age was significantly associated with the first diagnosis of common age-related conditions such as arthritis, lumps/growths, nuclear sclerosis and hepatopathy. These findings suggest that standard clinical measures from a large EMR database can be used to predict age in healthy pets and also provide insights into biological age, aiding in the assessment of individual health risks.