Background

Age Modelling consists of trying to predict the chronological age of an organism with a set of features that capture information about its aging status and dynamics. With a rich feature set one can accurately predict the chronological age of a normative (healthy) population using relatively simple machine learning models. When the feature set is sufficiently rich and the model can capture the complexity of the data, performing inference on non-normative populations leads to prediction errors in their chronological age. The mismatch between the chronological vs. predicted age (known as age delta) is a proxy of aberrant aging trajectories, often having high correlations with factors that condition ageing. Furthermore, classification of different clinical groups (e.g.: stable vs progressive) can also be done using the trained models.

AgeML

ageml allows age modelling with a set of simple-to-use CLIs that produce comprehensive figures of the modelling steps and detailed logs for exploring the effectiveness of the trained models.

There are 4 main CLIs:

• model_age: It takes a features file (in CSV format) from a set of subjects, which includes their chronological age, and it trains a model for predicting it. Categorical Covariates can be included to train models per category, using a CSV file. Systems, feature groups that correspond to a congruent physiological system (cardiac, muskuloskeletal, gastric, etc.) can also be included to train models per system using a simple .txt file. A CSV file is returned with the predicted ages, and the corresponding age delta. Also, a simple correlation analysis is performed to check how the input features correlate with the chronological age. The age distribution of the cohort is plotted too.
• factor_correlation: The correlation between the provided factors and the computed age deltas is analyzed, including the strength and significance. If the clinical category of each subject is provided (with a CSV file), this analysis runs for every clinical category.
• clinical_groups: To see how the age delta changes across clinical groups, a boxplot is created. The age distribution across the groups is also plotted.
• clinical_classify: Using the features file and the ages file (with the age delta) classification of two clinical groups is performed. A Receiver Operating Curve (ROC) is plotted for each model used in the classifications.