Prediction of Lung Function in Adolescence Using Epigenetic Aging: A Machine Learning Approach

Abstract

Epigenetic aging has been found associated with a number of phenotypes and diseases. Few studies investigated its effect on lung function in relatively older people. However, this effect has not been explored in younger population. This study examines whether lung function at adolescent can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (Forced Expiratory Volume in one second) and FVC (Forced Vital Capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over life span can be beneficial to assess the lung health in adolescence.

DNA methylation biomarkers in asthma and rhinitis: Are we there yet?

“…Machine learning approaches are increasingly used to address healthcare problems; up to date, only one study has been conducted to predict lung functions using machine learning approaches by utilizing the effect of DNAmAge and accelerating age on lung function. Arefeen et al 20 . suggested that apart from the previously described factors height, weight, and sex, changes in epigenetic age acceleration between 10 and 18 years can improve the prediction of FEV1 and FVC at 18 years of age and proposed five selected regression models for machine learning techniques to be used for lung function prediction.…”

“…Arefeen et al. 20 suggested that apart from the previously described factors height, weight, and sex, changes in epigenetic age acceleration between 10 and 18 years can improve the prediction of FEV1 and FVC at 18 years of age and proposed five selected regression models for machine learning techniques to be used for lung function prediction.…”