Preprint Technical Note Version 1 Preserved in Portico This version is not peer-reviewed

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

Version 1 : Received: 6 September 2020 / Approved: 8 September 2020 / Online: 8 September 2020 (03:15:28 CEST)

How to cite: Arefeen, M.A.; Nimi, S.T.; Rahman, M.S.; Arshad, S.H.; Holloway, J.W.; Rezwan, F.I. Prediction of Lung Function in Adolescence Using Epigenetic Aging: A Machine Learning Approach. Preprints 2020, 2020090168 (doi: 10.20944/preprints202009.0168.v1). Arefeen, M.A.; Nimi, S.T.; Rahman, M.S.; Arshad, S.H.; Holloway, J.W.; Rezwan, F.I. Prediction of Lung Function in Adolescence Using Epigenetic Aging: A Machine Learning Approach. Preprints 2020, 2020090168 (doi: 10.20944/preprints202009.0168.v1).

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.

Subject Areas

lung function; epigenetic aging; machine learning; feature selection; hyperparamter tuning

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