Machine learning estimation of human body time using metabolomic profiling

Abstract

Significance For individual timing of medical treatment or diagnosis of circadian disorders, it is essential that practical methods are being developed which allow estimation of individual circadian phase in the clinic. Our machine learning algorithm outperforms previously published approaches by using targeted metabolomics data from one or two optimally timed blood samples to reliably estimate circadian phase of melatonin specifically for men or women. It thereby provides a relatively cheap method for potential circadian applications in the clinic after appropriate validation.