Large‐Scale metabolomics: Predicting biological age using 10,133 routine untargeted LC–MS measurements

Untargeted metabolomics is the study of all detectable small molecules, and in geroscience, metabolomics has shown great potential to describe the biological age—a complex trait impacted by many factors. Unfortunately, the sample sizes are often insufficient to achieve sufficient power and minimize potential biases caused by, for example, demographic factors. In this study, we present the analysis of biological age in ~10,000 toxicologic routine blood measurements. The untargeted screening samples obtained from ultra‐high pressure liquid chromatography‐quadruple time of flight mass spectrometry (UHPLC‐ QTOF) cover + 300 batches and + 30 months, lack pooled quality controls, lack controlled sample collection, and has previously only been used in small‐scale studies. To overcome experimental effects, we developed and tested a custom neural network model and compared it with existing prediction methods. Overall, the neural network was able to predict the chronological age with an rmse of 5.88 years (r ( 2 ) = 0.63) improving upon the 6.15 years achieved by existing normalization methods. We used the feature importance algorithm, Shapley Additive exPlanations (SHAP), to identify compounds related to the biological age. Most importantly, the model returned known aging markers such as kynurenine, indole‐3‐aldehyde, and acylcarnitines along with a potential novel aging marker, cyclo (leu‐pro). Our results validate the association of tryptophan and acylcarnitine metabolism to aging in a highly uncontrolled large‐s cale sample. Also, we have shown that by using robust computational methods it is possible to deploy large LC‐MS datasets for metabolomics studies to reduce the risk of bias and empower aging studies.