Version 1
: Received: 4 October 2024 / Approved: 4 October 2024 / Online: 7 October 2024 (15:26:38 CEST)
How to cite:
Oravilahti, A.; Vangipurapu, J.; Laakso, M.; Fernandes Silva, L. Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health. Preprints2024, 2024100365. https://doi.org/10.20944/preprints202410.0365.v1
Oravilahti, A.; Vangipurapu, J.; Laakso, M.; Fernandes Silva, L. Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health. Preprints 2024, 2024100365. https://doi.org/10.20944/preprints202410.0365.v1
Oravilahti, A.; Vangipurapu, J.; Laakso, M.; Fernandes Silva, L. Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health. Preprints2024, 2024100365. https://doi.org/10.20944/preprints202410.0365.v1
APA Style
Oravilahti, A., Vangipurapu, J., Laakso, M., & Fernandes Silva, L. (2024). Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health. Preprints. https://doi.org/10.20944/preprints202410.0365.v1
Chicago/Turabian Style
Oravilahti, A., Markku Laakso and Lilian Fernandes Silva. 2024 "Metabolomics-Based Machine Learning for Predicting Mortality: Unveiling Multisystem Impacts on Health" Preprints. https://doi.org/10.20944/preprints202410.0365.v1
Abstract
Reliable predictors of long-term all-cause mortality are needed for middle-aged and older populations. Previous metabolomics mortality studies have limitations, a low number of participants and metabolites measured, measurements mainly using nuclear magnetic spectroscopy, and the use only of conventional statistical methods. To overcome these challenges, we applied liquid chromatography–tandem mass spectrometry and measured > 1000 metabolites in the METSIM study including 10.197 men. We applied the machine learning approach together with conventional statistical methods to identify metabolites associated with all-cause mortality. The three independent machine learning methods (logistic regression, XGBoost, Welch’test) identified 32 metabolites having the most im-pactful associations with all-cause mortality (25 increasing and 7 decreasing the risk). From these metabolites 20 were novel and encompassed various metabolic pathways, impacting cardiovascular, renal, respiratory, endocrine, and central nervous systems. In the In the Cox regression analyses (hazard rations and their 95% confidence intervals) clinical and laboratory risk factors increased the risk of all-cause mortality significantly by 1.76 (1.60-1.94), 25 metabolites by 1.89 (1.68-2.12), and 25 metabolites and clinical and labor-atory risk factors 2.00 (1.81-2.22). In our study the main causes of death were cancers (28%) and cardiovascular diseases (25%). We did not identify any metabolites associated with cancer but found 13 metabolites associated with an increased risk of cardiovascular dis-eases. Our study reports several novel metabolites associated with an increased risk of mortality and shows that these 25 metabolites improved the prediction of all-cause mor-tality beyond and above clinical and laboratory measurements.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
