Integration of Metabolomics Data in Cardiovascular Research
Recent breakthroughs in analytical chemistry allow us to measure low concentrations of small molecules (metabolites) in blood and other bodily fluids. Liquid Chromatography Mass Spectrometry (LC-MS) is one such method. Other methods can detect thousands of metabolomic features with low concentrations that are undetectable. We design and analyse metabolomic studies of plasma samples from large human cohorts. In one such study, we collected LC-MS metabolomic data to analyze changes in the metabolome in response to treatment and, subsequently their association with cardiovascular disease outcomes1. In another study, we integrated metabolomics and genetics data to study mechanistic pathways that explain the protective properties of physical activity against cardiovascular outcomes2.
Metabolomics data is noisy and presents exciting methodological challenges, such as instrument drifts and batch effects. We developed a novel method of drift correction for metabolomics data called White Noise Normalization (WiNN). WiNN provides a robust way to remove batch effects and drifts. We are working on a manuscript. In another project, we developed an annotation algorithm for metabolomic compounds measured using Nuclear Magnetic Resonance. Our method takes advantage of classical Machine Learning and Quantum Computers. Quantum Computing is a novel, rapidly developing technology that relies on quantum phenomena to surpass conventional computers in processing power. The results of this work were published in Nature Machine Intelligence3.
 Demler, O.V., Liu, Y., Luttmann-Gibson, H., Watrous, J.D., Lagerborg, K.A., Dashti, H., Giulianini, F., Heath, M., Camargo Jr, C.A., Harris, W.S. and Wohlgemuth, J.G., 2020. One-year effects of Omega-3 treatment on fatty acids, oxylipins, and related bioactive lipids and their associations with clinical lipid and inflammatory biomarkers: findings from a substudy of the Vitamin D and Omega-3 Trial (VITAL). Metabolites, 10(11), p.431.
 Hoshi, R.A., Liu, Y., Luttmann-Gibson, H., Tiwari, S., Giulianini, F., Andres, A.M., Watrous, J.D., Cook, N.R., Costenbader, K.H., Okereke, O.I. and Ridker, P.M., 2022. Association of Physical Activity With Bioactive Lipids and Cardiovascular Events. Circulation Research, 131(4), pp.e84-e99.
 Sels, D., Dashti, H., Mora, S., Demler, O. and Demler, E., 2020. Quantum approximate Bayesian computation for NMR model inference. Nature machine intelligence, 2(7), pp.396-402.