RESUMEN
Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Using a validated and efficient ICP-MS/MS-based workflow, a total of 30 metallomic features were profiled in a study comprising 101 AMI patients and 66 age-matched healthy controls. The metallomic features include 12 essential elements (Ca, Co, Cu, Fe, K, Mg, Mn, Na, P, S, Se, Zn), 8 non-essential/toxic elements (Al, As, Ba, Cd, Cr, Ni, Rb, Sr, U, V), and 10 clinically relevant element-pair product/ratios (Ca/Mg, Ca×P, Cu/Se, Cu/Zn, Fe/Cu, P/Mg, Na/K, Zn/Se). Preliminary linear regression with feature selection confirmed smoking status as a predominant determinant for the non-essential/toxic elements, and revealed potential routes of action. Univariate assessments with adjustments for covariates revealed insights into the ambivalent relationships of Cu, Fe, and P with AMI, while also confirming cardioprotective associations of Se. Also, beyond their roles as risk factors, Cu and Se may be involved in the response mechanism in AMI onset/intervention, as demonstrated via longitudinal data analysis with 2 additional time-points (1-/6-month follow-up). Finally, based on both univariate tests and multivariate classification modelling, potentially more sensitive markers measured as element-pair ratios were identified (e.g., Cu/Se, Fe/Cu). Overall, metallomics-based biomarkers may have utility for AMI prediction.
Asunto(s)
Espectrometría de Masas en Tándem , Oligoelementos , Humanos , Modelos Lineales , Oligoelementos/análisisRESUMEN
Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. This work aims to investigate the translational potential of a multi-omics study (comprising metabolomics, lipidomics, glycomics, and metallomics) in revealing biomechanistic insights into AMI. Following the N-glycomics and metallomics studies performed by our group previously, untargeted metabolomic and lipidomic profiles were generated and analysed in this work via the use of a simultaneous metabolite/lipid extraction and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow. The workflow was applied to blood plasma samples from AMI cases (n = 101) and age-matched healthy controls (n = 66). The annotated metabolomic (number of features, n = 27) and lipidomic (n = 48) profiles, along with the glycomic (n = 37) and metallomic (n = 30) profiles of the same set of AMI and healthy samples were integrated and analysed. The integration method used here works by identifying a linear combination of maximally correlated features across the four omics datasets, via utilising both block-partial least squares-discriminant analysis (block-PLS-DA) based on sparse generalised canonical correlation analysis. Based on the multi-omics mapping of biomolecular interconnections, several postulations were derived. These include the potential roles of glycerophospholipids in N-glycan-modulated immunoregulatory effects, as well as the augmentation of the importance of Ca-ATPases in cardiovascular conditions, while also suggesting contributions of phosphatidylethanolamine in their functions. Moreover, it was shown that combining the four omics datasets synergistically enhanced the classifier performance in discriminating between AMI and healthy subjects. Fresh and intriguing insights into AMI, otherwise undetected via single-omics analysis, were revealed in this multi-omics study. Taken together, we provide evidence that a multi-omics strategy may synergistically reinforce and enhance our understanding of diseases.