RESUMO
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.
RESUMO
Acute myocardial infarction (AMI) is a leading cause of mortality and morbidity worldwide. Diagnostic challenges remain in this highly time-sensitive condition. Using capillary electrophoresis-laser-induced fluorescence, we analyzed the blood plasma N-glycan profile in a cohort study comprising 103 patients with AMI and 69 controls. Subsequently, the data generated was subjected to classification modeling to identify potential AMI biomarkers. An area under the Receiving Operating Characteristic curve (AUCROC) of 0.81 was obtained when discriminating AMI vs. non-MI patients. We postulate that the glycan profile involves a switch from a pro- to an anti-inflammatory state in the AMI pathophysiology. This was supported by significantly decreased levels in galactosylation, alongside increased levels in sialylation, afucosylation and GlcNAc bisection levels in the blood plasma of AMI patients. By substantiating the glycomics analysis with immunoglobulin G (IgG) protein measurements, robustness of the glycan-based classifiers was demonstrated. Changes in AMI-related IgG activities were also confirmed to be associated with alterations at the glycosylation level. Additionally, a glycan-biomarker panel derived from glycan features and current clinical biomarkers performed remarkably (AUCROC = 0.90, sensitivity = 0.579 at 5% false positive rate) when discriminating between patients with ST-segment elevation MI (n = 84) and non-ST-segment elevation MI (n = 19). Moreover, by applying the model trained using glycomics information, AMI and controls can still be discriminated at 1 and 6 months after baseline. Thus, glycomics biomarkers could potentially serve as a valuable complementary test to current diagnostic biomarkers. Additional research on their utility and associated biomechanisms via a large-scale study is recommended.
