Differences in terms of connectivity in each couple of networks (survivors vs deceased patients) were analyzed for each risk parameters according to the low ( Figure 4A,C,E,G) or high ( Figure 4B,D,F,H) risk of death at 2 years. (45)ДЇor each risk parameter, namely ACS, Killip, GRACE, and metabolomics NOESY RF classifications, serum metaboliteвЂ“metabolite association networks distinct for high and low risk of death were estimated using the PCLRC (23) algorithm considering the two subgroups of survivor and deceased patients for a total of 16 different networks.

(44) Moreover, creatinine has been shown to be inversely associated with incident CVD while mannose was found to be directly associated with incident CVD. It has been proposed that adopting interventions in the patient management able to reduce rises in creatinine should improve outcomes of AMI patients. Moreover, increase of serum creatinine levels has been related to adverse outcomes, including an increased risk of in-hospital mortality, in patients undergoing primary percutaneous coronary intervention for AMI. Both these pieces of evidence suggest a central metabolic role of creatinine, which may condition the prognosis of AMI patients. As already shown in our previous study, (21) creatinine presents statistically higher concentrations in patients who died with respect to survivors ( Figure 3C). Possible implications of mannose are further discussed in Section 3.2. Disruption of this correlation could indicate a possible impairment or re-wiring of mannose metabolism, which has been considered as a potential biomarker for the prediction of CVD. Interestingly, results in our data set confirm this observation only in survivor patients: creatinine correlates significantly with mannose as well as with leucine, isoleucine, and methionine only in survivor patients, whereas it does not have any significant correlation in deceased patients. *The generic risk factor R assumes values: Killip, STEMI, GRACE, and Metabolomics RF (see Methods for more details).Д¬orrelation between creatinine and mannose has been previously observed, (42) suggesting an association between insulin resistance and diabetic kidney disease, (43) which is a well-established risk factor for CVD as well as for overt diabetic kidney disease. Overall, the analysis resulted in 16 different networks as shown in Figure 3.

The same analysis was performed on the sample of patients in the low-risk group. Patients in the high-risk group are then evaluated for mortality at 2 years after AMI, and samples are split according to the survival status (alive vs deceased.) MetaboliteвЂ“metabolite association networks were inferred from the two groups (alive vs deceased) using the PCLRC algorithm (23) and compared to detect metabolites with differential connectivity with respect to the survival status within patients initially classified to be at high risk of mortality. Given a risk factor R (with R equal to Killip, STEMI, GRACE, and Metabolomics RF), patient samples are divided into two groups, high risk of mortality and low risk of mortality. The AMI event is recorded at time t 0, and blood samples, analyzed in this study and the previous one, were collected at time t 1 (24вЂ“48 h after percutaneous coronary intervention and overnight fasting). Graphical overview of the analysis design to investigate differences between metaboliteвЂ“metabolite association networks underlying different mortality risk factors. Our results demonstrate that the characterization of metaboliteвЂ“metabolite association networks is a promising and powerful tool to investigate AMI patients according to their outcomes at a molecular level.ДЇigure 2. We show significant differences in the connectivity patterns of several low-molecular-weight molecules, implying variations in the regulation of several metabolic pathways regarding branched-chain amino acids, alanine, creatinine, mannose, ketone bodies, and energetic metabolism. We investigated differences in metabolite connectivity of patients who survived, at 2 years, the AMI event, and we characterized metaboliteвЂ“metabolite association networks specific to high and low risks of death according to four different risk parameters, namely, acute coronary syndrome classification, Killip, Global Registry of Acute Coronary Events risk score, and metabolomics NOESY RF risk score. We present here the differential analysis of metaboliteвЂ“metabolite association networks constructed from an array of 24 serum metabolites identified and quantified via nuclear magnetic resonance spectroscopy in a cohort of 825 patients of which 123 died within 2 years from acute myocardial infarction (AMI).

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