Metabolomic fingerprint of coronary blood in STEMI patients depends on the ischemic time and inflammatory state

In this study we investigated whether the metabolomic analysis could identify a specific fingerprint of coronary blood collected during primary PCI in STEMI patients. Fifteen samples was subjected to metabolomic analysis. Subsequently, the study population was divided into two groups according to the peripheral blood neutrophil-to-lymphocyte ratio (NLR), a marker of the systemic inflammatory response. Regression analysis was then applied separately to the two NLR groups. A partial least square (PLS) regression identified the most significant involved metabolites and the PLS-class analysis revealed a significant correlation between the metabolic profile and the total ischemic time only in patients with an NLR > 5.77.

Metabolomics analysis. Two patients were excluded from the final analysis because of the use of a different contrast agent.
After an unsupervised Principal Component Analysys (PCA) to visualize possible metabolic differences among the groups and to identify potential outliers, we performed a partial least square (PLS) regression ( Fig. 1) using total coronary ischemic time (the time from symptom onset until reperfusion) as the Y variable, achieving a good capacity for fitting and prediction (R 2 x = 0.499; R 2 y = 0.804; Q 2 = 0.500). PLS regression is a supervised extension of PCA (Principal Component Analysis) used to maximize the correlation between two sets of variables, e.g. spectral intensity values (X matrix) and ischemic time (Y matrix), so that the response variable Y can be predicted from X. The estimated predictive power of the model is expressed by R 2 Y and Q 2 Y, which represent the fraction of the variation of Y-variable and the predicted fraction of the variation of Y-variable, respectively. A good prediction model is achieved when Q 2 > 0.5.
The variable interdependent parameter (VIP) analysis allowed identification of the metabolites more important in determining the culprit coronary blood fingerprint by using S-line loadings plot, which shows an increase in choline, phosphocholine, orthinitine and myo-inositol concentrations as the ischemic time increases, while lysine and 2-phosphoglycerate levels decrease.
To evaluate the effects of inflammation in modulating the endothelial response, we divided the population into two groups on the basis of the NLR, using a cut-off of 5.77 4 , into a high NLR group (N = 6) and a medium-low NLR group (N = 7). The two NLR groups showed statistically significant differences with regard to NLR (8.09 ± 1.83 vs 3.86 ± 1.62; p = 0.01) and to anthropometrics data (Height: 165 ± 4.6 cm vs 173 ± 4.86 cm, p = 0.01; Weight: 66.4 ± 11.9 Kg vs 86.6 ± 8.1 Kg, p < 0.01; BMI: Kg/m 2 24.1 ± 4.0 vs 28.6 ± 1.6 kg/m 2 , p < 0.01), with a negative correlation between NLR and BMI as highlighted in previous findings 11 .
Next, we applied a PLS regression analysis separately for each group. The correlation between the metabolic pool and coronary ischemic time was not significant for the group with an NLR < 5.77. In contrast, the high NLR group showed highly significant correlations between the metabolic pool and coronary ischemic time (R 2 x = 0.624; R 2 y = 0.968; Q 2 = 0.843; Fig. 2).

Discussion
The present study was designed to assess whether coronary blood fingerprint in the culprit vessels change on the basis of ischemic time and inflammatory state. Our results show that (1) coronary blood fingerprint in STEMI patients are constituted by choline, phosphocholine, myo-inositol, lysine, ornithine, and 2-phosphoglycerate and (2) a highly significant correlation was demonstrated between the metabolic pool and coronary ischemic time in patients with a high NLR, but not in those with an NLR < 5.77.
Alterations in endothelial function are the basic mechanism responsible for the development of atherosclerosis. These alterations play a key role in plaque progression and the development of plaque rupture, plaque erosion, and subsequent thrombus formation 2 . The metabolomic fingerprint detected in the coronary blood of our STEMI patients is characterized by a limited number of metabolites.
Choline, the immediate precursor of betaine, serves as a methyl group donor in the conversion of homocysteine to methionine and is involved in phospholipid synthesis 12 . In fact, choline is also a precursor of phosphocholine, another key metabolite in the metabolomic fingerprint, and an intermediate in the synthesis of phosphatidylcholine and of its derivates. Phosphocholine is the main water-soluble metabolite secreted by monocytes 13 and is also the main bioactive lipid component of oxidized low density lipoprotein (LDL) 14   Myo-inositol is an essential component of the plasma membrane that, when phosphorylated, acts as a second messenger. Myo-inositol levels are increased in mice fed a high cholesterol diet 17 and in the platelets of patients with hypercholesterolemia 18 , suggesting its important role in arterial thrombosis 19 . Moreover, myo-inositol seemed to be part of the metabolic fingerprint of patients with acute coronary syndromes 20 .
Lysine is an essential amino acid that can negatively modulate the synthesis of nitric oxide (NO) 21 . In addition, ornithine is the product of the degradation of arginine by arginase. This enzyme also negatively impacts endothelial metabolism, since it degrades arginine, the precursor of NO. Higher activity of arginase II increases ornithine and reduces the production of NO, such as the inflow of lysine in endothelial cells. Higher arginase II activity has been demonstrated in the endothelial cells of patients with primary pulmonary hypertension 22 and in the coronary endothelium of diabetic rats 23 .
2-phosphoglycerate, produced during the glycolytic process, has been shown to increase in cultured endothelial cells after the addition of LDL 24 and is produced by activated macrophages in atherosclerotic lesions 25 , decreased as the ischemic time increases, suggesting a progressive modulation in the circulating and endothelial factors involved in the STEMI pathophysiology and a progressive impairment in energetic metabolism.
In summary, our data suggest that the systemic inflammatory state modulates the endothelial response to the occlusion, and the change of the metabolite pool becomes more intense over time. Our findings are in agreement with prior studies showing a lower patency of the infarcted artery at 60 and 90 minutes after thrombolysis in patients with neutrophilia with a relative lymphopenia 26 and with studies showing a strong correlation between a high NLR and the phenomenon of no-reflow after primary PCI 27 . The common basis of these findings is endothelial dysfunction induced by ischemic noxa and aggravated by an imbalanced immune response, as evidenced by a high NLR.

Conclusion
Our results indicate that (1) study of the coronary metabolomic fingerprint can allow a deeper understanding of STEMI pathophysiology, identifying involved biological pathways and (2) systemic inflammation is able to modulate the endothelial response to ischemic noxa.

Study
Limitations. The small number of samples included in this study is certainly a limitation, such as the lack of an independent validation cohort and our findings require validation in larger studies. Nevertheless, the statistical significance and the viability of the mechanisms suggested by the identified metabolites support the reliability of these preliminary results.
These preliminary data do not pretend to be exhaustive or conclusive, but want to stimulate further research in this direction, such as the relationship between metabolic disarrangement and no-reflow/slow-flow phenomenon or systolic function recovery after PCI.

Methods
Patients. Fifteen subjects ≥18 years of age with evidence of an STEMI according to the third universal definition of myocardial infarction 28 were enrolled in the study.
The study was approved by the Azienda Ospedaliero-Universitaria di Cagliari Ethics Committee and was carried out in accordance with the Declaration of Helsinki and with approved guidelines, particularly all patients were treated according to the ESC STEMI guidelines 1 . Patients were informed regarding the purpose and methodology of the study, their consent was obtained and a written confirm has been collected prior to enrolment. Exclusion criteria included previous coronary events of any type; significant (more than moderate) valvulopathy; cardiomyopathy; chronic inflammatory disease and/or cancer; and thromboembolic events or major surgery in the previous six months. Table 1 summarizes the clinical data of the study patients.
Blood samples during coronary angiography. All patients underwent coronary artery blood sampling near the culprit lesion using a micro-catheter inserted along a guide wire passed through the lesion. Blood samples were collected in heparinised tubes, immediately centrifuged at 4000 rpm for 15 min, aliquoted (800 μL) in cuvettes, and stored at −80 °C until metabolomics analysis.

Metabolomics analysis.
The extraction of water-soluble metabolites from plasma samples was performed on the basis of the Folch, Lees and Sloane-Stanley procedure and has been already described in previous papers of our group 29 . 400 μL of plasma were dissolved in 1.  1 H-NMR spectra were acquired with a spectral width of 6000 Hz, a 90° pulse, an acquisition time of 2 s, a relaxation delay of 2 s, and 256 scans. A presaturation sequence was used to suppress the residual H 2 O signal with low power radiofrequency irradiation for 2 s. 1 H-NMR spectra were imported into an ACDlab Processor Academic Edition (version 12.01, 2010, Advanced Chemistry Development, Toronto, Canada) and pre-processed with line broadening of 0.1 Hz, zero-filled to 64 K, prior to Fourier transformation. Spectra were manually phased and baseline corrected and chemical shifts referenced internally to TSP at δ = 0.0 ppm. The 1 H-NMR spectra were reduced into consecutive integrated spectral regions (bins) of 0.01 ppm. Were excluded from the analysis the spectral regions between 1.87-1.99 ppm, 2.36-2.50 ppm, 3.32, 3.55 ppm, 3.68-4.14 ppm and 4.74-4.49 in order to remove the resonances of contrast agent and the effect of variation in the pre-saturation of the residual water resonance Finally, the spectral data set was normalized to the total area and imported into the SIMCA-P + program (Version 14.0, Umetrics, Umea, Sweden). All imported data were Pareto scaled for multivariate analysis. Two Univariate statistical analysis. Continuous variables were compared with a non-paired t-test, and categorical variables were compared with Fisher's exact test. A two-tailed p value < 0.05 was considered statistically significant.