Baseline characteristics of the study patients.

*<p>By Fisher exact test.</p>§<p>by Wilcoxon Rank Sum Test.</p><p>ACE = angiotensin-converting enzyme; ARB = angiotensin II receptor blocker; CABG = coronary artery bypass graft surgery; CKD = chronic kidney disease; CRP = C-reactive protein; eGFR = estimated glomerular filtration rate; NA = not applicable; PCI = percutaneous coronary intervention.</p

Kaplan-Meier survival analyses during follow-up.

<p>Composite outcomes of cardiac death and myocardial infarction according to concentrations of plasma arginine (panel A), asymmetric dimethylarginine (ADMA; panel B), and symmetric dimethylarginine (SDMA; panel C), divided by median levels. P values by log-rank test are shown.</p

Relationships between NO synthesis inhibitors and renal function.

<p>Relationship between asymmetric (ADMA; upper panel) and symmetric (SDMA; lower panel) dimethylarginine plasma levels and estimated glomerular filtration rate (eGFR) in the study population.</p

Arginine, ADMA and SDMA plasma levels.

<p>Arginine (panel A), asymmetric dimethylarginine (ADMA; panel B), and symmetric dimethylarginine (SDMA; panel C) plasma levels (mean±SD values) in healthy subjects (n = 20), in controls with chronic kidney disease (CKD; n = 10), and in NSTEMI patients without (n = 71) and with (n = 33) CKD.</p

Cox regression analysis for the primary end point of the study (composite outcome of cardiac death and myocardial infarction).

<p>Hazard ratios (HR) in models 1–4 are adjusted for age, hemoglobin and left ventricular ejection fraction; HRs in models 5–7 are also mutually adjusted.</p><p>HRs for CKD are vs. no-CKD; for all other variables, HRs are for values above vs. below median.</p><p>ADMA = asymmetric dimethylarginine; CKD = chronic kidney disease; CI = confidence intervals; SDMA = symmetric dimethylarginine.</p

miRNA clusters efficiently differentiate SA and UA patients from Controls.

<p>Hierarchical clustering demonstrated that different miRNA “signatures” efficiently classify between matched controls (CTRLS) and CAD patients. The cluster composed by miR-1 and miR-126 and miR-485-3p can be used to correctly classify SA patients from controls with 90.2% (yellow bar) efficiency. Similarly the miR-1, miR-126 and miR-133a cluster can be used to correctly classify UA patients from controls with 87.2% (red bar) efficiency. No signatures of miRNAs could be found to efficiently discriminate SA from UA patients with an accuracy > 66% (miR-126 and miR-337-5p cluster, blue bar). </p

ROC curve analysis of CAD-miRNAs in Stable Angina patients and control subjects.

<p>The figure depicts calculated ROC curve and respective AUC values for miR-1, miR-126, and miR-485-3p, which exhibited good accuracy (AUC>0.85) in differentiating Stable Angina (SA) patients from matched controls (C).</p

ROC curve analysis of CAD-miRNAs in Unstable Angina patients and control subjects.

<p>The figure depicts calculated ROC curve and respective AUC values for miR-1, miR-126, and miR-133a, which exhibited good accuracy (AUC>0.85) in differentiating Unstable Angina (UA) patients from matched controls (C).</p

ROC curve analysis of CAD-miRNAs in Stable and Unstable Angina patients.

<p>None of the investigated miRNAs exhibited adequate accuracy in differentiating Stable (SA) from Unstable Angina (UA) patients: AUC values ranged between 0.404 (miR-145) and 0.678 (miR-337-5p).</p