Nitric oxide metabolites: associations with cardiovascular biomarkers and clinical parameters in patients with HFpEF

Abstract

Aims: Heart failure with preserved ejection fraction (HFpEF) is one of the most rapidly growing cardiovascular health burden worldwide, but there is still a lack of understanding about the HFpEF pathophysiology. The nitric oxide (NO) signalling pathway has been identified as a potential key element. The aim of our study was to investigate markers of NO metabolism [l-arginine (l-Arg), homoarginine (hArg), and asymmetric and symmetric dimethylarginine (ADMA and SDMA)], additional biomarkers [N-terminal pro-B-type natriuretic peptide (NT-proBNP), endothelin-1 (ET-1), mid-regional pro-adrenomedullin (MR-proADM), copeptin, and high-sensitivity C-reactive protein (hsCRP)], and the endothelial function in an integrated approach focusing on associations with clinical characteristics in patients with HFpEF.

Methods and results: Seventy-three patients, prospectively enrolled in the 'German HFpEF Registry', were analysed. Inclusion criteria were left ventricular ejection fraction (LVEF) ≥ 50%; New York Heart Association functional class ≥ II; elevated levels of NT-proBNP > 125 pg/mL; and at least one additional criterion for structural heart disease or diastolic dysfunction. All patients underwent transthoracic echocardiography, cardiopulmonary exercise testing, and pulse amplitude tonometry (EndoPAT™). Patients were categorized in two groups based on their retrospectively calculated HFA-PEFF score. Serum concentrations of l-Arg, hArg, ADMA, SDMA, NT-proBNP, ET-1, MR-proADM, copeptin, and hsCRP were determined. Patients had a median age of 74 years, 47% were female, and median LVEF was 57%. Fifty-two patients (71%) had an HFA-PEFF score ≥ 5 (definitive HFpEF), and 21 patients (29%) a score of 3 to 4 (risk for HFpEF). Overall biomarker concentrations were 126 ± 32 μmol/L for l-Arg, 1.67 ± 0.55 μmol/L for hArg, 0.74 (0.60;0.85) μmol/L for SDMA, and 0.61 ± 0.10 μmol/L for ADMA. The median reactive hyperaemia index (RHI) was 1.55 (1.38;1.87). SDMA correlated with NT-proBNP (r = 0.291; P = 0.013), ET-1 (r = 0.233; P = 0.047), and copeptin (r = 0.381; P = 0.001). ADMA correlated with ET-1 (r = 0.250; P = 0.033) and hsCRP (r = 0.303; P = 0.009). SDMA was associated with the left atrial volume index (β = 0.332; P = 0.004), also after adjustment for age, sex, and comorbidities. Biomarkers were non-associated with the RHI. A principal component analysis revealed two contrary clusters of biomarkers.

Conclusions: Our findings suggest an impaired NO metabolism as one possible key pathogenic determinant in at least a subgroup of patients with HFpEF. We argue for further evaluation of NO-based therapies. Upcoming studies should clarify whether subgroups of HFpEF patients can take more benefit from therapies that are targeting NO metabolism and pathway.

Keywords: Arginine; Endothelial dysfunction; HFpEF; Nitric oxide; SDMA.

Figure 1

Synthesis and interactions of methylated arginines, nitric oxide, and nitric oxide signalling pathway. Symmetric dimethylarginine (SDMA) and asymmetric dimethylarginine (ADMA) are formed within post‐translational modifications of protein‐bound arginine residues by protein arginine N‐methyltransferases (PRMT). ADMA is a direct inhibitor of nitric oxide synthases (NOS). NOS synthesize nitric oxide (NO) from l‐arginine. ADMA and SDMA can cause a reduced l‐arginine uptake through a competitive transport via the cationic amino acid transporter system and thereby reduce substrate availability for NO synthesis. Homoarginine (hArg) is formed from l‐arginine and lysine by the l‐arginine:glycine amidinotransferase (AGAT). Homoarginine can inhibit the enzyme arginase and thereby augment l‐arginine pools. A lower NO bioavailability leads to a suppression of the NO–soluble guanylate cyclase (sGC)–cyclic guanosine monophosphate (cGMP)–protein kinase G (PKG) axis. Rme1, monomethylated arginine residues; Rme2a, asymmetric dimethylated arginine residues; Rme2s, symmetric dimethylated arginine residues.

Figure 2

Heatmap for the simple linear regression analysis between biomarkers and clinical characteristics (Model 1). Scale for the regression coefficient β from 0.5 to −0.5; in cases of significance (P‐value < 0.05), β is given in the corresponding field. ADMA, asymmetric dimethylarginine; E/e′ mean, left ventricular filling index; e′ mean, mean early diastolic mitral annulus velocity; ET‐1, endothelin‐1; hArg, homoarginine; hsCRP, high‐sensitivity C‐reactive protein; l‐Arg, l‐arginine; LAVI, left atrial volume index; LVEF, left ventricular ejection fraction; LVMI, left ventricular mass index; MR‐proADM, mid‐regional pro‐adrenomedullin; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; peakVO₂, peak oxygen uptake; RHI, reactive hyperaemia index; SDMA, symmetric dimethylarginine; VE/VCO₂ Slope, exercise ventilatory efficiency.

Figure 3

Principal component analysis from the investigated biomarkers and the reactive hyperaemia index (RHI). Principal component 1 (PC1) and principal component 2 (PC2) explain 23.46% and 18.68% of the variance. ADMA, asymmetric dimethylarginine; ET‐1, endothelin‐1; hArg, homoarginine; hsCRP, high‐sensitivity C‐reactive protein; l‐Arg, l‐arginine; MR‐proADM, mid‐regional pro‐adrenomedullin; NT‐proBNP, N‐terminal pro‐brain natriuretic peptide; SDMA, symmetric dimethylarginine.