Literature DB >> 11596905

Metabolites of the L-arginine-NO pathway in patients with left-to-right shunt.

M Gorenflo1, C Zheng, A Pöge, M Bettendorf, E Werle, W Fiehn, H E Ulmer.   

Abstract

OBJECTIVES: The endogenous production of metabolites of the L-arginine-NO pathway has been found to be altered in patients with left-to-right shunt and pulmonary hypertension. The objective of this study was to analyze the influence of age and of the magnitude of the left-to-right shunt on plasma levels of L-arginine, cyclic guanosine monophosphate (cGMP), nitrite and nitrate in children and young adults presenting with left-to-right shunt.
METHODS: Twenty-nine patients with ventricular septal defect (n=18), atrial septal defect (n=6) and atrioventricular canal (n=5) were assigned to group I when the ratio of pulmonary to systemic blood flow (Qp/Qs) was less than 1.5 (n=10) and to group II when Qp/Qs > or = 1.5 (n=19). At cardiac catheterization blood samples were taken from the pulmonary vein or left ventricle. In 33 controls peripheral venous blood was obtained. cGMP levels were determined by radioimmunoassay, L-arginine, nitrite and nitrate by high performance liquid chromatography (HPLC).
RESULTS: L-arginine plasma levels were lower in group II than in controls (51.7 [23.3-82.2] versus 60.5 [32.4-85.9] pmol/l; p < 0.05 by KRUSKAL-WALLIS). Age did not influence the L-arginine plasma levels (p = 0.30). cGMP levels depended on age (p<0.01) and mean pulmonary artery pressure (p <0.01) but not on high pulmonary blood flow (p=0.85; ANOVA). Plasma nitrite and nitrate were not different in both groups and when compared with controls (nitrite: 26.0 [23.5-31.0] micromol/l; nitrate: 26.8 [24.0-32.0] micromol/l).
CONCLUSIONS: Age and pulmonary artery pressure exert important effects on plasma cGMP. Measurement of nitrite and nitrate in plasma alone may not reflect the endogenous NO production. Future studies should evaluate the role of plasma levels of L-arginine in patients with high pulmonary blood flow undergoing repair of their defect.

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Year:  2001        PMID: 11596905

Source DB:  PubMed          Journal:  Clin Lab        ISSN: 1433-6510            Impact factor:   1.138


  7 in total

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7.  Identifying Patients with Atrioventricular Septal Defect in Down Syndrome Populations by Using Self-Normalizing Neural Networks and Feature Selection.

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  7 in total

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