1H Nuclear Magnetic Resonance Spectroscopy Reveals Changes in Metabolic Phenotype Associated with Disease Stage in Patients with Chronic Venous Disease.

OBJECTIVE

Chronic venous disease (CVD) is a condition presenting a great burden to patients and society, with poorly characterised pathophysiology. Metabolic phenotyping can elucidate mechanisms of disease and identify candidate biomarkers. The aim of this study was to determine differences in the metabolic signature between symptomatic patients with CVD and asymptomatic volunteers using proton nuclear magnetic resonance spectroscopy (1H-NMR).

METHODS

This was a prospective case control study of consecutive patients with symptomatic CVD and asymptomatic volunteers recruited from a single centre. Participants underwent clinical assessment, venous duplex ultrasound, and blood and urine sampling. Disease stage was defined according to the Clinical-Etiology-Anatomy-Pathophysiology (CEAP) classification. 1H-NMR experiments were performed, with data analysed via multivariable statistical techniques.

RESULTS

A total of 622 participants were recruited, including 517 symptomatic patients with CVD (telangiectasia [C1] 0.6%, varicose veins [C2] 48.5%, swelling [C3] 12.0%, skin changes [C4] 27.7%, healed or active ulceration [C5/6] 11.2%) and 105 asymptomatic participants (no disease [C0] 69.5%, telangiectasia [C1] 29.6%). Multivariable analysis revealed differences between the metabolic profile of the symptomatic CVD and asymptomatic groups, and between CEAP clinical classes in the CVD group. Serum aromatic amino acids positively correlated with increasing CEAP clinical class (p < .001). Urinary formate, creatinine, glycine, citrate, succinate, pyruvate, and 2-hydroxyisobutyrate negatively correlated with increasing CEAP clinical class (p < .001). These metabolites are involved in the tricarboxylic acid cycle, hypoxia inducible factor pathway, and one carbon metabolism.

CONCLUSION

Untargeted biofluid analysis via 1H-NMR has detected metabolites associated with the presence and severity of CVD, highlighting biological pathways of relevance and providing candidate biomarkers to explore in future research.