Metabolic profiling links cardiovascular risk and vascular end organ damage. (August 2021)

• Record Type: Journal Article
• Authors: Streese, Lukas; Springer, Anna Maria; Deiseroth, Arne; Carrard, Justin; Infanger, Denis; Schmaderer, Christoph; Schmidt-Trucksäss, Arno; Madl, Tobias; Hanssen, Henner
• Is Part Of: Atherosclerosis. Volume 331(2021)
• Page Start: 45
• Page End: 53
• Publication Date: 2021-08
• Subjects: Cardiovascular risk -- Metabolomics -- Ageing -- Vascular function -- Screening; Arteriosclerosis -- Periodicals; Electronic journals; 616.136
• DOI: 10.1016/j.atherosclerosis.2021.07.005
• Languages: English
• ISSNs: 0021-9150

Abstract

Abstract: Background and aims: An untargeted metabolomics approach allows for a better understanding and identification of new candidate metabolites involved in the etiology of vascular disease. We aimed to investigate the associations of cardiovascular (CV) risk factors with the metabolic fingerprint and macro- and microvascular health in an untargeted metabolomic approach in predefined CV risk groups of aged individuals. Methods: The metabolic fingerprint and the macro- and microvascular health from 155 well-characterized aged (50–80 years) individuals, based on the EXAMIN AGE study, were analysed. Nuclear magnetic resonance spectroscopy was used to analyse the metabolic fingerprint. Carotid-femoral pulse wave velocity and retinal vessel diameters were assessed to quantify macro- and microvascular health. Results: The metabolic fingerprint became more heterogeneous with an increasing number of risk factors. There was strong evidence for higher levels of glutamine [estimate (95% CI): −14.54 (−17.81 to −11.27), p < 0.001], glycine [−5.84 (−7.88 to −3.79), p < 0.001], histidine [−0.73 (−0.96 to −0.50), p < 0.001], and acetate [−1.68 (−2.91 to −0.46), p = 0.007] to be associated with a lower CV risk profile. Tryptophan, however, was positively associated with higher CV risk [0.31 (0.06–0.56), p = 0.015]. The combination of a priori defined CV risk factors explained up to 45.4% of the metabolic variation. The metabolic fingerprint explained 20% of macro- and 23% of microvascular variation. Conclusions: Metabolic profiling has the potential to improve CV risk stratification by identifying new underlying metabolic pathways associated with atherosclerotic disease development, from cardiovascular risk to metabolites, to vascular end organ damage. Graphical abstract: Image 1 Highlights: Untargeted metabolomics identifies new metabolites associated with vascular disease. The selection of metabolites explains up to 23% of variation in vascular function. Metabolic profiling has the potential to improve cardiovascular risk stratification. New metabolic pathways are postulated from cardiovascular risk to end organ damage.