Untargeted metabolites and vascular health in cardiovascular risk patients. (11th May 2021) - Streese, L; Springer, AM; Deiseroth, A; Carrard, J; Infanger, D; Schmaderer, C; Schmidt-Trucksaess, A; Madl, T; Hanssen, H
Abstract: Funding Acknowledgements: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Swiss National Science Foundation Background: The mechanistic pathways how classical cardiovascular (CV) risk factors affect macro- and microvascular health are not fully understood. The metabolome reflects a chemical fingerprint of the upstream epigenetic processes and seems to have the potential to provide insights into multiple molecular mechanisms underlying vascular health or vascular abnormalities that contribute to the pathogenesis of CV disease. Purpose: We aimed to investigate the associations of CV risk factors with the metabolic fingerprint and macro- as well as microvascular health in an untargeted metabolic approach in predefined CV risk groups of aged individuals. Methods: This cross-sectional study investigated the metabolic fingerprint, as well as the macro- and microvascular health from a well-characterized aged (50-80 years) cohort, based on the EXAMIN AGE study. One hundred and fifty-five participants with no to ≥three previously defined CV risk factors were analysed. Nuclear magnetic resonance spectroscopy was used to analyse the metabolic fingerprint. Carotid-femoral pulse wave velocity as well as retinal vessel diameters were analysed to quantify macro- and microvascular health. Results: The metabolic fingerprint gradually changed with increasing number of risk factors. Whereas the metabolic profile of individuals with a small number of risk factors were more homogenous, the profile became more heterogeneous with increasing numbers of risk factors. There was strong evidence for a negative association 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] with a higher CV risk profile. Tryptophan was positively associated with higher CV risk [0.31 (0.06 to 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. Conclusion: Metabolic profiling has the potential to improve and individualise CV risk stratification. It may help better understand some of the underlying metabolic mechanisms of atherosclerotic disease development. Our findings support future preventive strategies to target specific metabolites and thereby reduce CV risk and associated vascular disease. Metabolic profiling may have added value to predict CV disease development and potentially guide future treatment strategies.