Lukas Streese1, Anna Maria Springer2, Arne Deiseroth1, Justin Carrard1, Denis Infanger1, Christoph Schmaderer3, Arno Schmidt-Trucksäss1, Tobias Madl4, Henner Hanssen1. 1. Department of Sport, Exercise and Health, Medical Faculty, University of Basel, Basel, Switzerland. 2. Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria. 3. Department of Nephrology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany. 4. Gottfried Schatz Research Center for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University of Graz, Graz, Austria; BioTechMed Graz, Graz, Austria. Electronic address: tobias.madl@medunigraz.at.
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.
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.
Authors: Christoph Walter Haudum; Ewald Kolesnik; Barbara Obermayer-Pietsch; Albrecht Schmidt; Caterina Colantonio; Ines Mursic; Marion Url-Michitsch; Andreas Tomaschitz; Theresa Glantschnig; Barbara Hutz; Alice Lind; Natascha Schweighofer; Clemens Reiter; Klemens Ablasser; Markus Wallner; Norbert Joachim Tripolt; Elisabeth Pieske-Kraigher; Tobias Madl; Alexander Springer; Gerald Seidel; Andreas Wedrich; Andreas Zirlik; Thomas Krahn; Rudolf Stauber; Burkert Pieske; Thomas R Pieber; Nicolas Verheyen Journal: BMJ Open Date: 2022-04-07 Impact factor: 2.692