Metabolic Fingerprint of Cardiorespiratory Fitness

Julia Bork (Greifswald)1, M. R. P. Markus (Greifswald)1, R. Ewert (Greifswald)1, M. Nauck (Greifswald)2, N. Friedrich (Greifswald)2, H. Völzke (Greifswald)3, M. Dörr (Greifswald)1, M. Bahls (Greifswald)1

1Universitätsmedizin Greifswald Klinik und Poliklinik für Innere Medizin B Greifswald, Deutschland; 2Universitätsmedizin Greifswald Institut für klinische Chemie u. Laboratoriumsmedizin Greifswald, Deutschland; 3Universitätsmedizin Greifswald Institut für Community Medicine Greifswald, Deutschland


High cardiorespiratory fitness (CRF) is associated with a lower risk for all-cause mortality and cardiovascular diseases (CVD). Metabolomics provide deep insight in cellular processes and are influenced by multiple factors. We aimed to identify a metabolic fingerprint related to CRF. 

Data from two independent cohorts from the Study of Health in Pomerania (SHIP) was used. CRF was measured by symptom-limited cardio-pulmonary exercise testing (CPET) on a cycle ergometer according to a modified Jones protocol. After testing the exclusion criteria n=1839 individuals were included. Data was log2 transformed. Linear regression models were adjusted for age, smoking and height and stratified by sex.

In the SHIP-START-2 cohort 132 associations with CRF were observed. Out of those, 99 metabolites had positive associations with CRF. These included lipids (n=90, especially glycerophospholipids (n=86)) and amino acids (n=9). A total of 33 metabolites had inverse associations with CRF. Including 16 acylcarnitines, three glycerophospholipids and three sphingolipids. Eight amino acids (including amino acids related (n=1)), two energy derivates and one biogenic amine showed inverse associations either. In the SHIP-TREND-0 cohort we identified 47 positive associations with CRF, mainly in lipids (n=31: including n=26 glycerophospholipids, three sphingolipids and two other lipids). Moreover, five amino acids (including two amino acids related), two xenobiotics, and one energy derivate had positive associations with CRF. An amount of 71 metabolites had inverse associations with CRF. The majority of those were lipids (n=19) including eight acylcarnitines, two glycerophospholipids, one sphingolipid and seven other lipids. Furthermore, 17 amino acids (amino acids related n=1), 10 peptides, four nucleotides, two carbohydrates such as two cofactors/vitamins showed inverse associations with CRF. A total of 17 metabolites had inverse and eight had positive associations to CRF with unknown functions.

The results show, that CRF has an impact on the metabolic fingerprint, which is notably represented by alterations in the lipid profile. Most of the associations were seen in the big group of glycerophospholipids. Moreover, we found reduced levels of branched chained amino acids (BCAA) in people with high CRF, probably related to a health promoting effect in terms of insulin resistance (IR). After a closer look at the subgroups, we saw that the metabolic profile varied by sex and that the female subgroup showed stronger associations. In the future more research needs to be done to understand the involved metabolic pathways and their health promoting effects related to high CRF.
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