ACUTE POSTPRANDIAL LIPIDOMIC RESPONSES TO AEROBIC, RESISTANCE, AND COMBINED EXERCISE IN NORMAL WEIGHT, OBESITY, AND TYPE 2 DIABETES
Description
Lipid metabolism is altered in obesity and type 2 diabetes (T2D) and is influenced by exercise modality, intensity, and duration. Although aerobic training (AT) and resistance training (RT) can modify circulating metabolite levels, the acute lipidomic response to a single bout of moderate-intensity exercise remains unclear, particularly in individuals with obesity and T2D. We used untargeted direct infusion high-resolution mass spectrometry (DI-HRMS) lipidomics to characterise acute lipidomic responses to AT, RT, and combined training (CT) in participants with normal weight (NW), obesity (OB), and T2D under standardised postprandial conditions. In a randomised crossover design, 57 participants (18 NW, 22 OB, 17 T2D) completed three supervised exercise sessions, with blood sampled before exercise, immediately after exercise, and at 30 and 60 min of recovery. NW participants showed lipid changes that were generally smaller in effect size and largely resolved within 60 min, whereas OB and T2D showed a greater number of altered species and, in several lipid classes, larger effect sizes, including prolonged elevations in triacylglycerols, diacylglycerols (DAG), and ceramides (Cer), patterns previously linked to insulin resistance (IR). RT and CT were associated with a broader, and in several cases larger-magnitude, set of changes in lipid species linked in prior literature to inflammation and IR (including DAG-, ganglioside GM3-, and Cer-related lipids). Hierarchical clustering revealed distinct, phenotype- and modality-specific patterns of lipid class enrichment. These findings indicate that both the number and magnitude of acute circulating lipid changes after exercise differ by metabolic health status and training modality, generating specific hypotheses about modality-dependent lipid pathways for future mechanistic and longitudinal study.
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Steps to reproduce
All samples were randomized for data acquisition and directly infused in a HESI LTQ Orbitrap-MS Discovery (Thermo Scientific, Bremen, Germany) with 30 000 FWHM of mass resolution. Direct injection of the sample was carried out in positive mode, and acquisitions were performed in quintuplicate within the mass range of m/z 200 to 1200. The flow rate utilized was 10 μL/min, whilst the temperature was set at 280°C. The spray voltage was set at 5 kV, and the sheath gas was maintained at 10 arbitrary units. For spectra acquisition and visualisation, the software employed was XCalibur 3.0.63 (Thermo Scientific, Bremen, Germany). Fragmentation of selected markers after statistical analysis was achieved by an MS/MS (MS2) experiment directly injected into the Thermo Q Exactive mass spectrometer (Thermo Scientific, Bremen, Germany). An inclusion list of markers was inserted, followed by the preparation of a sample pool for each group after the addition of 10 μL of sample and 990 μL of methanol. The pool was then centrifuged at 1088 g for 5 min at 5°C. Then 250 μL of the supernatant and 250 μL of water containing 0.1% (v/v) formic acid were transferred to an Eppendorf tube. A full scan of each spectrum was collected, followed by two experiments: the first performed with low collision energy at 20, 30, and 40 eV, and the second with high collision energy at 40, 50, and 60 eV. The flow rate was set to 15 μL/min, the temperature to 320°C, the spray voltage to 4 kV, and the sheath gas to 12 arbitrary units. A resolution of 17,500 in positive mode was used to acquire spectra in the mass range of m/z 200 to 1,200. Spectra were acquired and processed using XCalibur 3.0.63 (Thermo Scientific, Bremen, Germany). The DIMS data was processed through the Galaxy-M workflow in R. Once the table of peak intensities and mass-to-charge ratio (m/z) values was obtained, the data underwent a filtering process prior to statistical analyses. Initially, variables with a value of zero in more than 20% of samples were removed, as well as those with a coefficient of variation (CV) greater than 20% in technical replicates. The missing data were then imputed using a constant value of half the minimum (HM). The data were subsequently transformed using the logarithm to the base 10 (log10) function to decrease the variation in peak intensities among samples and then scaled using the Pareto-scaling method to reduce data variability between samples.
Institutions
- University of AberdeenAberdeen, Aberdeen