| Literature DB >> 33806196 |
Rosamaria Militello1, Simone Luti1, Matteo Parri1, Riccardo Marzocchini1, Riccardo Soldaini2, Alessandra Modesti1,3, Pietro Amedeo Modesti2.
Abstract
BACKGROUND: Most studies on oxidative stress markers and antioxidant levels have been conducted in male athletes, although female participation in sport has increased rapidly in the past few decades. In particular, it could be important to assess oxidative stress markers in relation to the training load because the anaerobic path becomes predominant in high-intensity actions.Entities:
Keywords: adiponectin; exercise training; female athletes; metabolomics; oxidative stress
Year: 2021 PMID: 33806196 PMCID: PMC8066547 DOI: 10.3390/healthcare9040368
Source DB: PubMed Journal: Healthcare (Basel) ISSN: 2227-9032
Participants’ characteristics.
| Characteristics | Mean (SD) | |||
|---|---|---|---|---|
| All the Study Participants | Basketball | Control |
| |
| Age (year) | 26 ± 4.13 | 25.13 ± 5.46 | 26.88 ± 2.32 | 0.4157 |
| Weight (kg) | 63.7 ± 10.4 | 68.7 ± 11.9 | 58.7 ± 5.8 | 0.0509 |
| Height (cm) | 169.5 ± 9.4 | 175.6 ± 8.3 | 163.4 ± 5.9 | 0.0043 ** |
| BMI (kg/m2) | 22.07 ± 2.12 | 22.12 ± 2.05 | 22.03 ± 2.32 | 0.9357 |
** Statistically significant difference p < 0.01. SD = standard deviation, BMI = body mass index.
Figure 1Plasma oxidative stress, adiponectin and cortisol determination. (A) The antioxidant capacity was evaluated using the BAP Test (Biological Antioxidant Potential), and (B) the levels of reactive oxygen metabolites using the d-ROM test (diacron-Reactive Oxygen Metabolite) by a free radical analyzer system (FREE Carpe Diem, DIACRON INTERNATIONAL s.r.l). (C) A representative immunoblot of adiponectin is shown together with the corresponding Coomassie-stained PVDF membrane. (D) Cortisol levels measured using enzyme-linked immunosorbent assay kit. All the measurements (n = 10 Control and 10 basketball) were performed in triplicate and are reported in the histograms as mean ± SD. The statistical analysis was carried out by two-tailed t-test using Graphpad Prism 8 (** p < 0.01; **** p < 0.0001).
Figure 2Plasma metabolomic profile using gas chromatography–mass spectrometry (GC-MS). Histogram representation of plasma metabolites whose relative abundance is statistically different between basketball athletes and controls (p value < 0.05).
List of metabolites with significant differences between female basketball players and controls, identified by gas chromatography–mass spectrometry (GC-MS) analysis.
| Metabolite | Metabolite Name | Retention Time | CAS Number * | KEGG ID ° |
|---|---|---|---|---|
| 1 | 2-hydroxybutyric acid | (7.852) | 565-70-8 | C05984 |
| 2 | L-lactic acid | (6.851) | 79-33-4 | C00186 |
| 3 | L-asparagine | (14.984) | 70-47-3 | C00152 |
| 4 | L-glutamic acid | (14.398) | 56-86-0 | C00025 |
| 5 | L-ornithine | (16.632) | 70-26-8 | C00077 |
| 6 | Urea | (9.599) | 57-13-6 | C00086 |
* Chemical Abstract Service number, ° KEGG identifier (https://www.genome.jp/kegg/ accessed on 5 November 2020).
Figure 3Selected interaction networks obtained using plasma metabolites identified by GC-MS showing a statistically significant increase/decrease in basketball players in comparison with controls (see Table 2). The network analysis was carried out by using the MetScape 3 App for Cytoscape (http://metscape.med.umich.edu accessed on 10 December 2020). (A) Butanoate metabolism, (B) vitamin B9 (folate) metabolism, (C) glycolysis and gluconeogenesis, (D) purine metabolism, (E) histidine metabolism, (F) urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine.