A pilot study comparing the metabolic profiles of elite-level athletes from different sporting disciplines

Study design

Study participants included in this study were 191 consented elite athletes (171 males and 20 females) from different sports disciplines who participated in national or international sports events and tested negative for doping substances at anti-doping laboratories in Qatar and Italy. Spare serum samples collected for anti-doping human growth hormone tests were used for metabolomics studies. Briefly, samples were either collected IN or OUT of competition. Once collected, samples were delivered to the anti-doping labs within 36 h under cooling conditions. Once received, samples were immediately centrifuged to separate the serum and then stored at − 20 °C until analysis. Only information related to type of sport and athlete’s gender were available to researches. All other information was not available, including age, ethnicity, or the time of recruitment (pre- or post-exercise), due to the strict anonymization process undertaken by anti-doping laboratories and those dictated by study’s ethics. This study was performed in line with the World Medical Association Declaration of Helsinki. All protocols were approved by the Institutional Research Board of anti-doping lab Qatar (F2014000009). Sport types can be dichotomized into low, moderate, and high dynamic or static groups based on associated peak dynamic (VO₂) and peak static (MVC) components achieved during competition, as suggested previously [21]. In our study, few athletes belonged to low levels of endurance and power, therefore were merged with the corresponding moderate class of endurance and power, respectively (Table 1A). For statistical analysis, endurance and power athletes were each represented by a categorical variable with two levels (high and moderate, Table 1B). Table 1 further lists the number of participants per sport type in each class and their genders.

(A) Distribution of elite athletes in various categories based on sport type-associated peak dynamic (maximal oxygen uptake percentage; VO₂) and peak static (maximal voluntary muscle contraction percentage; MVC) components achieved during competition as described previously [21]. The number and gender (M for males and F for females) of participants in each group are also indicated. (B) Categorization of sport types into classes based on power alone regardless of endurance (left) and similarly for endurance alone ignoring power (right); these classes were used in the statistical analysis

Metabolomics

Statistical analysis of metabolomics data

Multivariate analysis of athlete metabolomics data

Non-targeted metabolomics was applied to determine the metabolic signatures of 191 elite athletes. PCA components 1 and 2 (PC1 and PC2) captured together 25% of the variance in the data. PC1 revealed two clusters of samples, which were not explained by gender, sport types, or classes (Fig. 1a). Examination of the loading plot in Fig. 1b revealed a concentration of hemoglobin and heme metabolites at the positive end of PC1. Furthermore, a t test comparing the hemolysis measurement, between the two clusters of samples revealed by PC1, was significant at the 0.01 significance level. These results led to the conclusion that PC1 captured the extent of hemolysis in the samples. Interestingly, there was also an enrichment of arachidonate phospholipid metabolites at the positive end of PC1 as oppose to an enrichment of eicosanoids at the negative end. While the biochemical link between the two sets of metabolites is an obvious substrate/product relationship, the link to hemolysis was rather obscure. There were no clusters of samples according to PC2 (Fig. 1a). A closer look at the loading plot revealed that TCA energy metabolites and amino acids that feed into TCA cycle were mostly located at the positive end of PC2 (Fig. 1c). Moreover, a significant positive correlation between previously identified changes in metabolites following 1 hour post-endurance exercise [1], also listed in Additional file 2: Table S1, and our PC2 loading values for the same metabolites (R = 0.6, p = 0.005) was identified. The enrichment of dipeptides at the negative end of PC2 could indicate an opposing anabolic effect. Although PCA did not explain sport classes, it provided clues of possible confounders (hemolysis and pre/post exercise) that we corrected for subsequent analyses.

Unlike PCA, OPLS-DA can identify sets of metabolites that best distinguish between predefined classes of samples. An OPLS-DA analysis comparing moderate versus high classes of endurance revealed one class-discriminatory component accounting for 66.7% of the variation in the data due to endurance level (R-squared-Y = 0.66, Q-squared = 0.45) (Fig. 2a). The corresponding loading score, shown in Fig. 2b, suggests a reduction in diacyl glycerols and gamma-glutamyl amino acids as oppose to an increase in steroids, GABA derivatives, and monohydroxy fatty acids with higher endurance levels.

OPLS-DA also revealed a clear separation between moderate versus high power. One significant predictive component explaining 88% of the variation in the power (R-squared-Y = 0.88, Q-squared = 0.52) was identified (Fig. 3a). The loading plot on Fig. 3b suggests a decrease in gamma glutamyl amino acids as oppose to an increase in sterols, phospholipids, lysolipids, and xanthine metabolites with increased power. OPLS results were confirmed by linear model in the following section.

Univariate association tests and function enrichment analysis

Endurance-associated metabolites

A linear model was used to assess the significance of metabolite-associations with the athletes’ class (moderate versus high endurance) after correcting for gender, hemolysis levels, PC1, PC2, and power. Thirty-eight metabolites associated with endurance at a Bonferroni level of significance (p ≤ 0.05/743 = 6.72 × 10⁻⁵) were identified and their associated pathways listed (Table 2). More metabolites associated with endurance at FDR and nominal levels of significance are shown in Additional file 2: Table S2. Similar results were obtained when analysis was restricted to males only (Additional file 2: Table S3).

Enrichment analysis revealed an over-representation of diacylglycerols, gamma-glutamyl amino acids, eicosanoids, and monohydroxy fatty acids (FDR-corrected p-value 0.000122, 0.005, 0.017, and 0.04, respectively) among metabolites most strongly associated with endurance, irrespective of the direction of change. The steroid class scored a nominal p-value of 0.05 but failed to remain significant after FDR-based multiple testing. Interestingly, these results are in considerable agreement with metabolic effects identified through the OPLS-DA multivariate approach previously discussed (Fig. 2b).

The results pertaining to steroids are certainly remarkable if replicated and will be elaborated further in the “Discussion” section. It is important to note that in addition to the six Bonferroni significant steroids listed in Table 2, seven more steroid species were FDR significant at alpha = 0.05. These are etiocholanolone glucuronide (FDR p value = 0.003); 5alpha-pregnane-3beta,20alpha-diol disulfate (FDR p value = 0.01); 5alpha-pregnane-3beta,20beta-diol monosulfate (FDR p value = 0.02); androstenediol (3beta,17beta) disulfate (FDR p value = 0.025); 5alpha-pregnane-3beta,20alpha-diol monosulfate (FDR p value = 0.029); pregnen-dioldisulfate (FDR p value = 0.035); and androstenediol (3alpha, 17alpha) monsulfate (FDR p value = 0.04). All Bonferroni and FDR significant steroid metabolites were projected onto KEGG Steroid Biosynthesis Pathway to highlight their biochemical inter-relationships (Fig. 4). Significant correlations among the identified steroid metabolites were confirmed (Additional file 3: Fig. S1, Additional file 2: Table S4), suggesting activation of sex steroid biosynthesis pathway in high-endurance athletes.

A part of enrichments of functionally related sets of metabolites, endurance association analysis also revealed individual metabolic effects which are noteworthy. Among these are derivatives of GABA cyclic lactam 2-pyrrolidinone including succinimide (Bonferroni p value = 0.00263), acisoga or N-(3-acetamidopropyl)pyrrolidin-2-one (FDR p value = 0.004), and 2-pyrrolidinone itself (FDR p value = 0.03) as well as GABA derivative 4-guanidinobutanoate (Bonferroni p value = 0.004). There were significant correlations between 2-pyrrolidinone and its derivatives including succinimide (R = 0.15, p = 0.04), 4-guanidinobutanoate (R = − 0.146, p = 0.04), and guanidinosuccinate (R = − 0.186, p = 0.01), suggesting presence of this drug and its derivatives in high-endurance athletes, also seen in OPL-DA analysis (Fig. 2b).

Other interesting effects include a Bonferroni significant increase in citrate together with an FDR significant increase in 2-methylcitrate (FDR = 0.012). Other associations include acyl carnitines, phospholipids, and sphingolipids among others (Table 2).

Power associated metabolites

When considering power, the categorical variable “power” becomes the explanatory variable of interest in the previous model and “endurance” becomes a confounder that is corrected for. Thirty-three metabolites were significantly associated with power according to this model; these are listed in (Table 3). Enrichment analysis revealed an over-representation of phospholipids (p = 0.00042), lysolipids (p = 0.00042), gamma-glutamyl amino acids (p = 0.000846), and sterols (p = 0.005) amongst metabolites most strongly associated with power. Other significantly changed metabolites in moderate- versus high-power classes included guanidinoacetate, N-acetylcarnosine, cholate, imidazole lactate, indolelactate, and 3-methylxanthine (Table 3).

Among FDR significant changes, an increase in creatine (estimate = 0.6, p = 0.001) and a decrease in creatinine (estimate = − 0.1, p = 0.002) were also detected in the high-power group although did not reach Bonferroni significance. More metabolites associated with power at FDR level of significance are shown in Additional file 2: Table S5. Similar results were obtained when analysis was restricted to males only (Additional file 2: Table S6).

Metabolites with FDR corrected p values of less than 0.01 from the endurance and power models were projected on the heatmap in Figs. 5 and 6, respectively. The heatmaps give a snapshot summary of the actual intensities of these metabolites after correcting for confounders in the linear model described earlier. Samples were ordered by sports type within their respective sport groups (moderate power/moderate endurance, moderate power/high endurance and high power/high endurance).

Gender-sports class interaction

Gender-endurance interaction analysis identified 60 significant metabolites with a nominal p value (less than 0.05) amongst which none remained significant after FDR correction (Additional file 2: Table S7). As for power, 144 metabolites were differently associated with power between males and females, among which 35 metabolites remained significant after FDR correction (Additional file 2: Table S8).

Metabolites associated with endurance

Exercise can cause changes in sex steroid hormone concentrations in the serum of non-athletes as well as athletes [19, 24], including levels of testosterone and cortisol [25, 26]. One interesting finding in this study is the elevated levels of various metabolites involved in sex steroid hormone biosynthesis in the high-endurance athletes. Some of these metabolites were conjugated with one or more sulfate group(s) which renders them inactive. However, these can be reactivated through the activity of enzyme steroid sulfatase [27]. The list of elevated steroids included pregnenolone that mediates biosynthesis of corticosteroids and progesterone and 21-hydroxypregnenolone disulfate that mediates biosynthesis of corticosteroids, corticoids (cortisol and cortisone), various metabolites of progesterone (pregnanediol, 5alpha-pregnane-3beta,20alpha-diol, 5alpha-pregnane-3beta,20beta-diol), testosterone precursor (androstenediol (3beta,17beta)), and testosterone metabolites (etiocholanoloneglucuronide, androstenediol (3alpha, 17alpha)) (Fig. 4). Elevated cortisol-related metabolites in response to sustained aerobic exercise were shown to correlate positively with intensity of exercise as measured by oxygen uptake [28]. However, exercise-induced alterations in sex steroid hormone levels are usually short lived (1–3 h) [19]. The habitual exercise regiments of the elite endurance athletes may have accounted for this maintained systemic increase. Sex steroid hormones play a crucial role in glucose metabolism and protein synthesis in the muscle as well as in the regulation of redox homeostasis [29–31]. Some act as neurosteroids that alter neuronal excitability such as pregnen-dioldisulfate that works as a potent negative allosteric modulator of the GABAA receptor [32] and pregnenolone sulfate that acts as a potent negative allosteric modulator of the GABAA receptor and a weak positive allosteric modulator of the NMDA receptor [33]. The stimulatory effects of steroids on muscle mass, energy generation, and neuronal excitability may have accounted for the higher endurance ability of the high-endurance group compared to their lower endurance counterparts. Given that athletes included in this study have successfully passed anti-doping tests, changes in steroids levels may reflect either enhancement in endogenous anabolic steroids biosynthesis, physiological adaptation to exercise, and/or increased dietary intake. A genetic association study is needed to reveal the potential genetic variants underlying increased activity of enzymes involved in steroid biosynthesis. Interestingly, in addition to elevation in a number of neurosteroids, our data suggested increased elevated levels of a number of GABA derivatives including 2-pyrrolidinone, the cyclic lactate form of GABA [34], its derivatives succinimide, acisoga (N-(3-acetamidopropyl)pyrrolidin-2-one), and 4-guanidinobutanoate, perhaps contributing to GABA-mediated muscle growth in response to exercise [35].

Other metabolic changes associated with high endurance included reduced diacylglycerols (DAGs) and fatty acid (FA)-carnitine and increased acylated carnitine. Alterations in these lipids may suggest enhanced hydrolysis of DAGs, shuttling of FA intracellularly, followed by fatty acid oxidation and energy generation [36]. Fatty acids and lipids are preferred substrates for exercising the muscle, and the emerging data suggest a greater beta oxidation of fatty acids in athletes belonging to higher endurance sports. Hence, those athletes are perhaps more capable of activating lipolysis during physical activity than moderate-endurance athletes. Furthermore, accumulation of acylated carnitine may provide a favorable effect on the recovery from exercise stress since carnitine can reduce post-exercise plasma lactate and prevent cellular damage [37]. Citrate and isocitrate were also significantly increased in high-endurance elite athletes, indicating enhanced aerobic energy generation through TCA.

Metabolites associated with power athletes

Changes in creatine, creatinine, and guanidinoacetate were significant between high- and moderate-power athletes. Whereas creatine increased in the high-power group, its breakdown product (creatinine) and precursor (guanidinoacetate) were both significantly reduced, thus maintaining the previously reported balance of creatine metabolism [38]. Creatine (Cr) and creatine phosphate (CrP) play essential roles in the storage and transmission of phosphate-bound energy. Changes in creatine homeostasis in high-power athletes may suggest more adaptable muscular storage of CrP that during exercise can constitute an essential source for high energy to replenish ATP in the first few seconds of intense activity. Other energy-related metabolites elevated in high-power athletes were 3-methylxanthine and 7-methylxanthine (adenine breakdown products), perhaps reflecting heightened utilization of fuel substrates in several metabolic pathways [39]. Xanthine supplementation allows athletes to exercise at a greater power output for longer times [40]. Additionally, N-acetylcarnosine was significantly reduced in high-power athletes. This metabolite acts as oxidative stress scavenger in muscles especially against lipid peroxidation through its imidazolium group that stabilizes adducts formed at the primary amino group [41]. Various derivatives of phosphatidates were increased with increased power, perhaps reflecting changes in cellular membrane dynamics in response to oxidative stress [42]. Among those, inositol phospholipids were previously shown to accumulate in response to muscle contraction during hypoxia [43]. Another metabolite likely to be a result from stress-induced membrane dynamics is 12,13-DHOME. This long-chain fatty acid enhances adipogenesis and inhibits asteogenesis due to its role as a proliferator-activated receptor (PPAR) gamma 2 ligand [44].

Global stress response in both high-power and high-endurance athletes

Intensive exercise has been implicated in the promotion of free radical generation in active skeletal muscle resulting in the formation of oxidized lipids [42]. Overall in both power and endurance athletes, there was a clear stress metabolic response. Changes in gamma-gultamyl amino acids, associated with elevated cysteine-glutathione disulfide (change 0.24, nominal p value of 0.03), between high- and moderate-performance athletes may indicate active gamma-glutamyl cycle that plays an important role in the glutathione-mediated radical detoxification during oxidative stress [45]. The cycle involves synthesis and degradation of glutathione by transferring gamma-glutamyl functional groups from glutathione to an amino acid, leaving the cysteine products intact, which leads to the preservation of intracellular homeostasis in case of oxidative stress [46, 47]. Reduction in serum levels of gamma-glutamyl-amino acids in high-performance athletes (both high power and high endurance) may indicate increased glutathione synthesis. The accumulation of glutathione in the blood stream marks increased oxidative stress and reactive oxygen species scavenging activity.

Gender-related differences

Despite lack of FDR significant differences in metabolites associated with endurance in males versus females, differences in a number of metabolites were nominally significant, including a number of gamma-glutamyl amino acids and steroid metabolites among others. Differences in these metabolites between high and moderate levels of endurance were mostly going in the same direction in males and females but were more pronounced in females. As per power-associated metabolites, there were FDR significant differences between males and females in a number of metabolites including TCA-mediators such as malate, fumarate, succinate, and alpha keto glutarate as well as lactate where in females there was increase with higher power with no FDR significant effects in males. These gender-related differences need to be further investigated, especially in light of low number of studied females (n = 20).

Study limitations

One main limitation of this study is the relatively low number of participants, especially the females; therefore, a replication study is essential for confirmation of these findings. Furthermore, since athletes’ blood samples were collected at multiple sites, a batch effect was inevitable, likely attenuating correlations between metabolite concentrations and sports class. This batch effect may have included various crucial pre-analytical features that can significantly influence the metabolic profiling of samples such as the blood collection process and time (IN or OUT of competition) and transportation conditions, including time to reach anti-doping laboratories, sample processing, and sample storage [48]. Despite these factors, clear signatures were identified after correcting for potential confounders. Additionally, the lack of information about participants including their age, ethnicity, and body mass index was another major limitation of this study. However, the young age of elite athletes in general and the wide range of sports included in this study may have diluted out other potential confounders. Ambiguity in the exact description of the subcategories of athletes’ sports was an additional issue this study has faced due to the limited information provided by the anti-doping laboratories following the strict anonymization process. This has prompted the adoption of the general sports class grouping based on previously published work [21] despite the differences among different members of the same team such as such as breast-stroke and freestyle swimming or football midfielders and goal keepers. Another limitation of this study is the group number bias as some sports were overrepresented and others underrepresented. Finally, differences in dietary intake between high- and moderate-power and endurance elite athletes, including various supplements, medications, and other ergogenics, may have influenced their metabolic profile [49]. Such differences are difficult to account for as they vary among different sports and athletes and are not usually publicized. Taken all these limitations into account, it is critical to stress that this is a pilot study that needs further replication and validation as finding biomarkers from the identified differentiating significant compounds still requires optimization of target-specific analytical methods and validation of these methods with their reference materials and proficiency tests [50].