miRNAs and sports: tracking training status and potentially confounding diagnoses
- Anne Hecksteden†1,
- Petra Leidinger†2,
- Christina Backes3,
- Stefanie Rheinheimer2,
- Mark Pfeiffer4,
- Alexander Ferrauti5,
- Michael Kellmann5, 6,
- Farbod Sedaghat7,
- Benjamin Meder7,
- Eckart Meese2,
- Tim Meyer1 and
- Andreas Keller3Email authorView ORCID ID profile
© The Author(s) 2016
Received: 2 February 2016
Accepted: 11 July 2016
Published: 26 July 2016
The dependency of miRNA abundance from physiological processes such as exercises remains partially understood. We set out to analyze the effect of physical exercises on miRNA profiles in blood and plasma of endurance and strength athletes in a systematic manner and correlated differentially abundant miRNAs in athletes to disease miRNAs biomarkers towards a better understanding of how physical exercise may confound disease diagnosis by miRNAs.
We profiled blood and plasma of 29 athletes before and after exercise. With four samples analyzed for each individual we analyzed 116 full miRNomes. The study set-up enabled paired analyses of individuals. Affected miRNAs were investigated for known disease associations using network analysis.
MiRNA patterns in blood and plasma of endurance and strength athletes vary significantly with differences in blood outreaching variations in plasma. We found only moderate differences between the miRNA levels before training and the RNA levels after training as compared to the more obvious variations found between strength athletes and endurance athletes. We observed significant variations in the abundance of miR-140-3p that is a known circulating disease markers (raw and adjusted p value of 5 × 10−12 and 4 × 10−7). Similarly, the levels of miR-140-5p and miR-650, both of which have been reported as makers for a wide range of human pathologies significantly depend on the training mode. Among the most affected disease categories we found acute myocardial infarction. MiRNAs, which are up-regulated in endurance athletes inhibit VEGFA as shown by systems biology analysis of experimentally validated target genes.
We provide evidence that the mode and the extent of training are important confounding factors for a miRNA based disease diagnosis.
It is increasingly recognized that biomarkers are not only affected by pathological processes, but likewise by other environmental factors. Among the most popular examples are cardiovascular biomarkers such as cTnT, hs-cTnT, BNP or NT-proBNP. These biomarkers are prone to alterations due to strenuous exercise, as recently reviewed by Sedaghat-Hamedani . Similarly, more complex marker signatures may also depend significantly on the training status. One class of such novel marker candidates are small non-coding RNAs, so-called miRNAs.
Their importance for a wide range of conditions is currently explored and validated. Beyond tissue based miRNA profiles, circulating patterns have gained increasing importance. The role of circulating miRNAs in different disease classes has been reviewed in depth. Examples include the two most common causes for death in developed countries, cancer  and cardiovascular disorders . In addition to their promising role as diagnostic and prognostic markers for human pathologies, miRNAs have also been correlated to different exercise modes and the overall physical performance capacity. Especially in the light of more complex marker patterns that are not only correlated to diseases but also to physiological processes such as exercises it is essential to understand how respective physiological processes can confound disease diagnosis and prognosis.
Previously, we reported that exercise of elite endurance athletes had a limited direct influence on miRNAs in blood . In the same study we reported that respective changes in the blood have lower effect sizes as compared to the impact of pathophysiological changes. In other studies, the impact of exercise on different miRNAs has been explored. Melo and co-workers have discovered a key role of miR-214 in rats . While this miRNA was down-regulated in the cohort undergoing exercise, the target gene SERCA2a increased. Similarly, Liu and co-workers demonstrated that miR-222 is of key importance for cardiac growth, which is induced by exercise. Furthermore miR-222 protects against pathological cardiac remodeling . While these studies have been carried out in animals, case–control studies have been also conducted in human athletes. Wardle et al. have carried out a study on plasma of endurance and strength athletes . They investigated three cohorts (strength athletes, power athletes and untrained individuals, n = 10) and found differential regulation of circulating miRNAs such as miR-222. A more elaborate overview describing the role of circulating microRNAs in response to exercise can be found in two recently published comprehensive reviews by Xu  and Altana .
Especially with increasing evidence of miRNA profiles for diagnostic or prognostic purposes in human pathologies putative confounding variables are also gaining increasing attention. Beyond straightforward confounders of miRNA profiles such as the age and gender  regular exercise on a long term as well as short term increase after fatigue (e.g. in competitions or training camps) may also impact the miRNA profile. Concordantly, Gomes and co-workers described the implications on clinical diagnostics of using microRNA-based biomarkers in exercise .
Despite multiple studies on circulating miRNAs related to fatigue status, it is hard to compare different training forms, athlete types and the variations in blood and plasma. In the available studies, different protocols for extracting the miRNAs, profiling them and evaluating them have been applied. This renders a meta-analytical analysis error prone. We thus set to implement a standardized repository of miRNAs related to physical exercise.
In our previous proof-of-concept study  we did not distinguished between strength and endurance athletes, but analyzed less trained individuals as controls. In addition, we analyzed whole blood only. In the present study, we selected a study set-up that allows for paired and unpaired comparison of the most important factors. In detail, we measured the full miRNomes (1) of strength athletes and endurance athletes (2) before and after a 6-days simulated training camp in (3) plasma and blood. With all combinations included we profiled a total of 8 different groups. Since the comparisons “prior and post training” and “plasma and blood” were done analyzing same individuals, it was possible to do a paired testing for the respective comparisons. An unpaired analysis was required only for the comparison between strength and endurance athletes. Altogether, we profiled 29 athletes corresponding to 29 biological replicates. For each athlete we measured the miRNomes of the following four profiles: “prior training plasma”, “post training plasma”, “prior training blood” and “post training blood”, totaling 116 miRNomes.
Twenty nine well-trained male athletes [15 endurance athletes (cyclists) and 14 strength athletes] volunteered for this prospective, short-term training trial. Tests were conducted at baseline following a 2-day run in resting phase as well as after induction of fatigue by a discipline specific, strenuous training program. The 6-day training period consisted of two training sessions a day, with the exception of day 4 when no morning session was scheduled. Care was taken to implement a demanding, training design for either discipline resulting in high levels of physical strain and fatigue. To verify effective, reversible induction of fatigue established fatigue markers  were assessed.
The study was undertaken in accordance with the Declaration of Helsinki and approved by the local ethics committee (Ärztekammer des Saarlandes, Saarbrücken, Germany, ID 46/13). All athletes provided written informed consent prior to participation. All tests were conducted in a University department (Cyclists: Saarland University, Germany; Strength athletes: Ruhr Universität Bochum, Germany).
Blood sampling and miRNA measurement
After reporting to the laboratory at a standardized time (between 8 and 10 a.m., intra-individually same hour for all tests) subjects rested in the supine position for 10 min prior to blood collection. A winged cannula was inserted into the antecubital vein during a short stasis (max. 30 s.). Serum and plasma aliquots were frozen at −80 °C within 60 min from blood collection and stored for later analysis. Whole-blood samples for the determination of miRNA expression were collected and stored in special tubes (PAXgene blood RNA tube, Becton–Dickinson, Germany). For miRNA measurement, blood and plasma samples have been used while further laboratory parameters (details below) were determined from serum samples. miRNA extraction of blood and plasma samples have been carried out as described previously and according to manufacturer’s instructions . For the strength athletes, plasma has been diluted in a ratio of 1:2. The quality of the samples has been controlled by Bioanalyzer measurements and the RNA 6000 Nano kit. Microarray profiling has been carried out using Agilent SurePrint V16 Human miRNA Microarrays (miRBase v16) microarrays encompassing 1205 different miRNAs, as described previously  and following the manufacturer’s instructions. Each miRNA was measured for each patient by 40 on-chip technical replicates. The microarray data are available in Additional file 1: Table S1.
Further laboratory parameters
As further blood parameters we included the hemoglobin concentration (Hb), Erythrocyte, Leucocyte and Thrombocyte count, creatine kinase (CK), urea, free-testosterone, c reactive protein (CRP), cortisol, glutamine (Gln) and glutamate (Glu) concentration, insulin like growth factor 1 (IGF-1), IGF-1 binding protein 3 (IGF-BP3), tumor necrosis factor (TNF), interleukin 6 (IL-6), and human growth hormone (HGH) at different time points.
Following feature extraction using the Agilent’s image processing software with standard parameters, expression values were subjected to quantile normalization. All downstream statistical calculations have been carried out in R version 3.0.2. Principal Component Analysis has been done using the prcomp function, for ANOVA, the aov function was used. Hierarchical clustering was done using the Euclidian distance measure and complete linkage clustering by the hclust package. Visualization of heatmaps has been done by a modified version of the heatmap.2 function.
Correlations of the identified miRNA to diseases were calculated using the downloaded version of the Human microRNA Disease Database (HMDD) (Version 2.0, data accessed on May, 28th, 2016) . Before relating the miRNAs to the HMDD, mature forms were mapped to the precursors to ensure compatibility with the HMDD. We used only the “circulating” subset of the HMDD. Prediction of pathways has been carried out using the gene (miRNA) set enrichment tool miEAA  (http://www.ccb.uni-saarland.de/mieaa_tool/). KEGG pathway and gene ontology analysis has been performed using miRTargetLink  (http://www.ccb.uni-saarland.de/mirtargetlink) and the API to GeneTrail  and GeneTrail2 .
Availability of data and supporting materials
All miRNA measurements with meta-data have been made freely available (Additional file 1: Table S1).
Difference in composition of miRNA profiles
These results suggest that the type of training has a substantial impact on plasma and blood profiles of individuals. The miRNA profiles, especially from blood, can thus likely be used as molecular monitor for the strength and stamina of athletes.
Specific miRNAs as indicators for different training modes
Taken together, our results suggest that miRNAs, preliminary measured from blood, are well suited to indicate the overall status of an athlete and whether the respective athlete is trained mostly towards endurance or strength.
miRNA enrichment of markers depending on training mode
Pathway analysis of miRNA targets
To further annotate the genes in the network we carried out functional gene enrichment analysis. We compared the accumulation of the 162 unique miRNA targets on KEGG pathways and Gene Ontology categories to the background distribution of all genes that are targeted by at least one miRNA with strong evidence (2098 genes). Following adjustment for multiple testing according to Benjamini-Hochberg and excluding categories with a less than three-fold enrichment of genes, we found 8 significant categories from the gene ontology (biological process) and ten KEGG pathways. Remarkably, the first two categories were correlated to diseases, including the KEGG pathways “Melanoma” (4.6 times more genes than expected, p = 3.1 × 10−6) and “Bladder cancer” (5.4 times more genes than expected, p = 1.4 × 10−5). The third most significant category was the KEGG pathway “Cell Cycle” (3.6 times more genes than expected, p = 1.8 × 10−5). The previously described gene VEGFA was not only contained in disease pathways but also in the Gene Ontology category “cell maturation” and the KEGG pathway “PI3 K-Akt signaling pathway”.
Specific correlation of miRNAs to diseases
To find correlations of miRNAs to diseases we queried the Human miRNA and disease database (HMDD) as described in the “Methods” section. Importantly, we only used unique combinations of miRNAs to diseases in the “circulation” data set from the HMDD, that includes 512 associations for 240 miRNAs. Altogether, we found 74 matches, i.e. 74 of the 512 entries in the HMDD contained one of the 63 dys-regulated miRNAs. Almost 50 % of the 63 miRNAs in turn were associated with at least one disease. The largest number of associations was found for hsa-mir-17, being correlated with 18 cancerous disorders. The associations between miRNAs and diseases are summarized in Additional file 2: Table S2. The results indicate that the training mode potentially influence disease diagnosis by circulating miRNAs.
Specific miRNAs as indicators for fatigue
KEGG and GO enrichment analysis
Target genes on category
G1_phase(4) & mitotic_G1_phase(5)
Chronic myeloid leukemia
Non-small cell lung cancer
p53 signaling pathway
Pair-wise comparisons of prior- and post training for endurance and strength athletes in blood and plasma
Plasma strength p
Plasma strength AUC
Blood strength p
Blood strength AUC
Plasma endurance p
Plasma endurance AUC
Blood endurance p
Blood endurance AUC
Correlation of miRNAs to other laboratory parameters
The athletes participating in this study were characterized for other common blood parameters as detailed in the “Methods” section. We correlated all miRNAs to all clinical parameters in a pair wise manner using Spearman correlation. To correct for multiple testing we applied the Bonferroni correction to minimize the number of false positive hits. Following this adjustment, 14 correlations of miRNAs with blood parameters remained significant. We found three correlations of the creatine kinase (CK) at day 5 with miR-18-3p, miR-33b-3p or miR-650. Both miR-1305 and miR-3198 correlated with IGF-BP3 after training and miR-128-3p correlated with the free testosterone. MiR-140-5p in plasma was negative correlated to hemoglobin while plasma miR-188-5p was positive correlated to hemoglobin.
Levels of circulating miRNAs are not only altered in pathological processes but are also affected by training condition. In this study we examined the distribution of miRNAs in blood and plasma of strength and endurance athletes before and after training. The influence of physiological processes, such as physical exercises, on the profiles of circulating miRNA may confound disease diagnosis by miRNAs potentially limiting the translation of miRNA profiles to clinics .
First, we provide evidence that miRNAs in blood and plasma are different between strength and endurance athletes and are also indicative for the overall fatigue status of competitive athletes. The effect sizes of differences in blood outreach the differences in plasma clearly. Fatigue induced changes were only detectable when using a repeated measures approach, pointing to the need for an individualized interpretation of measured values. Moreover, we provide evidence that longitudinal measurement of blood miRNAs may add to individualized and personalized training.
Different miRNAs have been described as markers for fatigue status or altered between strength and power athletes (see “Background” section and [3, 9]). The respective miRNAs were also observed in our study. However usually other markers exceeded the effect sizes of the known markers substantially. This may be due to different factors. First, our study profiles over 12,000 miRNAs that are annotated in the version 16 of the miRBase (http://www.mirbase.org). Second, we carried out a paired study set-up and no unpaired case–control study. Third we measured more individuals as most other studies. Altogether, for 29 athletes four different full miRNomes were profiled, resulting in 116 profiles.
By investigating signaling cascades we discovered a high regulatory influence of miRNAs that are dys-regulated in strength and endurance athletes on several genes. Most significant was the regulatory influence of miRNAs on a vascular endothelia growth factor (VEGF), namely VEGFA. Increased capillary density in skeletal muscle is a well-known effect of exercise training which promotes oxygen supply during exercise by increasing diffusion area and reducing diffusion distance. Angiogenesis has been observed with various exercise modes including endurance  and strength training . However an increase in capillary density is generally observed with endurance training only, reflecting the requirements of aerobic energy production . VEGF is a key pro-angiogenic regulator of capillary growth in response to physical exercise . Therefore, at first sight, the higher expression of miRNAs which (down-) regulate VEGF seems surprising. However, capillary growth in response to exercise training is a highly regulated process involving a wealth of pro- as well as anti-angiogenic factors . Two of the most important regulators promoting VEGF expression during exercise are AMP-activated protein kinase (AMPK)  and the transcriptional co-activator PGC-1α , which are also fundamental for the adaptation to endurance training . Therefore, avoiding excessive angiogenesis caused by the targeted activation of these pathways with competitive endurance training seems to be a plausible explanation for this finding.
Besides, it is important to understand the variability of miRNAs dependent on the training (including long term effects but also short term effects) since miRNAs are important disease markers. This in turn means that intensive, long-term training or even one single exhaustive event may confound disease diagnosis substantially. Similar effects are e.g. known for cardiac markers such as cardiac troponin and BNP [26, 27]. As shown in the “Results” section, many miRNAs are associated with different diseases. Building on these results we investigated miRNA changes as result of the training condition. We carried out a comprehensive literature research for the most significantly changed markers in blood, serum and plasma, including 46 markers that had adjusted p-values of below 10−10 in any of the two comparisons. The most significantly altered miR-650 was correlated to a range of different pathologies, including heart failure , congenital heart disease , diabetic ischemic heart failure  and different cancer types (gastric cancer , melanoma , leukemia , hepatocellular carcinoma , glioma , colorectal cancer  and lung cancer ). Other key miRNAs included miR-221-3p as marker for Non-ST-Segment and ST-Segment-Elevation Myocardial Infarction  and stroke . In the latter publication another miRNA significantly affected in our study was described: miR-140-5p. The same holds for miR-221-3p and miR-98-5p in preeclamptic patients . Another study highlighted two miRNAs (again miR-140-5p as well as miR-532-5p) linked to type 2 diabetes that change with insulin sensitization . The same two miRNAs have been found as markers for obesity . Beyond these examples we extracted 231 hits for the 47 miRNAs (for 31 miRNAs at least a single hit was found) from PubMed (http://www.ncbi.nlm.nih.gov/pubmed) in 211 different publications.
The overall high correlation of miRNAs affected by training to diseases provide evidence that the training condition is an important confounding variable for miRNA biomarker studies that has to be taken into account in developing diagnostic and prognostic signatures.
This study represents the most comprehensive miRNA atlas of athletes to date. Our study set-up that includes four measurements per individual athlete allowed for quantifying differences in strength and endurance athletes, in serum and plasma and before and after a 6 days training camp.
Overall, we report a limited immediate influence of the 6 days training on the miRNA profiles overall but describe substantial differences between profiles of strength and endurance athletes. miRNAs especially expressed in endurance athletes but less expressed in strength athletes significantly targeted VEGFA. These differences in blood by far exceeded the differences in plasma, which have already been described. Additionally, we provide evidence that miRNA profiles prior to exercise correlate very well with measurements of creatine kinase carried out following 5 days of physical training.
Finally, we describe that the miRNAs that are affected by exercising according to our results are important disease markers. For a diagnosis or prognosis of human pathologies the amount and kind of physical training conducted prior to blood sampling is in consequence a very important confounding variable.
principal component analysis
vascular endothelial growth factor A
cardiac troponin T
B-type natriuretic peptide
c reactive protein
insulin like growth factor 1
IGF-1 binding protein 3
tumor necrosis factor
human growth hormone
AH contributed in study set-up, manuscript drafting and sample collection; PL performed microarray experiments; CB contributed in data analysis, SR contributed in data measurement; MP, AF, MK contributed in study-set up and correcting the manuscript draft; FS BM performed analysis with respect to cardial miRNAs and control experiments; EM contributed in writing the manuscript draft and study set-up; TM contributed in study set-up and recruitment of athletes; AK contributed in data analysis, study set-up and wrote manuscript draft. All authors read and approved the final manuscript.
We acknowledge the financial support of Saarland University and the EU and thank all study participants for contributing to our manuscript.
The authors declare that they have no competing interests.
This work has been funded by internal funds of Saarland University and in parts by the EU FP7 “BestAgeing” project.
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