An observational human study investigating the effect of anabolic androgenic steroid use on the transcriptome of skeletal muscle and whole blood using RNA-Seq
Bibliographic record
Abstract
BACKGROUND: The effects of Anabolic Androgenic Steroids (AAS) are largely illustrated through Androgen Receptor induced gene transcription, yet RNA-Seq has yet to be conducted on human whole blood and skeletal muscle. Investigating the transcriptional signature of AAS in blood may aid AAS detection and in muscle further understanding of AAS induced hypertrophy. METHODS: Males aged 20-42 were recruited and sampled once: sedentary controls (C), resistance trained lifters (RT) and resistance trained current AAS users (RT-AS) who ceased exposure ≤ 2 or ≥ 10 weeks prior to sampling. RT-AS were sampled twice as Returning Participants (RP) if AAS usage ceased for ≥ 18 weeks. RNA was extracted from whole blood and trapezius muscle samples. RNA libraries were sequenced twice, for validation purposes, on the DNBSEQ-G400RS with either standard or CoolMPS PE100 reagents following MGI protocols. Genes were considered differentially expressed with FDR < 0.05 and a 1.2- fold change. RESULTS: Cross-comparison of both standard reagent whole blood (N = 55: C = 7, RT = 20, RT-AS ≤ 2 = 14, RT-AS ≥ 10 = 10, RP = 4; N = 46: C = 6, RT = 17, RT-AS ≤ 2 = 12, RT-AS ≥ 10 = 8, RP = 3) sequencing datasets, showed that no genes or gene sets/pathways were differentially expressed between time points for RP or between group comparisons of RT-AS ≤ 2 vs. C, RT, or RT-AS ≥ 10. Cross-comparison of both muscle (N = 51, C = 5, RT = 17, RT-AS ≤ 2 = 15, RT-AS ≥ 10 = 11, RP = 3) sequencing (one standard & one CoolMPS reagent) datasets, showed one gene, CHRDL1, which has atrophying potential, was upregulated in RP visit two. In both muscle sequencing datasets, nine differentially expressed genes, overlapped with RT-AS ≤ 2 vs. RT and RT-AS ≤ 2 vs. C, but were not differentially expressed with RT vs. C, possibly suggesting they are from acute doping alone. No genes seemed to be differentially expressed in muscle after the long-term cessation of AAS, whereas a previous study found long term proteomic changes. CONCLUSION: A whole blood transcriptional signature of AAS doping was not identified. However, RNA-Seq of muscle has identified numerous differentially expressed genes with known impacts on hypertrophic processes that may further our understanding on AAS induced hypertrophy. Differences in training regimens in participant groupings may have influenced results. Future studies should focus on longitudinal sampling pre, during and post-AAS exposure to better control for confounding variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".