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Record W4368360480 · doi:10.1186/s12920-023-01512-z

An observational human study investigating the effect of anabolic androgenic steroid use on the transcriptome of skeletal muscle and whole blood using RNA-Seq

2023· article· en· W4368360480 on OpenAlexfundno aff
Alexander Kolliari-Turner, Giscard Lima, Guan Wang, Fernanda Rossell Malinsky, Antonia Karanikolou, Gregor Eichhorn, Kumpei Tanisawa, Jonathan Ospina‐Betancurt, Blair Hamilton, Paulette Y. O. Kumi, Jonathan Shurlock, Vasileios Skiadas, Richard Twycross-Lewis, Liam P. Kilduff, Renan Paulo Martin, Garrett I. Ash, Cynthia Potter, Fergus Guppy, Jane T. Seto, C Fossati, Fabio Pigozzi, Paolo Borrione, Yannis Pitsiladis

Bibliographic record

VenueBMC Medical Genomics · 2023
Typearticle
Languageen
FieldMedicine
TopicHormonal and reproductive studies
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsAnabolismTranscriptomeSkeletal muscleInternal medicineBiologyRNA-SeqEndocrinologyRNAWhole bloodAnabolic-Androgenic SteroidsGeneBlood samplingGene expressionBioinformaticsMedicineGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.140
GPT teacher head0.347
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes1
Has abstractyes

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