The Proteomic Signature of Recombinant Growth Hormone in Recreational Athletes
Bibliographic record
Abstract
OBJECTIVE: Administration of human growth hormone (hGH) is prohibited in competitive sport and its detection in an athlete's sample triggers an adverse analytical finding. However, the biological processes that are modulated by recombinant hGH are not well characterized and associated blood serum proteins may constitute new biomarkers for hGH misuse. METHODS: Thirty-five recreational athletes were enrolled in a study to investigate the time- and dose-dependent response of serum protein levels to recombinant hGH administration. Participants were randomly assigned to 4 groups, receiving 1 of 3 different doses of recombinant hGH or a placebo. Bio samples were collected at 22 time points over a period of 13 weeks, starting 4 weeks before treatment, during 3 weeks of treatment, and at 6 weeks' follow-up. A total of 749 serum samples were analyzed for 1305 protein markers using the SOMAscan proteomics platform. RESULTS: We identified 66 proteins that significantly associated with recombinant hGH administration and dosage, including well known hGH targets, such as IGF1, but also previously unknown hGH-related proteins (eg, protease inhibitors, WFIKKN1, and chemokines, CCL2). Network analysis revealed changes in specific biological pathways, mainly related to the immune system and glucose metabolism. CONCLUSION: Our analysis suggests that hGH administration affects biological processes more strongly than previously acknowledged. Some of the proteins were dysregulated even after hGH treatment and could potentially be developed into biomarkers for hGH misuse. Moreover, our findings suggest new roles for hGH-associated proteins in the etiology of hGH-related diseases and may indicate new risks that may be associated with hGH misuse.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".