Circulatory lipid signature in response to short-term testosterone gel treatment of healthy young females
Bibliographic record
Abstract
The impact of testosterone administration on the circulating lipidome in females remains unexplored, despite its relevance to understanding metabolic disorders like polycystic ovary syndrome (PCOS). This study addresses this gap by examining the effects of testosterone gel on the plasma lipidome of healthy women over three menstrual cycles. A cohort of 14 women aged 22-37 years with regular cycles was analyzed, with plasma samples collected at baseline, during peak testosterone levels (D45), and post-treatment (D59, D80). Testosterone gel treatment lasted 28 days, administered between day 29 and day 57 of the study. Using a deep-targeted lipidomic approach, 597 lipids were quantified to provide a detailed profile of the lipidome and capture subtle changes in lipid species and their associations with testosterone fluctuations. Extensive profiling revealed a significant decrease in 17 lipid species, especially ether- and ester-linked lysophosphatidylcholines (LPC), at peak testosterone. These lipid reductions were strongly negatively correlated with free and total testosterone, as well as dihydrotestosterone (DHT), and positively correlated with SHBG levels. Notably, intra-individual lipid variability was consistently lower than inter-individual variability, indicating a highly personalized lipidome regulation. Despite testosterone-induced changes, overall plasma lipidome alterations were minimal, suggesting mechanisms that maintain lipid homeostasis. This study highlights the complex interplay between testosterone and lipid metabolism in women. The minimal overall lipidome changes and high inter-individual variability point to the need for further research to assess the clinical relevance of these findings, particularly in hyperandrogenic conditions like PCOS. Clinical Trial Registration number: This study was registered on https://www.isrctn.com/ (ISRCTN10122130) on 09/01/2019.
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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 | 0.000 | 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".