Metabolomics as tool for the early detection of diagnostic biomarkers in antidoping field
Bibliographic record
Abstract
This thesis focuses on the exploration of the metabolomics approach as a guided tool in the field of anti-doping by defining a workflow based on the synergy between High-Resolution Mass Spectrometry and chemometric tools. First, the optimization of a low-energy electron ionization source to maximize the formation of molecular ion and minimize the fragmentation degree of steroid pathways, preserving the specific fragmentation pathway of the steroids considered and increasing the m/z coverage range. To this end, the effects of electron energy, emission current and source temperature on steroid fragmentation pathways were studied by performing full factorial experimental designs, using steroid reference materials chosen to cover the entire urinary steroid profile. Second, the development and validation of systematic metabolomics workflows to reduce the time and resources required to identify direct drug metabolites for GCHRMS. To do so, the administration of 7-keto-DHEA was studied as a Proof-of-Concept to highlight the strong synergy between high-resolution mass spectrometry and chemometric tools for early detection of drug metabolites in anti-doping. A comparison of the most significant features with the spectra library validated the proposed metabolomics approach, further supported by existing data in the literature. Third, extension of the previously proposed workflow on GCHRMS data to LCHRMS data, development and validation. The primary differences between the two workflows lie in the method validation, sample analysis processes, including preparation and acquisition, as well as in the raw data preprocessing steps. This knowledge gives the opportunity to gain insight into all possible metabolic changes, regardless of whether it is the formation of new compounds or the reduction of compounds. In contrast, the metabolite-focused approach generally reduces the scope of investigation to the formation of metabolites from the parent molecule, thus losing the response that other endogenous compounds might have as a result of its intake. Fourth, application of the developed workflow for the investigation of the physiological and post training effects of ecdisteroid supplementation on the human serum metabolome. These outcomes elucidates the effectiveness of a metabolomics-based approach in detecting specific trends related to the intake of performance-enhancing substances that would otherwise remain undetected through traditional analytical methods or be masked by physiological changes. The results presented in this thesis are of relevance for a more depth understanding of the complex relationships between different steroids, which may not be apparent when examining individual steroids in isolation, and in the identification of patterns or combinations of steroids that may discover new biomarkers for disease diagnosis, prognosis, or monitoring. This is a step forward in the metabolic characterization of different physio-pathological conditions that allow for the personalization of treatment strategies and optimization of individual performance outcomes. This personalized treatment enhances the value of the proposed metabolomics approach, making it beneficial not only for improving sports performance, but also in the clinical setting, where targeted supplementation can promote better health and recovery.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".