An Analytical Strategy for Reliable Metabolome Analysis of Clinical Leftover Sera Using Timed Aliquoting
Bibliographic record
Abstract
Accurate metabolome analysis depends not only on advanced analytical techniques but also on strict control of preanalytical variables. This study presents an analytical strategy for reliable untargeted metabolomics using clinical leftover sera, focusing on the impact of timed aliquoting during short-term storage. Leftover serum samples from routine clinical testing, typically stored at 4 °C for up to 7 days, offer a valuable and accessible resource for biomarker discovery. However, variable delays in sample aliquoting and storage can compromise metabolite stability. We used high-coverage 12 C-/ 13 C-dansylation LC–MS to profile the amine/phenol submetabolome in serum samples collected from healthy individuals at multiple time points postdraw. The study included 630 LC–MS runs (105 subjects × 6 time points) in the discovery set and 280 runs (70 subjects × 4 time points) in the validation set, quantifying 1382 and 1352 metabolites, respectively. Although time-dependent changes in metabolite abundances were observed, these shifts were relatively small between adjacent time points. Notably, clear sex-based metabolic separation was observed when using samples aliquoted at the same time or within 24-h intervals, whereas discriminatory power diminished when samples with longer storage time differences were combined. These findings demonstrate that, with carefully timed aliquoting, clinical leftover sera can be reliably used for metabolomics. Our study establishes a practical and scalable workflow to control preanalytical variation─specifically by minimizing storage time differences─thereby enabling broader use of clinical samples in biomarker discovery and population-scale metabolomics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".