A sensitive and cost‐effective high‐performance liquid chromatography/tandem mass spectrometry (multiple reaction monitoring) method for the clinical measurement of serum hepcidin
Bibliographic record
Abstract
RATIONALE: Hepcidin is a peptide hormone that plays a central role in regulating iron metabolism. It is a potential biomarker for the diagnosis, monitoring and treatment of iron metabolism disorders. Serum hepcidin level can differ by 3 orders of magnitude depending on the patient's condition. Existing liquid chromatography/mass spectrometry (LC/MS) assays lack clinical sensitivity or require costly sample preparation steps. A simple, sensitive, robust and cost-effective assay for serum hepcidin quantitation in routine clinical laboratories is needed. METHODS: A high-performance liquid chromatography/tandem mass spectrometry (HPLC/MS/MS) method was developed to quantify hepcidin in human serum using chemically synthesized hepcidin as a standard and stable-isotope-labeled hepcidin as the internal standard. The method was validated according to CLSI-C62A guidelines. Calibrators were prepared with hepcidin-free serum. Clinical samples were separately processed and compared using solid-phase extraction (SPE) and acetonitrile (ACN) protein precipitation. RESULTS: >0.99. Both the SPE and the ACN precipitation methods had excellent and comparable reproducibility. The intra-day and inter-day coefficients of variation (CVs) were <3% and <6%. There was 89% and 88% hepcidin recovery by SPE and ACN preparation. Measurement of secondary reference material using non-traceable calibrators yielded up to 30% positive bias, comparable with values obtained by an external comparator. Hepcidin was stable in serum at ambient temperature and at 4°C. The relative errors (REs) were ≤1.2% and ≤4.4%, respectively. The freeze-thaw (-70°C) stability after 3 cycles showed a relative error (RE) of ≤1.8%. The impact on hepcidin recovery due to hemolysis (4+), lipemia (4+) and Icterus (4+) was <3%. CONCLUSIONS: We have developed and validated a simple, sensitive, robust and cost-effective HPLC/MS/MS method for the quantitation of serum hepcidin. The method uses ACN protein precipitation for sample preparation and reversed-phase normal-flow HPLC. Sample preparation is inexpensive; it can be automated with a liquid handling system to allow high-throughput application.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".