Development of an LC-MS/MS Proposed Candidate Reference Method for the Standardization of Analytical Methods to Measure Lipoprotein(a)
Bibliographic record
Abstract
BACKGROUND: Use of lipoprotein(a) concentrations for identification of individuals at high risk of cardiovascular diseases is hampered by the size polymorphism of apolipoprotein(a), which strongly impacts immunochemical methods, resulting in discordant values. The availability of a reference method with accurate values expressed in SI units is essential for implementing a strategy for assay standardization. METHOD: A targeted LC-MS/MS method for the quantification of apolipoprotein(a) was developed based on selected proteotypic peptides quantified by isotope dilution. To achieve accurate measurements, a reference material constituted of a human recombinant apolipoprotein(a) was used for calibration. Its concentration was assigned using an amino acid analysis reference method directly traceable to SI units through an unbroken traceability chain. Digestion time-course, repeatability, intermediate precision, parallelism, and comparability to the designated gold standard method for lipoprotein(a) quantification, a monoclonal antibody-based ELISA, were assessed. RESULTS: A digestion protocol providing comparable kinetics of digestion was established, robust quantification peptides were selected, and their stability was ascertained. Method intermediate imprecision was below 10% and linearity was validated in the 20-400 nmol/L range. Parallelism of responses and equivalency between the recombinant and endogenous apo(a) were established. Deming regression analysis comparing the results obtained by the LC-MS/MS method and those obtained by the gold standard ELISA yielded y = 0.98*ELISA +3.18 (n = 64). CONCLUSIONS: Our method for the absolute quantification of lipoprotein(a) in plasma has the required attributes to be proposed as a candidate reference method with the potential to be used for the standardization of lipoprotein(a) assays.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".