Analysis of 2-methylcitric acid, methylmalonic acid, and total homocysteine in dried blood spots by LC-MS/MS for application in the newborn screening laboratory: A dual derivatization approach
Bibliographic record
Abstract
Inborn errors of propionate, cobalamin and methionine metabolism are targets for Newborn Screening (NBS) in most programs world-wide, and are primarily screened by analyzing for propionyl carnitine (C3) and methionine in dried blood spot (DBS) cards using tandem mass spectrometry (MS/MS). Single-tier NBS approaches using C3 and methionine alone lack specificity, which can lead to an increased false-positive rate if conservative cut-offs are applied to minimize the risk of missing cases. Implementation of liquid chromatography tandem mass spectrometry (LC-MS/MS) second-tier testing for 2-methylcitric acid (MCA), methylmalonic acid (MMA), and homocysteine (HCY) from the same DBS card can improve disease screening performance by reducing the false-positive rate and eliminating the need for repeat specimen collection. However, DBS analysis of MCA, MMA, and HCY by LC-MS/MS is challenging due to limited specimen size and analyte characteristics leading to a combination of low MS/MS sensitivity and poor reverse-phase chromatographic retention. Sufficient MS response and analytical performance can be achieved for MCA by amidation using DAABD-AE and by butylation for MMA and HCY. Herein we describe the validation of a second-tier dual derivatization LC-MS/MS approach to detect elevated MCA, MMA, and HCY in DBS cards for NBS. Clinical utility was demonstrated by retrospective analysis of specimens, an interlaboratory method comparison, and assessment of external proficiency samples. Imprecision was <10.8% CV, with analyte recoveries between 90.2 and 109.4%. Workflows and analytical performance characteristics of this second-tier LC-MS/MS approach are amenable to implementation in the NBS laboratory.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".