COMPLEMENTARY MASS SPECTROMETRIC ASSAYS AND AUTOMATED DATA PRE-PROCESSING TOOLS IN METABOLOMICS FOR STUDYING VITAMIN D INTERVENTIONS IN CRITICALLY ILL CHILDREN
Bibliographic record
Abstract
Vitamin D represents a group of pro-hormones with pleiotropic effects on human health that regulate calcium homeostasis, bone formation, immune function, and muscle protein turn-over. Despite the historic success of vitamin D3 to prevent nutritional rickets, vitamin D deficiency (VDD) remains a global health concern that disproportionately impacts vulnerable demographic groups, including critically ill children in pediatric intensive care units (PICUs) who are at greater risk for morbidity and mortality than vitamin D sufficient patients. This has recently inspired nutritional interventions using high-dose vitamin D3 supplementation as a safe, low-cost, and potentially efficacious treatment to improve the clinical outcomes of critically ill children with VDD, such as reduced illness severity, lower sepsis incidents, and earlier hospital discharge. This thesis will outline the development of new advances in high resolution mass spectrometry (MS)-based methods and data pre-processing tools for comprehensive metabolomic and lipidomic analyses that can support on-going vitamin D clinical trials that aim to shine greater light into the mechanisms and biological effects of the “sunshine vitamin” in high-risk critically ill children. Chapter II describes a high throughput “separation-free” assay for the rapid screening of vitamin D status in critically ill children based on direct infusion-tandem mass spectrometry (DI-MS/MS) in conjunction with pre-column chemical derivatization using 2-nitrosopyridine. This assay was extensively validated using certified reference samples for quality assurance and compared to both LC-MS/MS and a commercial competitive chemiluminescent immunoassay in PICU patients (n=30). The importance of high-throughput techniques in metabolomics is further highlighted in Chapter III, which focuses on the development of an automated data pre-processing tool for performing untargeted metabolite profiling (i.e., metabolomics) when using high throughput screening methods based on multisegment injection-capillary electrophoresis-mass spectrometry (MSI-CE-MS). Currently, manual data pre-processing techniques are required in metabolomic data workflows given the spectral complexity of multiplexed separations and the large migration time variability in MSI-CE-MS, which is time-consuming, resource intensive, and prone to bias. Herein, Chapter III introduces PeakMeister to accelerate data pre-processing when performing serum metabolomic analyses with MSI-CE-MS under positive ion mode that takes advantage of a robust peak detection strategy that relies on migration time indices that avoids complicated time warping and electrophoretic mobility transformations. MSI-CE-MS in conjunction with PeakMeister achieved excellent peak picking fidelity and integration accuracy that was 30-times faster than manual integration by an experienced analyst when analyzing over 5,000 serum samples from a Brazilian nutritional survey of young children/infants under 5 years of age. Lastly, the development of a reversed-phase ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) assay is described in Chapter IV, which can measure various vitamin D metabolites and their isomers in conjunction with untargeted lipidomic profiling to provide better insight into the treatment response differences of PICU patients with VDD following a single large bolus dose of up to 400,000 IU of vitamin D3 relative to placebo. MSI-CE-MS in conjunction with PeakMeister was also applied for untargeted serum metabolic profiling in this phase-II randomized clinical control trial involving a cohort of critically ill children (n=31). For the first time, Chapter IV reports the discovery of circulating 3-epi-D3 while also revealing that the 25-OH-D treatment response of supplemented critically ill children were modulated by differences in vitamin D3 bioavailability and C3-epimerase activity with evidence that individuals who attained vitamin D repletion (> 75 nmol/L) within 3 days had a metabolic phenotype consistent with lower oxidative stress, inflammation, and muscle wasting. As a whole, this thesis highlights the importance of rigorous analytical method validation from sample preparation to data pre-processing and contributes new advances to the field of precision nutrition to explain the conflicting results reported in high-dose vitamin D interventional trials.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".