Association between the plasma proteome and serum ascorbic acid concentrations in humans
Bibliographic record
Abstract
Vitamin C has been associated with a reduced risk of chronic diseases, but the biological pathways regulated by vitamin C are not all known. The objective was to use a proteomics approach to identify plasma proteins associated with circulating levels of ascorbic acid. Men and women (n=1,022) 20–29 years of age from the Toronto Nutrigenomics and Health Study completed a general health and lifestyle questionnaire and 196‐item food frequency questionnaire and provided a fasting blood sample. Circulating ascorbic acid was analyzed by HPLC and a mass spectrometry‐based multiple reaction monitoring method measured 54 proteins abundant in plasma. Mean protein concentrations were compared across tertiles of serum ascorbic acid using analysis of covariance. Levels of complement C9, ceruloplasmin, alpha‐1‐anti‐trypsin, angiotensinogen, complement C3, vitamin D binding protein, and plasminogen were inversely associated with levels of ascorbic acid (P<0.0009). The inverse association between ascorbic acid and vitamin D binding protein was highest in those with higher levels of total 25‐hydroxyvitamin D (interaction P=0.2). In conclusion, several plasma proteins from various physiological pathways are significantly associated with circulating levels of ascorbic acid in a population of young adults. This research was funded by the Advanced Foods and Materials Network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".