Vitamin D status and plasma proteomic profiles
Bibliographic record
Abstract
Vitamin D deficiency, defined as serum 25(OH)D <27.5 nmol/L, has been associated with biomarkers of inflammation and increased risk of type 2 diabetes (T2D) and cardiovascular disease (CVD). Our objective was to examine the association between vitamin D status and a panel of 55 common plasma proteins involved in inflammation, endothelial activation and lipid metabolism in an ethnically diverse population (n=1107) of young Canadian adults aged 20–29 years. Protein concentrations were measured with a tandem mass spectrometry‐based multiple reaction monitoring assay. Using principal components analysis, 4 main independent proteomic profiles were identified. Profiles 1 and 2 each included proteins involved in multiple pathways, while profile 3 consisted of pro‐inflammatory proteins and profile 4 included coagulation proteins. Generalized linear models adjusted for age, sex, BMI, season, ethnicity, physical activity and energy intake showed that vitamin D status was associated with differences in average profile loading scores. Profile 1 was positively associated with vitamin D status, while profiles 2 and 3 were inversely associated with vitamin D status. Vitamin D appears to be associated with distinct plasma proteomic profiles and may regulate physiologic pathways associated with T2D and CVD. Research Support from the Public Health Agency of Canada and the Advanced Foods and Materials Network. Grant Funding Source : Public Health Agency of Canada, Advanced Foods and Materials Network
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".