Serum 25‐hydroxyvitamin D and IgE – a significant but nonlinear relationship
Bibliographic record
Abstract
BACKGROUND: Hormonal vitamin D system affects the determination of T-cell responses. It is unknown if there is an association between vitamin D status and allergic conditions. Our aim was to investigate differences in serum IgE concentrations by vitamin D status [measured by 25(OH)D] and by a genetic variation in a key vitamin D activation enzyme (CYP27B1) previously shown to be associated with type 1 diabetes. METHODS: 9377 participants in the 1958 British birth cohort completed a biomedical assessment at 45 years of age ; 7288 eligible participants had data on 25(OH)D and IgE, with 6429 having further information on CYP27B1 genotype ()1260C>A). RESULTS: There was a nonlinear association between 25(OH)D and IgE (P-value for curvature = 0.0001). Compared with the reference group with the lowest IgE concentrations [25(OH)D 100-125 nmol/l], IgE concentrations were 29% higher (95% CI 9-48%) for participants with the 25(OH)D <25 nmol/l, and 56% higher (95% CI 17-95%) for participants with 25(OH)D >135 nmol/l (adjusted for sex, month, smoking, alcohol consumption, time spent outside, geographical location, social class, PC/TV time, physical activity, body mass index and waist circumference). CYP27B1 genotype was associated with both 25(OH)D (difference for A vs. C allele: 1.88%, 95% CI 0.37-3.4%, P = 0.01) and IgE concentrations ()6.59%, )11.6% to )1.42%, P = 0.01). CONCLUSIONS: These data suggest that there may be a threshold effect with both low and high 25(OH)D levels associated with elevated IgE concentrations. The same CYP27B1 allele that is protective of diabetes was associated with increased IgE concentrations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".