Vitamin D Binding Protein Is Not Involved in Vitamin D Deficiency in Patients with Chronic Kidney Disease
Bibliographic record
Abstract
OBJECTIVE: This study was designed to evaluate vitamin D status with separate determination of 25-OH D2 and 25-OH D3 and its relationship to vitamin D binding protein (VDBP) in patients with chronic kidney disease (CKD) and long-term haemodialysis patients (HD). METHODS: 45 CKD patients, 103 HD patients, and 25 controls (C) were included. Plasma vitamin D concentrations were determined using chromatography and VDBP in serum and urine in CKD using enzyme immunoassay. RESULTS: Plasma vitamin D levels were lower in CKD (30.16 ± 16.74 ng/mL) and HD (18.85 ± 15.85 ng/mL) versus C (48.72 ± 18.35 ng/mL), P < 0.0001. 25-OH D3 was the dominant form of vitamin D. Serum VDBP was higher in CKD (273.2 ± 93.8 ug/mL) versus C (222 ± 87.6 ug/mL) and HD (213.8 ± 70.9 ug/mL), P = 0.0003. Vitamin D/VDBP ratio was the highest in C and the lowest in HD; however, there was no correlation between vitamin D and VDBP. Urinary concentration of VDBP in CKD (0.25 ± 0.13 ug/mL) correlated with proteinuria (r = 0.43, P = 0.003). CONCLUSIONS: Plasma levels of vitamin D are decreased in CKD patients and especially in HD patients. 25-OH D3 was the major form of vitamin D. Despite urinary losses of VDBP, CKD patients had higher serum VDBP concentrations, indicating compensatory enhanced production. Vitamin D binding protein is not involved in vitamin D deficiency.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".