Associations of Calcium from Food Sources versus Phosphate Binders with Serum Calcium and FGF23 in Hemodialysis Patients
Bibliographic record
Abstract
Background: Dysregulated serum calcium and FGF23 are associated with increased mortality and morbidity rates in patients receiving hemodialysis. Preliminary data suggest serum calcium regulates FGF23 secretion independently of serum phosphate, parathyroid hormone, and 25-OH vitamin D. It is unclear to what extent dietary and prescription sources of calcium influence calcium and FGF23 levels, and whether they confound this relationship. In this cross-sectional analysis of a multi-ethnic cohort of prevalent hemodialysis patients, association of dietary calcium and prescribed calcium were examined against serum calcium and FGF23. Bi- and multivariable linear regression was used for all analyses. Results: 81 patients (mean age 58 years, dialysis vintage 2 years, 51 men) participated. Dietary calcium was inversely associated with FGF23 (p = 0.04) however association of FGF23 with prescribed calcium did not reach statistical significance (0.08). In multivariable models, dietary calcium and prescribed calcium were associated in opposing directions with serum calcium (prescribed calcium; ß-coefficient = −0.35, p = 0.005 versus dietary calcium; ß-coefficient = 0.35, p = 0.03). FGF23 was independently associated with serum calcium (p = 0.007). Conclusions: We found differing, sometimes opposing, associations between serum calcium and FGF23 levels when considering prescribed versus dietary sources of calcium. Serum calcium and FGF23 were strongly correlated regardless of possible confounders examined in this hemodialysis cohort. Dietary calcium was associated with higher serum calcium and lower FGF23 concentrations, while prescribed calcium was only inversely associated with serum calcium. Further studies are required to confirm these associations and determine causality.
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 | 0.001 | 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 | 0.000 | 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".