Ethnic and seasonal variations in FGF-23 and markers of chronic kidney disease–mineral and bone disorder
Bibliographic record
Abstract
Background: Fibroblast growth factor 23 (FGF-23) and other markers of chronic kidney disease-mineral and bone disorder (CKD-MBD) provide valuable insights into disease processes, treatment options and patient prognosis. However, limited research has explored potential associations with ethnicity or season, particularly in multi-ethnic populations residing in high-latitude regions. Methods: We evaluated CKD-BMD markers in a diverse cohort of CKD patients, who were participants of The CANADIAN AIM to PREVENT (the CAN AIM to PREVENT) study. FGF-23, calcium, phosphate, 25-hydroxyvitamin D (25-OHD) and intact parathyroid hormone (iPTH) in 1234 participants with pre-dialysis CKD (mean estimated glomerular filtration rate: 41.8 ± 14.3 mL/min) were analyzed. Mixed-effects general linear regression models adjusted for demographic and biological factors were used to compare repeated measurements across patient groups categorized by ethnicity (East Asian, White, South Asian, Black, Southeast Asian) and seasons. Results: Compared with other groups, White participants exhibited 8.0%-18.5% higher FGF-23 levels, Black participants had 0.17-0.32 mg/dL higher calcium levels, White participants had 10.0%-20.1% higher 25-OHD levels, South Asian participants had 7.3%-20.1% lower 25-OHD levels and Black participants had 22.1-73.8% higher iPTH levels, while East Asian participants had 10.7%-73.8% lower iPTH levels. Seasonal variations were also observed. FGF-23 levels were 11.9%-15.5% higher in summer compared with other seasons, while calcium levels were 0.03-0.06 mg/dL lower in summer. 25-OHD levels were 5.6%-10.6% higher in summer and autumn compared with other seasons. Conclusions: This study shows that FGF-23 and CKD-MBD markers in a Canadian pre-dialysis CKD cohort vary independently by ethnicity and season. Further research is needed to understand the reasons and clinical significance of these findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".