FGF23-Klotho Axis and Fractures in Patients Without and With Early CKD: A Case-Cohort Analysis of CARTaGENE
Bibliographic record
Abstract
CONTEXT: Whether fibroblast growth factor-23 (FGF23) and α-Klotho are associated with fractures, especially in chronic kidney disease (CKD), remains controversial. OBJECTIVE: We evaluated how FGF23, α-Klotho, and traditional mineral parameters predict fractures in individuals with and without early CKD. METHODS: We conducted a stratified case-cohort analysis using CARTaGENE, a population-based survey from Quebec, Canada. Individuals aged 40 to 69 years were selected according to outcome and CKD status (non-CKD: eGFR > 60 mL/min/1.73 m2; CKD stage 3: eGFR 30-60 mL/min/1.73 m2]). Baseline levels of c-terminal FGF23 (cFGF23), α-Klotho, parathyroid hormone (PTH), phosphate, and calcium were analyzed for associations with osteoporotic fracture incidence from recruitment (2009-2010) through March 2016. Adjusted Cox models were used, and predictors were treated linearly or flexibly using splines. RESULTS: A total of 312 patients (159 non-CKD; 153 CKD) were included; 98 had ≥ 1 fracture at any site during a median follow up of 70 months. Compared with non-CKD, CKD patients had increased levels of cFGF23 but similar levels of α-Klotho. cFGF23 was linearly associated with increased fracture incidence (adjusted HR = 1.81 [1.71, 1.93] per doubling for all participants). The association of α-Klotho with fracture followed a U-curve (overall P = 0.019) but was attenuated by adjustment for potential mediators (bone mineral density, phosphate, PTH). PTH and phosphate also had U-shaped associations with fracture. Associations were mostly similar between non-CKD and CKD. Adjustment for cFGF23 strongly attenuated the association between CKD status and fractures. CONCLUSION: cFGF23 is associated linearly with fracture incidence while α-Klotho, PTH, and phosphate levels have a U-shaped association.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".