Fibroblast growth factor 23 and renal function among young and healthy individuals
Bibliographic record
Abstract
BACKGROUND: Fibroblast growth factor 23 (FGF-23), an osteocyte hormone involved in the regulation of phosphate metabolism, is associated with incident and progressive chronic kidney disease. We aimed to assess the association of FGF-23 with renal parameters, vascular function and phosphate metabolism in a large cohort of young and healthy individuals. METHODS: Healthy individuals aged 25-41 years were included in a prospective population-based study. Fasting venous blood and morning urinary samples were used to measure plasma creatinine, cystatin C, endothelin-1, phosphate and plasma FGF-23 as well as urinary creatinine and phosphate. Multivariable regression models were constructed to assess the relationship of FGF-23 with parameters of renal function, endothelin-1 and fractional phosphate excretion. RESULTS: The median age of 2077 participants was 37 years, 46% were males. The mean estimated glomerular filtration rate (eGFR - CKD-EPI creatinine-cystatin C equation) and fractional phosphate excretion were 110 mL/min/1.73 m2 and 8.7%, respectively. After multivariable adjustment, there was a significant inverse relationship of FGF-23 with eGFR (β per 1 log-unit increase -3.81; 95% CI [-5.42; -2.20]; p<0.0001). Furthermore, we found a linear association between FGF-23 and endothelin-1 (β per 1 log-unit increase 0.06; [0.01, 0.11]; p=0.01). In addition, we established a significant relationship of FGF-23 with fractional phosphate excretion (β per 1 log-unit increase 0.62; [0.08, 1.16]; p=0.03). CONCLUSIONS: Increasing plasma FGF-23 levels are strongly associated with decreasing eGFR and increasing urinary phosphate excretion, suggesting an important role of FGF-23 in the regulation of kidney function in young and healthy adults.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".