Association between Serum Glycerol-3-Phosphate and Fibroblast Growth Factor 23 in Nondiabetic Incident Hemodialysis Patients
Bibliographic record
Abstract
Background: Glycerol-3-phosphate (G3P), a byproduct of glycolysis that can be derived from injured kidneys, has been identified as a stimulator of fibroblast growth factor-23 (FGF-23) production. Furthermore, G3P is strongly correlated with circulating FGF-23 in people without kidney disease and in the setting of AKI. Since FGF-23 increases risk of mortality in patients with ESKD, understanding the relationship between G3P and FGF-23 may inform our diagnostic and treatment practices. Methods: In a cross-sectional study of 99 nondiabetic patients with ESKD on incident hemodialysis from the Predictors of Arrhythmic and Cardiovascular Risk in End Stage Renal Disease (PACE) cohort, we measured baseline G3P by mass spectrometry and C-terminal FGF-23 by ELISA. Participant characteristics were compared among G3P terciles using the Fisher’s exact or Kruskal-Wallis’s tests. Linear regression was used to examine the association between G3P terciles with log transformed FGF-23 while adjusting for sociodemographics, prevalent coronary artery disease, phosphorus, and parathyroid hormone. Results: The median age of participants was 54 years (IQR 44-63), 38% were women, 71% were Black, 27% had coronary artery disease. Median FGF-23 level was 777 (IQR 222-1310) RU/mL. Participants with higher G3P were younger and had higher levels of serum phosphate and intact parathyroid hormone. Higher G3P was associated with higher FGF-23 in both unadjusted and adjusted models (p<0.001 and 0.004, respectively). Those with G3P in the highest tercile had 92% higher level of FGF-23 (95% CI: 5%, 252%; p=0.004) compared to participants within the lowest G3P tertile. Conclusion: Higher G3P is positively associated with higher FGF-23 in those with ESKD. Our findings suggest that there may be a kidney-independent role of G3P in FGF-23 regulation. Future studies will need to address the relationship between G3P and FGF-23 with earlier stages of CKD. Funding: NIDDK Support
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".