FGF23 as a Potential Pathophysiological Factor in Peripheral Arterial Disease Associated with Chronic Kidney Disease
Bibliographic record
Abstract
Fibroblast growth factor 23 (FGF23) levels are often elevated in chronic kidney disease (CKD). FGF23 and inflammation are common characteristics in CKD, and both are associated with worse disease progression and the occurrence of complications. The existence of an interaction between FGF23 and inflammation has been suggested, each of which influences the expression and activity of the other, leading to a vicious feedback loop with adverse outcomes, including cardiovascular disease and mortality. In this work, we determined circulating FGF23 levels in a group of patients with CKD stages 3 and 4 subjected to elective femoral endarterectomy due to established peripheral artery disease (PAD), a condition resulting from an athero-inflammatory process, and we studied its associations with different inflammatory markers and mediators. We evaluated its association with serum tumor necrosis factor (TNF)α, interleukin (IL) 6, and IL10, as well as with the gene expression levels of these parameters and A disintegrin and metalloproteinase domain-containing protein (ADAM) 17 in femoral vascular tissue and peripheral blood circulating cells (PBCCs). We also analyzed its association with serum concentrations of C-reactive protein (CRP), the systemic immune inflammation index (SII), and the neutrophil-to-lymphocyte ratio (NLR). Finally, we determined the vascular immunoreactivity of protein TNFα in a subgroup of patients. FGF23 concentrations were independently associated with circulating and PBCC mRNA levels of TNFα. Worst kidney function and diabetes were also found to be contributing to FGF23 levels. Patients with higher levels of FGF23 also had greater vascular immunoreactivity for TNFα.
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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 | 0.000 | 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 | 0.000 |
| 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".