The effect of frequent hemodialysis on matrix metalloproteinases, their tissue inhibitors, and FGF23: Implications for blood pressure and left ventricular mass modification in the Frequent Hemodialysis Network trials
Bibliographic record
Abstract
BACKGROUND: Frequent hemodialysis modifies serum phosphorus, blood pressure, and left ventricular mass (LVM). We ascertained whether frequent hemodialysis is associated with specific changes in biomarker profile among patients enrolled in the frequent hemodialysis network (FHN) trials. METHODS: This was a post hoc analysis of biomarkers among patients enrolled to the FHN trials. In particular, we hypothesized that frequent hemodialysis is associated with changes in a specific set of biomarkers which are linked with changes in blood pressure or LVM. RESULTS: Among 332 randomized patients, 243 had biomarker data available. Of these, 124 patients were assigned to 3-times-a-week hemodialysis (94 [Daily Trial] and 30 [Nocturnal Trial]) and 119 patients were assigned to 6-times-a-week hemodialysis (87 [Daily Trial] and 32 [Nocturnal Trial]). Frequent hemodialysis lowered phosphate, blood pressures, LVM, log fibroblast growth factor (FGF)23, and tissue inhibitors of metalloproteinase (TIMP)-2 levels. The fall in phosphate was associated with changes in FGF23 (r = 0.48, P < 0.001) [Daily Trial] and (r = 0.55, P < 0.001) [Nocturnal Trial]) and tended to be associated with changes in systolic blood pressure (r = 0.18, P = 0.057) [Daily Trial] and (r = 0.31, P = 0.04) [Nocturnal Trial]. Within the Daily Trial, changes in MMP2 (r = 0.20, P = 0.034) were associated with changes in LVM. In the Nocturnal Trial, changes in TIMP-1 (r = 0.37, P = 0.029) and MMP 9 (r = -0.38, P = 0.01) were associated with LVM changes. MMP2 changes were associated with changes in systolic blood pressure. CONCLUSIONS: Reduction of serum phosphate by frequent hemodialysis may modulate FGF23 levels and systolic blood pressure. Markers of matrix turnover are associated with LVM changes. Frequent hemodialysis may affect pathological mediators of chronic kidney disease-mineral bone-metabolism disorder.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".