Changes in Biomarker Profile and Left Ventricular Hypertrophy Regression: Results from the Frequent Hemodialysis Network Trials
Bibliographic record
Abstract
BACKGROUND: Regression of left ventricular hypertrophy (LVH) is feasible with more frequent hemodialysis (HD). We aimed to ascertain pathways associated with regression of left ventricular mass (LVM) in patients enrolled in the Frequent HD Network (FHN) trials. METHODS: This was a post hoc observational cohort study. We hypothesized LVH regression with frequent HD was associated with a different cardiovascular biomarker profile. Regressors were defined as patients who achieved a reduction of more than 10% in LVM at 12 months. Progressors were defined as patients who had a minimum of 10% increase in LVM at 12 months. RESULTS: Among 332 randomized patients, 243 had biomarker data available. Of these, 121 patients did not progress or regress, 77 were regressors, and 45 were progressors. Mean LVM change differed between regressors and progressors by -65.6 (-74.0 to -57.2) g, p < 0.001. Regressors had a median (interquartile range) increase in dialysis frequency (from 3.0 [3.0-3.0] to 4.9 [3-5.7] per week, p = 0.001) and reductions in pre-dialysis systolic (from 149.0 [136.0-162.0] to 136.0 [123.0-152.0] mm Hg, p < 0.001) and diastolic (from 83.0 [71.0-91.0] to 76.0 [68.0-84.0] mm Hg, p < 0.001) blood pressures. Klotho levels increased in regressors versus progressors (76.9 [10.5-143.3] pg/mL, p = 0.024). Tissue inhibitors of metalloproteinase (TIMP)-2 levels fell in regressors compared to progressors (-7,853 [-14,653 to -1,052] pg/mL, p = 0.024). TIMP-1 and log (brain natriuretic -peptide [BNP]) levels also tended to fall in regressors. Changes in LVM correlated inversely with changes in klotho (r = -0.24, p = 0.014). -Conclusions: Markers of collagen turnover and changes in klotho levels are potential novel pathways associated with regression of LVH in the dialysis population, which will require further prospective validation.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".