Proteome-Wide Changes in Blood Biomarkers During Hemodialysis
Bibliographic record
Abstract
Introduction During hemodialysis, proteins in the blood can decrease in concentration due to diffusion, convective clearance, dialyzer adsorption, or cellular uptake, while others may increase in concentration due to production, cellular release, secretion, or ultrafiltration of water. We examined the impact of hemodialysis on blood protein concentrations on a proteome-wide scale. Methods A nested cohort of 44 patients (25 male, 19 female) including 29 with intradialytic hypotension were selected from the prospective Hemodialysis Outcomes and SympToms assessment (HOST) cohort. 1,163 proteins were measured before and after a hemodialysis treatment using Olink. Pre- and post-dialysis concentrations were compared, and the impact of protein characteristics and intradialytic hypotension on protein concentration was evaluated. Results 189 proteins (16%) significantly decreased and 54 (5%) significantly increased in concentration. Change in concentration was associated with protein molecular weight (r = 0.37, P = 2.8 x 10 -16 ), isoelectric point (r = -0.26, P = 6.4 x 10 -14 ), and pre-dialysis concentration (r = -0.21, P = 3.0 x 10 -9 ). There was enrichment for cardiovascular biomarkers in those nominally associated with a drop in systolic blood pressure during treatment ( P = 2.8 x 10 -8 ). Conclusions Changes in the blood proteome are detectable during hemodialysis on a high throughput scale. Protein properties and intradialytic hypotension events appear associated with changes in biomarker concentration. Larger proteome-wide biomarker studies may identify measures of dialysis adequacy and reveal pathological processes contributing to adverse effects of dialysis.
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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 | 0.000 |
| 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".