Beta-2 microglobulin and all-cause mortality in the era of high-flux hemodialysis: results from the Dialysis Outcomes and Practice Patterns Study
Bibliographic record
Abstract
BACKGROUND: Beta-2 microglobulin (β2M) accumulates in hemodialysis (HD) patients, but its consequences are controversial, particularly in the current era of high-flux dialyzers. High-flux HD treatment improves β2M removal, yet β2M and other middle molecules may still contribute to adverse events. We investigated patient factors associated with serum β2M, evaluated trends in β2M levels and in hospitalizations due to dialysis-related amyloidosis (DRA), and estimated the effect of β2M on mortality. METHODS: = 5332). We evaluated time trends with linear and Poisson regression and mortality with Cox regression. RESULTS: Median β2M changed nonsignificantly from 2.71 to 2.65 mg/dL during 2011-18 (P = 0.87). Highest β2M tertile patients (>2.9 mg/dL) had longer dialysis vintage, higher C-reactive protein and lower urine volume than lowest tertile patients (≤2.3 mg/dL). DRA-related hospitalization rates [95% confidence interval (CI)] decreased from 1998 to 2018 from 3.10 (2.55-3.76) to 0.23 (0.13-0.42) per 100 patient-years. Compared with the lowest β2M tertile, adjusted mortality hazard ratios (95% CI) were 1.16 (0.94-1.43) and 1.38 (1.13-1.69) for the middle and highest tertiles. Mortality risk increased monotonically with β2M modeled continuously, with no indication of a threshold. CONCLUSIONS: DRA-related hospitalizations decreased over 10-fold from 1998 to 2018. Serum β2M remains positively associated with mortality, even in the current high-flux HD era.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".