Allopurinol use predicts lower low-density lipoprotein cholesterol in patients with pre-dialysis chronic kidney disease—a prospective cohort study
Bibliographic record
Abstract
ABSTRACT Background Hyperuricemia influences lipid metabolism, yet relationships between urate-lowering therapy with allopurinol, serum urate, and lipid levels in patients with chronic kidney disease remain underexplored. Methods This was a post-hoc analysis of 1970 participants of the CAN AIM to PREVENT who had pre-dialysis chronic kidney disease and were not receiving lipid-lowering therapy or febuxostat. Joint generalized structural equation modeling was used to investigate associations between allopurinol use (yes or no), serum urate [as a continuous or categorical variable (target if <6 mg/dl or high if ≥6 mg/dl)], and lipid levels [total cholesterol, low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides) assessed every 6 months for up to 3 years, along with time-to-event outcomes (death or initiation of renal replacement therapy), adjusting for demographic and clinical factors. Mediation analysis was used to determine allopurinol's direct and indirect effects (via urate) on lipid levels. Results Allopurinol use independently predicted lower total cholesterol (–7.94%, 95% CI: −12.13% to –3.54%, p < 0.001) and LDL-C [–13.84% (–21.14 to –5.87), p = 0.001]. Serum urate independently predicted a small increase in LDL-C [0.02% per mg/dl (0.009 to 0.03), p < 0.001]. Patients on allopurinol with target urate had lower LDL-C compared to those not on allopurinol with target urate [–4.46% (–8.25 to –0.50), p = 0.027] and those on allopurinol with high urate [–10.15% (–13.16 to –7.04), p < 0.001]. Mediation analysis showed that serum urate indirectly mediated only 24% of the effect of allopurinol on LDL-C. Conclusion Allopurinol use predicted lower total and especially LDL cholesterol independently of serum urate in this cohort of patients with pre-dialysis chronic kidney disease. Future studies could investigate underlying mechanisms, evaluate clinical implications, and confirm these findings in this and other populations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".