Association between LDL-C and Risk of Myocardial Infarction in CKD
Bibliographic record
Abstract
LDL cholesterol (LDL-C) is an important marker of coronary risk in the general population, but its utility in people with CKD is unclear. We studied 836,060 adults from the Alberta Kidney Disease Network with at least one measurement of fasting LDL-C, estimated GFR (eGFR), and proteinuria between 2002 and 2009. All participants were free of stage 5 CKD at cohort entry. We followed participants from first eGFR measurement to March 31, 2009; we used validated algorithms applied to administrative data to ascertain primary outcome (hospitalization for myocardial infarction) and Cox regression to calculate adjusted hazard ratios (HRs) for myocardial infarction by LDL-C categories within eGFR strata. During median follow-up of 48 months, 7762 patients were hospitalized for myocardial infarction, with incidence highest among participants with the lowest eGFR. Compared with 2.6-3.39 mmol/L (referent), the risk associated with having LDL-C above 4.9 mmol/L seemed greatest for GFR≥90 ml/min per 1.73 m(2) and least for eGFR=15-59.9 ml/min per 1.73 m(2). Specifically, the adjusted HRs (95% confidence intervals) of myocardial infarction associated with LDL-C of ≥4.9 compared with 2.6-3.39 mmol/L in participants with eGFR=15-59.9, 60-89.9, and ≥90 ml/min per 1.73 m(2) were 2.06 (1.59, 2.67), 2.30 (2.00, 2.65), and 3.01 (2.46, 3.69). In conclusion, the association between higher LDL-C and risk of myocardial infarction is weaker for people with lower baseline eGFR, despite higher absolute risk of myocardial infarction. Increased LDL-C may be less useful as a marker of coronary risk among people with CKD than the general population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".