Cholesterol Levels and Cardiovascular Outcomes Following Statin Initiation for Primary Prevention
Bibliographic record
Abstract
BACKGROUND: Non-high-density lipoprotein cholesterol (HDL-C) is increasingly incorporated into guidelines along with low-density lipoprotein cholesterol (LDL-C) to guide lipid-lowering therapy decisions. OBJECTIVES: The purpose of this study was to examine patterns of LDL-C and non-HDL-C levels after statin initiation for primary prevention and their association with incident cardiovascular events. METHODS: This was a population-based cohort study in Ontario, Canada, among persons aged ≥66 years starting a statin for primary prevention between January 1, 2012, and December 31, 2019. We identified those with a lipid panel in the 1-year after starting a statin and categorized individuals based on LDL-C and non-HDL-C thresholds for intensification in the 2021 Canadian Cardiovascular Society dyslipidemia guidelines. We stratified by diabetes/chronic kidney disease (CKD) status. The primary outcome was the composite of all-cause mortality or cardiovascular events, with follow-up to December 31, 2020. We used a Cox proportional hazards model for analysis. RESULTS: Our cohort comprised 125,013 people. The median follow-up was 2.5 years. Compared with those meeting both LDL-C and non-HDL-C thresholds, being above both thresholds was associated with an increased rate of the primary outcome for people without diabetes/CKD (HR: 1.10; 95% CI: 1.05-1.15) and for those with diabetes/CKD (HR: 1.16; 95% CI: 1.09-1.23). Being below the LDL-C threshold but above non-HDL-C threshold was associated with an increased rate of the primary outcome for people with diabetes/CKD (HR: 1.16; 95% CI: 1.03-1.30). CONCLUSIONS: These findings support the residual risk associated with incompletely controlled LDL-C or non-HDL-C levels after statin initiation for primary prevention.
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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) | 0.000 | 0.000 |
| 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".