Effectiveness and Safety of Statins in Type 2 Diabetes According to Baseline Cardiovascular Risk
Bibliographic record
Abstract
BACKGROUND: Whether statins benefit patients with type 2 diabetes mellitus (T2DM) with low predicted 10-year cardiovascular risk is uncertain. OBJECTIVE: To evaluate the effectiveness and safety of statin initiation for primary prevention among adults with T2DM stratified by predicted 10-year risk for cardiovascular disease (CVD). DESIGN: Cohort study using target trial emulation. SETTING: U.K. primary care using the IQVIA Medical Research Data database. PARTICIPANTS: Persons aged 25 to 84 years with a diagnosis of T2DM between 2005 and 2016 and no history of coronary artery disease, myocardial infarction, stroke, heart failure, myopathy, liver disease, rheumatic heart disease, schizophrenia, or cancer. INTERVENTION: Statin initiation versus noninitiation, with estimation of the observational analogues of the intention-to-treat effect. Statin initiators were propensity score-matched to noninitiators in a 1:4 ratio within 4 QRISK3 strata of 10-year predicted cardiovascular risk: low (<10%), intermediate (10% to 19%), high (20% to 29%), and very high (≥30%). MEASUREMENTS: Absolute risk differences (RDs) and risk ratios (RRs) at 10 years of follow-up for all-cause mortality and major CVD, as well as myopathy and liver dysfunction. RESULTS: Statin initiation was associated with reductions in all-cause mortality and major CVD across QRISK3 strata. In the low-risk stratum, RDs and RRs were -0.53% (95% CI, -0.90% to -0.08%) and 0.80 (95% CI, 0.67 to 0.97), respectively, for all-cause mortality and -0.83% (95% CI, -1.28% to -0.34%) and 0.78 (95% CI, 0.66 to 0.91), respectively, for major CVD. A small increased risk for myopathy was observed in the moderate-risk stratum only, and there was no associated increased risk for liver dysfunction in any stratum. LIMITATIONS: Unmeasured confounding and underascertainment of some hospitalization outcomes. CONCLUSION: Statin use in T2DM for primary prevention was associated with reductions in all-cause mortality and major CVD across the full spectrum of predicted cardiovascular risk. PRIMARY FUNDING SOURCE: National Natural Science Foundation of China.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".