Insulin Dose and Cardiovascular Mortality in the ACCORD Trial
Bibliographic record
Abstract
OBJECTIVE: In the ACCORD trial, intensive treatment of patients with type 2 diabetes and high cardiovascular (CV) risk was associated with higher all-cause and CV mortality. Post hoc analyses have failed to implicate rapid reduction of glucose, hypoglycemia, or specific drugs as the causes of this finding. We hypothesized that exposure to injected insulin was quantitatively associated with increased CV mortality. RESEARCH DESIGN AND METHODS: We examined insulin exposure data from 10,163 participants with a mean follow-up of 5 years. Using Cox proportional hazards models, we explored associations between CV mortality and total, basal, and prandial insulin dose over time, adjusting for both baseline and on-treatment covariates including randomized intervention assignment. RESULTS: More participants allocated to intensive treatment (79%) than standard treatment (62%) were ever prescribed insulin in ACCORD, with a higher mean updated total daily dose (0.41 vs. 0.30 units/kg) (P < 0.001). Before adjustment for covariates, higher insulin dose was associated with increased risk of CV death (hazard ratios [HRs] per 1 unit/kg/day 1.83 [1.45, 2.31], 2.29 [1.62, 3.23], and 3.36 [2.00, 5.66] for total, basal, and prandial insulin, respectively). However, after adjustment for baseline covariates, no significant association of insulin dose with CV death remained. Moreover, further adjustment for severe hypoglycemia, weight change, attained A1C, and randomized treatment assignment did not materially alter this observation. CONCLUSIONS: These analyses provide no support for the hypothesis that insulin dose contributed to CV mortality in ACCORD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".