Intensive Risk Factor Management and Cardiovascular Autonomic Neuropathy in Type 2 Diabetes: The ACCORD Trial
Bibliographic record
Abstract
OBJECTIVE The effects of preventive interventions on cardiovascular autonomic neuropathy (CAN) remain unclear. We examined the effect of intensively treating traditional risk factors for CAN, including hyperglycemia, hypertension, and dyslipidemia, in individuals with type 2 diabetes (T2D) and high cardiovascular risk participating in the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. RESEARCH DESIGN AND METHODS CAN was defined as heart rate variability indices below the fifth percentile of the normal distribution. Of 10,251 ACCORD participants, 71% (n = 7,275) had a CAN evaluation at study entry and at least once after randomization. The effects of intensive interventions on CAN were analyzed among these subjects through generalized linear mixed models. RESULTS As compared with standard intervention, intensive glucose treatment reduced CAN risk by 16% (odds ratio [OR] 0.84, 95% CI 0.75–0.94, P = 0.003)—an effect driven by individuals without cardiovascular disease (CVD) at baseline (OR 0.73, 95% CI 0.63–0.85, P < 0.0001) rather than those with CVD (OR 1.10, 95% CI 0.91–1.34, P = 0.34) (Pinteraction = 0.001). Intensive blood pressure (BP) intervention decreased CAN risk by 25% (OR 0.75, 95% CI 0.63–0.89, P = 0.001), especially in patients ≥65 years old (OR 0.66, 95% CI 0.49–0.88, P = 0.005) (Pinteraction = 0.05). Fenofibrate did not have a significant effect on CAN (OR 0.91, 95% CI 0.78–1.07, P = 0.26). CONCLUSIONS These data confirm a beneficial effect of intensive glycemic therapy and demonstrate, for the first time, a similar benefit of intensive BP control on CAN in T2D. A negative CVD history identifies T2D patients who especially benefit from intensive glycemic control for CAN prevention.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".