130-OR: Effects of Intensive Risk Factor Management on Cardiovascular Autonomic Neuropathy in Type 2 Diabetes: Findings from the ACCORD Trial
Bibliographic record
Abstract
Cardiovascular autonomic neuropathy (CAN) is a common complication that independently predicts cardiovascular (CV) morbidity and mortality in persons with type 2 diabetes (T2D). The effect of preventive interventions on CAN remains unclear. We examined the effects of intensively targeting hyperglycemia, hypertension, and dyslipidemia on CAN, in persons with T2D and high CV risk from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial. CAN was defined as heart rate variability indices below the 5th percentile of the normal distribution (standard deviation of all normal-to-normal R-R intervals [SDNN] <8.2 ms and root mean square of successive differences between normal-to-normal R-R intervals [rMSSD] <8.0 ms). Among 10,251 ACCORD participants, 71% (n=7,275) had valid CAN measures at study entry and at least once during follow-up. As compared to standard treatment, intensive glycemic control had a protective effect on CAN (OR=0.83, 95% CI 0.74 - 0.93, p=0.002), especially in persons with no CVD history (OR= 0.72, 0.62 - 0.83, p<0.0001). Intensive BP therapy also decreased the odds of CAN (OR=0.82, 0.72 - 0.94, p=0.01), especially in persons with positive CVD history (OR=0.71, 0.53 - 0.96, p=0.03). Fenofibrate did not have a significant impact on the dichotomous CAN outcome (OR=0.91, 0.77 - 1.06, p=0.22), but showed a significant benefit on continuous CAN outcomes SDNN (β=1.04, 1.01 - 1.08, p=0.02) and rMSSD (β=1.06, 1.02 - 1.10, p=0.003). No significant interactions were observed between treatments. This study is, to our knowledge, the first to demonstrate a clear benefit of intensive glycemic and BP control, and fenofibrate therapy on CAN in a large T2D cohort and high CV risk. The finding of possible heterogeneity in the benefit of these interventions on CAN across clinical strata suggests personalization of these treatments as a path forward to optimize their use. Disclosure Y. Tang: None. H. Shah: None. C. Bueno Junior: None. X. Sun: None. J. Mitri: Consultant; Spouse/Partner; Janssen Pharmaceuticals, Inc., kymera. Consultant; Self; national dairy council/local dairy council. Research Support; Spouse/Partner; AbbVie Inc., beigene, Janssen Pharmaceuticals, Inc. Research Support; Self; Kowa Pharmaceuticals America, Inc., national dairy council. Research Support; Spouse/Partner; pharma cyclic, TG therapeutics. M. Sambataro: None. L. Sambado: None. H.C. Gerstein: Advisory Panel; Self; Abbott, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Consultant; Self; Kowa Pharmaceuticals America, Inc. Research Support; Self; AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Merck & Co., Inc., Novo Nordisk Inc., Sanofi. Other Relationship; Self; Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Sanofi. V. Fonseca: Consultant; Self; Abbott, Asahi Kasei Corporation, AstraZeneca, Bayer Inc., Novo Nordisk Inc., Sanofi. Research Support; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Stock/Shareholder; Self; Amgen, Bravo4health. A. Doria: Research Support; Self; Sanofi. R. Pop-Busui: Advisory Panel; Self; Boehringer Ingelheim Pharmaceuticals, Inc. Consultant; Self; Bayer Healthcare Pharmaceuticals Inc., Novo Nordisk Inc. Research Support; Self; AstraZeneca. Other Relationship; Self; American Diabetes Association. Funding National Institutes of Health (N01HC95178, N01HC95179, N01HC95180, N01HC95181, N01HC95182, N01HC95183, N01HC95184, IAAY1HC-9035, IAAY1HC1010)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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) | 0.003 | 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".