Risk Factors for Longitudinal Resting Heart Rate and Its Associations With Cardiovascular Outcomes in the DCCT/EDIC Study
Bibliographic record
Abstract
OBJECTIVE Individuals with diabetes have higher resting heart rate compared with those without, which may be predictive of long-term cardiovascular disease (CVD) risk. Using data from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study, we evaluated whether the beneficial effect of intensive versus conventional diabetes therapy on heart rate persisted, the factors mediating the differences in heart rate between treatment groups, and the effects of heart rate on future CVD risk. RESEARCH DESIGN AND METHODS Longitudinal changes in heart rate, from annual electrocardiograms over 22 years of EDIC follow-up, were evaluated in 1,402 participants with type 1 diabetes. Linear mixed models were used to assess the effect of DCCT treatment group on mean heart rate over time, and Cox proportional hazards models were used to estimate the effect of heart rate on CVD risk during DCCT/EDIC. RESULTS At DCCT closeout, 52% of participants were male and mean ± SD age was 33 ± 7 years, diabetes duration 12 ± 5 years, and HbA1c 7.4 ± 1.2% (intensive) and 9.1 ± 1.6% (conventional). Through EDIC, participants in the intensive group had significantly lower heart rate in comparison with the conventional group. While significant group differences in heart rate were fully attenuated by DCCT/EDIC mean HbA1c, higher heart rate predicted CVD and major adverse cardiovascular events independent of other risk factors. CONCLUSIONS After 22 years of follow-up, former intensive versus conventional therapy remained significantly associated with lower heart rate, consistent with the long-term beneficial effects of intensive therapy on CVD. DCCT treatment group effects on heart rate were explained by differences in DCCT/EDIC mean HbA1c.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".