The Cardiovascular Legacy of Good Glycemic Control: Clues About Mediators From the DCCT/EDIC Study
Bibliographic record
Abstract
Diabetes is defined by hyperglycemia, but whether optimizing glycemic control can reduce its complications was long in doubt. In 1968 Siperstein et al. proposed the hypothesis that microvascular injury accompanying diabetes could be genetically determined (1), and there was concern that, even if the “glucose hypothesis” (2,3) were valid, seeking good control of hyperglycemia was overly risky. Nevertheless, epidemiologic evidence linking hyperglycemia with both microvascular and cardiovascular complications prompted assessment of the effects of intensive glycemic control in the Diabetes Control and Complications Trial (DCCT) for type 1 diabetes (T1D) and the UK Prospective Diabetes Study (UKPDS) for type 2 diabetes (T2D). When the results of the DCCT were reported in Las Vegas on a hot (>110°F) day in June 1993, many people were surprised by the approximately 50% reduction of microvascular changes that was associated with 6.5 years of maintaining hemoglobin A1c (HbA1c) close to 7% (53 mmol/mol) compared with 9% (75 mmol/mol) in the control arm (4). Because the DCCT participants were young, cardiovascular events were too few to analyze and long-term effects on cardiovascular risk remained unknown. Results in T2D from the UKPDS reinforced the message of the DCCT. More intensive glycemic control attaining about a 1% difference in HbA1c in T2D for 10 years led to 25% lower rates of microvascular outcomes (5) and a nonsignificant 16% lower rate of myocardial infarction (5). For more than two decades after these reports the dominant view has been that improving glycemic control reduces eye, nerve, and kidney complications but not cardiovascular risk. Four large randomized clinical trials testing whether seeking HbA1c levels at or below 7% could reduce cardiovascular events showed limited benefits during their periods of active treatment (5–8). Although analysis of data pooled from these four trials …
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".