Impact of Glycemic Control Strategies on the Progression of Diabetic Peripheral Neuropathy in the Bypass Angioplasty Revascularization Investigation 2 Diabetes (BARI 2D) Cohort
Bibliographic record
Abstract
OBJECTIVE: The Bypass Angioplasty Revascularization Investigation 2 Diabetes (BARI 2D) trial demonstrated similar long-term clinical effectiveness of insulin-sensitizing (IS) versus insulin-providing (IP) treatments for type 2 diabetes on cardiovascular outcomes in a cohort with documented coronary artery disease. We evaluated the effects of randomized glycemic control strategy (IS vs. IP) on the prevalence and incidence of diabetic peripheral neuropathy (DPN). RESEARCH DESIGN AND METHODS: DPN (defined as Michigan Neuropathy Screening Instrument [MNSI] clinical examination score>2) was assessed at baseline and yearly for 4 years. DPN prevalence and incidence were compared by intention-to-treat modeling by logistic generalized estimating equation models for prevalence and Kaplan-Meier estimates and Cox regression models for incidence rates. RESULTS: Results are reported for 2,159 BARI 2D participants (70% males) with valid baseline and at least one follow-up MNSI score (mean age 62±9 years, mean HbA1c 7.7±1.6%, diabetes duration 10±9 years). There were no differences in the prevalence of DPN between the IS and the IP groups throughout the 4 years of follow-up. In 1,075 BARI 2D participants with no DPN at baseline, the 4-year cumulative incidence rate of DPN was significantly lower in the IS (66%) than in the IP (72%) strategy group (P=0.02), which remained significant after adjusting for the in-trial HbA1c (P=0.04). In subgroup analyses, IS strategy had a greater benefit in men (hazard ratio 0.75 [99% CI 0.58-0.99], P<0.01). CONCLUSIONS: Among patients with type 2 diabetes followed for up to 4 years during BARI 2D, a glycemic control therapy with IS significantly reduced the incidence of DPN compared with IP therapy and may add further benefit for men.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".