Continuous Positive Airway Pressure Treatment for Obstructive Sleep Apnea Does Not Reduce Arterial Stiffness in Patients With Type 2 Diabetes After One Year of Follow-Up
Bibliographic record
Abstract
Background: The aim of this study is to evaluate the effects of 12-month continuous positive airway pressure (CPAP) treatment on arterial stiffness in patients with type 2 diabetes. Methods: Obstructive sleep apnea (OSA) and type 2 diabetes frequently co-exists. Both diseases increase arterial stiffness, a marker of cardiovascular risk. Treating OSA with CPAP may lower arterial stiffness. In a recent randomized trial, we found that CPAP treatment for 12 weeks did not reduce arterial stiffness in type 2 diabetes patients with OSA. Participants from the randomized trial were invited to a follow-up study 12 months after inclusion. We evaluated arterial stiffness by measuring carotid-femoral pulse wave velocity (cfPWV) using SphygmoCor. Results: Forty-six patients (63.9% of the original 72 patients, age 63.8 ± 6.5 years, diabetes duration 16.1 ± 9.7 years, body mass index (BMI) 34.7 ± 3.9 kg/m 2 ) partook in the study. Mean duration of CPAP treatment was 10.5 ± 1.5 months. Baseline cfPWV was 10.7 m/s. At follow-up cfPWV was 10.6 m/s, change in cfPWV: -0.12 m/s, 95% confidence interval (CI): -0.6, 0.4, P = 0.6. Baseline systolic blood pressure (BP) was 136.2 mm Hg. At follow-up BP was 137.9 mm Hg, change in BP: 1.6 mm Hg, 95% CI: -2.3, 5.5. Conclusions: We found no effect of 9 - 12-month CPAP treatment on arterial stiffness or BP in patients with long duration of type 2 diabetes and OSA. J Endocrinol Metab. 2021;11(5):134-139 doi: https://doi.org/10.14740/jem773
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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".