Relationships Among Conventional Cardiovascular Risk Factors and Lifestyle Habits With Arterial Stiffness in Type 2 Diabetic Patients
Bibliographic record
Abstract
BACKGROUND: While conventional cardiovascular risk factors and certain lifestyle habits are associated with arterial stiffness in patients with type 2 diabetes mellitus (T2DM), it is still unknown whether they are actually associated with arterial stiffness even after adjustment for conventional cardiovascular risk factors and lifestyle habits. The aim of this study was to identify variables that are associated with brachial-ankle pulse wave velocity (baPWV). METHODS: The study participants comprised 724 Japanese T2DM outpatients free of history of cardiovascular diseases. Lifestyle habits were analyzed using self-reported questionnaires. The associations among conventional cardiovascular risk factors and lifestyle habits with baPWV were investigated by multivariable linear regression analysis. RESULTS: The mean age of the study subjects was 57.8 ± 8.6 years, and 62.8% of those were males. The mean HbA1c was 7.0±1.0%, and the estimated duration of T2DM was 9.9 ± 7.2 years. Multiple linear regression analysis that included age and gender demonstrated that age and male sex were positively associated with baPWV. In a model adjusted for numerous conventional cardiovascular risk factors and lifestyle habits, age, duration of T2DM, systolic blood pressure, serum uric acid, urinary albumin excretion and poor sleep quality were positively associated with baPWV, while body mass index was negatively associated with baPWV. CONCLUSIONS: In Japanese T2DM, in addition to several conventional cardiovascular risk factors, poor sleep quality was associated with baPWV even after adjustment for numerous conventional cardiovascular risk factors and lifestyle habits.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".