The Impact of Glycolipid Metabolic Disorders on Severity Stage-Specific Variation of Cardiac Autonomic Function in Obstructive Sleep Apnea: A Data-Driven Clinical Study
Bibliographic record
Abstract
BACKGROUND: Cardiac autonomic dysfunction (CAD) is a common pathology in cardiovascular diseases; however, the role of glycolipid metabolic disorders in CAD development in obstructive sleep apnea (OSA) remains poorly understood. METHODS: In total, 4152 patients with suspected OSA were recruited in our sleep center. Metabolic characteristics including anthropometric and glycolipid data were collected. Heart rate variability (HRV) was measured to assess the risk of CAD; its dose-response relationship with OSA severity was evaluated via restricted cubic spline (RCS) analysis. A segmented multivariate linear regression (SMLR) model was used to evaluate the roles of metabolic variables in different stages of OSA. RESULTS: The RCS showed that CAD risk increased in a nonlinear relationship pattern with OSA severity, from slow fluctuation at earlier stages to rapid change in later stages. After integrating the clinical definition and RCS selected knots, we obtained the new four OSA severity stages. SMLR model showed that the overall value of glycolipid variables for prediction of HRV abnormalities was greater than the value of OSA variables at earlier stages, while OSA variables were more effective predictors in more severe stages. The discordance in respective relationship of HRV with metabolic and OSA variables sheds the light how metabolic disorders promoted the development of CAD in OSA, the later further in turn deteriorates cardiac function. CONCLUSION: These results are indicative of stage-specific involvement of glycolipid metabolic factors underlying CAD nonlinear changes in patients with OSA. Early control glycolipid disorders may help the control of CAD development in patients with OSA.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".