Characteristics of Cardiorespiratory Function During Sleep Related to Depression and Antidepressant Medication Use
Bibliographic record
Abstract
Through a series of original research articles, this thesis explores the characteristics of autonomic cardiac regulation and respiratory function during sleep in association with depression and antidepressant medication use and validates a novel diagnostic biomarker of depression. Cardiorespiratory dysfunction during sleep may contribute to the increased risk of developing cardiovascular disease amongst individuals with depression. Sleep represents a unique physiological state shielded from many external confounding factors and may be a more relevant window to observe the effects of depression on cardiorespiratory function. In a first study, we found that depression was associated with abnormal autonomic modulation of cardiac activity during sleep. Specifically, depression was associated with reduced heart rate variability compared to healthy controls, and this difference was most prominent during sleep as compared to wake, which may indicate impairments in the parasympathetic modulation of the cardiac sinoatrial node. Secondly, we validated a machine-learning algorithm that uses patterns of heart rate during sleep to identify depression. This algorithm was found to have 79.9% classification accuracy, based on the differences in autonomic modulation associated with distinct mental states. The algorithm was highly generalizable across different depression subgroups and thus may be useful as an adjunct diagnostic tool. Finally, we found that the use of antidepressants, particularly serotonergic agents, was associated with worse sleep-related respiratory disturbances compared to non-medicated individuals with depression and those using non-serotonergic antidepressants. We proposed that depression-related alterations in serotonin receptor expression and binding may shape the response of the respiratory system to the use of serotonergic agents. Considering the high comorbidity between depression and sleep-related breathing disturbances and their impact on cardiovascular health, this has great clinical implications for the management of depression. Taken together, these results show that depression is associated with several sleep-related abnormalities in terms of cardiorespiratory function, which may represent a valid biomarker of depression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".