Multidimensional analysis of heart rate variability and burden of illness in bipolar disorder
Bibliographic record
Abstract
Introduction: Patients with bipolar disorder (BD) face disproportionate rates of cardiovascular disease. While several mechanisms have been proposed for this association, both are multifactorial and heterogeneous illnesses, and it is unlikely that any individual factor can account for a significant portion of their association. Potential mediators have been mostly studied in isolation even though they are interrelated. Furthermore, few studies have accounted for baseline cardiovascular functioning or burden of illness. Methods: We sought to analyze the association between burden of illness, phase of the illness (e.g., depressive) and heart rate variability (HRV) in 48 patients diagnosed with BD using canonical correlation analysis. We hypothesized that the association between burden of illness and HRV measures would be different depending on the clinical phase (i.e., euthymia, depression or (hypo)mania). Results: A longer duration of (hypo)manic episodes was associated with higher rates of hypertension and increased sympathetic activation, along with lower rates of migraine and family history of suicide. In the second canonical variable, a later age at onset of (hypo)manic episodes was associated with higher parasympathetic activity. Limitations: Small sample size; while the magnitudes of correlations for these top functions were large, none of the models were statistically significant. Conclusions: There are different dimensions to the association between burden of illness and HRV in BD. The first dimension could comprise higher sympathetic activation in the context of longer (hypo)manic episodes, with lower rates for clinical variables that have been associated with a predominant depressive polarity (e.g., family history of suicide and migraine).
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 | 0.001 | 0.004 |
| 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".