Heart rate variability: Evaluating a potential biomarker of anxiety disorders
Bibliographic record
Abstract
Establishing quantifiable biological markers associated with anxiety will increase the objectivity of phenotyping and enhance genetic research of anxiety disorders. Heart rate variability (HRV) is a physiological measure reflecting the dynamic relationship between the sympathetic and parasympathetic nervous systems, and is a promising target for further investigation. This review summarizes evidence evaluating HRV as a potential physiological biomarker of anxiety disorders by highlighting literature related to anxiety and HRV combined with investigations of endophenotypes, neuroimaging, treatment response, and genetics. Deficient HRV shows promise as an endophenotype of pathological anxiety and may serve as a noninvasive index of prefrontal cortical control over the amygdala, and potentially aid with treatment outcome prediction. We propose that the genetics of HRV can be used to enhance the understanding of the genetics of pathological anxiety for etiological investigations and treatment prediction. Given the anxiety-HRV link, strategies are offered to advance genetic analytical approaches, including the use of polygenic methods, wearable devices, and pharmacogenetic study designs. Overall, HRV shows promising support as a physiological biomarker of pathological anxiety, potentially in a transdiagnostic manner, with the heart-brain entwinement providing a novel approach to advance anxiety treatment development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".