Genetic and polygenic investigation of heart rate variability to identify biomarkers associated with Anxiety disorders
Bibliographic record
Abstract
Given that anxiety disorders (AD) are associated with reduced vagally-mediated heart rate variability (HRV), genetic variants related to HRV may provide insight into anxiety etiology. This study used polygenic risk scores (PRS) to explore the genetic overlap between AD and HRV, and investigated whether HRV-related polymorphisms influence anxiety risk. Resting vagally-mediated HRV was measured using a wearable device in 188 European individuals (AD=101, healthy controls=87). AD PRS was tested for association with resting HRV, and HRV PRS for association with AD. We also investigated 15 significant hits from an HRV genome-wide association study (GWAS) for association with resting HRV and AD and if this association is mediated through resting HRV. The AD PRS and HRV PRS showed nominally significant associations with resting HRV and anxiety disorders, respectively. HRV GWAS variants associated with resting HRV were rs12980262 (NDUFA11), rs2680344 (HCN4), rs4262 and rs180238 (GNG11), and rs10842383 (LINC00477). Mediation analyses revealed that NDUFA11 rs12980262 A-carriers and GNG11 rs180238 and rs4262 C-carriers had higher anxiety risk through lower HRV. This study supports an anxiety-HRV genetic relationship, with HRV-related genetic variants translating to AD. This study encourages exploration of HRV genetics to understand mechanisms and identify novel treatment targets for anxiety.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".