W5. INVESTIGATING THE GENETIC LINK BETWEEN HEART RATE VARIABILITY (HRV) AND ANXIETY DISORDERS: POLYGENIC RISK SCORE OVERLAP AND ASSOCIATION OF HRV GENOME-WIDE POLYMORPHISMS WITH ANXIETY
Bibliographic record
Abstract
Background Anxiety disorders are associated with reduced heart rate variability (HRV), a heritable measure of difference in time between heartbeats. Given this link, genetic variants related to HRV may provide insight into the risk for pathological anxiety. This study examined the genetic overlap between anxiety risk and HRV using polygenic risk scores (PRS). In addition, polymorphisms related to HRV were investigated to determine if they influence anxiety disorder risk, potentially mediated by HRV. Methods In 184 European individuals (100 anxiety disorder, 84 controls, age 18-65, 69% female), we measured 5-minute resting HRV via photoplethysmography with the Empatica E4 wristband and genotyped their DNA samples with the Global Screening Array. Blood volume pulse data was processed via Kubios HRV software, and HRV was calculated using the root mean square of successive beat-to-beat interval differences. Anxiety and HRV PRSs were computed using the summary statistics from a meta-analysis of anxiety disorder GWASs and meta-analysis of HRV GWASs, respectively, using a clumping and thresholding approach with standard p-value thresholds (5e-8 to 1) followed by high-resolution analysis (PRSice-2). To test for anxiety disorder PRS association with resting HRV in our sample, and the HRV PRS association with anxiety disorder, we used linear and logistic regression , respectively. Additionally, using the 15 significant SNPs from a pre-existing HRV GWAS, we performed mediation analyses (SPSS macro PROCESS) to study their associations with anxiety disorder status in our sample through resting HRV. Covariates in all analyses included age, sex, and the first three principal components of ancestry. Results For the association of anxiety disorders PRS with resting HRV, none of the standard p-value thresholds explained a significant amount of variance. The high-resolution analysis revealed a p-value threshold of 0.0004 for the maximum variance explained in resting HRV, which was nominally significant (R2=0.023, p=0.029, β=-0.16, 501 SNPs). For the association of HRV PRS with anxiety disorder status, among the standard p-value thresholds, 5e-05 explained the largest amount of variance and was nominally associated with anxiety disorder (R2=0.027, p=0.039, OR=1.43, 55 SNPs). In the high-resolution analysis, we observed a p-value threshold of 3.34e-05 for the maximum variance explained in anxiety disorder status, displaying nominal significance (R2=0.035, p=0.018, OR=1.51, 48 SNPs). When testing whether the HRV GWAS top-hit SNPs are associated with anxiety disorder mediated through resting HRV, NDUFA11 rs12980262 A-carriers and GNG11 rs180238 and rs4262 C-carriers had higher anxiety risk through lower HRV (b=0.35, 95%CI=0.09–0.76; b=0.15, 95%CI=0.01–0.39; b=0.17, 95%CI=0.02–0.42), and LINC00477 rs10842383 T-carriers had lower anxiety risk through higher HRV (b=-0.17, 95%CI=-0.42–-0.004). Discussion This study provides preliminary support for a genetic overlap between anxiety disorders and HRV. We also identified genetic variants from HRV investigations that link to anxiety, with effects influenced by HRV. These variants have not been studied in the context of psychiatry and are therefore novel markers to explore further. Limitations include the modest sample size and restriction to Europeans. The anxiety-HRV association thus supports the potential of HRV genetic variations as novel therapy targets that may alleviate pathological anxiety symptoms.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.020 | 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".