444. POLYGENIC RISK ANALYSES OF VENLAFAXINE-RELATED SIDE EFFECTS IN OLDER ADULTS WITH DEPRESSION
Bibliographic record
Abstract
Abstract Background Older adults are more susceptible to antidepressant-induced side effects due to higher comorbidity, cognitive decline, and polypharmacy. Onset of side effects is also associated with antidepressant discontinuation, which might increase the risk of relapse or recurrence of depression. Therefore, it is important to investigate genomic factors underlying antidepressant-induced side effects. Aims & Objectives We performed polygenic risk score (PRS) analyses to evaluate the shared genetic architecture between venlafaxine-induced side effects and various disorders of potentially related to a higher risk for specific side effects. Method We analyzed genetic and clinical data from participants enrolled in the Incomplete Response in Late-Life Depression: Getting to Remission study (IRL-GRey, NCT00892047) phase 1, where participants received venlafaxine for 12 weeks, with dosage up to 300mg/day (Lenze et al., 2015). Side effects were assessed at the end of phase 1 using the 46-item Udvalg for Kliniske Undersøgelser (UKU) rating scale. The presence of side effects was identified by a two-point increase in UKU scores compared to baseline measurements. Additionally, according to UKU categorization, adverse effects were categorized as psychic (10 items), neurological (9 items), autonomic (11 items), and other (16 items). PRSice-2 was utilized to construct the PRSs on our target sample (IRL-GRey) using 14 summary statistics from the PGC, UK Biobank, IGAP, and MEGASTROKE consortium. We tested the association between the total 14 PRSs and the presence of at least one adverse effect. Furthermore, the presence of any psychic side effects was tested for their associations with six PRSs for psychiatric disorders (i.e., depression, bipolar disorder, schizophrenia, and antidepressant treatment response), while the presence of neurological side effects was analyzed with three PRSs for Alzheimer’s disease. Logistic regression was utilized to test for these associations adjusting for age, sex, and first three ancestry principal components. Due to the limited sample size in other populations, our PRS analyses were restricted to the European-ancestry subsample, and only discovery cohorts with predominantly European ancestry were included. The results were corrected for multiple testing using a stringent Bonferroni correction with adjusted α = 0.05/(14+6+3) = 0.0022. Results A total of 297 individuals were included in the analyses. Overall, higher PRSs for all stroke (OR = 1.43 [1.11, 1.85], p = 0.006, empirical-p = 0.03), ischemic stroke (OR = 1.44 [1.10, 1.88], p = 0.008, empirical-p = 0.04), and small vessel stroke (OR = 1.41 [1.10, 1.82], p = 0.007, empirical-p = 0.03), were nominally associated with a higher likelihood of experiencing at least one side effect after venlafaxine treatment in the IRL-GRey sample. However, none of these nominally significant associations survived multiple testing corrections before or after permutation. Discussion & Conclusions Our findings indicate nominal genetic associations between venlafaxine-related side effects and PRSs for stroke in older adults. Notably, our previous analyses indicated PRS for stroke also to be associated with non-remission in the same sample (Marshe et al., 2021). By identifying individuals at higher risk of side effects based on their genetic profile, treatment plans could be tailored to minimize side effects and optimize treatment.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".